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This article is not about how to reduce or control these costs, but rather how the costs are justified."/><meta name="robots" content="index, follow"/><meta name="googlebot" content="index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1"/><link rel="canonical" href="https://signoz.io/blog/justifying-a-million-dollar-observability-bill/"/><link rel="alternate" type="application/rss+xml" href="https://signoz.io/rss/"/><meta property="og:title" content="Is a $1 million Datadog bill worth it?"/><meta property="og:description" content="I’d like to write a bit about how Observability costs are significant, how these costs tend to be justified, and how precise amount a company spends on *anything* tends to be more subjective than you’d think. This article is not about how to reduce or control these costs, but rather how the costs are justified."/><meta property="og:url" content="https://signoz.io/blog/justifying-a-million-dollar-observability-bill/"/><meta property="og:site_name" content="SigNoz"/><meta property="og:locale" content="en_US"/><meta property="og:image" content="https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/10/million-dollar-bill-cover-min.jpg"/><meta property="og:type" content="article"/><meta property="article:published_time" content="2023-10-12T00:00:00.000Z"/><meta property="article:modified_time" content="2023-10-12T00:00:00.000Z"/><meta property="article:author" content="Nočnica Mellifera"/><meta name="twitter:card" content="summary_large_image"/><meta name="twitter:title" content="Is a $1 million Datadog bill worth it?"/><meta name="twitter:description" content="I’d like to write a bit about how Observability costs are significant, how these costs tend to be justified, and how precise amount a company spends on *anything* tends to be more subjective than you’d think. This article is not about how to reduce or control these costs, but rather how the costs are justified."/><meta name="twitter:image" content="https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/10/million-dollar-bill-cover-min.jpg"/><script src="/_next/static/chunks/0cz1d0mv5g_q7.js?dpl=dpl_pGGjk7JXoCmMBwKvWimjoH9rJupT" noModule=""></script></head><body class="text-[var(--l1-foreground)] antialiased"><div hidden=""><!--$--><!--/$--></div><!--$!--><template data-dgst="BAILOUT_TO_CLIENT_SIDE_RENDERING"></template><!--/$--><!--$!--><template data-dgst="BAILOUT_TO_CLIENT_SIDE_RENDERING"></template><!--/$--><noscript><iframe src="https://www.googletagmanager.com/ns.html?id=GTM-N9B6D4H" height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript><section class="relative mx-auto"><div class="relative flex min-h-screen flex-col justify-between"><div class="sr-only" data-markdown-ignore="true">For the complete documentation index, see<!-- --> <a href="https://signoz.io/llms.txt">llms.txt</a>. 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This article is not about how to reduce or control these costs, but rather how the costs are justified.","image":{"@type":"ImageObject","url":"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/10/million-dollar-bill-cover-min.jpg","width":1200,"height":630},"mainEntityOfPage":{"@type":"WebPage","@id":"https://signoz.io/blog/justifying-a-million-dollar-observability-bill"},"url":"https://signoz.io/blog/justifying-a-million-dollar-observability-bill","datePublished":"2023-10-12","dateModified":"2023-10-12","inLanguage":"en-us","wordCount":1764,"author":[{"@type":"Person","name":"Nočnica Mellifera","url":"https://github.com/serverless-mom"}],"publisher":{"@type":"Organization","@id":"https://signoz.io/#organization","name":"SigNoz","logo":{"@type":"ImageObject","url":"https://signoz.io/svgs/icons/signoz.svg","width":600,"height":60},"sameAs":["https://www.linkedin.com/company/signozio","https://x.com/SigNozHQ","https://github.com/SigNoz","https://www.youtube.com/@signoz","https://www.ycombinator.com/companies/signoz"]},"articleSection":"operations"}</script><script type="application/ld+json">{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"SigNoz","item":"https://signoz.io/"},{"@type":"ListItem","position":2,"name":"Blog","item":"https://signoz.io/blog/"},{"@type":"ListItem","position":3,"name":"Is a $1 million Datadog bill worth it?","item":"https://signoz.io/blog/justifying-a-million-dollar-observability-bill/"}]}</script><main id="article-main"><section class="relative mx-auto"><div class="mx-auto flex h-full w-full max-w-ot-hub items-start justify-center gap-4 overflow-clip px-3 pt-8 max-lg:flex-col max-lg:gap-3 md:px-6 md:pt-12 lg:px-8"><div class="mx-auto box-border w-full min-w-0 max-w-[780px] flex-auto md:px-0 lg:px-4"><div class="mb-4 lg:hidden"></div><nav aria-label="Breadcrumb" class="not-prose mb-4"><ol class="flex flex-wrap items-center gap-1.5 p-0 text-sm"><li class="items-center gap-1.5 flex"><a class="flex items-center text-[var(--l2-foreground)] transition-colors hover:text-[var(--accent-primary-hover)]" href="/"><svg xmlns="http://www.w3.org/2000/svg" width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-house" aria-label="Home"><path d="M15 21v-8a1 1 0 0 0-1-1h-4a1 1 0 0 0-1 1v8"></path><path d="M3 10a2 2 0 0 1 .709-1.528l7-5.999a2 2 0 0 1 2.582 0l7 5.999A2 2 0 0 1 21 10v9a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2z"></path></svg></a></li><li class="items-center gap-1.5 flex"><svg xmlns="http://www.w3.org/2000/svg" width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-chevron-right shrink-0 text-[var(--l3-foreground)]" aria-hidden="true"><path d="m9 18 6-6-6-6"></path></svg><a class="whitespace-nowrap text-[var(--l2-foreground)] transition-colors hover:text-[var(--accent-primary-hover)]" href="/blog/">Blog</a></li><li class="items-center gap-1.5 flex"><svg xmlns="http://www.w3.org/2000/svg" width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-chevron-right shrink-0 text-[var(--l3-foreground)]" aria-hidden="true"><path d="m9 18 6-6-6-6"></path></svg><span aria-current="page" class="max-w-[300px] truncate font-medium text-[var(--l1-foreground)]">Is a $1 million Datadog bill worth it?</span></li></ol></nav><article class="prose prose-slate max-w-none px-3 py-6 dark:prose-invert"><h1 class="text-3xl font-bold">Is a $1 million Datadog bill worth it?</h1><div class="mb-2 mt-3 flex flex-wrap gap-3 text-xs text-[var(--l2-foreground)] lg:hidden"><span>Last Updated: <!-- -->October 12, 2023</span><span>9 min read</span></div><p>In a recent reddit thread, I got into a conversation about <a href="https://devops.com/observability-costs-are-too-damn-high/" rel="noopener noreferrer nofollow" target="_blank">justifying the cost of observability</a>. It got to a really basic question about running a tech company: how do you know that any cost is justified? While a small number of expenses have clear and direct business values, a bunch of other costs, I would even say <em>most</em> costs, just aren’t that clear cut.</p>
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<p>I’d like to write a bit about how Observability costs are significant, how these costs tend to be justified, and how precise amount a company spends on <em>anything</em> tends to be more subjective than you’d think. This article is not about how to reduce or control these costs, but rather how the costs are justified.</p>
|
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<p><img src="https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/10/million-dollar-bill-cover.webp" alt="Cover Image"/></p>
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<h2 id="observability-is-expensive" class="content-header">Observability is expensive<a href="#observability-is-expensive" aria-hidden="true" tabindex="-1"><span class="content-header-link"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5 linkicon"><path d="M12.232 4.232a2.5 2.5 0 0 1 3.536 3.536l-1.225 1.224a.75.75 0 0 0 1.061 1.06l1.224-1.224a4 4 0 0 0-5.656-5.656l-3 3a4 4 0 0 0 .225 5.865.75.75 0 0 0 .977-1.138 2.5 2.5 0 0 1-.142-3.667l3-3Z"></path><path d="M11.603 7.963a.75.75 0 0 0-.977 1.138 2.5 2.5 0 0 1 .142 3.667l-3 3a2.5 2.5 0 0 1-3.536-3.536l1.225-1.224a.75.75 0 0 0-1.061-1.06l-1.224 1.224a4 4 0 1 0 5.656 5.656l3-3a4 4 0 0 0-.225-5.865Z"></path></svg></span></a></h2>
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<p>SaaS tools to do observability have significant costs for any large-scale user, and no observability tool has a cost of 0. While tools like SigNoz have self-hosted solutions that will cut your Datadog bill down to zero dollars, you still have to consider both the infrastructure costs of hosting your own data backend and the support you’ll need to do on such a tool. Even with the (amazing) open-source options, the cost is never zero when you count implementation effort and storage costs.</p>
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<p>While everyone knows that observability costs <em>something</em> I think most people don’t really understand just how expensive it can be. While many organizations may target spending 10% of their Ops budget for full-stack observability, it’s common for observability to be the <a target="_blank" rel="noopener noreferrer nofollow" href="https://devops.com/observability-costs-are-too-damn-high/">second-highest cost after infrastructure</a>. Why so expensive? And why so universally? For an object lesson, consider my demo flask app before and after I add OpenTelemetry instrumentation.</p>
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<div class="code-block-module__pEb5LG__root" data-slot="code-block" data-sz-codeblock=""><div class="code-block-module__pEb5LG__body"><div class="code-block-module__pEb5LG__codePane"><button type="button" aria-label="Copy code" class="code-block-module__pEb5LG__copy code-block-module__pEb5LG__copyFloating" style="width:68px"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"></path></svg><span>Copy</span></button><pre tabindex="0" data-language="python" data-theme="github-dark-dimmed github-light" class="code-block-module__pEb5LG__pre"><code data-language="python" data-theme="github-dark-dimmed github-light" style="display:grid" data-line-numbers="" data-line-numbers-max-digits="2"><span data-line=""><span style="--shiki-dark:#768390;--shiki-light:#6A737D"># Acquire a tracer</span></span>
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<span data-line=""> </span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">app </span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> Flask(</span><span style="--shiki-dark:#6CB6FF;--shiki-light:#005CC5">__name__</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
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||
<span data-line=""> </span>
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<span data-line=""><span style="--shiki-dark:#DCBDFB;--shiki-light:#6F42C1">@app.route</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"/rolldice"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
|
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<span data-line=""><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">def</span><span style="--shiki-dark:#DCBDFB;--shiki-light:#6F42C1"> roll_dice</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">():</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> do_work(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"busywork"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#F47067;--shiki-light:#D73A49"> return</span><span style="--shiki-dark:#6CB6FF;--shiki-light:#005CC5"> str</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">(do_roll())</span></span>
|
||
<span data-line=""> </span>
|
||
<span data-line=""><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">def</span><span style="--shiki-dark:#DCBDFB;--shiki-light:#6F42C1"> do_roll</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">():</span></span>
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||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> res </span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> randint(</span><span style="--shiki-dark:#6CB6FF;--shiki-light:#005CC5">1</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">, </span><span style="--shiki-dark:#6CB6FF;--shiki-light:#005CC5">6</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
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||
<span data-line=""><span style="--shiki-dark:#768390;--shiki-light:#6A737D"> # Do the thing</span></span>
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||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> rollspan.set_attribute(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"roll.value"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">, res)</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#F47067;--shiki-light:#D73A49"> return</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> res</span></span>
|
||
<span data-line=""> </span>
|
||
<span data-line=""><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">def</span><span style="--shiki-dark:#DCBDFB;--shiki-light:#6F42C1"> do_work</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">(work_type):</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#6CB6FF;--shiki-light:#005CC5"> print</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"doing some work..."</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span></code></pre></div></div></div>
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<p>Now let’s add <a target="_blank" href="https://signoz.io/opentelemetry/">OpenTelemetry</a> manual instrumentation:</p>
|
||
<div class="code-block-module__pEb5LG__root" data-slot="code-block" data-sz-codeblock=""><div class="code-block-module__pEb5LG__body"><div class="code-block-module__pEb5LG__codePane"><button type="button" aria-label="Copy code" class="code-block-module__pEb5LG__copy code-block-module__pEb5LG__copyFloating" style="width:68px"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"></path></svg><span>Copy</span></button><pre tabindex="0" data-language="python" data-theme="github-dark-dimmed github-light" class="code-block-module__pEb5LG__pre"><code data-language="python" data-theme="github-dark-dimmed github-light" style="display:grid" data-line-numbers="" data-line-numbers-max-digits="2"><span data-line=""><span style="--shiki-dark:#768390;--shiki-light:#6A737D"># Acquire a tracer</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">tracer </span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> trace.get_tracer(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"diceroller.tracer"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
|
||
<span data-line=""> </span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">app </span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> Flask(</span><span style="--shiki-dark:#6CB6FF;--shiki-light:#005CC5">__name__</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
|
||
<span data-line=""> </span>
|
||
<span data-line=""><span style="--shiki-dark:#DCBDFB;--shiki-light:#6F42C1">@app.route</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"/rolldice"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">def</span><span style="--shiki-dark:#DCBDFB;--shiki-light:#6F42C1"> roll_dice</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">():</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> do_work(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"busywork"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#F47067;--shiki-light:#D73A49"> return</span><span style="--shiki-dark:#6CB6FF;--shiki-light:#005CC5"> str</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">(do_roll())</span></span>
|
||
<span data-line=""> </span>
|
||
<span data-line=""><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">def</span><span style="--shiki-dark:#DCBDFB;--shiki-light:#6F42C1"> do_roll</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">():</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#768390;--shiki-light:#6A737D"> # This creates a new span that's the child of the current one</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#F47067;--shiki-light:#D73A49"> with</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> tracer.start_as_current_span(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"do_roll"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">) </span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">as</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> rollspan:</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> res </span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> randint(</span><span style="--shiki-dark:#6CB6FF;--shiki-light:#005CC5">1</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">, </span><span style="--shiki-dark:#6CB6FF;--shiki-light:#005CC5">6</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> current_span </span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> trace.get_current_span()</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> current_span.add_event(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"Gonna try it!"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#768390;--shiki-light:#6A737D"> # Do the thing</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> current_span.add_event(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"Did it!"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> rollspan.set_attribute(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"roll.value"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">, res)</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#F47067;--shiki-light:#D73A49"> return</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> res</span></span>
|
||
<span data-line=""> </span>
|
||
<span data-line=""> </span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">metric_reader </span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> PeriodicExportingMetricReader(ConsoleMetricExporter())</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">provider </span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> MeterProvider(</span><span style="--shiki-dark:#F69D50;--shiki-light:#E36209">metric_readers</span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">[metric_reader])</span></span>
|
||
<span data-line=""> </span>
|
||
<span data-line=""><span style="--shiki-dark:#768390;--shiki-light:#6A737D"># Sets the global default meter provider</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">metrics.set_meter_provider(provider)</span></span>
|
||
<span data-line=""> </span>
|
||
<span data-line=""><span style="--shiki-dark:#768390;--shiki-light:#6A737D"># Creates a meter from the global meter provider</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">meter </span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> metrics.get_meteFr(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"otel.nica.meter.name"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
|
||
<span data-line=""> </span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">work_counter </span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> meter.create_counter(</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62"> "work.counter"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">, </span><span style="--shiki-dark:#F69D50;--shiki-light:#E36209">unit</span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"1"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">, </span><span style="--shiki-dark:#F69D50;--shiki-light:#E36209">description</span><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">=</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"Counts the amount of work done"</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#F47067;--shiki-light:#D73A49">def</span><span style="--shiki-dark:#DCBDFB;--shiki-light:#6F42C1"> do_work</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">(work_type):</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#768390;--shiki-light:#6A737D"> # count the work being doing</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E"> work_counter.add(</span><span style="--shiki-dark:#6CB6FF;--shiki-light:#005CC5">1</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">, {</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"work.type"</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">: work_type})</span></span>
|
||
<span data-line=""><span style="--shiki-dark:#6CB6FF;--shiki-light:#005CC5"> print</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">(</span><span style="--shiki-dark:#96D0FF;--shiki-light:#032F62">"doing some work..."</span><span style="--shiki-dark:#ADBAC7;--shiki-light:#24292E">)</span></span></code></pre></div></div></div>
|
||
<p>Obviously a contrived example, but it’s worth noting that there were more lines involved in monitoring the app than the app doing its work. With detailed enough measurement and tracing, along with transmitting and storing that measurement, observability can easily rival the application’s footprint.</p>
|
||
<h3 id="cost-reductions-are-possible" class="content-header">Cost reductions are possible<a href="#cost-reductions-are-possible" aria-hidden="true" tabindex="-1"><span class="content-header-link"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5 linkicon"><path d="M12.232 4.232a2.5 2.5 0 0 1 3.536 3.536l-1.225 1.224a.75.75 0 0 0 1.061 1.06l1.224-1.224a4 4 0 0 0-5.656-5.656l-3 3a4 4 0 0 0 .225 5.865.75.75 0 0 0 .977-1.138 2.5 2.5 0 0 1-.142-3.667l3-3Z"></path><path d="M11.603 7.963a.75.75 0 0 0-.977 1.138 2.5 2.5 0 0 1 .142 3.667l-3 3a2.5 2.5 0 0 1-3.536-3.536l1.225-1.224a.75.75 0 0 0-1.061-1.06l-1.224 1.224a4 4 0 1 0 5.656 5.656l3-3a4 4 0 0 0-.225-5.865Z"></path></svg></span></a></h3>
|
||
<p>Again, this is not the focus of this piece (geez don’t you read the introductory paragraphs??), but it’s important to note that it’s completely possible to significantly reduce the costs of observability. Tools like the <a target="_blank" href="https://signoz.io/blog/opentelemetry-collector-complete-guide/">OpenTelemetry Collector</a> have tons of processors available to filter, parse, and compress data so that you’re sending way less data than your app’s actual workload.</p>
|
||
<p>And open-source options are absolutely going to give more bang for your buck than closed-source SaaS tools. While companies like Datadog and New Relic may claim support for open standards like OpenTelemetry, <a target="_blank" href="https://signoz.io/blog/is-opentelemetry-a-first-class-citizen-in-your-dashboard-a-datadog-and-newrelic-comparison/">the reality is a little different</a>.</p>
|
||
<p>However costs exist, they’re never going to be smaller than, say, what your office spends on sugar packets. How do we justify these expenses?</p>
|
||
<h2 id="justifying-the-cost" class="content-header">Justifying the cost<a href="#justifying-the-cost" aria-hidden="true" tabindex="-1"><span class="content-header-link"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5 linkicon"><path d="M12.232 4.232a2.5 2.5 0 0 1 3.536 3.536l-1.225 1.224a.75.75 0 0 0 1.061 1.06l1.224-1.224a4 4 0 0 0-5.656-5.656l-3 3a4 4 0 0 0 .225 5.865.75.75 0 0 0 .977-1.138 2.5 2.5 0 0 1-.142-3.667l3-3Z"></path><path d="M11.603 7.963a.75.75 0 0 0-.977 1.138 2.5 2.5 0 0 1 .142 3.667l-3 3a2.5 2.5 0 0 1-3.536-3.536l1.225-1.224a.75.75 0 0 0-1.061-1.06l-1.224 1.224a4 4 0 1 0 5.656 5.656l3-3a4 4 0 0 0-.225-5.865Z"></path></svg></span></a></h2>
|
||
<p>Back to the Reddit thread where we started, the question is really how do we decide that a certain value is ‘worth it’ as a cost of observability? Let’s talk about some costs that are a lot easier to justify and specify, than observability:</p>
|
||
<h3 id="costs-with-a-clear-quantifiable-benefit" class="content-header">Costs with a clear, quantifiable benefit<a href="#costs-with-a-clear-quantifiable-benefit" aria-hidden="true" tabindex="-1"><span class="content-header-link"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5 linkicon"><path d="M12.232 4.232a2.5 2.5 0 0 1 3.536 3.536l-1.225 1.224a.75.75 0 0 0 1.061 1.06l1.224-1.224a4 4 0 0 0-5.656-5.656l-3 3a4 4 0 0 0 .225 5.865.75.75 0 0 0 .977-1.138 2.5 2.5 0 0 1-.142-3.667l3-3Z"></path><path d="M11.603 7.963a.75.75 0 0 0-.977 1.138 2.5 2.5 0 0 1 .142 3.667l-3 3a2.5 2.5 0 0 1-3.536-3.536l1.225-1.224a.75.75 0 0 0-1.061-1.06l-1.224 1.224a4 4 0 1 0 5.656 5.656l3-3a4 4 0 0 0-.225-5.865Z"></path></svg></span></a></h3>
