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lg:pointer-events-none lg:block lg:py-10">Learn <svg width="11" height="12" viewBox="0 0 11 12" fill="none" xmlns="http://www.w3.org/2000/svg" class="lg:hidden"><path d="M5 1L10 6L5 11" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></svg></button><div class="jsx-62ee9791d3e4bddb menu fixed left-0 z-50 hidden w-full bg-[linear-gradient(to_right,_#f6f6f6_50%,_white_50%)] lg:top-[var(--navBottom,104px)] lg:group-focus-within:block lg:group-hover:block"><div class="jsx-62ee9791d3e4bddb container flex gap-12 xl:max-w-[1180px]"><div class="jsx-62ee9791d3e4bddb max-w-[170px] pt-12 pb-16"><h4 class="jsx-62ee9791d3e4bddb font-roboto mb-2 text-2xl font-bold">Observability Engineering</h4><p class="jsx-62ee9791d3e4bddb mb-6 text-sm tracking-wide"> <!-- -->Start your journey with the definitive guide to observability. 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href="https://ui.honeycomb.io/login">Login</a></div></div></header><main class="mx-auto w-full px-6 py-12 md:container"><script type="application/ld+json">{"@context":"https://schema.org","@graph":[{"@type":"Article","@id":"https://www.honeycomb.io/blog/cost-crisis-observability-tooling#article","isPartOf":{"@id":"https://www.honeycomb.io/blog/cost-crisis-observability-tooling"},"author":{"name":"Charity Majors","@id":"https://www.honeycomb.io/#/schema/person/charity"},"headline":"The Cost Crisis in Observability 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But I hear a special edge in people’s voices when it comes to their observability bill, and I don’t think it’s just about the cost of goods sold. I think it’s because people are beginning to correctly intuit that the value they get out of their tooling has become radically decoupled from the price they are paying.</p></div></div><div class="flex w-full flex-col gap-3"><div class="flex w-full flex-col items-center justify-between gap-5 md:flex-row"><div class="flex flex-row items-center px-5 md:px-0"><div class="flex items-center gap-[14px]"><img alt="Charity Majors" loading="lazy" width="80" height="80" decoding="async" data-nimg="1" class="bg-hc-gray-50 size-10 rounded-full" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F6c9e651a4b8db4eed6db22230b68cf4bba1ce0dd-1092x1092.png%3Fw%3D80%26h%3D80&amp;w=96&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F6c9e651a4b8db4eed6db22230b68cf4bba1ce0dd-1092x1092.png%3Fw%3D80%26h%3D80&amp;w=256&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2F6c9e651a4b8db4eed6db22230b68cf4bba1ce0dd-1092x1092.png%3Fw%3D80%26h%3D80&amp;w=256&amp;q=75"/><p class="text-hc-slate text-base"><span>By: </span><a class="text-hc-cobalt underline hover:no-underline" href="/author/charity">Charity Majors</a></p></div><span class="text-hc-slate font-roboto text-base font-medium"><span class="ml-1"> | </span><span>Updated: December 18, 2024</span></span></div><div class="bg-hc-gray-100 h-0.5 w-full md:hidden"></div><div class="flex flex-wrap gap-3 px-5 md:px-0"><a href="/blog/category/observability"><div class="font-roboto focus:ring-ring inline-flex items-center rounded-lg font-medium transition-colors focus:ring-2 focus:ring-offset-2 focus:outline-none px-2 py-1 text-xs leading-normal bg-hc-gray-200 text-hc-slate">Observability</div></a></div></div><div class="bg-hc-gray-100 hidden h-0.5 w-full md:block"></div></div></header><div class="mx-auto mt-10 mb-5 max-w-[1116px]"><img alt="The Cost Crisis in Observability Tooling" fetchPriority="high" loading="eager" width="2561" height="2006" decoding="async" data-nimg="1" class="mx-auto block aspect-[1116/628] h-auto w-full max-w-[1116px] rounded-4xl object-cover object-center" style="color:transparent" sizes="(max-width: 1116px) 100vw, 1116px" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fb94183af5739d03d187c898809788a3c9b6e744b-2561x2006.png&amp;w=640&amp;q=75 640w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fb94183af5739d03d187c898809788a3c9b6e744b-2561x2006.png&amp;w=750&amp;q=75 750w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fb94183af5739d03d187c898809788a3c9b6e744b-2561x2006.png&amp;w=828&amp;q=75 828w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fb94183af5739d03d187c898809788a3c9b6e744b-2561x2006.png&amp;w=1080&amp;q=75 1080w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fb94183af5739d03d187c898809788a3c9b6e744b-2561x2006.png&amp;w=1200&amp;q=75 1200w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fb94183af5739d03d187c898809788a3c9b6e744b-2561x2006.png&amp;w=1920&amp;q=75 1920w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fb94183af5739d03d187c898809788a3c9b6e744b-2561x2006.png&amp;w=2048&amp;q=75 2048w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fb94183af5739d03d187c898809788a3c9b6e744b-2561x2006.png&amp;w=3840&amp;q=75 3840w" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fb94183af5739d03d187c898809788a3c9b6e744b-2561x2006.png&amp;w=3840&amp;q=75"/></div><div class="m-auto w-full max-w-[1116px] justify-between lg:flex lg:gap-[80px] lg:py-10"><div class="h-fit lg:sticky lg:top-28 lg:w-[280px]"><div class="hidden overflow-hidden rounded-[22px] bg-gray-50 lg:block"><img alt="Whitepaper: The Cost