|
||
<ul>
|
||
<li><em>Infrastructure & Operations -</em> When we talk about infrastructure costs, it’s possible to calculate the marginal cost of a single user and show the value of a single additional user to our business, and therefore justify the costs. The same is true of the Operations team and SRE teams: they’re needed to keep the system up and running, so easy to put a dollar value on the benefit.</li>
|
||
<li><em>Sales & Marketing -</em> Here again, the calculation is fairly simple: calculate the benefit of new business, and you can show whether what you’re paying to acquire it makes sense</li>
|
||
</ul>
|
||
<p>Okay, if these costs can be justified to the last clipped Scottish groat, can’t we do that for all business expenses? Not at all:</p>
|
||
<h3 id="costs-that-are-less-clear-cut" class="content-header">Costs that are less clear-cut<a href="#costs-that-are-less-clear-cut" aria-hidden="true" tabindex="-1"><span class="content-header-link"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5 linkicon"><path d="M12.232 4.232a2.5 2.5 0 0 1 3.536 3.536l-1.225 1.224a.75.75 0 0 0 1.061 1.06l1.224-1.224a4 4 0 0 0-5.656-5.656l-3 3a4 4 0 0 0 .225 5.865.75.75 0 0 0 .977-1.138 2.5 2.5 0 0 1-.142-3.667l3-3Z"></path><path d="M11.603 7.963a.75.75 0 0 0-.977 1.138 2.5 2.5 0 0 1 .142 3.667l-3 3a2.5 2.5 0 0 1-3.536-3.536l1.225-1.224a.75.75 0 0 0-1.061-1.06l-1.224 1.224a4 4 0 1 0 5.656 5.656l3-3a4 4 0 0 0-.225-5.865Z"></path></svg></span></a></h3>
|
||
<p>Here are some costs where the benefit is harder to quantify</p>
|
||
<ul>
|
||
<li><strong>Literally, everyone else who works at the office -</strong> Not to start with a bold one, but the benefit of literally everyone else isn’t a number you can type into a spreadsheet. Some examples of people at your company who have a nebulous, hard-to-exactly define benefit:<!-- -->
|
||
<ul>
|
||
<li>All executives - At publicly traded companies, we often give credit for all stock moves to the CEO. But everyone knows this can’t be an accurate number, and the direct benefit of all other executives is similarly hard to pin down. Again, this doesn’t mean they have zero benefit, just that it can’t be quantified as an exact figure.</li>
|
||
<li>All Managers - If we set an arbitrary value for the contributions of a team of engineers, say (shudder) a dollar value on each line of code added, we can arrive at some vague estimate of the engineers’ contribution to value. Even this rough estimate isn’t possible with the team’s management. And as such, the benefit of management is impossible to get a perfect read on. We know that a great manager can get great things from their team. But the dollar value is quite difficult to calculate.</li>
|
||
</ul>
|
||
</li>
|
||
<li><strong>The Office itself</strong> - More on this in later sections. But the value of all those cubicles does not fit on a spreadsheet.</li>
|
||
</ul>
|
||
<p>So, we can see that a ton of the expenses a company runs up, especially at a tech company, can’t be calculated against the exact value they produce.</p>
|
||
<p>If this situation is hard to believe, consider that most airlines can’t specify the exact cost (and therefore the precise profit) <a href="https://revenue-hub.com/revenue-management-hotels-learn-delta-airlines/" rel="noopener noreferrer nofollow" target="_blank">of a single passenger on a single flight</a>, and you’ll see that very often businesses must make decisions heuristically rather than with precise valuations.</p>
|
||
<h3 id="the-real-argument-for-observability-the-cost-of-not-having-it" class="content-header">The real argument for observability: the cost of not having it<a href="#the-real-argument-for-observability-the-cost-of-not-having-it" aria-hidden="true" tabindex="-1"><span class="content-header-link"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5 linkicon"><path d="M12.232 4.232a2.5 2.5 0 0 1 3.536 3.536l-1.225 1.224a.75.75 0 0 0 1.061 1.06l1.224-1.224a4 4 0 0 0-5.656-5.656l-3 3a4 4 0 0 0 .225 5.865.75.75 0 0 0 .977-1.138 2.5 2.5 0 0 1-.142-3.667l3-3Z"></path><path d="M11.603 7.963a.75.75 0 0 0-.977 1.138 2.5 2.5 0 0 1 .142 3.667l-3 3a2.5 2.5 0 0 1-3.536-3.536l1.225-1.224a.75.75 0 0 0-1.061-1.06l-1.224 1.224a4 4 0 1 0 5.656 5.656l3-3a4 4 0 0 0-.225-5.865Z"></path></svg></span></a></h3>
|
||
<p>When a developer uses a cool <a target="_blank" href="https://signoz.io/blog/distributed-tracing-tools/">tracing tool</a> to find the bit of code that’s slowing down performance, the dollar benefit of that act to the company is extremely difficult to show. You might try calculating how long the developer would have spent trying to fix the problem without the tool, but that’s always going to be a <em>very</em> rough estimate since a proper observability tool <a href="https://www.cncf.io/blog/2022/12/16/why-opentelemetry-is-taking-cloud-native-to-new-heights/" rel="noopener noreferrer nofollow" target="_blank">fundamentally alters the way dev teams operate</a> so a simple time estimate is unlikely to be accurate.</p>
|
||
<p>No, the real way to show what observability has to offer is the cost of <em>not</em> having it. The cost of downtime, system slowdowns, and dissatisfied users is one that every exec can estimate.</p>
|
||
<p>No one wants to be responsible for the site going down, and the cost of downtime can be millions of dollars an hour for a large enough system.</p>
|
||
<p>This, then, is the core of observability’s cost justification: it may be hard to justify the cost of uptime, but the cost of extended downtime is enough to tank a successful company.</p>
|
||
<h3 id="the-birth-of-million-dollar-observability-bills" class="content-header">The Birth of Million Dollar Observability Bills<a href="#the-birth-of-million-dollar-observability-bills" aria-hidden="true" tabindex="-1"><span class="content-header-link"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5 linkicon"><path d="M12.232 4.232a2.5 2.5 0 0 1 3.536 3.536l-1.225 1.224a.75.75 0 0 0 1.061 1.06l1.224-1.224a4 4 0 0 0-5.656-5.656l-3 3a4 4 0 0 0 .225 5.865.75.75 0 0 0 .977-1.138 2.5 2.5 0 0 1-.142-3.667l3-3Z"></path><path d="M11.603 7.963a.75.75 0 0 0-.977 1.138 2.5 2.5 0 0 1 .142 3.667l-3 3a2.5 2.5 0 0 1-3.536-3.536l1.225-1.224a.75.75 0 0 0-1.061-1.06l-1.224 1.224a4 4 0 1 0 5.656 5.656l3-3a4 4 0 0 0-.225-5.865Z"></path></svg></span></a></h3>
|
||
<p>In the deep mists of observability history, I remember the healthcare.gov launch, and a now-famous line in a meeting with Mikey Dickerson and the IT firms who had bungled the website launch</p>
|
||
<blockquote>
|
||
<p>“If I hear one more person tell me I can’t use New Relic,” he said. “I’ll punch them in the face.”</p>
|
||
</blockquote>
|
||
<p>And that, truly, is the story of how our Datadog, New Relic, and Splunk bills got so high. After a spate of outages, failures, bugs, and other problems that users notice; some executive says ‘add observability, I don’t care what it costs.’ And the <a href="https://twitter.com/kellabyte/status/1704947999414063465" rel="noopener noreferrer nofollow" target="_blank">million-dollar observability bill</a> was born.</p>
|
||
<h2 id="observability-shouldnt-get-a-blank-check" class="content-header">Observability Shouldn’t Get a Blank Check<a href="#observability-shouldnt-get-a-blank-check" aria-hidden="true" tabindex="-1"><span class="content-header-link"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5 linkicon"><path d="M12.232 4.232a2.5 2.5 0 0 1 3.536 3.536l-1.225 1.224a.75.75 0 0 0 1.061 1.06l1.224-1.224a4 4 0 0 0-5.656-5.656l-3 3a4 4 0 0 0 .225 5.865.75.75 0 0 0 .977-1.138 2.5 2.5 0 0 1-.142-3.667l3-3Z"></path><path d="M11.603 7.963a.75.75 0 0 0-.977 1.138 2.5 2.5 0 0 1 .142 3.667l-3 3a2.5 2.5 0 0 1-3.536-3.536l1.225-1.224a.75.75 0 0 0-1.061-1.06l-1.224 1.224a4 4 0 1 0 5.656 5.656l3-3a4 4 0 0 0-.225-5.865Z"></path></svg></span></a></h2>
|
||
<p>The initial rush to implement observability tools like Datadog, New Relic, and Splunk often comes with little regard for cost optimization. This urgency is understandable; system failures and outages can be catastrophic for business. However, as the dust settles, organizations begin to scrutinize those hefty monthly bills. This scrutiny often leads to a more nuanced approach to observability. Teams start to explore open-source alternatives, like Prometheus and Grafana, or build custom solutions tailored to their specific needs. The aim shifts from simply "adding observability" to achieving meaningful insights in a cost-effective manner. This evolution reflects a maturing understanding of observability, where quality and cost-efficiency are balanced to meet the organization's unique requirements.</p>
|
||
<h3 id="opentelemetry-and-signoz-can-help-with-out-of-control-costs" class="content-header">OpenTelemetry and SigNoz can help with out-of-control-costs<a href="#opentelemetry-and-signoz-can-help-with-out-of-control-costs" aria-hidden="true" tabindex="-1"><span class="content-header-link"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" fill="currentColor" class="w-5 h-5 linkicon"><path d="M12.232 4.232a2.5 2.5 0 0 1 3.536 3.536l-1.225 1.224a.75.75 0 0 0 1.061 1.06l1.224-1.224a4 4 0 0 0-5.656-5.656l-3 3a4 4 0 0 0 .225 5.865.75.75 0 0 0 .977-1.138 2.5 2.5 0 0 1-.142-3.667l3-3Z"></path><path d="M11.603 7.963a.75.75 0 0 0-.977 1.138 2.5 2.5 0 0 1 .142 3.667l-3 3a2.5 2.5 0 0 1-3.536-3.536l1.225-1.224a.75.75 0 0 0-1.061-1.06l-1.224 1.224a4 4 0 1 0 5.656 5.656l3-3a4 4 0 0 0-.225-5.865Z"></path></svg></span></a></h3>
|
||
<p>This piece helps explain why observability SaaS offerings have often received a blank check as long as they reduced the risk of downtime. We haven't discussed why, so often, observability bills continue to grow and often outpace the growth in infrastructure costs. To explain that, we have to admit that part of the story is lock-in. With a closed-source SaaS offering for observability, switching service providers means at least an arduous change of installed software agents. In the worst case, teams will have added thousands of custom metric calls to their application code which will all have to be changed to switch <a target="_blank" href="https://signoz.io/blog/open-source-apm-tools/">APM tools</a>. Inevitably, this leaves customers 'stuck' and unable to do much as their observability bill grows.</p>
|
||
<p>OpenTelemetry can solve this problem. By implementing open standards for how observability data is gathered and transmitted, OpenTelemetry makes it very easy to switch service providers. If you're using the OpenTelemetry Collector (and you should be), all you have to do is reconfigure your collection endpoint in a single place.</p>
|
||
<p>Along with OpenTelemetry, you'll need a backend to report and chart data. The <a target="_blank" href="https://signoz.io/blog/opentelemetry-apm/">OpenTelemetry project</a> is neutral about your data backend, but a tool like <a href="https://github.com/SigNoz/signoz" rel="noopener noreferrer nofollow" target="_blank">SigNoz</a> uses the power of Clickhouse to store data efficiently, and it even has a self-hosted option.</p></article><div class="mt-8 max-lg:py-6 lg:hidden"><div class="relative w-full font-sans"><p class="m-0 mb-3 text-xs font-medium uppercase tracking-wide text-[var(--l2-foreground)]">Is this page helpful</p><div class="flex items-center gap-3" data-slot="popover-anchor"><div class="flex items-center gap-1.5"><button data-color="secondary" data-variant="outlined" data-size="icon" class="_button_1vo0j_1 !rounded-full" type="button" aria-label="Yes, this page was helpful" aria-pressed="false"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" 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flex-1 flex-col"><div class="mb-3 shrink-0 text-xs font-medium uppercase tracking-wide text-[var(--l2-foreground)]">On this page</div><div class="min-h-0 flex-1 overflow-y-auto overscroll-contain [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden"><div class="relative flex items-start gap-[3px]"><div class="relative min-h-full w-[2px] shrink-0 self-stretch"><div class="absolute inset-y-0 left-0 w-[2px] rounded-full bg-[var(--l2-border)]" aria-hidden="true"></div></div><div class="flex min-w-0 flex-1 flex-col gap-px"><div class="w-full"><a href="#observability-is-expensive" class="inline-block w-full rounded-md py-2 pl-3 transition-colors focus-visible:text-[var(--l2-foreground-hover)] focus-visible:outline-none text-sm text-[var(--l2-foreground)] hover:text-[var(--l2-foreground-hover)]">Observability is expensive</a></div><div class="w-full" style="padding-left:20px"><a href="#cost-reductions-are-possible" class="inline-block w-full rounded-md py-2 pl-3 transition-colors focus-visible:text-[var(--l2-foreground-hover)] focus-visible:outline-none text-sm text-[var(--l2-foreground)] hover:text-[var(--l2-foreground-hover)]">Cost reductions are possible</a></div><div class="w-full"><a href="#justifying-the-cost" class="inline-block w-full rounded-md py-2 pl-3 transition-colors focus-visible:text-[var(--l2-foreground-hover)] focus-visible:outline-none text-sm text-[var(--l2-foreground)] hover:text-[var(--l2-foreground-hover)]">Justifying the cost</a></div><div class="w-full" style="padding-left:20px"><a href="#costs-with-a-clear-quantifiable-benefit" class="inline-block w-full rounded-md py-2 pl-3 transition-colors focus-visible:text-[var(--l2-foreground-hover)] focus-visible:outline-none text-sm text-[var(--l2-foreground)] hover:text-[var(--l2-foreground-hover)]">Costs with a clear, quantifiable benefit</a></div><div class="w-full" style="padding-left:20px"><a href="#costs-that-are-less-clear-cut" class="inline-block w-full rounded-md py-2 pl-3 transition-colors focus-visible:text-[var(--l2-foreground-hover)] focus-visible:outline-none text-sm text-[var(--l2-foreground)] hover:text-[var(--l2-foreground-hover)]">Costs that are less clear-cut</a></div><div class="w-full" style="padding-left:20px"><a href="#the-real-argument-for-observability-the-cost-of-not-having-it" class="inline-block w-full rounded-md py-2 pl-3 transition-colors focus-visible:text-[var(--l2-foreground-hover)] focus-visible:outline-none text-sm text-[var(--l2-foreground)] hover:text-[var(--l2-foreground-hover)]">The real argument for observability: the cost of not having it</a></div><div class="w-full" style="padding-left:20px"><a href="#the-birth-of-million-dollar-observability-bills" class="inline-block w-full rounded-md py-2 pl-3 transition-colors focus-visible:text-[var(--l2-foreground-hover)] focus-visible:outline-none text-sm text-[var(--l2-foreground)] hover:text-[var(--l2-foreground-hover)]">The Birth of Million Dollar Observability Bills</a></div><div class="w-full"><a href="#observability-shouldnt-get-a-blank-check" class="inline-block w-full rounded-md py-2 pl-3 transition-colors focus-visible:text-[var(--l2-foreground-hover)] focus-visible:outline-none text-sm text-[var(--l2-foreground)] hover:text-[var(--l2-foreground-hover)]">Observability Shouldn’t Get a Blank Check</a></div><div class="w-full" style="padding-left:20px"><a href="#opentelemetry-and-signoz-can-help-with-out-of-control-costs" class="inline-block w-full rounded-md py-2 pl-3 transition-colors focus-visible:text-[var(--l2-foreground-hover)] focus-visible:outline-none text-sm text-[var(--l2-foreground)] hover:text-[var(--l2-foreground-hover)]">OpenTelemetry and SigNoz can help with out-of-control-costs</a></div></div></div></div></div><div class="shrink-0 rounded-xl border border-[var(--l2-border)] bg-[var(--l1-background)] p-4"><div class="relative w-full font-sans"><p class="m-0 mb-3 text-xs font-medium uppercase tracking-wide 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1.0807-.17.3521-.6071.2125-.6679-.1214-1.3721-1.9246L14.38 17.959l-1.1414-1.9428-.1397.079-.674 7.2552-.3156.3703-.7286.2793-.6071-.4614-.3218-.7468.3218-1.4753.3886-1.9246.3157-1.53.2853-1.9004.17-.6314-.0121-.0425-.1397.0182-1.4328 1.9672-2.1796 2.9446-1.7243 1.8456-.4128.164-.7164-.3704.0667-.6618.4008-.5889 2.386-3.0357 1.4389-1.882.929-1.0868-.0062-.1579h-.0546l-6.3385 4.1164-1.1293.1457-.4857-.4554.0608-.7467.2307-.2429 1.9064-1.3114Z"></path></svg><span class="whitespace-nowrap">Claude</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-arrow-up-right size-3 shrink-0" aria-hidden="true"><path d="M7 7h10v10"></path><path d="M7 17 17 7"></path></svg></button><button type="button" class="inline-flex h-8 items-center gap-1.5 rounded-[4px] border border-[var(--l1-border)] bg-[var(--l2-background-60)] px-2.5 text-[13px] 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a $1 million Datadog bill worth it? | SigNoz\"}],[\"$\",\"meta\",\"1\",{\"name\":\"description\",\"content\":\"I’d like to write a bit about how Observability costs are significant, how these costs tend to be justified, and how precise amount a company spends on *anything* tends to be more subjective than you’d think. This article is not about how to reduce or control these costs, but rather how the costs are justified.\"}],[\"$\",\"meta\",\"2\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"3\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"4\",{\"rel\":\"canonical\",\"href\":\"https://signoz.io/blog/justifying-a-million-dollar-observability-bill/\"}],[\"$\",\"link\",\"5\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://signoz.io/rss/\"}],[\"$\",\"meta\",\"6\",{\"property\":\"og:title\",\"content\":\"Is a $1 million Datadog bill worth it?\"}],[\"$\",\"meta\",\"7\",{\"property\":\"og:description\",\"content\":\"I’d like to write a bit about how Observability costs are significant, how these costs tend to be justified, and how precise amount a company spends on *anything* tends to be more subjective than you’d think. 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It got to a really basic question about running a tech company: how do you know that any cost is justified? While a small number of expenses have clear and direct business values, a bunch of other costs, I would even say _most_ costs, just aren’t that clear cut.\n\nI’d like to write a bit about how Observability costs are significant, how these costs tend to be justified, and how precise amount a company spends on _anything_ tends to be more subjective than you’d think. This article is not about how to reduce or control these costs, but rather how the costs are justified.\n\n\n\n\n\n## Observability is expensive\n\nSaaS tools to do observability have significant costs for any large-scale user, and no observability tool has a cost of 0. While tools like SigNoz have self-hosted solutions that will cut your Datadog bill down to zero dollars, you still have to consider both the infrastructure costs of hosting your own data backend and the support you’ll need to do on such a tool. Even with the (amazing) open-source options, the cost is never zero when you count implementation effort and storage costs.\n\nWhile everyone knows that observability costs _something_ I think most people don’t really understand just how expensive it can be. While many organizations may target spending 10% of their Ops budget for full-stack observability, it’s common for observability to be the [second-highest cost after infrastructure](https://devops.com/observability-costs-are-too-damn-high/). Why so expensive? And why so universally? For an object lesson, consider my demo flask app before and after I add OpenTelemetry instrumentation.\n\n```python\n# Acquire a tracer\n\napp = Flask(__name__)\n\n@app.route(\"/rolldice\")\ndef roll_dice():\n do_work(\"busywork\")\n return str(do_roll())\n\ndef do_roll():\n res = randint(1, 6)\n # Do the thing\n rollspan.set_attribute(\"roll.value\", res)\n return res\n\ndef do_work(work_type):\n print(\"doing some work...\")\n```\n\nNow let’s add [OpenTelemetry](https://signoz.io/opentelemetry/) manual instrumentation:\n\n```python\n# Acquire a tracer\ntracer = trace.get_tracer(\"diceroller.tracer\")\n\napp = Flask(__name__)\n\n@app.route(\"/rolldice\")\ndef roll_dice():\n do_work(\"busywork\")\n return str(do_roll())\n\ndef do_roll():\n # This creates a new span that's the child of the current one\n with tracer.start_as_current_span(\"do_roll\") as rollspan:\n res = randint(1, 6)\n current_span = trace.get_current_span()\n current_span.add_event(\"Gonna try it!\")\n # Do the thing\n current_span.add_event(\"Did it!\")\n rollspan.set_attribute(\"roll.value\", res)\n return res\n\n\nmetric_reader = PeriodicExportingMetricReader(ConsoleMetricExporter())\nprovider = MeterProvider(metric_readers=[metric_reader])\n\n# Sets the global default meter provider\nmetrics.set_meter_provider(provider)\n\n# Creates a meter from the global meter provider\nmeter = metrics.get_meteFr(\"otel.nica.meter.name\")\n\nwork_counter = meter.create_counter(\n \"work.counter\", unit=\"1\", description=\"Counts the amount of work done\"\n)\ndef do_work(work_type):\n # count the work being doing\n work_counter.add(1, {\"work.type\": work_type})\n print(\"doing some work...\")\n```\n\nObviously a contrived example, but it’s worth noting that there were more lines involved in monitoring the app than the app doing its work. With detailed enough measurement and tracing, along with transmitting and storing that measurement, observability can easily rival the application’s footprint.\n\n### Cost reductions are possible\n\nAgain, this is not the focus of this piece (geez don’t you read the introductory paragraphs??), but it’s important to note that it’s completely possible to significantly reduce the costs of observability. Tools like the [OpenTelemetry Collector](https://signoz.io/blog/opentelemetry-collector-complete-guide/) have tons of processors available to filter, parse, and compress data so that you’re sending way less data than your app’s actual workload.\n\nAnd open-source options are absolutely going to give more bang for your buck than closed-source SaaS tools. While companies like Datadog and New Relic may claim support for open standards like OpenTelemetry, [the reality is a little different](https://signoz.io/blog/is-opentelemetry-a-first-class-citizen-in-your-dashboard-a-datadog-and-newrelic-comparison/).\n\nHowever costs exist, they’re never going to be smaller than, say, what your office spends on sugar packets. How do we justify these expenses?\n\n## Justifying the cost\n\nBack to the Reddit thread where we started, the question is really how do we decide that a certain value is ‘worth it’ as a cost of observability? Let’s talk about some costs that are a lot easier to justify and specify, than observability:\n\n### Costs with a clear, quantifiable benefit\n\n- _Infrastructure \u0026 Operations -_ When we talk about infrastructure costs, it’s possible to calculate the marginal cost of a single user and show the value of a single additional user to our business, and therefore justify the costs. The same is true of the Operations team and SRE teams: they’re needed to keep the system up and running, so easy to put a dollar value on the benefit.\n- _Sales \u0026 Marketing -_ Here again, the calculation is fairly simple: calculate the benefit of new business, and you can show whether what you’re paying to acquire it makes sense\n\nOkay, if these costs can be justified to the last clipped Scottish groat, can’t we do that for all business expenses? Not at all:\n\n### Costs that are less clear-cut\n\nHere are some costs where the benefit is harder to quantify\n\n- **Literally, everyone else who works at the office -** Not to start with a bold one, but the benefit of literally everyone else isn’t a number you can type into a spreadsheet. Some examples of people at your company who have a nebulous, hard-to-exactly define benefit:\n - All executives - At publicly traded companies, we often give credit for all stock moves to the CEO. But everyone knows this can’t be an accurate number, and the direct benefit of all other executives is similarly hard to pin down. Again, this doesn’t mean they have zero benefit, just that it can’t be quantified as an exact figure.