Crisis in Metrics Tooling by honeycomb.io, with a colorful abstract graphic." width="280" height="184" decoding="async" data-nimg="1" class="h-[184px] object-cover" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fbf85c73ef06682760586b9bf4a6d7a4c2c3c5ec1-1920x1080.png&amp;w=384&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fbf85c73ef06682760586b9bf4a6d7a4c2c3c5ec1-1920x1080.png&amp;w=640&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fbf85c73ef06682760586b9bf4a6d7a4c2c3c5ec1-1920x1080.png&amp;w=640&amp;q=75"/><div class="p-[30px] pt-5"><div class="mb-3 flex items-center gap-4"><div class="font-roboto focus:ring-ring inline-flex items-center rounded-lg font-medium transition-colors focus:ring-2 focus:ring-offset-2 focus:outline-none px-2 py-1 text-xs leading-normal bg-hc-sky-100 text-hc-sky-900">Whitepapers</div><span class="h-3.5 border-r border-r-black/30"></span><span class="font-medium font-roboto text-hc-slate/70 text-xs"><span>May 4, 2024</span></span></div><div class="space-y-4"><h4 class="font-roboto text-sm font-medium md:text-base">The Cost Crisis in Metrics Tooling</h4><a target="_self" _type="link" class="text-hc-slate hover:text-hc-cobalt group/arrow-link items-center gap-2 font-roboto focus-visible:ring-ring cursor-pointer rounded-sm text-center font-bold tracking-[1.4px] uppercase transition-[color,background-color] [transition-duration:300ms,500ms] focus-visible:ring-1 focus-visible:outline-none disabled:pointer-events-none disabled:opacity-50 flex !p-0 px-4 py-[13px] text-[14px] leading-[14px] [&amp;_span]:text-hc-cobalt [&amp;_svg]:text-hc-cobalt" href="/resources/whitepapers/cost-crisis-metrics-tooling"><span class="flex items-center justify-center gap-2.5">Learn More</span><svg width="11" height="12" viewBox="0 0 11 12" fill="none" xmlns="http://www.w3.org/2000/svg" class="transition-transform group-hover/arrow-link:translate-x-2"><path d="M5 1L10 6L5 11" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></svg></a></div></div></div></div><div class="min-w-0 flex-1" style="content-visibility:auto;contain-intrinsic-size:auto 1500px"><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">The cost of services is on everybody’s mind right now, with interest rates rising, economic growth slowing, and organizational budgets increasingly feeling the pinch. But I hear a special edge in people’s voices when it comes to their observability bill, and I don’t think it’s just about the cost of goods sold. I think it’s because people are beginning to correctly intuit that the value they get out of their tooling has become radically decoupled from the price they are paying.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">In the happiest cases, the price you pay for your tools is “merely” rising at a rate several times faster than the value you get out of them. But that’s actually the best case scenario. For an alarming number of people, <strong>the value they get actually <em>decreases</em> as their bill goes up</strong>.</p><h2 id="observability-1.0-and-the-cost-multiplier-effect" class="text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24">Observability 1.0 and the cost multiplier effect</h2><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Are you familiar with this chestnut?</p><blockquote class="not-prose font-roboto border-hc-gray-200 text-hc-slate mx-2 mt-10 mb-10 border-l-2 pl-4 text-base leading-normal font-normal tracking-normal lg:mt-16 lg:mr-11 lg:mb-16 lg:ml-11 lg:pl-[31px]">“Observability has three pillars: metrics, logs, and traces.”</blockquote><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">This isn’t exactly true, but it’s definitely true of a particular generation of tools—one might even say <em>definitionally</em> true of a particular generation of tools. Let’s call it “observability 1.0.”</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">From an evolutionary perspective, you can see how we got here. Everybody has logs… so we spin up a service for log aggregation. But logs are expensive and everybody wants dashboards… so we buy a metrics tool. Software engineers want to instrument their applications… so we buy an APM tool. We start unbundling the monolith into microservices, and pretty soon we can’t understand anything without traces… so we buy a tracing tool. The front-end engineers point out that they need sessions and browser data… so we buy a RUM tool. On and on it goes.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Logs, metrics, traces, APM, RUM. You’re now paying to store telemetry five different ways, in five different places, for every single request. And a 5x multiplier is on the modest side of the spectrum, given how many companies pay for multiple overlapping tools in the same category. You may also also be collecting:</p><ul data-testid="article-list-bullet" class="text-hc-slate not-prose font-roboto mt-6 list-disc pl-6 text-sm leading-normal font-normal tracking-normal lg:text-base"><li class="mb-2">Profiling data</li><li class="mb-2">Product analytics</li><li class="mb-2">Business intelligence data</li><li class="mb-2">Database monitoring/query profiling tools</li><li class="mb-2">Mobile app telemetry</li><li class="mb-2">Behavioral analytics</li><li class="mb-2">Crash reporting</li><li class="mb-2">Language-specific profiling data</li><li class="mb-2">Stack traces</li><li class="mb-2">CloudWatch or hosting provider metrics</li><li class="mb-2">…and so on.</li></ul><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">So, how many times are <em>you</em> paying to store data about your user requests? What’s <em>your</em> multiplier? (If you have one consolidated vendor bill, this may require looking at your itemized bill.)</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">There are many types of tools, each gathering slightly different data for a slightly different use case, but underneath the hood there are really only three basic data types: the metric, unstructured logs, and structured logs. Each of these have their own distinctive trade-offs when it comes to how much they cost and how much value you can get out of them.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24"><strong>Download our whitepaper</strong> on the cost crisis in metrics tooling.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24"><a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/resources/cost-crisis-metrics-tooling">read now</a> </p><h3 class="font-poppins leading-heading lg:tracking-heading text-hc-slate not-prose mt-6 text-xl font-medium lg:text-3xl xl:scroll-mt-24">Metrics</h3><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Metrics are the great-granddaddy of telemetry formats; tiny, fast, and cheap. A “metric” consists of a single number, often with tags appended. All of the context of the request gets discarded at write time; each individual metric is emitted separately. This means you can never correlate one metric with another from the same request, or select all the metrics for a given request ID, user, or app ID, or ask arbitrary new questions about your metrics data.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Metrics-based tools include vendors like Datadog and open-source projects like Prometheus. RUM tools are built on top of metrics to understand browser user sessions; APM tools are built on top of metrics to understand application performance.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">When you set up a metrics tool, it generally comes prepopulated with a bunch of basic metrics, but the useful ones are typically the custom metrics you emit from your application.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Your metrics bill is usually dominated by the cost of these custom metrics. At minimum, <strong>your bill goes up linearly with the number of custom metrics you create</strong>. Which is unfortunate, because to restrain your bill from unbounded growth, you have to regularly audit your metrics, do your best to guess which ones are going to be useful in the future, and prune any you think you can afford to go without. Even in the hands of experts, these tools require significant oversight.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Linear cost growth is the goal, but it’s rarely achieved. The cost of each metric varies wildly depending on how you construct it, what the values are, how often it gets hit, etc. I’ve seen a <em>single custom metric</em> cost $30k per month. You probably have dozens of custom metrics per service, and it’s almost impossible to tell how much each of them costs you. Metrics bills tend to be incredibly opaque (possibly by design).</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Nobody can understand their software or their systems with a metrics tool alone, because the <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/blog/ask-miss-o11y-metric-or-trace">metric is extremely limited</a> in what it can do. No context, no cardinality, no strings… only basic static dashboards. For richer data, we must turn to logs.</p><h3 class="font-poppins leading-heading lg:tracking-heading text-hc-slate not-prose mt-6 text-xl font-medium lg:text-3xl xl:scroll-mt-24">Unstructured logs</h3><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">You can understand much more about your code with logs than you can with metrics. Logs are typically emitted multiple times throughout the execution of the request, with one or a small number of nouns per log line, plus the request ID. Unstructured logs are still the default, although this is slowly changing.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">The cost of unstructured logs is driven by a few things:</p><ul data-testid="article-list-bullet" class="text-hc-slate not-prose font-roboto mt-6 list-disc pl-6 text-sm leading-normal font-normal tracking-normal lg:text-base"><li class="mb-2"><strong>Write amplification</strong>. If you want to capture lots of rich context about the request, you need to emit a lot of log lines. If you are printing out just 10 log lines per request, per service, and you have half a dozen services, that’s 60 log events for every request.</li><li class="mb-2"><strong>Noisiness</strong>. It’s extremely easy to accidentally blow up your log footprint yet add no value—e.g., by putting a print statement <em>inside</em> a loop instead of <em>outside</em> the loop. Here, the usefulness of the data goes <em>down</em> as the bill shoots up.