\n - All Managers - If we set an arbitrary value for the contributions of a team of engineers, say (shudder) a dollar value on each line of code added, we can arrive at some vague estimate of the engineers’ contribution to value. Even this rough estimate isn’t possible with the team’s management. And as such, the benefit of management is impossible to get a perfect read on. We know that a great manager can get great things from their team. But the dollar value is quite difficult to calculate.\n- **The Office itself** - More on this in later sections. But the value of all those cubicles does not fit on a spreadsheet.\n\nSo, we can see that a ton of the expenses a company runs up, especially at a tech company, can’t be calculated against the exact value they produce.\n\nIf this situation is hard to believe, consider that most airlines can’t specify the exact cost (and therefore the precise profit) \u003ca href = \"https://revenue-hub.com/revenue-management-hotels-learn-delta-airlines/\" rel=\"noopener noreferrer nofollow\" target=\"_blank\" \u003eof a single passenger on a single flight\u003c/a\u003e, and you’ll see that very often businesses must make decisions heuristically rather than with precise valuations.\n\n### The real argument for observability: the cost of not having it\n\nWhen a developer uses a cool [tracing tool](https://signoz.io/blog/distributed-tracing-tools/) to find the bit of code that’s slowing down performance, the dollar benefit of that act to the company is extremely difficult to show. You might try calculating how long the developer would have spent trying to fix the problem without the tool, but that’s always going to be a _very_ rough estimate since a proper observability tool \u003ca href = \"https://www.cncf.io/blog/2022/12/16/why-opentelemetry-is-taking-cloud-native-to-new-heights/\" rel=\"noopener noreferrer nofollow\" target=\"_blank\" \u003efundamentally alters the way dev teams operate\u003c/a\u003e so a simple time estimate is unlikely to be accurate.\n\nNo, the real way to show what observability has to offer is the cost of _not_ having it. The cost of downtime, system slowdowns, and dissatisfied users is one that every exec can estimate.\n\nNo one wants to be responsible for the site going down, and the cost of downtime can be millions of dollars an hour for a large enough system.\n\nThis, then, is the core of observability’s cost justification: it may be hard to justify the cost of uptime, but the cost of extended downtime is enough to tank a successful company.\n\n### The Birth of Million Dollar Observability Bills\n\nIn the deep mists of observability history, I remember the healthcare.gov launch, and a now-famous line in a meeting with Mikey Dickerson and the IT firms who had bungled the website launch\n\n\u003e “If I hear one more person tell me I can’t use New Relic,” he said. “I’ll punch them in the face.”\n\nAnd that, truly, is the story of how our Datadog, New Relic, and Splunk bills got so high. After a spate of outages, failures, bugs, and other problems that users notice; some executive says ‘add observability, I don’t care what it costs.’ And the \u003ca href = \"https://twitter.com/kellabyte/status/1704947999414063465\" rel=\"noopener noreferrer nofollow\" target=\"_blank\" \u003emillion-dollar observability bill\u003c/a\u003e was born.\n\n## Observability Shouldn’t Get a Blank Check\n\nThe initial rush to implement observability tools like Datadog, New Relic, and Splunk often comes with little regard for cost optimization. This urgency is understandable; system failures and outages can be catastrophic for business. However, as the dust settles, organizations begin to scrutinize those hefty monthly bills. This scrutiny often leads to a more nuanced approach to observability. Teams start to explore open-source alternatives, like Prometheus and Grafana, or build custom solutions tailored to their specific needs. The aim shifts from simply \"adding observability\" to achieving meaningful insights in a cost-effective manner. This evolution reflects a maturing understanding of observability, where quality and cost-efficiency are balanced to meet the organization's unique requirements.\n\n### OpenTelemetry and SigNoz can help with out-of-control-costs\n\nThis piece helps explain why observability SaaS offerings have often received a blank check as long as they reduced the risk of downtime. We haven't discussed why, so often, observability bills continue to grow and often outpace the growth in infrastructure costs. To explain that, we have to admit that part of the story is lock-in. With a closed-source SaaS offering for observability, switching service providers means at least an arduous change of installed software agents. In the worst case, teams will have added thousands of custom metric calls to their application code which will all have to be changed to switch [APM tools](https://signoz.io/blog/open-source-apm-tools/). Inevitably, this leaves customers 'stuck' and unable to do much as their observability bill grows.\n\nOpenTelemetry can solve this problem. By implementing open standards for how observability data is gathered and transmitted, OpenTelemetry makes it very easy to switch service providers. If you're using the OpenTelemetry Collector (and you should be), all you have to do is reconfigure your collection endpoint in a single place.\n\nAlong with OpenTelemetry, you'll need a backend to report and chart data. The [OpenTelemetry project](https://signoz.io/blog/opentelemetry-apm/) is neutral about your data backend, but a tool like \u003ca href = \"https://github.com/SigNoz/signoz\" rel=\"noopener noreferrer nofollow\" target=\"_blank\" \u003eSigNoz\u003c/a\u003e uses the power of Clickhouse to store data efficiently, and it even has a self-hosted option.\n"])</script><script>self.__next_f.push([1,"2d:T4541,"])</script><script>self.__next_f.push([1,"\nIf you've used or looked into Datadog, you already know Datadog is not one product with one price. It is a platform with more than 40 separately metered products stacked onto a single bill, each charged by its own unit, whether that is hosts, gigabytes, events, sessions, committers, or AI credits. That structure is why teams routinely see invoices two to three times higher than their first estimate.\n\nThis blog breaks down how Datadog actually charges you, from the plans and per-unit prices for the products that drive most bills to the billing mechanics that catch people off guard and the levers that keep costs under control. Every price here comes from Datadog's official pricing pages, captured in July 2026.\n\nBy the end of this article, you will be able to read a Datadog quote and estimate your bill before signing, with a clear sense of where costs grow fastest. We have also added a more affordable [Datadog alternative](https://signoz.io/datadog-alternative/) at the end for you to consider.\n\n## How Datadog pricing works (the billing model in plain English)\n\nDatadog is, at its core, a modular platform. There is **no single Datadog subscription**. You assemble a bill from individual products you choose, and the total depends entirely on which ones you turn on and how much data each one sees.\n\nThe following three ideas explain almost every line on a Datadog invoice.\n\n### 1. Price is Modular\n\nDatadog fragments its pricing across different products like Infrastructure Monitoring, [Application Performance Monitoring (APM)](https://signoz.io/application-performance-monitoring/), [Log Management](https://signoz.io/log-management/), Real User Monitoring (RUM), and the security suite, all billed separately. While this is often marketed as flexibility, the reality is that each new service brings its own unique pricing meter. As your observability needs grow, bolting on new features means juggling multiple billing dimensions, making it increasingly difficult to forecast your overall spend and avoid surprise overages.\n\n### 2. The Usage-Based Billing Matrix\n\nDatadog bills every product using a different yardstick: hosts for infrastructure, dual-metrics (GBs and events) for logs, sessions for RUM, and active committers for CI tools. This matrix of usage metrics means there is no single lever to control your costs. If you don't monitor every single one of these disparate units, you are practically guaranteed a billing surprise at the end of the month.\n\n### 3. Three Billing Options\n\nDatadog offers three billing tiers.\n\n- `Annual` is the lowest rate and requires a committed volume.\n- `Month-to-month` is a higher monthly rate with no annual commitment.\n- `On-demand` is the highest rate, billed by actual usage with no commitment. \n\nFor example, while Infrastructure Pro is advertised at \\$15 per host annually, falling short on your estimation forces you into the \\$18 on-demand rate, turning any unexpected scaling into a costly premium penalty.\n\n## Datadog's Core Product Pricing Overview\n\nTo give you a clearer picture, here’s an overview of how Datadog prices its main offerings.\n\n\u003cKeyPointCallout defaultCollapsed=\"false\"\u003e\nBelow prices are based on publicly available information for billing as of July 2026 and may be subject to change. It's always best to consult Datadog's official pricing page for the latest figures.\n\u003c/KeyPointCallout\u003e\n\n| **Product** | **Annual Billing** | **Key Details \u0026 Inclusions** |\n| --- | --- | --- |\n| **Infrastructure (Pro)** | \\$15 per host/month | • On-demand: \\$18/host\u003cbr/\u003e• 5 containers/host\u003cbr/\u003e• 100 custom metrics/host\u003cbr/\u003e• 500 custom events/host\u003cbr/\u003e• 15-month retention |\n| **Infrastructure (Enterprise)** | \\$23 per host/month | • On-demand: \\$27/host\u003cbr/\u003e• 10 containers/host\u003cbr/\u003e• 200 custom metrics/host\u003cbr/\u003e• 1,000 custom events/host\u003cbr/\u003e• ML alerts + governance |\n| **APM** | \\$31 per host/month | • On-demand: \\$36/host\u003cbr/\u003e• 150 GB ingested spans/host\u003cbr/\u003e• 1M indexed spans/host\u003cbr/\u003e• Distributed tracing included |\n| **APM Pro** | \\$35 per host/month | • On-demand: \\$42/host\u003cbr/\u003e• Everything in APM + Data Streams Monitoring |\n| **APM Enterprise** | \\$40 per host/month | • On-demand: \\$48/host\u003cbr/\u003e• Everything in APM Pro + Continuous Profiler |\n| **Continuous Profiler (standalone)** | \\$19 per profiled host/month | • On-demand: \\$23/host\u003cbr/\u003e• 4 containers/profiled host\u003cbr/\u003e• Extra containers: \\$2 each |\n| **Log Management - Ingest** | \\$0.10 per GB/month | • Per GB uncompressed ingested\u003cbr/\u003e• Covers processing, live tail, archiving\u003cbr/\u003e• Charged even if not indexed |\n| **Log Management - Index** | \\$1.70 per 1M events/month (15-day) | • On-demand: \\$2.55/1M\u003cbr/\u003e• Retention: \\$1.06 (3-day), \\$1.27 (7-day), \\$2.50 (30-day)\u003cbr/\u003e• Makes logs searchable + alertable\u003cbr/\u003e• Flex Logs \\$0.05/1M for long retention |\n| **Custom Metrics** | \\$5 per 100 metrics/month (overage) | • 100/host (Pro), 200/host (Ent.) included\u003cbr/\u003e• Priced on cardinality\u003cbr/\u003e• OTel counts as custom |\n| **Real User Monitoring (RUM)** | \\$0.15 per 1,000 sessions (Measure) | • On-demand: \\$0.22/1K\u003cbr/\u003e• Investigate: \\$3/1K sessions\u003cbr/\u003e• Session Replay: +\\$2.50/1K\u003cbr/\u003e• Scales with traffic |\n| **Database Monitoring** | \\$70 per DB host/month | • On-demand: \\$84/host\u003cbr/\u003e• Query-level metrics\u003cbr/\u003e• Separate from infra host count |\n| **Synthetic API Tests** | \\$5 per 10,000 runs/month | • On-demand: \\$7.20/10K\u003cbr/\u003e• Scales with frequency \u0026 test locations |\n| **Synthetic Browser Tests** | \\$12 per 1,000 runs/month | • On-demand: \\$18/1K\u003cbr/\u003e• Full browser journey\u003cbr/\u003e• Mobile testing: \\$50/100 runs |\n| **Cloud Security Management** | \\$10/host (Pro)\u003cbr/\u003e\\$25/host (Enterprise) | • On-demand: \\$12 / \\$30\u003cbr/\u003e• Workload Protection separate (\\$15) |\n| **AI Credits** | \\$500 per 500-credit bundle/month | • On-demand: \\$1.30/credit\u003cbr/\u003e• No rollover\u003cbr/\u003e• 0.3–6.5 credits per action |\n\n## Understanding the Datadog Billing Mechanics \u0026 Hidden Costs\n\nDatadog does not simply multiply your average host count by the rate. It uses complex billing mechanics that often lead to bills significantly higher than initial estimates. Here are the four most common hidden traps to watch out for:\n\n### High-Water-Mark Host Billing Trap\n\nDatadog's pricing for Infrastructure and APM is often tied to the number of hosts you're monitoring. It measures your host count every hour, discards the top 1% of hours with the highest usage, and then bills the entire month based on the next-highest hour, roughly the 99th percentile. This protects you from very brief spikes, but it means any period of sustained high usage sets the price for the whole month.\n\nFor example: Suppose you normally run 50 hosts, and for a 5-day marketing event you scale to 200. At the end of the month, Datadog ignores about the top 1% of hours (roughly 7 hours of your peak usage), but it still sees 200 hosts for the rest of that stretch, your host count was consistently 200. Your APM bill for the month is calculated on 200 hosts, not your typical 50.\n\n- **Your Expectation (based on average):** Closer to `$2,260`\n- **Billed (based on 5-days peak usage):** `$6,200` (200 hosts x \\$31/month)\n\nYou end up paying for your busiest week across the entire month, which is where most \"why is my bill so high\" surprises originate.\n\n### The Container Overallocation Trap\n\nThe same host model gets dangerous in containerized environments. The recommended setup is one Datadog Agent per host, for example, one per Kubernetes node. If an agent is misconfigured to run in every pod, each pod can be counted as a separate host. On a 50-node cluster running hundreds of pods, that single mistake can multiply the bill many times over.\n\n### Double Tariff for Log Management\n\nDatadog's log pricing is a two-part tariff. You pay once to collect logs (`Ingest`) and again to make them searchable (`Index`).\n\n| **Billing Type** | **Price** | **What it covers** |\n| --- | --- | --- |\n| **Ingest** | \\$0.10 per GB | Collect, process, and archive all logs |\n| **Index** | \\$1.70 per 1M events (15-day) | Make logs searchable + alertable. Other retention tiers: \\$1.06 (3-day), \\$1.27 (7-day), \\$2.50 (30-day) |\n\nThe Problem is that this model forces you to pay twice for the same data to make it useful for debugging. You must pay to collect every gigabyte of logs, but to get real-time query and alerting capabilities, you must again pay the much higher per-event indexing fee. This creates a strong financial disincentive to log comprehensively.\n\nImagine your application generates a modest 200 GB of logs in a month, which equates to roughly 100 million log events.\n\n1. **Ingest Cost:** You are first charged **\\$20/month** (200 GB x \\$0.10) just to get the logs into Datadog's system.\n2. **Indexing Cost:** To make these logs searchable for your developers during an incident, you then pay an additional **\\$170/month** (100 million events x \\$1.70) for 15-days of retention.\n\nYour total bill is **\\$190/month**. To cut that, teams index only a fraction of their logs, which means most of their log data is not searchable during an incident, exactly when they need it.\n\nDatadog's newer Flex Logs tier softens this for long-retention use cases. Flex storage is `$0.05` per million events stored and keeps data queryable for up to `15 months`, but it does not support monitors or Watchdog Insights, so it fits audit and compliance data more than active debugging. For a fuller breakdown of ingestion, indexing, and retention tiers, see our dedicated [Datadog logs pricing](https://signoz.io/blog/datadog-logs-pricing/) guide.\n\n\u003cInlineCTA message=\"SigNoz is one of the few platforms with transparent, ingestion-based pricing on top of unified telemetry storage and faster queries. Every log you ingest is indexed and query-ready at no additional cost.\" ctaText = 'Read more' ctaLink = 'https://signoz.io/blog/log-storage-and-analysis-in-signoz/'/\u003e\n\n### The Custom Metrics Pricing Trap\n\nCustom metrics are the most unpredictable part of a Datadog bill. A custom metric can be defined as any metric that does not come from a standard Datadog integration, which includes almost every metric you create for your own application. Critically, metrics sent through OpenTelemetry, the open-source observability standard, are also billed as custom. Our [Datadog custom metrics pricing](https://signoz.io/blog/datadog-custom-metrics-pricing/) guide goes deeper, but here is the short version.\n\n| **Custom Metrics** | **Price** |\n| --- | --- |\n| **Pro** | 100 custom metrics per host |\n| **Enterprise** | 200 custom metrics per host |\n| **Overage** | \\$5 per 100 metrics/mo |\n| **Ingested metrics** (Metrics without Limits) | \\$0.10 per 100 metrics/mo |\n\nThe cost is driven by cardinality, meaning the number of unique combinations of a metric name and its tags.\n\nCardinality grows faster than people expect. Say you track `api.request.latency` across 10 endpoints, 3 status codes, and 3 customer tiers. That is `10 x 3 x 3 = 90 unique metrics` from a single metric name. Add one [high cardinality](https://signoz.io/blog/high-cardinality-data/) tag such as `customer_id` and the count jumps into the thousands, triggering significant overage fees.\n \nTo manage this, Datadog offers `Metrics without Limits`, which lets you control which tags are indexed (and thus searchable). While this can lower costs, it introduces its own complexity and a new charge: \n\n- **Indexed Metrics:** You are billed the standard overage rate for the metrics you choose to index with specific tags.\n\n- **Ingested Metrics:** You also pay a separate, smaller fee (**\\$0.10 per 100 metrics**) for **all** the original metric combinations you sent to Datadog before your tags were filtered.\n \nEssentially, you are either paying a high price for full visibility or juggling two different charges to reduce it, adding another layer of complexity to your bill. If you want to bring an existing Datadog bill down, our guide on [how to reduce Datadog costs](https://signoz.io/guides/how-to-reduce-datadog-costs/) walks through the practical levers.\n\n\u003cKeyPointCallout title=\"Another Model: Metric Name Pricing\" defaultCollapsed=\"false\"\u003e\nThe pricing discussed in above section is Datadog's cardinality-based model, which applies unless your contract opts into the alternative. That alternative, **Metric Name pricing** model, bills by the number of unique metric names you submit and the volume of datapoints they produce, rather than by tag combinations. The two are mutually exclusive, so confirm which one your contract uses before forecasting.\n\u003c/KeyPointCallout\u003e\n\n## SigNoz Cloud: The Cost-Effective Datadog Alternative\n\nLook back at your own Datadog bill for a moment. For most teams, the spend concentrates in a handful of places like infrastructure hosts, APM hosts, indexed logs, and custom metrics. Everything covered in the sections above points to the same question. How much of that bill is buying you insight, and how much is just the cost of how the tool meters usage?\n\nAnswer it with your own numbers, because what you find decides whether a different pricing model would actually help you. Here is how Datadog and SigNoz Cloud compare on each cost driver from the sections above:\n\n| **Dimension** | **Datadog** | **SigNoz Cloud** |\n| --- | --- | --- |\n| **Billing model** | Per host + per-product usage | Usage-based on data volume |\n| **Hosts** | `$15 to $40` per host/month | No per-host charge |\n| **Custom metrics** | `$5 per 100` over allotment; OTel is billed as custom | Not charged separately; OTel is not penalized |\n| **Logs** | `$0.10/GB` ingest + `$1.70/1M` indexed | `$0.30/GB`ingested; No extra cost for indexing|\n| **Traces** | `$31 to $40` per host + span overages | `$0.30/GB` ingested |\n| **Metrics** | Cardinality-based custom metric fees | `$0.10 per million` samples |\n| **Forecasting** | Multiple meters + high-water-mark host counting | Single volume-based meter |\n| **Vendor lock-in** | Proprietary agent and data formats. Leaving means re-instrumenting | OpenTelemetry-native. Instrumentation stays portable if you switch |\n\nRun your last invoice against the above table. If it is dominated by host counts and custom metrics, the gap is not a rounding difference, and you can see exactly where it comes from.\n\n[SigNoz Cloud](https://signoz.io/) is an all-in-one, OpenTelemetry-native observability platform that unifies metrics, traces, and logs in a single application. Because it is built around [OpenTelemetry](https://signoz.io/opentelemetry/), sending your data through OTel does not trigger a custom-metric surcharge, and there is no per-host fee to optimize around. The instrumentation belongs to you, not the vendor, so your telemetry stays portable and you are not locked into proprietary agents that make leaving expensive. [SigNoz Cloud pricing](https://signoz.io/pricing/) is easy to understand: `$0.30 per GB of logs`, `$0.30 per GB of traces`, and `$0.10 per million metric samples`.\n\nWhether Datadog's model fits your architecture is a call only you can make, and it should be made with your own bill in front of you. If the pattern you keep seeing is rising host counts and overages you did not forecast, it is worth putting a predictable, volume-based model side by side with your next Datadog quote before you renew. For teams already on Datadog, our [Datadog migration guide](https://signoz.io/docs/migration/migrate-from-datadog-to-signoz/) walks through moving dashboards and instrumentation instead of rebuilding them by hand.\n\n### Get started with SigNoz\n\nThe easiest way to try SigNoz is [SigNoz Cloud](https://signoz.io/teams/), which comes with a 30-day free trial with access to all features.\n\nTeams with data-privacy requirements that prevent sending data outside their infrastructure can use the [enterprise self-hosted or BYOC offering](https://signoz.io/contact-us/). Teams that prefer to manage everything themselves can start with the free, self-hosted [community edition](https://signoz.io/docs/install/self-host/).\n\n## FAQs\n\n#### How much does Datadog cost per month?\n\nThere is no single number. Infrastructure Monitoring starts at \\$15 per host per month on annual billing, and you add per-product charges for APM, logs, metrics, and anything else you enable. A small production stack typically lands in the low hundreds; large environments reach five and six figures.\n\n#### Why is my Datadog bill so high?\n\nThe usual causes are high-water-mark host billing during traffic spikes, the double charge for log ingest and indexing, and custom metric overages from high-cardinality tags. These are billed on top of the base plan and often appear in the second month of usage.\n\n#### Does Datadog have a free plan?\n\nYes. The Free tier covers up to 5 hosts with 1-day metric retention. It is fine for evaluation but not for production, given the retention limit and host cap.\n\n#### How does Datadog count hosts?\n\nA host is any physical or virtual OS instance, including VMs and Kubernetes nodes. Datadog measures host count hourly, drops the top 1% of hours, and bills the month on the next-highest hour.\n\n#### Why are custom metrics so expensive in Datadog?\n\nCustom metrics are billed by cardinality, the number of unique metric-and-tag combinations. Pro includes 100 per host, then \\$5 per 100 metrics. A single high-cardinality tag can push the count into the thousands. Metrics sent via OpenTelemetry are billed as custom.\n\n#### Is Datadog cheaper billed annually or on-demand?\n\nAnnual is cheaper but requires a committed volume. Infrastructure Pro is \\$15 per host annually versus \\$18 on-demand, and usage above your commitment is billed at the higher on-demand rate.\n"])</script><script>self.__next_f.push([1,"2e:Ta626,"])</script><script>self.