</li><li class="mb-2"><strong>Constraints on physical resources</strong>. Due to the write amplification of log lines per request, it’s often physically impossible to log everything you want to log for all requests or all users—it would saturate your NIC or disk. Therefore, people tend to use blunt instruments like these to blindly slash the log volume: <ul data-testid="article-list-bullet" class="text-hc-slate not-prose font-roboto mt-6 list-disc pl-6 text-sm leading-normal font-normal tracking-normal lg:text-base"><li class="mb-2">Log levels</li><li class="mb-2">Consistent hashes</li><li class="mb-2">Dumb sample rates</li></ul></li></ul><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">When you emit multiple log lines per request, you end up duplicating a lot of raw data; sometimes over half the bits are consumed by request ID, process ID, timestamp. This can be quite meaningful from a cost perspective.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">All of these factors can be annoying. But the worst thing about unstructured logs is that the only thing you can do to query them is full text search. The more data you have, the slower it becomes to search that data, and there’s not much you can do about it.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Searching your logs over any meaningful length of time can take minutes or even hours, which means experimenting and looking around for unknown-unknowns is prohibitively time-consuming. You have to know what to look for in order to find it. Once again, <strong>as your logging bill goes up, the value goes down.</strong></p><h3 class="font-poppins leading-heading lg:tracking-heading text-hc-slate not-prose mt-6 text-xl font-medium lg:text-3xl xl:scroll-mt-24">Structured logs</h3><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Structured logs are gaining adoption across the industry, especially as <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/resources/the-directors-guide-to-observability">OpenTelemetry</a> picks up steam. The nice thing about structured logs is that you can actually <em>do</em> things with the data other than slow, dumb string searches. If you’ve structured your data properly, you can perform calculations! Compute percentiles! Generate heatmaps!</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24"><strong>Tools built on structured logs are so clearly the future</strong>. But just taking your existing logs and adding structure isn’t quite good enough. If all you do is stuff your existing log lines into key/value pairs, the problems of amplification, noisiness, and physical constraints remain unchanged—you can just search more efficiently and do some math with your data.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">There are a number of things you can and should do to your structured logs in order to use them more effectively and efficiently. In order of achievability:</p><ul data-testid="article-list-bullet" class="text-hc-slate not-prose font-roboto mt-6 list-disc pl-6 text-sm leading-normal font-normal tracking-normal lg:text-base"><li class="mb-2">Instrument your code using the principles of <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="https://baselime.io/blog/canonical-log-lines">canonical logs</a>, which collects all the vital characteristics of a request into one wide, dense event. It is difficult to overstate the value of doing this, for reasons of usefulness and usability as well as cost control.</li><li class="mb-2"><a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/blog/observability-is-about-confidence">Add trace IDs and span IDs</a> so you can <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="https://docs.honeycomb.io/concepts/tracing/">trace your code</a> using <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/blog/ask-miss-o11y-trace-vs-log">the same events</a> instead of having to use an entirely separate tool.</li><li class="mb-2">Feed your data into a <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/blog/why-observability-requires-distributed-column-store">columnar storage engine</a> so you don’t have to predefine a schema or indexes to decide which dimensions future you can search or compute based on.</li><li class="mb-2">Use a storage engine that supports <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="https://docs.honeycomb.io/concepts/high-cardinality/#high-cardinality-and-high-dimensionality-are-critical-for-observability">high cardinality</a>, with an explorable interface.</li></ul><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">If you go far enough down this path of enriching your structured events, instrumenting your code with the right data, and displaying it in real time, you will reach an entirely <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/blog/so-you-want-to-build-an-observability-tool">different set of capabilities</a>, with a cost model so distinct it can only be described as “observability 2.0.” More on that in a second.</p><h2 id="ballooning-costs-are-baked-into-observability-1.0" class="text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24">Ballooning costs are baked into observability 1.0</h2><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">To recap: high costs are baked into the observability 1.0 model. Every pillar has a price.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">You have to collect and store your data—and pay to store it—again and again and again, for every single use case. Depending on how many tools you use, <strong>your observability bill may be growing at a rate 3x faster than your traffic is growing, or 5x, or 10x, or even more</strong>.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">It gets worse. As your costs go up, the value you get out of your tools goes down.</p><ul data-testid="article-list-bullet" class="text-hc-slate not-prose font-roboto mt-6 list-disc pl-6 text-sm leading-normal font-normal tracking-normal lg:text-base"><li class="mb-2">Your logs get slower and slower to search.</li><li class="mb-2">You have to know what you’re searching for in order to find it.</li><li class="mb-2">You have to use blunt force sampling technique to keep log volume from blowing up.</li><li class="mb-2">Any time you want to be able to ask a new question, you first have to commit new code and deploy it.</li><li class="mb-2">You have to guess which custom metrics you’ll need and which fields to index in advance.</li><li class="mb-2">As volume goes up, your ability to find a needle in the haystack—any unknown-unknowns—goes down commensurately.</li></ul><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">And <em><a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="#correction">nothing connects any of these tools</a>.</em> You cannot correlate a spike in your metrics dashboard with the same requests in your logs, nor can you trace one of the errors. It’s impossible. If your APM and metrics tools report different error rates, you have no way of resolving this confusion. The only thing connecting any of these tools is the intuition and straight-up guesses made by your most senior engineers. Which means that the cognitive costs are immense, and your bus factor risks are very real. The most important connective data in your system—connecting metrics with logs, and logs with traces—exists only in the heads of a few people.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">At the same time, the engineering overhead required to manage all these tools (and their bills) rises inexorably. With metrics, an engineer needs to spend time auditing your metrics, tracking people down to fix poorly constructed metrics, and reaping those that are too expensive or don’t get used. With logs, an engineer needs to spend time monitoring the log volume, watching for spammy or duplicate log lines, pruning or consolidating them, choosing and maintaining indexes.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">But all this the time spent wrangling observability 1.0 data types isn’t even the costliest part. <strong>The most expensive part is the unseen costs inflicted on your engineering organization as development slows down and tech debt piles up</strong>, due to low visibility and thus low confidence.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Is there an alternative? Yes.</p><h2 id="the-cost-model-of-observability-2.0-is-very-different" class="text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24">The cost model of observability 2.0 is very different</h2><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Observability 2.0 has no three pillars; it has a single source of truth. Observability 2.0 tools are built on top of <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/blog/structured-events-basis-observability">arbitrarily-wide structured log events</a>, also known as spans. From these wide, context-rich structured log events you can <em>derive</em> the other data types (metrics, logs, or traces).</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Since there is only one data source, you can correlate and cross-correlate to your heart’s content. You can switch fluidly back and forth between slicing and dicing, breaking down or grouping by events, and viewing them as a trace waterfall. You don’t have to worry about cardinality or key space limitations.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">You also effectively get <em>infinite custom metrics</em>, since you can append as many as you want to the same events. Not only does your cost <em>not</em> go up linearly as you add more custom metrics, your telemetry just gets richer and more valuable the more key-value pairs you add! Nor are you limited to numbers; you can add any and all types of data, including valuable high-cardinality fields like “App Id” or “Full Name.”</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Observability 2.0 has its own amplification factor to consider. As you instrument your code with more spans per request, the number of events you have to send (and pay for) goes up. However, you have some very powerful tools for dealing with this: you can perform dynamic head-based sampling or even <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/blog/evolution-sampling-refinery">tail-based sampling</a>, where you decide whether or not to keep the event after it’s finished, allowing you to capture 100% of slow requests and other outliers.