__next_f.push([1,"\n\n[New Relic vs](https://signoz.io/blog/open-source-newrelic-alternative/) DataDog: Both tools are popular for application and [infrastructure monitoring](https://signoz.io/blog/opentelemetry-powered-infrastructure-monitoring/), offering a wide range of features. This post compares New Relic and DataDog on key aspects like [APM](https://signoz.io/blog/open-source-apm-tools/), log management, and OpenTelemetry support.\n\n\u003cAdmonition type=\"info\"\u003e\n 💡 I instrumented a sample Spring Boot Application and sent data to Datadog and New Relic to\n evaluate my experience. Some takeaways are subjective and based on personal preference.\n\u003c/Admonition\u003e\n\n## Datadog vs New Relic: Overview\n\nNew Relic and Datadog represent two leading approaches to observability platforms. Datadog started as an [infrastructure monitoring tool](https://signoz.io/comparisons/infrastructure-monitoring-tools/) and expanded into a full-stack solution, while New Relic pioneered application performance monitoring and evolved into a comprehensive observability suite.\n\nFor application monitoring, both Datadog and New Relic offer similar core features. The difference lies in the actual user experience and specialized capabilities. My research found that Datadog gives you more granular controls and has stronger security features like Cloud SIEM, while New Relic feels simpler to start with and offers a more application-centric approach.\n\nHere's a quick overview of the overall platform features and functionality of DataDog and New Relic:\n\n| Feature | DataDog | New Relic |\n| ------------------------- | ---------------- | ---------------------------------------- |\n| APM | ✅ Available | ✅ Available |\n| Log Management | ✅ Available | ✅ Available |\n| Infrastructure Monitoring | ✅ Available | ✅ Available |\n| Network Monitoring | ✅ Available | ✅ Available |\n| Security Monitoring | ✅ Comprehensive | 🟡 Limited (in preview) |\n| Synthetic Monitoring | ✅ Available | ✅ Available |\n| Real User Monitoring | ✅ Available (with session replay) | ✅ Available |\n| Kubernetes Monitoring | ✅ Strong (dedicated Cluster Agent) | ✅ Strong (one-step observability) |\n| OpenTelemetry Support | 🟡 Limited | 🟡 Limited |\n| Free Tier | 🟡 Limited | ✅ Available (100GB free data per month) |\n| Integrations | ✅ 800+ | ✅ 780+ |\n\nThese differences highlight the strengths and weaknesses of DataDog and New Relic, helping you choose the right observability platform for 2026 based on your specific needs.\n\n**For a deeper dive into the specific features and performance of [New Relic vs DataDog](https://signoz.io/blog/datadog-vs-newrelic/#new-relic-apm), let's explore the detailed comparison in the sections below.**\n\n## APM: Datadog for More Control, New Relic for Simplicity\n\nI instrumented a sample Java application and sent data to both DataDog and New Relic for APM. The steps are almost the same in both DataDog and New Relic, with New Relic having a few extra steps. Both New Relic and DataDog require you to install their agent as well as a programming language-specific agent, which, in my case, was a Java agent.\n\n### DataDog APM\n\nIn DataDog, I had a hard time figuring out whether my setup was complete or not, and I found the onboarding flow of New Relic much better.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/02/datadog-onboarding-tabs.webp\"\n alt=\"Datadog's onboarding tab\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eDataDog's onboarding flow is a bit overwhelming, with too many horizontal tabs.\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\nThe good thing about DataDog is it gives you a lot of control. You can set up things like collecting custom metrics (which might be [expensive](https://signoz.io/blog/datadog-pricing/)), sampling rate, and telemetry correlation between traces and logs right from the beginning.\n\nOnce the setup is done, you can see your list of [spans](https://signoz.io/blog/distributed-tracing-span/) and corresponding [flamegraphs](https://signoz.io/blog/flamegraphs/) for your traces. DataDog does a good job of correlating different types of signals. You can relate info from infrastructure, metrics, network, etc., right from trace data if you have those products enabled.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg className=\"box-shadowed-image\" src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/02/datadog-apm.webp\" alt=\"Datadog APM\" /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eDataDog's APM showing breakdown of an internal server error\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\n**Some of the key features of DataDog APM include:**\n\n- ✅ Distributed tracing to track requests from user sessions to services and databases.\n- ✅ Correlation of [distributed traces](https://signoz.io/blog/distributed-tracing/) to infrastructure and network metrics.\n- ✅ Ingest 100% of traces from the last 15 minutes with retention of error and high latency traces.\n- ✅ Code-level performance inspection and breakdown of slow requests.\n\n### New Relic APM\n\nNew Relic's traces page shows trace groups instead of a list of spans, which feels like a cleaner representation, and you can filter by root spans, which comes in handy in case of large trace groups.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/02/new-relic-apm.webp\"\n alt=\"New Relic APM\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eNew Relic groups spans in trace groups and shows important metrics about them.\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\nYou can get flamegraphs for your traces in New Relic, too. Compared to DataDog, New Relic has fewer options for correlation. But it is interesting to note that New Relic shows many more spans for the same call in my Java application.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/02/new-relic-traces.webp\"\n alt=\"New Relic Traces\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eNew Relic's Flamegraph view of traces - you can also check out corresponding logs\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\n**Some of the key features of New Relic APM include:**\n\n- ✅ Auto-instrumentation of eight programming languages: Java, .Net, Node.js, PHP, Python, Ruby, Go, and C/C++\n- ✅ [Distributed tracing](https://signoz.io/distributed-tracing/) and sampling options for a wide range of technology stacks\n- ✅ Correlation of tracing data with other aspects of application infrastructure and user monitoring\n- ✅ Fully managed cloud-native experience with on-demand scalability\n\n### Verdict: New Relic vs DataDog\n\nOverall, if you need a simpler experience, then choose New Relic's APM. But if you need more control over what things you can do with your data, then choose DataDog's APM.\n\nLet's dive deeper into the specific features and performance of New Relic vs DataDog in the next sections.\n\n## Log Management: DataDog for More Filters, New Relic for Quick-Start\n\n### New Relic Log Management\n\nNew Relic automatically collected logs from my Java application and displayed them in the logs tab. It allows you to [search logs](https://signoz.io/docs/logs-management/logs-api/search-logs/) using Lucene, an open-source search library, and query log data using NRQL, a SQL-like query language developed by New Relic.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/02/new-relic-log-management-dashboard.webp\"\n alt=\"Log Management Dashboard in New Relic\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eLog Management Dashboard in New Relic showing logs from instrumented Java application\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\n**Key features of New Relic's log management:**\n\n- Automatically extracts attributes from logs.\n \u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/02/new-relic-log-attributes.webp\"\n alt=\"Log Attributes in New Relic\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eAttributes filtered from Java application logs in New Relic\u003c/i\u003e\n \u003c/figcaption\u003e\n \u003c/figure\u003e\n- Provides a feature called patterns for log data discoverability, though it didn't detect any pattern in my Java application logs.\n- Tools to manage log data by optimizing storage with dropping filters.\n\n### DataDog Log Management\n\nFor DataDog, automatic log collection is disabled by default and needs to be enabled in the agent's config file, along with activating a Java integration for application logs collection.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/02/datadog-logs-tab.webp\"\n alt=\"Log Tab in Datadog\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eLog Tab in DataDog showing logs from my Spring Boot Application\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\nBoth DataDog and New Relic offer log pattern detection, but DataDog identified patterns in my logs, while New Relic did not. DataDog provides more options to filter and visualize log data and allows the use of cloud storage for logs, useful for long-term storage.\n\nSetting up log collection in DataDog took more time compared to New Relic, but DataDog offers more visualization options.\n\n### Verdict: New Relic vs DataDog\n\nIf you need a [log management solution](/log-management/) that's quick to set up, New Relic is the better choice with its automatic log collection and easy setup. However, if you require more control over filtering and visualizing logs, DataDog's log management provides more advanced options.\n\n\u003cDatadogVsSigNoz /\u003e\n\n## Infrastructure Monitoring: Tie, Decide Based on Cost\n\nHost monitoring in both DataDog and New Relic is good, and choosing one over the other can be a matter of personal choice. I personally like the color theme of New Relic and the representation of things like disk usage in a table.\n\n### DataDog Infrastructure Monitoring\n\nWhat's interesting about DataDog is that it showed me a glimpse of the JVM metrics dashboard while clicking on my host. DataDog has done a really good job at correlating different types of information collected from your application and host.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/02/datadog-infrastructure.webp\"\n alt=\"Datadog infrastructure tab\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eDatadog showing JVM metrics without any configuration was a good experience\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/02/datadog-infrastructure-2.webp\"\n alt=\"Datadog host monitoring dashboard\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eDatadog's dashboard for host monitoring\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\nSome of the key features of DataDog's infrastructure monitoring include:\n\n- ✅ You can see all your machines in the infrastructure list. Each machine/host has tags, aliases, and metrics attached to it.\n- ✅ DataDog provides a Host map to visualize all your hosts on one screen.\n- ✅ It also provides a container map and real-time monitoring of containers.\n\n### New Relic Infrastructure Monitoring\n\nNew Relic's infrastructure monitoring provides robust features for connecting host performance with configuration changes.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/02/new-relic-host-monitoring.webp\"\n alt=\"New Relic Host Monitoring Dashboard\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eNew Relic's Dashboard for Host monitoring\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\nSome of the key features of New Relic infrastructure monitoring include:\n\n- ✅ Connect changes in your host performance with your configuration changes.\n- ✅ Troubleshoot performance issues by connecting the server-side to the application side if your infrastructure account is connected with the APM account.\n- ✅ Provides integrations to collect metrics for popular platforms like AWS, GCP, Azure, Kubernetes, etc.\n\n### Verdict: New Relic vs DataDog\n\nIf your use case is only infrastructure monitoring, then the decision comes down to cost. However, it's not easy to figure out how much each tool will cost on a head-to-head basis as their pricing structures are very different. I recommend you sign up and do a trial for both tools, including factors like user seats (New Relic charges for user seats).\n\n## Network Monitoring\n\n### DataDog Network Monitoring\n\nDataDog offers robust network monitoring capabilities. Some of the key features include:\n\n- ✅ Provides metrics for point-to-point communication within your infrastructure.\n- ✅ Granular data for network flows in a multi-cloud environment with aggregation capabilities supported by tags.\n- ✅ Automatically collects tags from more than 450 integrations, allowing you to see network volume between any two sets of tags.\n\n### New Relic Network Monitoring\n\nNew Relic provides comprehensive network monitoring solutions. Some of the key features include:\n\n- ✅ Pre-configured dashboards for monitoring popular cloud services like Azure, AWS, and GCP, with dynamic alerting.\n- ✅ Integrations with over 100 services, including advanced Kubernetes monitoring that correlates metrics from the application and the infrastructure. Check the full list of [AWS](https://docs.newrelic.com/docs/integrations/amazon-integrations/), [Azure](https://docs.newrelic.com/docs/integrations/microsoft-azure-integrations/azure-integrations-list/), and [GCP](https://docs.newrelic.com/docs/integrations/google-cloud-platform-integrations/) integrations.\n\n### Verdict: New Relic vs DataDog Network Monitoring\n\nBoth DataDog and New Relic offer strong network monitoring features. DataDog excels in granular data and multi-cloud environments, while New Relic shines with its pre-configured dashboards and extensive integrations. The choice between the two may come down to specific needs and preferences regarding integration and visualization capabilities.\n\n## Browser or Real-User Monitoring\n\nReal User Monitoring (RUM) is critical for understanding actual end-user experience with your applications. Both platforms offer robust solutions but with different approaches.\n\n### DataDog Real-User Monitoring\n\nDataDog provides comprehensive end-to-end visibility into user journeys for mobile and web applications. Some of the key features include:\n\n- ✅ Session Replay with up to 30-day retention, allowing you to watch recordings of user sessions\n- ✅ Capture and track Core Web Vitals (LCP, FID, CLS) with the ability to set alerts\n- ✅ Detailed filtering by user attributes, device, geography, and other dimensions\n- ✅ Strong integration with backend [APM traces](https://signoz.io/blog/apm-vs-distributed-tracing/) for full-stack visibility\n- ✅ Root cause analysis for slow loading times with visibility into code, network, and infrastructure\n- ✅ Customer segmentation using tags for real-time error tracking\n\nDataDog's RUM provides powerful analytics but requires additional configuration to enable all features, and it's priced per session.\n\n### New Relic Browser Monitoring\n\nNew Relic monitors end-users using your application across web browsers, devices, operating systems, and networks. Some of the key features include:\n\n- ✅ Quick implementation with automatic tracking of \"four golden signals\" of user experience\n- ✅ Built-in Apdex scoring for user satisfaction measurement\n- ✅ Event trails and breadcrumbs (though no full session video replay)\n- ✅ Full-stack visibility to identify end-user latency from backend or network issues\n- ✅ Session performance heatmaps showing user interaction with the webpage\n- ✅ JavaScript error analytics to track end-user steps leading to errors\n\nNew Relic's approach is simpler to implement and provides core functionality out-of-the-box, though it lacks some advanced features like session replay that Datadog offers.\n\n### Verdict: New Relic vs DataDog Browser Monitoring\n\nIf you need detailed user behavior insights with session replay and fine-grained analysis, Datadog RUM is more powerful. If you prefer quick implementation of essential UX monitoring with minimal setup, New Relic's Browser Monitoring provides what most teams need. Both capture Core Web Vitals and integrate with backend monitoring.\n\n## Synthetic Monitoring\n\nSynthetic monitoring simulates user interactions to proactively detect issues before real users encounter them. Both platforms offer comprehensive solutions in this area.\n\n### New Relic Synthetics\n\nNew Relic Synthetics provides various monitor types including:\n\n- ✅ Simple pings and API tests to check availability\n- ✅ Scripted browser interactions for complex user flows\n- ✅ Broken link detection and SSL certificate checks\n- ✅ Geographically distributed testing from multiple locations\n- ✅ Private locations for monitoring internal applications\n- ✅ SLA/SLO tracking with over 300 optimization recommendations\n\nNew Relic Synthetics offers an easy-to-use scripted browser recorder and straightforward dashboards, making it accessible for teams new to synthetic monitoring.\n\n### Datadog Synthetics\n\nDatadog's Synthetic Monitoring supports a wide range of test types:\n\n- ✅ HTTP checks, SSL, DNS, WebSocket, TCP, UDP, ICMP ping, gRPC tests\n- ✅ Multistep browser tests with screenshots of simulated sessions\n- ✅ Detailed waterfall views for web tests\n- ✅ Strong correlation with infrastructure metrics, traces, and logs\n- ✅ CI/CD integration for running tests in deployment pipelines\n- ✅ Performance budgets for build pass/fail criteria\n\nDatadog excels in providing screenshots and detailed diagnostics when tests fail, as well as supporting lower-level network protocol testing that New Relic doesn't cover.\n\n### Verdict: Synthetic Monitoring\n\nBoth platforms offer strong synthetic monitoring capabilities, with Datadog providing more granular test types and CI/CD integration, while New Relic offers excellent out-of-the-box scenarios and SLA reporting. Your choice might depend on whether you need specific protocol checks (advantage Datadog) or prefer simpler setup for web app testing (advantage New Relic).\n\n## Security Monitoring\n\nSecurity observability has become increasingly important, and the two platforms differ significantly in this area.\n\n### Datadog Security Monitoring\n\nDatadog has invested heavily in security observability with a comprehensive suite of products:\n\n- ✅ Cloud SIEM for log-based threat detection\n- ✅ Cloud Workload Security for host/container protection\n- ✅ Application Security Management for runtime protection\n- ✅ Cloud Security Posture Management for configuration security\n- ✅ Real-time threat detection analyzing logs, metrics, and traces\n- ✅ Integration with user data from authentication systems\n- ✅ Customizable detection rules with severity levels\n\nDatadog's approach brings observability data into the security realm, enabling robust DevSecOps capabilities, though these features come at additional cost.\n\n### New Relic Security Features\n\nAs of 2026, New Relic's security monitoring capabilities are still developing:\n\n- ✅ Vulnerability management (in public preview)\n- ✅ Security Health views\n- ✅ Basic security events integration\n- ✅ Limited security insights in the same dashboards as performance data\n\nNew Relic is beginning to integrate security signals into its observability platform, but lacks a dedicated security monitoring product comparable to Datadog's offerings.\n\n### Verdict: Security Monitoring\n\nDatadog is the clear winner for security monitoring in 2026. Its comprehensive suite covers cloud and application security extensively. New Relic currently doesn't offer comparable security monitoring features, so organizations requiring integrated security visibility typically either extend New Relic with third-party tools or consider Datadog for that domain.\n\n## Cloud-Native \u0026 Kubernetes/Serverless\n\nModern infrastructure heavily leverages cloud-native architectures, and both platforms have invested in supporting these paradigms.\n\n### Datadog Cloud-Native Capabilities\n\nDatadog's infrastructure heritage gives it strong Kubernetes monitoring:\n\n- ✅ Dedicated Datadog Cluster Agent for Kubernetes\n- ✅ Auto-discovery of containerized services\n- ✅ Integration with Kubernetes Events for deployment changes\n- ✅ Live container maps and detailed resource metrics\n- ✅ First-class support for AWS Lambda, Azure Functions, and Google Cloud Functions\n- ✅ Serverless monitoring with cold start tracking and cost visibility\n- ✅ 800+ integrations covering cloud-native tools\n\nDatadog's extensive integrations and purpose-built components for container and serverless environments make it particularly strong for complex cloud-native architectures.\n\n### New Relic Cloud-Native Capabilities\n\nNew Relic has made significant advances in Kubernetes monitoring:\n\n- ✅ \"One-step [Kubernetes observability](https://signoz.io/blog/opentelemetry-kubernetes/)\" for automatic instrumentation\n- ✅ Kubernetes integration and on-cluster agent (Pixie)\n- ✅ Kubernetes cluster explorer views\n- ✅ Native OpenTelemetry data support\n- ✅ AWS Lambda monitoring via Lambda layer\n- ✅ Distributed tracing through serverless functions\n- ✅ Instrumentation for multiple serverless platforms\n\nNew Relic focuses on simplifying Kubernetes monitoring by instrumenting both applications and the platform in one step, with an emphasis on ease of use rather than raw depth.\n\n### Verdict: Cloud-Native\n\nBoth platforms are well-suited for cloud-native environments. Datadog may be preferable for organizations that are all-in on infrastructure monitoring at massive scale with fine-tuned control. New Relic fits teams that want to instrument applications running on Kubernetes or serverless with minimal effort. The choice often depends on which platform you're already using and whether you need Datadog's extra cloud-security features.\n\n## Pricing: Beware of These Things\n\nThe two tools differ fundamentally in pricing shape: Datadog is modular and largely per-host plus per-feature, which can stack up quickly as you add products, while New Relic uses a usage-based model built around data ingest (per GB) and billable users. The cost winner depends heavily on your host count, data volume, and team size.\n\n### New Relic\n\nNew Relic uses a usage-based pricing model centered on users and data ingest:\n\n- **Users:** Three tiers of users:\n - Full Platform Users (~$99/user/month)\n - Core Users (~$49/user/month) \n - Basic Users (free)\n- **Data Ingest:** 100GB of free data per month, then pay per GB (~$0.25/GB)\n- **Entity-based pricing:** No per-host or per-CPU charges\n- **Perpetual free tier:** Good for evaluation and small projects\n- **Pricing Gotchas:** User seats can get expensive (Full users), and ingesting huge log volumes beyond the free tier adds up quickly\n\nNew Relic's pricing is often more predictable for smaller usage and encourages broad monitoring since you're not penalized per host.\n\n### DataDog\n\nDataDog uses a modular, per-product pricing structure:\n\n- **Infrastructure Monitoring:** ~$15 per host/month\n- **APM \u0026 Continuous Profiler:** ~$31 per host/month\n- **Logs:** ~$0.10/GB ingestion + separate retention costs\n- **RUM:** ~$1.50 per 1,000 sessions\n- **Synthetics:** Priced per test run\n- **Security Products:** Add-on pricing (SIEM, workload security, etc.)\n- **Custom Metrics:** Additional charges beyond standard metrics\n- **Pricing Gotchas:** Complex SKU-based pricing makes forecasting difficult; custom metrics can dramatically increase costs; using all components on all hosts gets very expensive\n\nDataDog's granular pricing allows you to pay only for what you use, but requires discipline and careful management to avoid surprises.\n\n### Verdict: Pricing\n\nIf you want a predictable pricing model with many hosts but moderate data volumes, New Relic might be simpler and possibly cheaper. If you want to pay only for specific features on specific hosts and can limit usage carefully, Datadog can be cost-optimized but requires discipline. Large enterprises that negotiate custom contracts may find either workable, but Datadog is generally perceived as more expensive, while New Relic offers better bang-for-buck upfront but watch for data overages.\n\n## OpenTelemetry Support: Not Great in Both Datadog \u0026 New Relic\n\nOpenTelemetry is quietly emerging as the open-source standard for collecting all types of telemetry signals. There are numerous [benefits](https://signoz.io/blog/opentelemetry-use-cases/#opentelemetry-vs-vendor-based-agents-for-application-instrumentation) to using OpenTelemetry for collecting telemetry data from your applications and host.\n\nBoth Datadog's and New Relic's support for OpenTelemetry is [not up to the mark](https://signoz.io/blog/is-opentelemetry-a-first-class-citizen-in-your-dashboard-a-datadog-and-newrelic-comparison/), which seems reasonable as their entire product is anchored around their specific agents.\n\nFor example, Datadog cannot link traces and logs automatically with the [DataDog OpenTelemetry](https://signoz.io/blog/opentelemetry-vs-datadog/) tools. New Relic's documentation is better for using OpenTelemetry, but once the data gets reported, you can see the difference again.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/09/firsclass-6.webp\"\n alt=\"OpenTelemetry data in New Relic\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eOpenTelemetry data is segregated in New Relic and not included in the APM experience.\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\nIf you are looking to use OpenTelemetry, then I would recommend [SigNoz](https://signoz.io/) (of course) - an OpenTelemetry-native APM. And just like OpenTelemetry, SigNoz is also open-source. So you can have a full-stack open-source [observability stack](https://signoz.io/guides/observability-stack/) with SigNoz and OpenTelemetry.\n\n## User Experience\n\n### DataDog User Experience\n\nDataDog offers a powerful but complex user interface with extensive customization options. The interface provides a lot of flexibility with multiple menus, time scopes, and tag-based filtering on nearly every screen.\n\nKey aspects of the Datadog UI:\n- ✅ Extremely detailed dashboards with many visualization options\n- ✅ Powerful dashboard layout editor for custom views\n- ✅ Deep integration between different data types\n- ❌ Can be overwhelming for newcomers with many features in submenus\n- ❌ Navigation sometimes feels cluttered with many related features bundled together\n- ❌ Steeper learning curve, especially for the query syntax\n\nAdvanced users appreciate the granular control, but new users often find the interface challenging until they gain experience.\n\n### New Relic User Experience\n\nNew Relic underwent a UI redesign in mid-2023, creating a cleaner, more intuitive interface. The platform organizes features in a straightforward way with clear navigation categories.\n\nKey aspects of the New Relic UI:\n- ✅ User-friendly interface with guided onboarding\n- ✅ NRQL [query builder](https://signoz.io/blog/query-builder-v5/) for easy data exploration\n- ✅ Both click-driven exploration and deep query options\n- ✅ Dark mode with consistent visual design\n- ✅ Logical organization of features\n- ❌ Fewer customization options than Datadog\n- ❌ Some settings can be hard to find initially\n\nNew Relic excels at guiding users through setup and exploration, making the platform more approachable, though it might have fewer advanced options than Datadog.\n\n### Verdict: New Relic vs DataDog\n\nNew Relic wins on overall ease-of-use and intuitive design, especially for new users or smaller teams. Datadog provides a feature-rich interface preferred by power users who invest time to learn it. If you value a clean UI that onboards you smoothly, New Relic has an edge; if you need fine-grained control and don't mind density, Datadog works well once mastered.\n\n## Datadog vs New Relic: Final Verdict\n\nYou should choose Datadog over New Relic if you are an observability expert and want more granular control over your data. That said, New Relic is not far behind in terms of features offered and can be a good solution for application observability.\n\nHere's a use-case-based guide for [Datadog vs](https://signoz.io/comparisons/datadog-vs-dynatrace/) New Relic:\n\n- If you want better correlation between your signals, choose Datadog.\n\n- If you want comprehensive security monitoring, choose Datadog.\n\n- If you need deep, customizable infrastructure monitoring, choose Datadog.\n\n- If you want a simpler pricing model based on usage, choose New Relic.\n\n- If you prefer quick implementation with minimal learning curve, choose New Relic.\n\n- If your team is application-centric (developers focused on APM), choose New Relic.\n\n- If you need a free tier or more cost-effective startup pricing, choose New Relic.\n\nBoth platforms are excellent, but they cater to slightly different priorities. Datadog excels in depth and power across infrastructure, security, and customization, while New Relic offers a more unified experience with faster time-to-value, especially for application-centric teams.\n\nIn case you're also considering Splunk, you will find our [Datadog vs Splunk](https://signoz.io/comparisons/datadog-vs-splunk/) guide useful as it goes deeper on how Datadog stacks up against another enterprise observability solution.\n\n## Issues with Datadog and New Relic\n\nDatadog and New Relic are, of course, popular monitoring tools used by a lot of users worldwide. But they come with their own issues. Here are some issues that users encounter with Datadog and New Relic.\n\n**Complex billing practices**\n\nDatadog is particularly famous for its [complex and unpredictable billing practices](https://signoz.io/blog/datadog-pricing/). Host-based pricing, charging separately for custom metrics and charging for both log ingestion and indexing are some of the common challenges with Datadog’s pricing. \n\nNew Relic charges for user seats, which doesn't make sense for a tool that any developer in the team can need based on what needs troubleshooting.\n\n**Cloud Only**\nBoth New Relic and Datadog are cloud-only products. That means that to use them, you need to send data outside your infrastructure. If you have strict compliance or data privacy issues, you cannot use these products.\n\n**Closed product roadmap**\nFor any small feature, you are dependent on their roadmap. We think this is an unnecessary restriction for a product which developers use. A product used by developers should be extendible\n\n### What the Community is Saying about New Relic vs DataDog\n\nReddit users have shared their experiences with Datadog and New Relic, highlighting various issues and insights:\n\n- **Pricing Transparency and Cost Issues**:\n Users often mention the unpredictable billing and high costs associated with both Datadog and New Relic. For instance, a user shared their frustration with Datadog's billing practices, noting a lack of transparency and delayed refunds for overcharges [here](https://www.reddit.com/r/devops/comments/120rjz5/heads_up_datadog_overcharging_customers_and/).\n\n- **Customer Support**:\n Opinions on customer support vary. Some users report positive experiences with Datadog's customer support, finding it responsive and helpful. Others have had less favorable interactions, especially with New Relic, citing slow response times and a lack of proactive support for smaller companies [here](https://www.reddit.com/r/devops/comments/xhaev0/if_youre_an_existing_datadog_or_new_relic/).\n\n- **Cost Management Strategies**:\n Users discuss various strategies to manage and reduce costs, such as setting up retention filters in Datadog to minimize data ingestion and storage costs. These strategies can lead to significant savings, but they also highlight the complexity and effort required to optimize usage [here](https://www.reddit.com/r/devops/comments/13ky2iq/datadog_where_does_it_hurt/).\n\nThe other alternative is an open-source solution. Many open-source products require substantial work to set up, maintain, and scale. [Self-Hosted SigNoz](https://signoz.io/) is a full-stack, open-source APM platform for teams that want to manage the infrastructure themselves.\n\nIf you're evaluating Datadog against AWS-native tooling, [Datadog vs CloudWatch](https://signoz.io/blog/datadog-vs-cloudwatch/) covers that angle. And if New Relic's APM positioning is what you're testing, [New Relic vs AppDynamics](https://signoz.io/comparisons/new-relic-vs-appdynamics/) is a useful read.\n\n## Introducing SigNoz Cloud - an alternative to Datadog and New Relic\n\nSigNoz Cloud is a managed [alternative to Datadog](https://signoz.io/blog/datadog-alternatives/#choosing-the-right-datadog-alternative) and New Relic. It provides APM, distributed tracing, and log management in one OpenTelemetry-native platform. Here are some top reasons to choose SigNoz Cloud over Datadog or New Relic.\n\n### OpenTelemetry Native - No vendor lock-in in your code\n\nSigNoz is built from the ground up to be OpenTelemetry native. This means we fully leverage OTel's semantic conventions, providing deeper, out-of-the-box insights. Unlike Datadog, **we don't charge you extra for \"custom metrics\" when you're using OpenTelemetry**. This fundamental difference means you can embrace open standards without the fear of a massive bill.\n\nWe've also recently launched features that double down on our OTel-native approach, including:\n\n- [Trace Funnels](https://signoz.io/blog/tracing-funnels-observability-distributed-systems/): Intelligently sample and analyze traces to focus on what's important.\n- [External API Monitoring](https://signoz.io/docs/apm-and-distributed-tracing/application-details/): Gain visibility into the performance of third-party APIs your application depends on.\n- [Out-of-the-box Messaging Queue Monitoring](https://signoz.io/blog/opentelemetry-powered-kafka-celery-monitoring/): Effortlessly monitor popular queuing systems.\n\n### Flexible Hosting Options for Every Stage of Growth\n\nWe believe you shouldn't be locked into a single deployment model. SigNoz offers a range of options to meet your needs as you scale:\n\n- [SigNoz Cloud](https://signoz.io/docs/cloud/): A fully-managed, scalable solution for teams that want to focus on their core business without the overhead of managing an observability platform.\n- [Self-Hosted SigNoz Enterprise](https://signoz.io/enterprise/): For organizations with strict data residency or privacy requirements, this self-hosted enterprise edition supports bring-your-own-cloud or on-premise deployment, dedicated support, and advanced security features.\n- [Self-Hosted SigNoz Community Edition](https://signoz.io/docs/install/self-host/): A self-hosted, open-source version for teams with the capability to manage their own infrastructure.\n\n### Simple, Transparent, Usage-Based Pricing\n\nThe [SigNoz Cloud pricing model](https://signoz.io/pricing/) is designed to be straightforward and predictable. It is based on the volume of data you send: \\$0.3 per GB of ingested logs, \\$0.3 per GB of ingested traces, and \\$0.1 per million samples.\n\nThere are no \"weird\" pricing levers like per-host charges that force you to alter your architecture, or charges based on user seats which can limit your team’s ability to set up robust observability practices. Our pricing plan is a simple, scalable model that grows with you.\n\nWith SigNoz, you get:\n\n- **No surprise bills:** Our pricing is easy to understand and forecast.\n- **Cost-effective at scale:** As your data volume grows, our pricing remains competitive.\n- **Freedom to architect your systems as you see fit:** We don't penalize you for using modern, dynamic infrastructure.\n\nSigNoz comes with out of box visualization of things like RED metrics.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n alt=\"SigNoz UI showing the popular RED metrics\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/common/signoz_charts_application_metrics.webp\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003e\n SigNoz UI showing application overview metrics like RPS, 50th/90th/99th Percentile latencies,\n and Error Rate\n \u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\nYou can also use flamegraphs to visualize spans from your trace data. All of this comes out of the box with SigNoz.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n alt=\"Flamegraphs used to visualize spans of distributed tracing in SigNoz UI\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/common/signoz_flamegraphs.webp\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eFlamegraphs showing exact duration taken by each spans - a concept of distributed tracing\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\nYou can use logs to dig deeper into application issues.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/common/signoz_logs.webp\" alt=\"Logs management in SigNoz\" /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eLogs management in SigNoz\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\nYou can also build custom metrics dashboard for your infrastructure.\n\nThe logs tab in SigNoz has advanced features like a log query builder, search across multiple fields, structured table view, JSON view, etc.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/common/signoz_logs.webp\" alt=\"Log management in SigNoz\" /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eLog management in SigNoz\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\n## Getting Started with SigNoz\n\nAs discussed earlier, you can choose between various deployment options in SigNoz. The easiest way to get started with SigNoz is [SigNoz cloud](https://signoz.io/teams/). We offer a 30-day free trial account with access to all features. \n\nThose who have data privacy concerns and can’t send their data outside their infrastructure can sign up for either [enterprise self-hosted or BYOC offering](https://signoz.io/contact-us/).\n\nThose who have the expertise to manage SigNoz themselves or just want to start with a free self-hosted option can use our [community edition](https://signoz.io/docs/install/self-host/).\n\nHope we answered all your questions regarding choosing SigNoz as the Datadog alternative. If you have more questions, feel free to use the SigNoz AI chatbot, or join our [slack community](https://signoz.io/slack/).\n\n\n## FAQs\n\n### Is Datadog better than New Relic?\n\nThe choice between DataDog and New Relic depends on your specific needs. DataDog offers more granular control and better correlation between signals, while New Relic provides a simpler user experience and pricing based on usage.\n\n### What is the difference between Grafana vs Datadog vs New Relic?\n\nGrafana is primarily a visualization tool, whereas DataDog and New Relic are comprehensive monitoring solutions. DataDog provides extensive integrations and detailed monitoring capabilities, while New Relic offers an easy-to-use platform with strong real-user monitoring features.\n\n### Is New Relic a monitoring tool?\n\nYes, New Relic is a comprehensive monitoring tool that provides application performance monitoring (APM), infrastructure monitoring, real-user monitoring, and more.\n\n### What is better than Datadog?\n\nDepending on your specific requirements, alternatives like New Relic, Splunk, or open-source solutions like Self-Hosted SigNoz might be better suited for your needs.\n\n### What is the weakness of Datadog?\n\nDataDog's primary weaknesses include its complex pricing model, high costs, and cloud-only deployment, which may not suit all organizations.\n\n### Which is better Splunk or Datadog?\n\nSplunk is better for log management and analysis, while DataDog excels in providing comprehensive monitoring and observability solutions. The best choice depends on your specific use case. Alternatively, SigNoz offers a full-stack open-source APM solution that combines the best of both worlds with easy configuration and scalability.\n\n### Is New Relic a good tool?\n\nYes, New Relic is a good tool for monitoring and observability, offering a wide range of features and a user-friendly interface. However, if you're looking for an open-source alternative, consider Self-Hosted SigNoz.\n\n### Is New Relic a SIEM tool?\n\nNo, New Relic is not a SIEM (Security Information and Event Management) tool. It focuses on application performance monitoring and observability.\n\n### Is New Relic a DevOps tool?\n\nYes, New Relic is used in DevOps for monitoring application performance, infrastructure, and real-user interactions, aiding in continuous delivery and operational efficiency.\n\n### Is Datadog a good monitoring tool?\n\nYes, DataDog is a robust monitoring tool that provides extensive features for application performance, infrastructure, and network monitoring. However, if you're looking for an open-source alternative, consider Self-Hosted SigNoz.\n\n### What is the advantage of Datadog?\n\nDataDog's advantages include its granular control over monitoring data, extensive integrations, and comprehensive visibility across various aspects of an application's infrastructure. Additionally, SigNoz offers a similar comprehensive monitoring solution with the benefits of being open-source.\n\n### Datadog vs New Relic Cost\n\nDatadog is a more expensive product. It has complex SKU-based pricing, which makes it difficult for engineering teams to use the platform freely. While New Relic provides free access to its entire platform and charges based on usage. But New Relic has user-based pricing - and that can be a significant portion of your entire bill at scale.\n\n---\n\n**Related Content**\n\n**[9x more value for money than Datadog and New Relic](https://signoz.io/blog/pricing-comparison-signoz-vs-datadog-vs-newrelic-vs-grafana/)**\n\n**[SigNoz vs Datadog](https://signoz.io/datadog-alternative/)**\n\n**[SigNoz vs New Relic](https://signoz.io/newrelic-alternative/)**\n"])</script><script>self.__next_f.push([1,"2f:T297c,"])</script><script>self.__next_f.push([1,"\n\n\u003e “The will must be stronger than the skill.” \n\u003e Muhammad Ali\n\nWelcome to our monthly product newsletter - SigNal 24!\n\nLast month, our team worked on the upcoming trace and logs explorer page. With the new update, our users will be able to drive deeper insights into their application performance quickly.\n\n\n\n\n\nWe also attended open source focused meetups and published a cost comparison blog comparing SigNoz with other popular observability tools.\n\nLet’s dive in to see what humans at SigNoz were up to in the month of April 2023.\n\n## What we’re working on?\n\nWe’re working on building a new trace explorer and a log explorer page. The new explorer pages will give our users much more granular control over querying logs and trace data.\n\nThere are also multiple views like List, Time Series, and Table view for a much better user experience. With powerful filtering, aggregation, and group-by capabilities, you can make dive deeper into your observability data like never before.\n\n\u003cfigure data-zoomable align='center'\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/05/trace_explorer_page.webp\" alt=\"The new Trace explorer page in SigNoz\"/\u003e\n \u003cfigcaption\u003e\u003ci\u003eThe new Trace Explorer page comes with advanced querying capabilities\u003c/i\u003e\u003c/figcaption\u003e\n\u003c/figure\u003e\n\n\n\nSimilarly, the logs explorer page comes with updated filtering capabilities, multiple views, and the ability to create metrics from logs to plot them.\n\n\u003cfigure data-zoomable align='center'\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/05/logs_explorer_page.webp\" alt=\"The new Logs explorer page\"/\u003e\n \u003cfigcaption\u003e\u003ci\u003eThe new Logs Explorer page with updated filtering capabilities and multiple views\u003c/i\u003e\u003c/figcaption\u003e\n\u003c/figure\u003e\n\n\n\n## Shocking Datadog bill of \\$65 million\n\nLast week Datadog had its Q1 earnings call. It was revealed that they charged a cryptocurrency company a bill of \\$65 million USD. A \u003ca href = \"https://news.ycombinator.com/item?id=35837330\" rel=\"noopener noreferrer nofollow\" target=\"_blank\" \u003e\u003cb\u003eHacker News thread\u003c/b\u003e\u003c/a\u003e discussing the report went viral, and there was an outpour of user stories around Datadog’s unpredictable billing practices.\n\nA lot of users also pointed out how the sales team of Datadog relentlessly pursue engineers for signing up for their services.\n\n\u003cfigure data-zoomable align='center'\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/05/dd_65_mill_bill.webp\" alt=\"Datadog's \\$65 mil bill\"/\u003e\n \u003cfigcaption\u003e\u003ci\u003eHackernews thread discussing issues with Datadog\u003c/i\u003e\u003c/figcaption\u003e\n\u003c/figure\u003e\n\n\n\nWe have come across many other horror stories around Datadog billing while interacting with our users. We recently did a [deep dive into pricing](https://signoz.io/blog/pricing-comparison-signoz-vs-datadog-vs-newrelic-vs-grafana/) to compare the cost of SigNoz compared to other observability tools.\n\nDatadog's billing has two key issues:\n\n- Very complex SKU-based pricing, which makes it impossible to predict how much it would cost.\n- Custom metrics billing (\\$0.05 per custom metric) - we found that custom metrics can account for [up to 52% of the total billing](https://signoz.io/blog/pricing-comparison-signoz-vs-datadog-vs-newrelic-vs-grafana/#no-limits-on-custom-metrics-with-signoz), which just does not make sense.\n\n## SigNoz provides up to 9x more value for money than Datadog\n\nWe did a cost analysis of SigNoz and compared it with other vendors like DataDog, New Relic, and Grafana. SigNoz can provide up to 9x more value for money than vendors like Datadog and let your engineering team do so much more.\n\n\u003cfigure data-zoomable align='center'\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/07/full-stack-observability-cost-comparison.webp\" alt=\"full-stack observability cost comparison\"/\u003e\n \u003cfigcaption\u003e\u003ci\u003eSigNoz provides the best in class value for money as compared to other observability tools\u003c/i\u003e\u003c/figcaption\u003e\n\u003c/figure\u003e\n\n\n\nAt SigNoz, we strive to provide the most value for your money. Check out the entire blog [here](https://signoz.io/blog/pricing-comparison-signoz-vs-datadog-vs-newrelic-vs-grafana/).\n\n## Featured Issue\n\n\u003ca href = \"https://github.com/SigNoz/signoz/issues/2600\" rel=\"noopener noreferrer nofollow\" target=\"_blank\" \u003e\u003cb\u003eMonitoring Query service\u003c/b\u003e\u003c/a\u003e\n\nOur query service talks to the ClickHouse DB to query telemetry data and is a critical part of SigNoz architecture. The Query service APIs should be monitored for latency and memory usage to help debug issues. This will help make SigNoz more robust for production users.\n\nFeel free to share your thoughts on this \u003ca href = \"https://github.com/SigNoz/signoz/issues/2600\" rel=\"noopener noreferrer nofollow\" target=\"_blank\" \u003e\u003cb\u003eGitHub issue\u003c/b\u003e\u003c/a\u003e.\n\n## SigNoz News\n\n### Podcast with Scaling DevTools\n\nPranay joined Jack from Scaling DevTools to share the journey of how SigNoz grew to 12k+ GitHub stars. Pranay also touched upon other aspects of building an open-source dev tool like adoption, the path to monetization, etc. You can check out the full video. 👇\n\n\n\n\u003cYouTube id=\"pOhyOrtqUp0\" mute=\"false\" /\u003e\n\n\n\n### Attended ObservabilityCon and Open Source focused meetups\n\nWe attended the ObservabilityCon Bay Area meetup that took place in Palo Alto. It was exciting to see the interest in open source observability and hear about people’s experiences. At SigNoz, we believe open source observability is the way to go ahead.\n\nWe also attended an open source focused meetup organized by Heavybit, a dev tool focused venture capital. It was an interesting half-day conference in San Francisco where top leaders from the OSS world shared their insights.\n\n\u003cfigure data-zoomable align='center'\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/05/oss_meetup_heavybit.webp\" alt=\"Slide at OSS meetup by Heavybit\"/\u003e\n \u003cfigcaption\u003e\u003ci\u003eOpen source software is ruling the world of software!