</p><h2 id="engineering-time-is-your-most-precious-resource" class="text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24">Engineering time is your most precious resource</h2><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">But the biggest <strong>difference between observability 1.0 and 2.0 won’t show up on any invoice. The difference shows up in your engineering team’s ability to move quickly, with confidence</strong>.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Modern software engineering is all about hooking up fast feedback loops. And observability 2.0 tooling is what unlocks the kind of fine-grained, exploratory experience you need in order to accelerate those feedback loops.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Where observability 1.0 is about MTTR, MTTD, reliability, and operating software, observability 2.0 is what underpins the entire software development lifecycle, setting the bar for how swiftly you can build and ship software, find problems, and iterate on them. Observability 2.0 is about being in conversation with your code, understanding each user’s experience, and building the right things.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Observability 2.0 isn’t exactly cheap either, although it is often less expensive. But the key difference between o11y 1.0 and o11y 2.0 has never been that either is cheap; it’s that with observability 2.0, when your bill goes up, the value you derive from your telemetry goes up too. <strong>You pay more money, you get more out of your tools.</strong> As you should.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">Interested in learning more? We’ve written at length about the <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/blog/so-you-want-to-build-an-observability-tool">technical prerequisites</a> for observability with a single source of truth (“observability 2.0” as we’ve called it here). Honeycomb was built to this spec; <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="https://www.servicenow.com/products/observability.html">ServiceNow (formerly Lightstep)</a> and <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="https://baselime.io/">Baselime</a> are other vendors that qualify. <a class="not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline" href="/get-a-demo">Click here to get a Honeycomb demo</a>.</p><p class="text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24">CORRECTION: The original version of this document said that “nothing connects any of these tools.” If you are using a single unified vendor for your metrics, logging, APM, RUM, and tracing tools, this is not strictly true. Vendors like New Relic or Datadog now let you define certain links between your traces and metrics, which allows you to correlate between data types in a few limited, predefined ways. This is better than nothing! But it’s very different from the kind of fluid, open-ended correlation capabilities that we describe with o11y 2.0. With o11y 2.0, you can slice and dice, break down, and group by your complex data sets, then grab a trace that matches any specific set of criteria at any level of granularity. With o11y 1.0, you can define a metric up front, then grab a random exemplar of that metric, and that’s it. All the limitations of metrics still apply; you can’t correlate any metric with any other metric from that request, app, user, etc, and you certainly can’t trace arbitrary criteria. But you’re right, it’s not nothing. 😸</p></div></div><aside id="relatedArticles" class="col-span-full py-12 lg:py-24"><div class="z-20 flex flex-col items-center lg:w-1/2 lg:gap-8 mx-auto mb-0 gap-8 text-center lg:mb-0"><h3 _type="title" class="font-medium text-pretty font-poppins text-center text-4xl lg:text-5xl"><span>Latest posts</span></h3></div><div class="gap-4 py-10 lg:py-16 xl:flex xl:gap-6"><div class="full flex flex-col gap-4 md:flex-row lg:flex-col xl:w-[831px] xl:gap-6"><a class="flex-1 lg:h-[245px]" href="/blog/ai-norms-values-part-3-things-we-hold-true"><div class="bg-hc-gray-50 flex h-full flex-col gap-6 rounded-4xl p-2.5 lg:flex-row"><div class="relative h-[172px] w-auto md:h-[177px] lg:h-[225px] lg:w-[396px] xl:flex-1"><img alt="AI Norms &amp; Values, Part 3 of 3: Things We Hold True" loading="lazy" decoding="async" data-nimg="fill" class="aspect-video rounded-4xl object-cover" style="position:absolute;height:100%;width:100%;left:0;top:0;right:0;bottom:0;color:transparent" sizes="(max-width: 768px) 100vw, 396px" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fff1c82783367c2a69702c80f1248dc907313059e-3840x2160.png&amp;w=640&amp;q=75 640w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fff1c82783367c2a69702c80f1248dc907313059e-3840x2160.png&amp;w=750&amp;q=75 750w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fff1c82783367c2a69702c80f1248dc907313059e-3840x2160.png&amp;w=828&amp;q=75 828w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fff1c82783367c2a69702c80f1248dc907313059e-3840x2160.png&amp;w=1080&amp;q=75 