\u003c/i\u003e\u003c/figcaption\u003e\n\u003c/figure\u003e\n\n\n\n### OpenTelemetry End-User Group Discussion\n\nThe team at SigNoz facilitates the monthly OpenTelemetry End-user group discussions for the APAC region. The OTel end-user group discussion is a place where you can discuss challenges related to OpenTelemetry implementations and learn from other OpenTelemetry users in a vendor-neutral space.\n\nIn our April session, we discussed evangelizing OpenTelemetry in a big organization and how to optimize observability data at scale. You can find the notes from the discussion \u003ca href = \"https://docs.google.com/document/d/1eDYC97LfvE428cpIf3A_hSGirdNzglPurlxgKCmw8o4/\" rel=\"noopener noreferrer nofollow\" target=\"_blank\" \u003e\u003cb\u003ehere\u003c/b\u003e\u003c/a\u003e.\n\n### Contributor Highlight\n\nEvery month, contributors from our community help make SigNoz better. We want to thank the following contributors who made contributions to SigNoz last month 🤗\n\n\u003cdiv class=\"row\"\u003e\n \u003cdiv class=\"col col--6\"\u003e\n \u003cdiv class=\"avatar\"\u003e\n \u003ca\n class=\"avatar__photo-link avatar__photo avatar__photo--lg\"\n href=\"https://github.com/yeshev\"\n \u003e\n \u003cimg\n alt=\"Yevhen Shevchenko\"\n src=\"https://avatars.githubusercontent.com/u/90138953?v=4\"\n /\u003e\n \u003c/a\u003e\n \u003cdiv class=\"avatar__intro\"\u003e\n \u003cdiv class=\"avatar__name\"\u003eYevhen Shevchenko\u003c/div\u003e\n \u003csmall class=\"avatar__subtitle\"\u003e\n \u003c/small\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"col col--6\"\u003e\n \u003cdiv class=\"avatar\"\u003e\n \u003ca\n class=\"avatar__photo-link avatar__photo avatar__photo--lg\"\n href=\"https://github.com/GermaVinsmoke\"\n \u003e\n \u003cimg\n alt=\"GermaVinsmoke\"\n src=\"https://avatars.githubusercontent.com/u/32815509?v=4\"\n /\u003e\n \u003c/a\u003e\n \u003cdiv class=\"avatar__intro\"\u003e\n \u003cdiv class=\"avatar__name\"\u003eGermaVinsmoke\u003c/div\u003e\n \u003csmall class=\"avatar__subtitle\"\u003e\n \u003c/small\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\n\u003cp\u003e\u003c/p\u003e\n\n\u003cdiv class=\"row\"\u003e\n \u003cdiv class=\"col col--6\"\u003e\n \u003cdiv class=\"avatar\"\u003e\n \u003ca\n class=\"avatar__photo-link avatar__photo avatar__photo--lg\"\n href=\"https://github.com/daniel-t4e\"\n \u003e\n \u003cimg\n alt=\"Daniël\"\n src=\"https://avatars.githubusercontent.com/u/130993189?v=4\"\n /\u003e\n \u003c/a\u003e\n \u003cdiv class=\"avatar__intro\"\u003e\n \u003cdiv class=\"avatar__name\"\u003eDaniël\u003c/div\u003e\n \u003csmall class=\"avatar__subtitle\"\u003e\n \u003c/small\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"col col--6\"\u003e\n \u003cdiv class=\"avatar\"\u003e\n \u003ca\n class=\"avatar__photo-link avatar__photo avatar__photo--lg\"\n href=\"https://github.com/erplsf\"\n \u003e\n \u003cimg\n alt=\"Andriy Mykhaylyk\"\n src=\"https://avatars.githubusercontent.com/u/4072364?v=4\"\n /\u003e\n \u003c/a\u003e\n \u003cdiv class=\"avatar__intro\"\u003e\n \u003cdiv class=\"avatar__name\"\u003eAndriy Mykhaylyk\u003c/div\u003e\n \u003csmall class=\"avatar__subtitle\"\u003e\n \u003c/small\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\n\u003cp\u003e\u003c/p\u003e\n\n\u003cdiv class=\"row\"\u003e\n \u003cdiv class=\"col col--6\"\u003e\n \u003cdiv class=\"avatar\"\u003e\n \u003ca\n class=\"avatar__photo-link avatar__photo avatar__photo--lg\"\n href=\"https://github.com/czchen\"\n \u003e\n \u003cimg\n alt=\"ChangZhuo Chen\"\n src=\"https://avatars.githubusercontent.com/u/98758?v=4\"\n /\u003e\n \u003c/a\u003e\n \u003cdiv class=\"avatar__intro\"\u003e\n \u003cdiv class=\"avatar__name\"\u003eChangZhuo Chen\u003c/div\u003e\n \u003csmall class=\"avatar__subtitle\"\u003e\n \u003c/small\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\n## From the blog\n\nIn 2018, RBI came out with a guideline on storing payment system data of Indian users. According to the guideline, all payment data need to be stored in data centers located in India. So how does this affect the performance monitoring of fintech applications?\n\nThe circular is applicable to all payment system providers authorized/approved by RBI to set up and operate a payment system in India. At SigNoz, we provide our users the option to host their data in India. Read on to find out more.\n\n**[Challenges in Choosing an APM tool for Fintech Companies in India](https://signoz.io/blog/open-source-apm-tools/)**\n\n---\n\nThank you for taking out the time to read this issue :) If you have any feedback or want any changes to the format, please create an \u003ca href = \"https://github.com/SigNoz/signoz/issues\" rel=\"noopener noreferrer nofollow\" target=\"_blank\" \u003eissue\u003c/a\u003e.\n\nFeel free to join our Slack community and say hi! 👋\n\n[](https://signoz.io/slack/)\n"])</script><script>self.__next_f.push([1,"30:T33f1,"])</script><script>self.__next_f.push([1,"\nDemocratize observability for engineering teams of all sizes!\n\nThat’s the vision that drives us every day. SigNoz is open source, provides three signals (logs, metrics, and traces) under a single pane, and is OpenTelemetry-native. And it also costs lesser than other popular observability tools.\n\n\n\n\n\n\u003cInlineCTA message=\"Up to 9x more value for money than Datadog. No user seats, no custom metrics surcharge — just simple per-GB pricing.\" /\u003e\n\nWe did a cost analysis of SigNoz Cloud and compared it with DataDog, New Relic, and Grafana Cloud. SigNoz Cloud can provide up to 9x more value for money than Datadog and let your engineering team do more with the same budget.\n\nHere are some key takeaways from our cost analysis:\n\n- SigNoz Cloud can provide up to **9x more value for money** than Datadog. The cost savings can enable engineering teams to send more data while spending less.\n\n- User-based SaaS pricing limits the ability of engineering teams to collaborate seamlessly. SigNoz Cloud does not charge for user seats. Vendors like **New Relic can charge up to 66%** of its total bill amount just **for adding users**.\n\n- Custom metrics are important for understanding your application. If you use Datadog, your custom metrics bill can be **up to 52% of your total billing**. [SigNoz Cloud](https://signoz.io/docs/introduction/) does not charge separately for custom metrics and charges \\$0.1 per million samples.\n\nBelow is the snapshot of our full stack observability cost comparison. You can have a look at our complete \u003ca href = \"https://docs.google.com/spreadsheets/d/1EEw48D7SmC-DHKanT5hoiShT-AZcIfZDc9HQiVYdZBY/edit#gid=0\" rel=\"noopener noreferrer nofollow\" target=\"_blank\" \u003e\u003cb\u003ecost comparison analysis\u003c/b\u003e\u003c/a\u003e.\n\n\u003cfigure data-zoomable align='center'\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/07/full-stack-observability-cost-comparison.webp\" alt=\"full-stack observability cost comparison\"/\u003e\n \u003cfigcaption\u003e\u003ci\u003eSigNoz Cloud pricing compared with other managed observability tools\u003c/i\u003e\u003c/figcaption\u003e\n\u003c/figure\u003e\n\n\nYou can check the paid SigNoz Cloud plans [here](https://signoz.io/pricing/).\n\n## Issues with Datadog pricing\n\nDatadog has a very complex SKU-based [pricing](https://signoz.io/blog/datadog-pricing/) structure. The complex billing structure makes it hard to predict how much you will be charged at the end of the month.\n\nRecently, it was revealed that they charged a cryptocurrency company a bill of $65 million USD. A **[Hacker News thread](https://news.ycombinator.com/item?id=35837330)** discussing the report went viral, and there was an outpour of user stories around Datadog’s unpredictable billing practices.\n\nA lot of users also pointed out how the sales team of Datadog relentlessly pursue engineers for signing up for their services.\n\n\u003cfigure data-zoomable align='center'\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/05/dd_65_mill_bill.webp\" alt=\"Datadog's $65 mil bill\"/\u003e\n \u003cfigcaption\u003e\u003ci\u003eHackernews thread discussing issues with Datadog pricing\u003c/i\u003e\u003c/figcaption\u003e\n\u003c/figure\u003e\n\n\nDatadog’s pricing for custom metrics is also insane. We deep dive into it later in the blog.\n\n## Cost comparison of SigNoz Cloud with Datadog, New Relic, and Grafana Cloud\n\nDepending on the size of the engineering team, we have done cost benchmarking of three hypothetical scenarios.\n\n- Small engineering team - 25 engineers\n- Midsize engineering team - 100 engineers\n- Large engineering team - 200 engineers\n\nDatadog has a complex SKU-based pricing structure. New Relic charges based on data ingest and user seats. Grafana Cloud charges based on the amount of telemetry data sent and user seats. SigNoz Cloud charges only on the amount of telemetry data sent.\n\n## Small engineering team comparison\n\nObservability should be set up from day one. For small engineering teams, getting the most value for their money is critical. Below is a breakdown of full-stack observability cost comparison for a team of 25 engineers.\n\nWe have assumed 20 APM hosts, 50 infra hosts, and 2500 GB ingested logs.\n\nYou can find the assumptions we have taken in this \u003ca href = \"https://docs.google.com/spreadsheets/d/1EEw48D7SmC-DHKanT5hoiShT-AZcIfZDc9HQiVYdZBY\" rel=\"noopener noreferrer nofollow\" target=\"_blank\" \u003e\u003cb\u003esheet\u003c/b\u003e\u003c/a\u003e.\n\n| | SigNoz Cloud | Grafana Cloud | New Relic | Datadog |\n| --------------------------------------------------------------------------- | ---------- | ---------- | ---------- | ----------- |\n| **APM** \u003cbr\u003e\u003c/br\u003e20 APM hosts, 50 M indexed spans | | | | $671 |\n| **Infra** \u003cbr\u003e\u003c/br\u003e50 infra hosts, 750k container hours, 75k custom metrics | | | | $5,600 |\n| **Logs** \u003cbr\u003e\u003c/br\u003e2500 GB ingested, 1560 million log events | | | | $4,150 |\n| **Logs** \u003cbr\u003e\u003c/br\u003e2500 GB ingested | $750 | $1,200 | | |\n| **Metrics** \u003cbr\u003e\u003c/br\u003e13 million samples per infra host (1) | $65 | $124 | | |\n| **Traces** \u003cbr\u003e\u003c/br\u003e43.8 GB per APM host | $263 | $388 | | |\n| **Data Ingest** | | | $1,178 | |\n| **Users** | | $200 | $2,333 | |\n| Total | **$1,078** | **$1,912** | **$3,511** | **$10,421** |\n| Up to **9.7x more value for money** with SigNoz Cloud | | | | |\n\n## Midsize engineering team comparison\n\nAs your business grows, the engineering team needs to scale too. Here’s a cost comparison for a hypothetical team of 100 engineers. The tech stack consists of 125 APM hosts, 200 infra hosts, and 10,000 GB ingested logs.\n\n| | SigNoz Cloud | Grafana Cloud | New Relic | Datadog |\n| ------------------------------------------------------------------------------ | ------ | ------- | --------- | ------- |\n| **APM** \u003cbr\u003e\u003c/br\u003e125 APM hosts, 500 M indexed spans | | | | $4,513 |\n| **Infra** \u003cbr\u003e\u003c/br\u003e200 infra hosts, 1.5 M container hours, 250k custom metrics | | | | $17,200 |\n| **Logs** \u003cbr\u003e\u003c/br\u003e10,000 GB ingested, 3000 million log events | | | | $8,500 |\n| **Logs** \u003cbr\u003e\u003c/br\u003e10,000 GB ingested | $3,000 | $4,950 | | |\n| **Metrics** \u003cbr\u003e\u003c/br\u003e13 million samples per infra host (1) | $260 | $494 | | |\n| **Traces** \u003cbr\u003e\u003c/br\u003e43.8 GB per APM host | $1,643 | $2,688 | | |\n| **Data Ingest** | | | $5,393 | |\n| **Users** | | $800 | $9,430 | |\n| Total | $4,903 | $8,932 | $14,823 | $30,213 |\n| Up to **6.2x more value for money** with SigNoz Cloud | | | | |\n\n## Large engineering team comparison\n\nLarge businesses need observability at scale. Here’s a cost comparison for a hypothetical team of 200 engineers. The tech stack consists of 225 APM hosts, 350 infra hosts, and 20,000 GB ingested logs.\n\n| | SigNoz Cloud | Grafana Cloud | New Relic | Datadog |\n| ------------------------------------------------------------------------------ | ------ | ------- | --------- | ------- |\n| **APM** \u003cbr\u003e\u003c/br\u003e225 APM hosts, 2 Billion indexed spans | | | | $9,993 |\n| **Infra** \u003cbr\u003e\u003c/br\u003e350 infra hosts, 2.5 M container hours, 250k custom metrics | | | | $45,500 |\n| **Logs** \u003cbr\u003e\u003c/br\u003e20,000 GB ingested, 4,500 million log events | | | | $13,250 |\n| **Logs** \u003cbr\u003e\u003c/br\u003e20,000 GB ingested | $6,000 | $9,950 | | |\n| **Metrics** \u003cbr\u003e\u003c/br\u003e13 million samples per infra host (1) | $455 | $865 | | |\n| **Traces** \u003cbr\u003e\u003c/br\u003e43.8 GB per APM host | $2,957 | $4,878 | | |\n| **Data Ingest** | | | $10,292 | |\n| **Users** | | $1,600 | $18,860 | |\n| Total | $9,412 | $17,292 | $29,152 | $68,743 |\n| Up to **7.3x more value for money** with SigNoz Cloud | | | | |\n\n## No limits on custom metrics with SigNoz Cloud\n\nCustom metrics give deeper insights into the performance of your application. It can help you track key application KPIs. For a robust observability setup, your engineering and DevOps teams need the flexibility and freedom to create and send as many custom metrics as needed.\n\nBut vendors like Datadog charge $0.05 per custom metric, which limits a team’s ability to send and analyze custom metrics for monitoring.\n\n\u003cfigure data-zoomable align='center'\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/05/custom_metrics_pricing.webp\" alt=\"Datadog custom metrics billing\"/\u003e\n \u003cfigcaption\u003e\u003ci\u003eDatadog charges $0.05 per custom metric\u003c/i\u003e\u003c/figcaption\u003e\n\u003c/figure\u003e\n\n\nAt scale, it can constitute up to 52% of your total billing.\n\n\u003cfigure data-zoomable align='center'\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/05/datadog_billing_custom_metrics.webp\" alt=\"Custom metrics billing in Datadog\"/\u003e\n \u003cfigcaption\u003e\u003ci\u003eCustom metrics billing can constitute a significant portion of your total bill with Datadog\u003c/i\u003e\u003c/figcaption\u003e\n\u003c/figure\u003e\n\n\nSigNoz Cloud does not treat custom metrics differently. The charge remains $0.1 per million samples for every metric type.\n\n## No user-based pricing, collaborate seamlessly with SigNoz Cloud\n\nUser-based pricing limits access during debugging because you never know which engineer may need the observability tool. SigNoz Cloud does not charge based on user seats.\n\nNew Relic’s \u003ca href = \"https://newrelic.com/pricing\" rel=\"noopener noreferrer nofollow\" target=\"_blank\" \u003e\u003cb\u003euser pricing\u003c/b\u003e\u003c/a\u003e can go up to $549/user. At scale, the cost of adding users can go up to 66% of the total bill.\n\n\u003cfigure data-zoomable align='center'\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/05/new_relic_user_seat_pricing.webp\" alt=\"User seats billing in New Relic\"/\u003e\n \u003cfigcaption\u003e\u003ci\u003eUser seat billing can constitute a significant portion of your total bill with New Relic.\u003c/i\u003e\u003c/figcaption\u003e\n\u003c/figure\u003e\n\n\n## Why choose SigNoz?\n\nWe believe in transparent and flexible pricing. As we meet with developers, engineering leaders, and executives around the world, we realized engineering teams want two things:\n\n- Best value for their money\n- Predictability of how much they will pay\n\nWe are working tirelessly to improve our offering and value to our users. After careful examination, we identified the following issues with other tools.\n\n| Tool | Issue |\n| --------- | ------------------------------------------------------------------------------------------------------------------------------------------- |\n| Datadog | Has the most complex pricing structure. You will never know what you might end up paying. The internet is full of many such horror stories. |\n| New Relic | High user-based pricing limits collaboration. As teams become more diverse and cross-functional, you need to collaborate seamlessly. |\n| Grafana | It does not have a seamless three signals (logs, metrics, traces) in a single pane experience. |\n\nAt SigNoz, we strive to provide the most value for your money. The SigNoz open-source project reflects our focus on transparency. Using SigNoz Cloud can help engineering teams do more with their data for the same budget as other managed observability tools.\n\nReady to make the switch? Use our [automated migration tool](https://signoz.io/datadog-migration-tool/) to migrate your Datadog dashboards in minutes.\n\n\u003cSignUps /\u003e\n\n#### References\n\n1. [Pricing Comparison Sheet](https://docs.google.com/spreadsheets/d/1EEw48D7SmC-DHKanT5hoiShT-AZcIfZDc9HQiVYdZBY/)\n"])</script><script>self.__next_f.push([1,"31:T82d3,"])</script><script>self.__next_f.push([1,"\nThe rising costs and complexities of monitoring cloud infrastructure are pushing many organizations to explore [alternatives to Datadog](https://signoz.io/blog/datadog-alternatives/#signoz-cloud). If you're making that move, you need simpler billing without losing the ability to investigate an issue across logs, metrics, and traces. SigNoz Cloud brings these signals together in one OpenTelemetry-native product with usage-based pricing. In this article, we'll look at nine Datadog alternatives and what each offers for your monitoring needs.\n\n\u003cInlineCTA message=\"Simple usage-based pricing — no per-host fees, no custom metrics surcharge. Start with SigNoz Cloud, self-host anytime with the same open-source codebase.\" /\u003e\n\n## Why Organizations Look for Datadog Alternatives\n\nIn one of the earning calls of Datadog, it was revealed that they charged a cryptocurrency company a bill of \\$65 million USD. A \u003ca href=\"https://news.ycombinator.com/item?id=35837330\" rel=\"noopener noreferrer nofollow\" target=\"_blank\"\u003e\u003cb\u003eHacker News thread\u003c/b\u003e\u003c/a\u003e discussing the report went viral, and there was an outpouring of user stories around Datadog's unpredictable billing practices.\n\n \u003cdiv className=\"flex space-x-4 overflow-x-auto snap-x snap-mandatory scroll-smooth items-center\"\u003e\n \u003cdiv className=\"flex-shrink-0 w-full md:w-8/12 h-60 snap-start\"\u003e\n \u003ciframe\n id=\"reddit-embed-3\"\n src=\"https://www.redditmedia.com/r/kubernetes/comments/1d1onw2/need_help_with_datadog_alternatives/?ref_source=embed\u0026amp;ref=share\u0026amp;embed=true\u0026amp;showmedia=false\u0026amp;theme=dark\"\n sandbox=\"allow-scripts allow-same-origin allow-popups\"\n className=\"w-full h-full border-none\"\n scrolling=\"no\"\n \u003e\u003c/iframe\u003e\n \u003c/div\u003e\n \n \u003cdiv className=\"flex-shrink-0 w-full md:w-8/12 h-60 snap-start\"\u003e\n \u003ciframe\n id=\"reddit-embed-1\"\n src=\"https://www.redditmedia.com/r/devops/comments/zz4naq/datadog_i_do_not_understand_the_pricing_model/?ref_source=embed\u0026amp;ref=share\u0026amp;embed=true\u0026amp;showmedia=false\u0026amp;theme=dark\"\n sandbox=\"allow-scripts allow-same-origin allow-popups\"\n className=\"w-full h-full border-none\"\n scrolling=\"no\"\n \u003e\u003c/iframe\u003e\n \u003c/div\u003e\n\n \u003cdiv className=\"flex-shrink-0 w-full md:w-8/12 h-60 snap-start\"\u003e\n \u003ciframe\n id=\"reddit-embed-2\"\n src=\"https://www.redditmedia.com/r/devops/comments/zz4naq/datadog_i_do_not_understand_the_pricing_model/j2am1eg/?depth=1\u0026amp;showmore=false\u0026amp;embed=true\u0026amp;showmedia=false\u0026amp;theme=dark\"\n sandbox=\"allow-scripts allow-same-origin allow-popups\"\n className=\"w-full h-full border-none\"\n scrolling=\"no\"\n \u003e\u003c/iframe\u003e\n \u003c/div\u003e \n \n \u003cdiv className=\"flex-shrink-0 w-full md:w-8/12 h-60 snap-start\"\u003e\n \u003ciframe\n id=\"reddit-embed-4\"\n src=\"https://www.redditmedia.com/r/sysadmin/comments/12qa7n4/how_on_earth_do_people_deal_with_datadogs_billing/?ref_source=embed\u0026amp;ref=share\u0026amp;embed=true\u0026amp;showmedia=false\u0026amp;theme=dark\"\n sandbox=\"allow-scripts allow-same-origin allow-popups\"\n className=\"w-full h-full border-none\"\n scrolling=\"no\"\n \u003e\u003c/iframe\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\nA lot of users also pointed out how the sales team of Datadog relentlessly pursues engineers for signing up for their services.\n\nHere are the [three main caveats of Datadog's pricing](https://signoz.io/blog/datadog-pricing/):\n\n- **Per-Host, High-Water Mark Billing:** On Datadog's high-watermark plan, you're billed based on peak usage after the top 1% of hourly readings are excluded, not your average. This model can make common scaling events like handling peak-season traffic expensive.\n\n- **Expensive Custom Metrics:** You're charged a premium for custom metrics based on unique combinations of metric names and tag values (cardinality), so costs can explode unexpectedly. OpenTelemetry metrics outside supported integration allowances are billed as custom metrics.\n\n- **Dual-Cost Log Management:** You pay to ingest logs and separately to index them for searching, with indexing costs based on event count and retention. This dual-charge model can make comprehensive logging expensive, forcing a choice between visibility and cost.\n\n\u003cKeyPointCallout title=\"Breaking down Datadog pricing\"\u003e\n\nThese billing triggers are why teams evaluating a [cost-effective Datadog alternative](https://signoz.io/blog/cost-effective-datadog-alternative/) or an [open-source Datadog alternative](https://signoz.io/blog/open-source-datadog-alternative/) increasingly look for OTLP ingestion, BYOC deployment, and efficient telemetry storage that can handle high-cardinality telemetry without making log indexing unaffordable. For a deeper breakdown of the log side of the bill, see this [Datadog logs pricing analysis](https://signoz.io/blog/datadog-logs-pricing/).\n\n\u003c/KeyPointCallout\u003e\n\nIn this article we will go through the best DataDog alternatives which you can consider.\n\nList of top DataDog alternatives:\n\n- [SigNoz Cloud](#signoz-cloud)\n- [New Relic](#new-relic)\n- [Dynatrace](#dynatrace)\n- [Grafana](#grafana)\n- [LogicMonitor](#logicmonitor)\n- [AppDynamics](#appdynamics)\n- [Splunk](#splunk)\n- [Sematext](#sematext)\n- [Sumo Logic](#sumo-logic)\n\n## SigNoz Cloud\n\nSigNoz Cloud is an excellent alternative to Datadog for APM, distributed tracing, log management, and metrics monitoring. It brings these signals into one product built on [OpenTelemetry](https://signoz.io/opentelemetry/), with usage-based pricing instead of separate host and user charges. If you're tired of complex and unpredictable billing practices, or if you want to use OpenTelemetry, then SigNoz Cloud is the right choice. Here are some top reasons to choose SigNoz Cloud over Datadog.\n\n### OpenTelemetry Native - No vendor lock-in in your code\n\nSigNoz Cloud is built from the ground up to be OpenTelemetry native. You can filter by resource and span attributes, inspect span events and links, and follow a trace ID to the related logs. Instrumentation scope and metric metadata stay available when you need to understand where data came from. For example, you can start with a slow request, find the span responsible, and check its logs and host metrics without starting a separate investigation. **We don't charge you extra for \"custom metrics\" when you're using OpenTelemetry**.\n\nThe same [visual query builder](https://signoz.io/docs/userguide/query-builder-v5/) works across logs, metrics, and traces, so you don't need to learn a different query language for each signal. Quick filters, autocomplete, and saved views help you return to an investigation. You can also scrape Prometheus endpoints with the [OpenTelemetry Collector](https://signoz.io/docs/metrics-management/send-metrics/) and use PromQL when you prefer it.\n\nWe've also recently launched features that double down on our [OTel](https://signoz.io/opentelemetry/)-native approach, including:\n\n- **[LLM Observability](https://signoz.io/llm-observability/):** Monitor your LLM applications (OpenAI, Anthropic, LangChain) with out-of-the-box dashboards.\n- **[Trace Funnels](https://signoz.io/docs/trace-funnels/overview/):** Measure completion rates and delays between steps in a request flow.\n- **[External API Monitoring](https://signoz.io/docs/apm-and-distributed-tracing/application-details/):** Gain visibility into the performance of third-party APIs your application depends on.\n- **[Out-of-the-box Messaging Queue Monitoring](https://signoz.io/blog/opentelemetry-powered-kafka-celery-monitoring/):** Effortlessly monitor popular queuing systems.\n\n### Flexible Hosting Options for Every Stage of Growth\n\nWe believe you shouldn't be locked into a single deployment model. You can choose managed SigNoz Cloud or self-hosted SigNoz to meet your needs as you scale:\n\n- **[SigNoz Cloud](https://signoz.io/docs/cloud/):** A fully-managed, scalable solution for teams that want to focus on their core business without the overhead of managing an observability platform.\n- **[SigNoz Cloud: Dedicated Enterprise](https://signoz.io/enterprise/):** A dedicated environment managed by SigNoz, with enterprise support and security options.\n- **[SigNoz Cloud: Managed BYOC](https://signoz.io/enterprise/):** SigNoz runs the platform in your cloud account, so you can meet data residency requirements without operating it yourself.\n- **[Self-hosted SigNoz Enterprise](https://signoz.io/enterprise/):** Your team runs the platform in your own cloud or on-premise, including storage, scaling, upgrades, and backups, with enterprise support.\n- **[Self-hosted SigNoz Community Edition](https://signoz.io/docs/install/self-host/):** The MIT-licensed, open-source option for teams that want to manage their own infrastructure.\n\n### Simple, Transparent, Usage-Based Pricing\n\nThe SigNoz Cloud [pricing model](https://signoz.io/pricing/) is designed to be straightforward and predictable. Plans start at \\$49/month, including \\$49 of usage. Logs and traces cost \\$0.30 per ingested GB with 15-day retention; metrics cost \\$0.10 per million samples with one-month retention. You pay for usage above the included amount at these rates.\n\nThere are no \"weird\" pricing levers like per-host charges that force you to alter your architecture. Our goal is a simple, scalable model that grows with you.\n\nWith SigNoz Cloud, you get:\n\n- **No surprise bills:** Our pricing is easy to understand and forecast.\n- **Cost-effective at scale:** As your data volume grows, our pricing remains competitive.\n- **Freedom to architect your systems as you see fit:** We don't penalize you for using modern, dynamic infrastructure.\n\nHere's a feature overview of SigNoz Cloud:\n\n\u003cProductFeatureShowcase /\u003e\n\n\u003cDatadogVsSigNoz /\u003e\n\n## New Relic\n\n\u003ca href=\"https://newrelic.com/\" rel=\"noopener noreferrer nofollow\" target=\"_blank\"\u003e**New Relic**\u003c/a\u003e is one of the oldest companies in this domain and can be a good DataDog alternative. If you opt for a full user plan, you can get access to all the tools New Relic provides in its observability stack.\n\n### Key Features\n\n- **Application Performance Monitoring**: End-to-end visibility into application performance\n- **Browser \u0026 Mobile Monitoring**: Track frontend and mobile app performance\n- **Infrastructure Monitoring**: Monitor hosts, containers and cloud services\n- **Log Management**: Centralized log aggregation and analysis\n- **Synthetic Monitoring**: [Proactive monitoring](https://signoz.io/guides/proactive-monitoring/) of user journeys\n- **Serverless Monitoring**: Monitor AWS Lambda and other serverless platforms\n- **AIOps \u0026 Analytics**: AI-powered insights and anomaly detection\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/10/datadog-alternatives-image 4.webp\"\n alt=\"New Relic Dashboard (Source: New Relic Docs)\"\n caption=\"New Relic Dashboard (Source: New Relic Docs)\"\n/\u003e\n\nNew Relic's \u003ca href=\"https://newrelic.com/pricing\" rel=\"noopener noreferrer nofollow\" target=\"_blank\"\u003epricing\u003c/a\u003e includes 100 GB of free data ingest each month. Beyond that, Original Data costs \\$0.40/GB and Data Plus costs \\$0.60/GB. It offers data-plus-users or data-plus-compute pricing; the Standard user-based edition supports up to five full-platform users. On the user-based model, adding engineers can increase the bill alongside data ingestion.\n\nIf you're leaving Datadog with a production workload above the free allowance, compare the paid bill, not just the free tier. SigNoz Cloud has no per-user fees, so more engineers can investigate issues without adding seat costs. Use the same telemetry volume, team size, and retention in our [pricing comparison](https://signoz.io/blog/pricing-comparison-signoz-vs-datadog-vs-newrelic-vs-grafana/) and [calculator](https://signoz.io/pricing/) to check the saving for your workload.\n\n### Pros\n\n- Comprehensive full-stack observability platform\n- Strong APM capabilities with detailed transaction tracing\n- Generous free tier with 100GB data ingestion\n- AI-powered analytics and troubleshooting\n- Extensive integrations ecosystem\n- Proven enterprise-grade platform\n\n### Cons\n\n- Separate data and user or compute charges to budget for\n- Adding full-platform users increases costs on the user-based model\n- Learning curve for new users\n- Limited customization options\n\n### Best For\n\n- Enterprise organizations needing comprehensive observability\n- Teams wanting proven, mature monitoring solutions\n- Organizations with dedicated observability budgets\n- Companies needing strong APM capabilities\n- Teams requiring extensive integration options\n\nMoving from Datadog does not have to mean rebuilding every dashboard by hand. The [migration guide](https://signoz.io/docs/migration/migrate-from-datadog-to-signoz/) and [dashboard converter](https://signoz.io/datadog-migration-tool/) help you move to SigNoz Cloud in stages; dashboard migration support is available for spends above \\$999. For a detailed comparison of the two vendors, read [DataDog vs New Relic](https://signoz.io/blog/datadog-vs-newrelic/).\n\n## Dynatrace\n\n\u003ca href=\"https://www.dynatrace.com/\" rel=\"noopener noreferrer nofollow\" target=\"_blank\"\u003e**Dynatrace**\u003c/a\u003e is another strong contender among Datadog competitors, providing a broad spectrum of monitoring\nservices aimed at large-scale enterprises. Its standard host-monitoring setup uses OneAgent to\ncollect application and infrastructure telemetry. Dynatrace also supports OpenTelemetry ingestion.\nIt can serve the following use-cases for monitoring:\n\n### Key Features\n\n- **Application Performance Monitoring**: End-to-end visibility with OneAgent technology\n- **[Infrastructure Monitoring](https://signoz.io/guides/infrastructure-monitoring/)**: Monitor hosts, cloud services and virtual machines\n- **Container Monitoring**: Support for Docker, Kubernetes environments\n- **Network Monitoring**: Deep network visibility and analysis\n- **Root Cause Analysis**: AI-powered problem detection and analysis\n- **User Experience Monitoring**: Track and analyze user interactions\n- **Cloud Automation**: Automated deployment and configuration\n\nFull-stack monitoring, the Dynatrace product aimed to provide observability for apps, is \u003ca href=\"https://www.dynatrace.com/pricing/\" rel=\"noopener noreferrer nofollow\" target=\"_blank\"\u003epriced at \\$0.01 per memory-GiB-hour\u003c/a\u003e. An 8 GiB host running for 730 hours would cost about \\$58.40 for full-stack monitoring. Logs and other separately metered services add to that bill.\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/10/datadog-alternatives-image 5.webp\"\n alt=\"Dynatrace Dashboard (Source: Dynatrace Website)\"\n caption=\"Dynatrace Dashboard (Source: Dynatrace Website)\"\n/\u003e\n\n### Pros\n\n- Single agent (OneAgent) deployment model\n- Strong AI-powered analytics and automation\n- Comprehensive infrastructure monitoring\n- Enterprise-grade security features\n- Detailed root cause analysis\n- Extensive cloud platform support\n\n### Cons\n\n- Full-stack costs scale with host memory and running hours\n- Complex pricing structure\n- Steep learning curve\n- Enterprise focus may not suit smaller teams\n- Resource-intensive agent\n\n### Best For\n\n- Large enterprise organizations\n- Teams needing AI-powered analytics\n- Organizations with complex infrastructure\n- Companies requiring detailed root cause analysis\n- Teams wanting unified agent deployment\n\nFor detailed comparison, please read [DataDog vs Dynatrace.](https://signoz.io/comparisons/datadog-vs-dynatrace/)\n\n## Grafana\n\nGrafana is an open-source data visualization tool that you can use as part of a Datadog alternative. Grafana Cloud is its managed service for logs, metrics, traces, APM, and infrastructure monitoring, so you do not have to operate the backends yourself.\n\nIf you're looking to self-host a Grafana-based Datadog alternative, you'll typically use the core LGTM stack. It is particularly popular in cloud-native environments, covering logs, metrics, traces, and visualization. Grafana can also query external data sources.\n\n**The LGTM Stack Components**\n\nGrafana's observability platform is built on the LGTM stack:\n\n- **Loki:** An efficient log aggregation system that indexes log metadata, enabling cost-effective storage and fast querying, integrated seamlessly with Grafana.\n- **Grafana**: A versatile visualization tool offering customizable dashboards to display and analyze data from various sources, including metrics, logs, and traces.\n- **Tempo**: A [**distributed tracing**](https://signoz.io/blog/distributed-tracing-in-microservices/) backend focused on scalability, storing and querying trace data, with easy integration into Grafana for visualization.\n- **Mimir**: A long-term, scalable storage system for [Prometheus metrics](https://signoz.io/guides/what-are-the-4-types-of-metrics-in-prometheus/), ensuring high performance and availability for large-scale metric data.\n\nFor teams using OpenTelemetry, the difference is also in how you explore the data. Grafana's backends use PromQL, LogQL, and TraceQL for metrics, logs, and traces. SigNoz Cloud gives you one visual query builder across all three, with resource attributes, span events, and span links available during an investigation. Grafana Cloud accepts OTLP but \u003ca href=\"https://grafana.com/docs/grafana-cloud/observe-and-act/send-data/otlp/otlp-format-considerations/\" rel=\"noopener noreferrer nofollow\" target=\"_blank\"\u003emaps OpenTelemetry metrics to its Prometheus representation\u003c/a\u003e. SigNoz Cloud supports native OpenTelemetry metrics as well as Prometheus scraping through the Collector, with PromQL available when you need it.\n\n### Key Features\n\n- **Visualization \u0026 Dashboarding**: Create customizable dashboards to analyze and display data from multiple sources\n- **[LGTM Stack](https://signoz.io/docs/migration/migrate-from-grafana-to-signoz/) Integration**: Core observability components working together seamlessly\n- **Cloud-Native Support**: Designed for modern containerized and microservices architectures\n- **Open Source Foundation**: Built on open-source technologies with strong community support\n- **Multi-Backend Support**: Connect to various data sources and backends\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/10/datadog-alternatives-CleanShot_2024-10-24_at_15.58.162x.webp\"\n alt=\"Grafana Dashboards (Source: Grafana Website)\"\n caption=\"Grafana Dashboards (Source: Grafana Website)\"\n/\u003e\n\n### Pros\n\n- Open-source foundation with enterprise features\n- Highly customizable dashboards\n- Support for multiple data sources\n- Strong community and ecosystem\n- Cost-effective compared to proprietary solutions\n\n### Cons\n\n- **Managing multiple backend components can be complex when self-hosting**\n- Steeper learning curve for advanced features\n- Self-hosted deployment requires operational expertise\n- Enterprise features require paid subscription\n\nGrafana Cloud's free tier can cover small workloads, but a production workload moving from Datadog may exceed those limits. Compare paid plans at the same telemetry volume and retention, including the engineers who need access. SigNoz Cloud's usage-based pricing makes it a cost-effective option to evaluate at that scale. Our [pricing comparison](https://signoz.io/blog/pricing-comparison-signoz-vs-datadog-vs-newrelic-vs-grafana/) shows the assumptions, and the [Grafana migration guide](https://signoz.io/docs/migration/migrate-from-grafana-to-signoz/) covers the move. Read more about [Grafana vs Datadog](https://signoz.io/blog/datadog-vs-grafana/).\n\n## LogicMonitor\n\n\u003ca href=\"https://www.logicmonitor.com/\" rel=\"noopener noreferrer nofollow\" target=\"_blank\"\u003eLogicMonitor\u003c/a\u003e is a SaaS-based hybrid observability platform powered by AI that helps organizations monitor\nand optimize their IT infrastructure and applications.{' '}\n\nIt can be used as a DataDog alternative if you're looking for infrastructure monitoring. It also provides AIOps features, including root cause analysis, anomaly detection, and forecasting.\n\n### Key Features\n\n- **Unified Platform**: Single platform for infrastructure, cloud, and application monitoring\n- **AIOps Capabilities**: AI-powered anomaly detection, root cause analysis, and forecasting\n- **Dynamic Topology Mapping**: Automated discovery and visualization of infrastructure dependencies\n- **Extensive Integrations**: Over 3000 pre-built integrations and monitoring templates\n- **Automated Deployment**: Quick setup with automatic discovery and configuration\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/10/datadog-alternatives-image 6.webp\"\n alt=\"LogicMonitor Dashboard (Source: LogMonitor Website)\"\n caption=\"LogicMonitor Dashboard (Source: LogMonitor Website)\"\n/\u003e\n\nLogicMonitor's dynamic topology mapping is a feature that distinguishes it from other Datadog competitors.\n\nWith dynamic topology mapping, you can have an overview of your network devices and their inter-dependency. Some of the key monitoring capabilities provided by LogicMonitor are:\n\n- Infrastructure Monitoring\n- Cloud Monitoring (AWS, Azure, GCP)\n- Application Performance Monitoring\n- Log Analytics\n- [Distributed Tracing](https://signoz.io/blog/distributed-tracing/)\n- Configuration Monitoring\n- Service Insights\n\n### Pros\n\n- Comprehensive hybrid observability solution\n- \u003ca href=\"https://www.logicmonitor.com/platform\" rel=\"noopener noreferrer nofollow\" target=\"_blank\"\u003eLogicMonitor reports\u003c/a\u003e 90% less alert noise\n- LogicMonitor reports a 46% reduction in mean time to resolution (MTTR)\n- Easy deployment and automation\n- Strong AIOps capabilities\n\n### Cons\n\n- Can be complex for small organizations\n- Pricing may be higher for extensive deployments\n- Some advanced features require additional configuration\n\n## AppDynamics\n\n\u003ca href=\"https://www.appdynamics.com/\" rel=\"noopener noreferrer nofollow\" target=\"_blank\"\u003e**AppDynamics**\u003c/a\u003e is an extensive SaaS tool and a significant player among Datadog competitors, that promises to\ncorrelate business metrics and application performance metrics. It can be used as a good DataDog\nalternative. Its platform includes an APM tool that provides code-level observability.\n\n### Key Features\n\n- **Full-Stack Observability**: End-to-end visibility across applications, infrastructure and business context\n- **Business iQ**: Correlates technical performance with business metrics\n- **AI/ML Capabilities**: Automated anomaly detection and root cause analysis\n- **Code-Level Diagnostics**: Deep application performance insights\n- **Multi-Cloud Support**: Monitoring for cloud, hybrid and on-premise environment\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/09/appdynamics_splunk_alternative.webp\"\n alt=\"Datadog Alternative - AppDynamics\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eAppDynamics observability platform for full visibility of application performance\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\n### Pros\n\n- Deep code-level visibility\n- Strong business metrics correlation\n- Enterprise-grade security\n- Extensive language support\n- Powerful analytics capabilities\n\n### Cons\n\n- Complex initial setup\n- Higher price point\n- Steep learning curve\n- Resource intensive agents\n\n## Splunk\n\n\u003ca href=\"https://www.splunk.com/\" rel=\"noopener noreferrer nofollow\" target=\"_blank\"\u003eSplunk\u003c/a\u003e is a software platform designed for searching, monitoring, and analyzing machine-generated\ndata. It's primarily used for log and event data analysis, but its capabilities extend to various\ntypes of data, including metrics, security information, and more. Splunk provides a centralized\nplatform where you can collect, index, and visualize data from a wide range of sources, such as\nservers, applications, network devices, sensors, and websites.\n\n### Key Features\n\n- **Log Management:** Splunk can ingest, index, and store log data from various sources.\n- **Security Information and Event Management (SIEM):** It can be used for security monitoring and threat detection.\n- **Monitoring and alerting**: Splunk can set up real-time alerts based on predefined conditions, helping organizations respond to issues as they occur.\n- **Unified Data Platform**: Centralized collection and analysis of logs, metrics, traces and more\n- **Advanced Analytics**: Powerful search and visualization capabilities\n- **Security Intelligence**: Built-in SIEM functionality and threat detection\n- **Machine Learning**: Automated anomaly detection and predictive analytics\n- **Real-time Monitoring**: Live data ingestion and alerting capabilities\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2023/09/splunk_dashboard.webp\" alt=\"Splunk Dashboard\" /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eSplunk Dashboard\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\nIt offers both on-premises and cloud-based solutions, and it has a wide range of apps and integrations to support specific use cases and industries.\n\n### Pros\n\n- Powerful search capabilities\n- Extensive integration options\n- Strong security features\n- Flexible deployment options\n- Large ecosystem of apps\n\n### Cons\n\n- Complex pricing model\n- Resource intensive\n- Steep learning curve\n- Can be expensive at scale\n\n## Sematext\n\n\u003ca href=\"https://sematext.com/\" rel=\"noopener noreferrer nofollow\" target=\"_blank\"\u003eSematext\u003c/a\u003e is a monitoring tool that specializes in providing monitoring, logging, and observability\nsolutions for modern software applications and infrastructure. The company offers a range of tools\nand services designed to help organizations gain insights into their systems, troubleshoot issues,\nand improve overall performance.\n\n### Key Features\n\n- **Metrics Monitoring**: Sematext offers monitoring solutions that allow organizations to collect and visualize metrics from various sources, including servers, applications, containers, and cloud services.\n- **Log Management**: Sematext provides log management capabilities comparable to those found in [open source logging tools](https://signoz.io/blog/best-log-management-tools/), enabling the collection, aggregation, and analysis of log data from different components of an organization’s technology stack.\n- **Tracing and APM:** Sematext offers application performance monitoring and tracing capabilities, allowing organizations to trace requests and transactions through their applications.\n- **Infrastructure Monitoring:** Sematext's solutions cover infrastructure monitoring, allowing organizations to [monitor server health](https://signoz.io/guides/server-health-monitoring/), resource utilization, and network performance.\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/10/datadog-alternatives-image 7.webp\"\n alt=\"Sematext Dashboard (Source: Sematext)\"\n caption=\"Sematext Dashboard (Source: Sematext)\"\n/\u003e\n\n### Pros\n\n- Easy to set up and use\n- Good documentation\n- Flexible deployment options\n- Competitive pricing\n- Built-in alerting\n\n### Cons\n\n- Limited third-party integrations\n- Basic reporting capabilities\n- UI can be improved\n- Some features lack depth\n\n## Sumo Logic\n\nSumo Logic is a cloud-based log management and analytics platform that helps organizations collect, manage, and analyze data generated by their applications, systems, and infrastructure. It is designed to provide real-time insights into the performance, security, and operational aspects of an organization's IT environment.\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/10/datadog-alternatives-image 8.webp\"\n alt=\"SumoLogic dashboard (Source: Sumologic)\"\n caption=\"SumoLogic dashboard (Source: Sumologic)\"\n/\u003e\n\n### Key Features\n\n- **Log Management:** Sumo Logic allows organizations to centralize the collection and storage of log data from various sources, including servers, applications, cloud services, and network devices.\n- **Real-Time Monitoring:** Sumo Logic provides real-time monitoring and alerting capabilities, enabling organizations to proactively detect and respond to issues as they occur.\n- **Log Analysis:** Users can perform advanced log analysis using Sumo Logic's query language and search capabilities. This allows for the identification of patterns, anomalies, and trends within log data.\n- **Security Information and Event Management (SIEM):** Sumo Logic can be used as a SIEM solution, helping organizations detect and investigate security threats by correlating and analyzing security-related data from logs and other sources.\n\n### Pros\n\n- Scalable cloud-based solution\n- Real-time monitoring and alerting\n- Advanced log analysis capabilities\n- Integrated SIEM functionality\n\n### Cons\n\n- Can be complex to set up and configure\n- Pricing can be high for smaller organizations\n- Limited offline capabilities\n\n## Choosing the Right Datadog Alternative\n\nIf you're looking for a Datadog alternative, start with the problems you want to solve: a bill that is hard to forecast, a complex interface, or too much work connecting logs, metrics, and traces. SigNoz Cloud addresses these with usage-based pricing and a shared query builder in one OpenTelemetry-native product.\n\nThe above DataDog alternatives can be a good option to meet your monitoring needs. If one of those tools stands out, it might be worth checking out the more focused guides on [Dynatrace alternatives](https://signoz.io/blog/dynatrace-alternatives/), [Grafana alternatives](https://signoz.io/blog/grafana-alternatives/), and [Splunk alternatives](https://signoz.io/blog/splunk-alternatives/) for pricing, strengths, and migration tradeoffs.\n\nFor an [OpenTelemetry backend](https://signoz.io/blog/opentelemetry-backend/), SigNoz Cloud is a great choice. You can keep instrumentation based on open standards and use the resulting context to investigate issues across signals. Start a trial with one service, follow the [Datadog migration guide](https://signoz.io/docs/migration/migrate-from-datadog-to-signoz/), and compare the workflow and cost with your current setup.\n\n## Getting started with SigNoz Cloud\n\n\u003cGetStartedSigNoz /\u003e\n\n## FAQs\n\n### Who is Datadog's biggest competitor?\n\nDatadog's biggest competitors include SigNoz Cloud, New Relic, Dynatrace and Grafana. In all of these, the best choice depends on your specific needs. For APM, logs, metrics, and traces with OpenTelemetry-native workflows and usage-based pricing, SigNoz Cloud is a great choice. New Relic and Dynatrace also provide broad observability platforms. Grafana can be used with self-hosted backends or through the managed Grafana Cloud service.\n\n### Is Datadog better than Splunk?\n\nDatadog and Splunk serve different purposes with some overlap. Datadog excels in cloud infrastructure monitoring and APM, while Splunk is renowned for log management and SIEM. The choice depends on your specific needs. For a comprehensive open-source alternative that integrates metrics, logs, and traces, consider self-hosted SigNoz.\n\n### How to replace Datadog?