1080w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fff1c82783367c2a69702c80f1248dc907313059e-3840x2160.png&amp;w=1200&amp;q=75 1200w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fff1c82783367c2a69702c80f1248dc907313059e-3840x2160.png&amp;w=1920&amp;q=75 1920w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fff1c82783367c2a69702c80f1248dc907313059e-3840x2160.png&amp;w=2048&amp;q=75 2048w, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fff1c82783367c2a69702c80f1248dc907313059e-3840x2160.png&amp;w=3840&amp;q=75 3840w" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F927dxq0h%2Fproduction%2Fff1c82783367c2a69702c80f1248dc907313059e-3840x2160.png&amp;w=3840&amp;q=75"/></div><div class="flex flex-1 flex-col gap-4 max-lg:px-2.5 lg:pt-4"><div class="flex items-center gap-4"><div class="font-roboto focus:ring-ring inline-flex items-center rounded-lg font-medium transition-colors focus:ring-2 focus:ring-offset-2 focus:outline-none px-2 py-1 text-xs leading-normal bg-hc-gray-200 text-hc-slate">Blog</div><span class="h-3.5 border-r border-r-black/30"></span><span class="font-medium font-roboto text-hc-slate/70 text-xs"><span>September 9, 2026</span></span></div><h3 class="font-roboto line-clamp-2 text-base font-medium">AI Norms &amp; 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CONSULTATION\"},\"icon\":\"$undefined\",\"modalVideo\":null,\"modalForm\":null}]]}]]}]}]]}]}]\n"])</script><script>self.__next_f.push([1,"40:[\"$\",\"blockquote\",\"d7c31068697f\",{\"className\":\"not-prose font-roboto border-hc-gray-200 text-hc-slate mx-2 mt-10 mb-10 border-l-2 pl-4 text-base leading-normal font-normal tracking-normal lg:mt-16 lg:mr-11 lg:mb-16 lg:ml-11 lg:pl-[31px]\",\"children\":[\"“Observability has three pillars: metrics, logs, and traces.”\"]}]\n41:[\"$\",\"p\",\"d595070146eb\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"This isn’t exactly true, but it’s definitely true of a particular generation of tools—one might even say \",[\"$\",\"em\",\"dfa6a2dc3e40\",{\"children\":[\"definitionally\"]}],\" true of a particular generation of tools. Let’s call it “observability 1.0.”\"]}]\n42:[\"$\",\"p\",\"ba5eaf537d0c\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"From an evolutionary perspective, you can see how we got here. Everybody has logs… so we spin up a service for log aggregation. But logs are expensive and everybody wants dashboards… so we buy a metrics tool. Software engineers want to instrument their applications… so we buy an APM tool. We start unbundling the monolith into microservices, and pretty soon we can’t understand anything without traces… so we buy a tracing tool. The front-end engineers point out that they need sessions and browser data… so we buy a RUM tool. On and on it goes.\"]}]\n43:[\"$\",\"p\",\"309c8322c3d8\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Logs, metrics, traces, APM, RUM. You’re now paying to store telemetry five different ways, in five different places, for every single request. And a 5x multiplier is on the modest side of the spectrum, given how many companies pay for multiple overlapping tools in the same category. You may also also be collecting:\"]}]\n44:[\"$\",\"ul\",\"9f8fcbad271e-parent\",{\"data-testid\":\"article-list-bullet\",\"className\":\"text-hc-slate not-prose font-roboto mt-6 list-disc pl-6 text-sm leading-normal font-normal tracking-normal lg:text-base\",\"children\":[[\"$\",\"li\",\"9f8fcbad271e\",{\"className\":\"mb-2\",\"children\":[\"Profiling data\"]}],[\"$\",\"li\",\"c99e75c0854c\",{\"className\":\"mb-2\",\"children\":[\"Product analytics\"]}],[\"$\",\"li\",\"a2216c315d82\",{\"className\":\"mb-2\",\"children\":[\"Business intelligence data\"]}],[\"$\",\"li\",\"ee80a920065d\",{\"className\":\"mb-2\",\"children\":[\"Database monitoring/query profiling tools\"]}],[\"$\",\"li\",\"755ff98a37df\",{\"className\":\"mb-2\",\"children\":[\"Mobile app telemetry\"]}],[\"$\",\"li\",\"970999960469\",{\"className\":\"mb-2\",\"children\":[\"Behavioral analytics\"]}],[\"$\",\"li\",\"a353083ef82a\",{\"className\":\"mb-2\",\"children\":[\"Crash reporting\"]}],[\"$\",\"li\",\"b9587213954f\",{\"className\":\"mb-2\",\"children\":[\"Language-specific profiling data\"]}],[\"$\",\"li\",\"ed8fbc212452\",{\"className\":\"mb-2\",\"children\":[\"Stack traces\"]}],[\"$\",\"li\",\"43618d41a849\",{\"className\":\"mb-2\",\"children\":[\"CloudWatch or hosting provider metrics\"]}],[\"$\",\"li\",\"76cb76fd2c94\",{\"className\":\"mb-2\",\"children\":[\"…and so on.\"]}]]}]\n45:[\"$\",\"p\",\"3190c90a4971\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"So, how many times are \",[\"$\",\"em\",\"6e7d5a88a457\",{\"children\":[\"you\"]}],\" paying to store data about your user requests? What’s \",[\"$\",\"em\",\"13bbd88e0937\",{\"children\":[\"your\"]}],\" multiplier? (If you have one consolidated vendor bill, this may require looking at your itemized bill.)\"]}]\n46:[\"$\",\"p\",\"627f3812b2a1\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"There are many types of tools, each gathering slightly different data for a slightly different use case, but underneath the hood there are really only three basic data types: the metric, unstructured logs, and structured logs. Each of these have their own distinctive trade-offs when it comes t"])</script><script>self.