\n\nYou can replace Datadog in stages with SigNoz Cloud. Instrument one service with OpenTelemetry SDKs or auto-instrumentation, then configure the Collector to send its telemetry to SigNoz Cloud. The [dashboard migration tool](https://signoz.io/datadog-migration-tool/) converts Datadog dashboard JSON into SigNoz Cloud dashboards; review the converted queries and widgets against your data. Validate the signals, dashboards, and alerts before turning off the matching Datadog collection. Follow the [migration guide](https://signoz.io/docs/migration/migrate-from-datadog-to-signoz/) for the full process. Dashboard migration support is available for spends above \\$999.\n\n### What makes Datadog so good?\n\nDatadog is popular due to its comprehensive monitoring capabilities, ease of use, and numerous integrations. Its powerful dashboards and alerting system also contribute to its appeal. However, its complex and unpredictable pricing can be a drawback. For a managed alternative with usage-based pricing, consider SigNoz Cloud.\n\n### What is the weakness of Datadog?\n\nThe primary weakness of Datadog is its complex SKU-based pricing, which can lead to unpredictable and often high costs, especially for smaller organizations.\n\n### Can Grafana replace Datadog?\n\nGrafana can replace Datadog for many monitoring and visualization needs when paired with backends such as Loki, Tempo, and Mimir. Self-hosting that stack requires configuration and ongoing operations; Grafana Cloud manages the backends for you. SigNoz Cloud is another managed option, with one query builder and connected logs, metrics, and traces in one product.\n\n### Is Datadog SaaS only?\n\nYes, Datadog is a SaaS-only platform, offering its services via the cloud, which simplifies deployment and scaling for users. For an open-source alternative that you can deploy on-premise or in your own cloud, consider self-hosted SigNoz.\n\n### Is Sentry better than Datadog?\n\nSentry and Datadog serve different purposes. Sentry focuses on error tracking and performance monitoring for applications, while Datadog offers a broader range of monitoring and observability features. The choice depends on specific needs.\n\n### Is Datadog like Grafana?\n\nDatadog and Grafana both provide monitoring and visualization capabilities. Datadog is an all-in-one SaaS platform, while Grafana is a visualization tool used with data backends. You can operate a Grafana-based stack yourself or use the managed Grafana Cloud service.\n\n\u003cPricingCTA /\u003e\n\nRead More:\n\n[**SigNoz vs Datadog**](https://signoz.io/datadog-alternative/)\n\n[**New Relic Alternatives**](https://signoz.io/blog/open-source-newrelic-alternative/)\n\n[**New Relic vs DataDog**](https://signoz.io/blog/datadog-vs-newrelic/)\n"])</script><script>self.__next_f.push([1,"32:T486a,"])</script><script>self.__next_f.push([1,"If you’re looking for the best open-source alternative to Datadog, Self-Hosted SigNoz provides an OpenTelemetry-native observability platform that you can run on your own infrastructure.\n\nOne reason for building the SigNoz open-source project was the lack of a robust, one-stop observability solution that provided a polished experience similar to SaaS tools such as Datadog. We also wanted a tool built on OpenTelemetry, with no proprietary agents in your application code, so that you never get locked in with a vendor.\n\n\n\n\u003cInlineCTA message=\"Start with SigNoz Cloud in minutes. Simple usage-based pricing, no surprise bills. Migrate to self-hosted anytime — same open-source codebase.\" /\u003e\n\nSigNoz has been built on [OpenTelemetry](https://signoz.io/opentelemetry/) (OTel) from day one and uses OTel data across the product, enabling deep correlation and a faster debugging experience. Teams moving from Datadog can use our [dashboard migration tool](https://signoz.io/datadog-migration-tool/) to translate exported dashboards, then migrate their telemetry and workflows in stages.\n\n## The issues with Datadog and why SigNoz\n\nFor many teams, the first problem with Datadog is unpredictable billing. That problem becomes harder to escape when instrumentation depends on a proprietary agent and there is no self-hosted deployment option. Let’s look at these issues in detail.\n\n### Complex and Unpredictable billing practices\n\nThe core of the confusion with Datadog's pricing lies in its multi-dimensional, usage-based model. You're not just paying for one thing; you're charged across various products, each with its own pricing metric. This can lead to unpredictable bills that are difficult to forecast and control. \n\nMany users who migrated from Datadog to SigNoz came to us after receiving bills they did not expect.\n\n\u003cFigure src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2025/06/datadog-pricing-datadog-pricing-reddit-rant-dark.webp\" alt=\"Surprise bills are one of the common complaints againts Datadog (Source: Reddit)\" caption=\"Surprise bills are one of the common complaints againts Datadog (Source: Reddit)\" /\u003e\n\nHost-based pricing, special pricing for custom metrics, and paying twice for logs through ingestion and indexing are some of the [common issues with Datadog billing](https://signoz.io/blog/datadog-pricing/). With Self-Hosted SigNoz Community Edition, there is no SigNoz license fee, per-host fee, or per-user fee; you pay for and operate the infrastructure. If you want a managed experience, SigNoz Cloud uses simple, usage-based pricing. Some highlights:\n\n- **Simple usage-based pricing:** \\$0.30 per GB for ingested logs or traces, \\$0.10 per million samples for metrics. Use our [cost estimator](https://signoz.io/pricing/#estimate-your-monthly-bill) to estimate your monthly bill.\n- **No special pricing for custom metrics:** Datadog can bill metrics outside its standard integrations as custom metrics, with allowances and overages based on the plan or contract. SigNoz Cloud prices all metric samples the same way, so you do not have to classify application metrics by integration type before estimating cost.\n- **No host-based, no user-based pricing:** Monitor as many hosts as you like and add unlimited team members based on your requirements. Both of these make sense for modern architecture and modern engineering teams.\n\n\u003cInterlinkCard title=\"Check out SigNoz pricing details\" href=\"/pricing/\" /\u003e\n\n### Vendor Lock-in Due to Proprietary Agent\n\nA significant issue with Datadog is vendor lock-in, driven by its proprietary agent. To use Datadog, you must embed their agent throughout your infrastructure. If you ever decide to switch platforms, you face a major migration project: ripping out the Datadog agent everywhere and re-instrumenting your entire application stack for a new tool.\n\nOpenTelemetry is now the default standard for instrumenting cloud-native applications. If you don’t know much about OpenTelemetry, here’s a good read on [what is OpenTelemetry](https://signoz.io/opentelemetry/) and [top reasons to use OpenTelemetry](https://signoz.io/opentelemetry/).\n\nBy using OTel, you instrument your applications with a vendor-neutral standard, not a proprietary tool. This decouples your instrumentation from your observability backend, giving you the freedom to send your data to any OTel-compatible platform, including SigNoz, without being locked in.\n\nSigNoz is OpenTelemetry-native, designed to leverage the full power of [OTel](https://signoz.io/opentelemetry/) data for a unified view of logs, metrics, and traces. We've also recently launched features that double down on our OTel-native approach, including:\n\n- [Trace Funnels](https://signoz.io/blog/tracing-funnels-observability-distributed-systems/): Intelligently sample and analyze traces to focus on what's important.\n- [External API Monitoring](https://signoz.io/docs/apm-and-distributed-tracing/application-details/): Gain visibility into the performance of third-party APIs your application depends on.\n- [Out-of-the-box Messaging Queue Monitoring](https://signoz.io/blog/opentelemetry-powered-kafka-celery-monitoring/): Effortlessly monitor popular queuing systems.\n\n## Why SigNoz is a better open-source Datadog alternative than Grafana\n\nGrafana is the other open-source option many teams consider when moving from Datadog. It started as a data visualization tool, and it remains excellent at that job. If you just want to build dashboards from different data sources, Grafana may be a good fit. But a team replacing Datadog usually needs an opinionated observability product that helps it ingest, explore, correlate, and troubleshoot telemetry without assembling the experience itself. (If you want to explore the wider landscape, see our roundup of the best [open-source APM tools](https://signoz.io/blog/open-source-apm-tools/).)\n\nGrafana is being marketed as an all-in-one observability tool, but its open-source stack combines separate projects: Prometheus or Mimir for metrics, Loki for logs, Tempo for traces, and Grafana for visualization. Each signal keeps its own storage and query layer.\n\n**SigNoz stores logs, metrics, and traces in a single columnar datastore and exposes them through one product.**\n\nThis matters during an investigation. Engineers can use the same Query Builder experience across signals instead of moving between PromQL for metrics, LogQL for logs, and TraceQL for traces. A shared datastore also reduces the operational overhead of self-hosting multiple backends and makes cross-signal correlation and complex aggregations easier.\n\nSigNoz also exposes OpenTelemetry context inside the product. Engineers can query resource and span attributes, inspect instrumentation scope, span events, span links, and trace identifiers, and use metric metadata without treating OTLP as only an ingestion format. Grafana Cloud accepts OTLP, but stores metrics in Mimir, its Prometheus-compatible database, and maps OTLP metric names and resource attributes to the Prometheus data model. This means teams still work with Prometheus naming and label conventions for those metrics.\n\nSigNoz also provides first-class infrastructure monitoring for Kubernetes and Linux hosts, plus cloud-service monitoring for AWS, Azure, and GCP. Teams can investigate application and infrastructure signals together.\n\n### Deployment flexibility as your needs change\n\nDatadog is a closed SaaS product, so the only deployment option is to send data to Datadog Cloud. SigNoz lets you start with open source and move to a managed deployment as your requirements change, without changing observability products. You can choose from five deployment models:\n\n- [SigNoz Cloud](https://signoz.io/teams/): A fully managed service for teams that do not want to operate the observability platform.\n- [Dedicated Enterprise Cloud](https://signoz.io/enterprise/): A dedicated environment managed by SigNoz for organizations that need stronger isolation and enterprise support.\n- [Managed BYOC](https://signoz.io/enterprise/): SigNoz operates the platform in your cloud account, so the data stays in your cloud environment.\n- [Enterprise Self-Hosted](https://signoz.io/enterprise/): Your team operates SigNoz in its own infrastructure with enterprise features and support from SigNoz.\n- [Self-Hosted Community Edition](https://signoz.io/docs/install/self-host/): The open-source, MIT-licensed option for teams that want to operate SigNoz themselves.\n\nIf a team later moves from self-hosted to managed observability, SigNoz Cloud keeps the same product experience and uses telemetry-volume pricing without extra per-host, per-user, or custom metric charges. Grafana Cloud has free allowances, but after those allowances, each signal uses a different pricing unit: metrics are billed by active series, while logs and traces are billed by data volume. Some services also add host or user charges. As telemetry grows, teams leaving Datadog for cost reasons can quickly outgrow those free allowances in Grafana Cloud. In those cases, SigNoz Cloud can be easier to forecast and more cost-effective. See our detailed [SigNoz, Datadog, New Relic, and Grafana pricing comparison](https://signoz.io/blog/pricing-comparison-signoz-vs-datadog-vs-newrelic-vs-grafana/).\n\n\u003cInterlinkCard title=\"In-depth SigNoz vs Grafana\" href=\"/grafana-alternative/\" /\u003e\n\n## An Overview of SigNoz Features\n\n### Application metrics\n\nGet out of the box p90, p99 latencies, RPS, Error rates and top endpoints for a service out of the box.\n\n\u003cfigure data-zoomable\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/common/signoz_charts_application_metrics.webp\"\n alt=\"SigNoz dashboard showing popular RED metrics\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003e\n SigNoz UI showing application overview metrics like 50th/90th/99th Percentile latencies,\n request rate and Apdex\n \u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\n### Seamless flow between telemetry signals\n\nPowered by OpenTelemetry's [semantic conventions](https://opentelemetry.io/docs/concepts/semantic-conventions/), you can quickly jump between telemetry signals in SigNoz. Found something suspicious in a metric, just click that point in the graph \u0026 get details of traces which may be causing the issues. Seamless, Intuitive.\n\nSimilarly, we have enabled correlation between other telemetry signals.\n\n#### APM Metrics to Traces \u0026 Logs\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/09/introducing-correlation-of-signals-apm-charts-to-logs.webp\"\n alt=\"[APM metrics](https://signoz.io/guides/apm-metrics/) to Traces \u0026 Logs\"\n caption=\"Quickly jump from APM metrics to traces or logs to investigate issues further\"\n/\u003e\n\n#### Traces to Logs\n\nIf you see a API call taking more time than usual, you can go to related logs to investigate further.\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/common/traces-to-logs.webp\"\n alt=\"Traces to related logs\"\n caption=\"Go from traces to related logs to get more context in debugging performance issues\"\n/\u003e\n\nSimilarly you can click on detailed view of logs and then go to related [trace ID](https://signoz.io/comparisons/opentelemetry-trace-id-vs-span-id/) to see the flow of user requests.\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/common/logs-to-trace.webp\"\n alt=\"Logs to traces\"\n caption=\"Go from logs to related trace ID in flamegraph view to see the flow of user requests\"\n/\u003e\n\n#### Logs with Infrastructure metrics\n\nWhile troubleshooting with logs, you can investigate the related infrastructure metrics to see if issues are happening becuase of that.\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/09/introducing-correlation-of-signals-logs-to-infra-metrics.webp\"\n alt=\"Logs to infra metrics\"\n caption=\"Check Pod and Node metrics while troubleshooting with logs\"\n/\u003e\n\n### Quick filters and an advanced Query Builder for all telemetry signals\n\nSigNoz provides quick filters and a consistent Query Builder experience for logs, metrics, and traces. You can filter by telemetry attributes, build aggregations, and move across signals without learning a separate query language for each backend.\n\nFor example, query builder for traces allows you to create queries for finding the p99 latency of services.\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/common/trace-query-builder.webp\"\n alt=\"Trace Query Builder\"\n caption=\"Create queries on your trace data by using various aggregate functions and group by options\"\n/\u003e\n\nSimilarly, use quick filters to quickly filter the data that you need.\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/common/logs-quick-filter.webp\"\n alt=\"Logs quick filter\"\n caption=\"Use quick filter in logs to quickly filter out specific logs based on your use case\"\n/\u003e\n\nYou can create custom metrics from filtered traces to find metrics of any type of request. Want to find p99 latency of `customer_type: premium` who are seeing `status_code:400`. Just set the filters, and you have the graph. Boom!\n\n### Flamegraphs \u0026 Gantt charts\n\nDetailed flamegraph \u0026 Gantt charts to find the exact cause of the issue and which underlying requests are causing the problem. Is it a SQL query gone rogue or a Redis operation is causing an issue? Get more context on your spans with tags and events.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n className=\"box-shadowed-image\"\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/common/signoz_flamegraphs.webp\"\n alt=\"Detailed Flamegraphs \u0026 Gantt charts\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003e\n Spans of a trace visualized with the help of flamegraphs and gantt charts in SigNoz dashboard\n \u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\n### Logs Management\n\nSigNoz provides Logs management with advanced log query builder. You can also monitor your logs in real-time using live tailing. SigNoz uses a columnar database ClickHouse to store logs, which is [very efficient at ingesting and storing logs data](https://signoz.io/blog/logs-performance-benchmark/). Columnar databases like ClickHouse are very effective in storing log data and making it available for analysis.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/common/signoz_logs.webp\" alt=\"Logs tab in SigNoz\" /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eLogs tab in SigNoz comes equipped with advanced logs query builder and live tailing\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\n### Metrics \u0026 Dashboards\n\nMonitor any metrics important to you. Ingest metrics from your infrastructure or applications and create customized dashboards to monitor them.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2024/11/metrics-n-dashboards.webp\" alt=\"A hostmetrics dashboard\" /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003e\n You can create any kind of customized dashboards using different visualization panel types and\n an advanced [query builder](https://signoz.io/blog/query-builder-v5/)\n \u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\n### Exceptions Monitoring\n\nMonitor exceptions automatically in Python, Java, Ruby, and Javascript. For other languages, just drop in a few lines of code and start monitoring exceptions.\n\n\u003cfigure data-zoomable align=\"center\"\u003e\n \u003cimg\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/features/exceptions/exceptions-overview.webp\"\n alt=\"Exceptions Monitoring in SigNoz\"\n /\u003e\n \u003cfigcaption\u003e\n \u003ci\u003eExceptions Monitoring in SigNoz\u003c/i\u003e\n \u003c/figcaption\u003e\n\u003c/figure\u003e\n\n### LLM Observability\n\nBuilding with LLMs? SigNoz provides end-to-end observability for your LLM applications. Track token usage, costs, and latency for providers like OpenAI, Anthropic, and frameworks like LangChain and LlamaIndex.\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/blog/2026/02/llm-observability-dashboard.webp\"\n alt=\"LLM Observability Dashboard\"\n caption=\"Monitor token usage, cost, and latency for your LLM applications\"\n/\u003e\n\n### Agent Native Observability\n\nSigNoz also brings production context into AI-assisted debugging through [Agent Native Observability](https://signoz.io/agent-native-observability/). The [SigNoz MCP server](https://signoz.io/docs/ai/signoz-mcp-server/) lets coding agents such as Claude Code, Cursor, Codex, and Gemini query traces, logs, metrics, service topology, and deployment history from the development environment. Because the coding agent already has the codebase in context, it can relate production telemetry to the code behind an issue.\n\n[Noz](https://signoz.io/docs/ai/noz/) is the AI teammate built into SigNoz Cloud. Engineers can ask questions about their telemetry in natural language, let Noz investigate across signals, and use it to create dashboards, alerts, and views inside SigNoz.\n\n\u003cFigure\n src=\"https://d3nu8xzr1i9u95.cloudfront.net/web/img/agent-native-observability/mcp-and-noz.webp\"\n alt=\"SigNoz MCP Server in a coding agent next to the Noz AI assistant\"\n caption=\"Use the SigNoz MCP Server from a coding agent, or investigate telemetry with Noz in SigNoz Cloud\"\n/\u003e\n\n## Getting started with SigNoz\n\nAs discussed earlier, you can choose among five SigNoz deployment models. The fastest way to get started is [SigNoz Cloud](https://signoz.io/teams/). You can start with a free trial and test it with your own telemetry.\n\nOrganizations that need stronger isolation or data control can choose Dedicated Enterprise Cloud, Managed BYOC, or [Enterprise Self-Hosted](https://signoz.io/contact-us/).\n\nTeams that want to manage an open-source deployment themselves can use our [Self-Hosted Community Edition](https://signoz.io/docs/install/self-host/).\n\nIf you are migrating from Datadog, our [dashboard migration tool](https://signoz.io/datadog-migration-tool/) translates exported dashboard JSON into SigNoz dashboard definitions. Review imported queries, units, variables, and thresholds, and plan telemetry pipelines, alerts, integrations, permissions, and historical data as separate migration work. If you have more questions, use the SigNoz AI chatbot or join our [Slack community](https://signoz.io/slack/).\n\n\n\n\n---\n\n**Related Content**\n\n**[SigNoz vs Datadog](https://signoz.io/datadog-alternative/)**\u003cbr\u003e\u003c/br\u003e\n**[Beware these surprises in Datadog pricing](https://signoz.io/blog/datadog-pricing/)**\u003cbr\u003e\u003c/br\u003e\n**[LLM-powered Datadog to SigNoz Migration Tool](https://signoz.io/datadog-migration-tool/)**\u003cbr\u003e\u003c/br\u003e\n\u003cDatadogVsSigNoz /\u003e\n"])</script><script>self.__next_f.push([1,"21:[[[\"$\",\"script\",null,{\"type\":\"application/ld+json\",\"dangerouslySetInnerHTML\":{\"__html\":\"$29\"}}],[\"$\",\"script\",null,{\"type\":\"application/ld+json\",\"dangerouslySetInnerHTML\":{\"__html\":\"{\\\"@context\\\":\\\"https://schema.org\\\",\\\"@type\\\":\\\"BreadcrumbList\\\",\\\"itemListElement\\\":[{\\\"@type\\\":\\\"ListItem\\\",\\\"position\\\":1,\\\"name\\\":\\\"SigNoz\\\",\\\"item\\\":\\\"https://signoz.io/\\\"},{\\\"@type\\\":\\\"ListItem\\\",\\\"position\\\":2,\\\"name\\\":\\\"Blog\\\",\\\"item\\\":\\\"https://signoz.io/blog/\\\"},{\\\"@type\\\":\\\"ListItem\\\",\\\"position\\\":3,\\\"name\\\":\\\"Is a $1 million Datadog bill worth it?\\\",\\\"item\\\":\\\"https://signoz.io/blog/justifying-a-million-dollar-observability-bill/\\\"}]}\"}}]],[\"$\",\"$L2a\",null,{\"children\":[\"$\",\"$L2b\",null,{\"content\":{\"id\":900,\"documentId\":\"yrrrq5c3fl8vkykg9xucz9yr\",\"title\":\"Is a $1 million Datadog bill worth it?\",\"description\":\"I’d like to write a bit about how Observability costs are significant, how these costs tend to be justified, and how precise amount a company spends on *anything* tends to be more subjective than you’d think. 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The same is true of the Operations team and SRE teams: they’re needed to keep the system up and running, so easy to put a dollar value on the benefit.\"]}],\"\\n\",[\"$\",\"li\",null,{\"children\":[[\"$\",\"em\",null,{\"children\":\"Sales \u0026 Marketing -\"}],\" Here again, the calculation is fairly simple: calculate the benefit of new business, and you can show whether what you’re paying to acquire it makes sense\"]}],\"\\n\"]}]\n54:[\"$\",\"p\",null,{\"children\":\"Okay, if these costs can be justified to the last clipped Scottish groat, can’t we do that for all business expenses? 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You might try calculating how long the developer would have spent trying to fix the problem without the tool, but that’s always going to be a \",[\"$\",\"em\",null,{\"children\":\"very\"}],\" rough estimate since a proper observability tool \",[\"$\",\"a\",null,{\"href\":\"https://www.cncf.io/blog/2022/12/16/why-opentelemetry-is-taking-cloud-native-to-new-heights/\",\"rel\":\"noopener noreferrer nofollow\",\"target\":\"_blank\",\"children\":\"fundamentally alters the way dev teams operate\"}],\" so a simple time estimate is unlikely to be accurate.\"]}]\n5c:[\"$\",\"p\",null,{\"children\":[\"No, the real way to show what observability has to offer is the cost of \",[\"$\",\"em\",null,{\"children\":\"not\"}],\" having it. 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