__next_f.push([1,"o how much they cost and how much value you can get out of them.\"]}]\n47:[\"$\",\"p\",\"c1c48640e4ba\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[[\"$\",\"strong\",\"f23896d21e35\",{\"children\":[\"Download our whitepaper\"]}],\" on the cost crisis in metrics tooling.\"]}]\n48:[\"$\",\"p\",\"cf91c422348b\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[[\"$\",\"$L25\",\"34b24475030e\",{\"href\":\"/resources/cost-crisis-metrics-tooling\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"read now\"]}],\" \"]}]\n49:[\"$\",\"h3\",\"49aa8fae8589\",{\"className\":\"font-poppins leading-heading lg:tracking-heading text-hc-slate not-prose mt-6 text-xl font-medium lg:text-3xl xl:scroll-mt-24\",\"children\":[\"Metrics\"]}]\n4a:[\"$\",\"p\",\"8aa36c0acd1a\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Metrics are the great-granddaddy of telemetry formats; tiny, fast, and cheap. A “metric” consists of a single number, often with tags appended. All of the context of the request gets discarded at write time; each individual metric is emitted separately. This means you can never correlate one metric with another from the same request, or select all the metrics for a given request ID, user, or app ID, or ask arbitrary new questions about your metrics data.\"]}]\n4b:[\"$\",\"p\",\"c8208bb58bf2\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Metrics-based tools include vendors like Datadog and open-source projects like Prometheus. RUM tools are built on top of metrics to understand browser user sessions; APM tools are built on top of metrics to understand application performance.\"]}]\n4c:[\"$\",\"p\",\"d5070e1a3f9f\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"When you set up a metrics tool, it generally comes prepopulated with a bunch of basic metrics, but the useful ones are typically the custom metrics you emit from your application.\"]}]\n4d:[\"$\",\"p\",\"7e5926acfebf\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Your metrics bill is usually dominated by the cost of these custom metrics. At minimum, \",[\"$\",\"strong\",\"4c23025e635b\",{\"children\":[\"your bill goes up linearly with the number of custom metrics you create\"]}],\". Which is unfortunate, because to restrain your bill from unbounded growth, you have to regularly audit your metrics, do your best to guess which ones are going to be useful in the future, and prune any you think you can afford to go without. Even in the hands of experts, these tools require significant oversight.\"]}]\n4e:[\"$\",\"p\",\"af63b4db4221\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Linear cost growth is the goal, but it’s rarely achieved. The cost of each metric varies wildly depending on how you construct it, what the values are, how often it gets hit, etc. I’ve seen a \",[\"$\",\"em\",\"ab595338f826\",{\"children\":[\"single custom metric\"]}],\" cost $30k per month. You probably have dozens of custom metrics per service, and it’s almost impossible to tell how much each of them costs you. Metrics bills tend to be incredibly opaque (possibly by design).\"]}]\n4f:[\"$\",\"p\",\"5f7c5b32f316\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Nobody can understand their software or their systems with a metrics tool alone, because the \",[\"$\",\"$L25\",\"18e21c0cdc02\",{\"href\":\"/blog/ask-miss-o11y-metric-or-trace\",\"className\":\"not-prose text-hc-cobalt mt"])</script><script>self.__next_f.push([1,"-6 font-normal underline hover:no-underline\",\"children\":[\"metric is extremely limited\"]}],\" in what it can do. No context, no cardinality, no strings… only basic static dashboards. For richer data, we must turn to logs.\"]}]\n50:[\"$\",\"h3\",\"32783dbfc22e\",{\"className\":\"font-poppins leading-heading lg:tracking-heading text-hc-slate not-prose mt-6 text-xl font-medium lg:text-3xl xl:scroll-mt-24\",\"children\":[\"Unstructured logs\"]}]\n51:[\"$\",\"p\",\"b8f5d694aabc\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"You can understand much more about your code with logs than you can with metrics. Logs are typically emitted multiple times throughout the execution of the request, with one or a small number of nouns per log line, plus the request ID. Unstructured logs are still the default, although this is slowly changing.\"]}]\n52:[\"$\",\"p\",\"a6bba3d934ae\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"The cost of unstructured logs is driven by a few things:\"]}]\n"])</script><script>self.__next_f.push([1,"53:[\"$\",\"ul\",\"2ba125d428c6-parent\",{\"data-testid\":\"article-list-bullet\",\"className\":\"text-hc-slate not-prose font-roboto mt-6 list-disc pl-6 text-sm leading-normal font-normal tracking-normal lg:text-base\",\"children\":[[\"$\",\"li\",\"2ba125d428c6\",{\"className\":\"mb-2\",\"children\":[[\"$\",\"strong\",\"db056db8f882\",{\"children\":[\"Write amplification\"]}],\". If you want to capture lots of rich context about the request, you need to emit a lot of log lines. If you are printing out just 10 log lines per request, per service, and you have half a dozen services, that’s 60 log events for every request.\"]}],[\"$\",\"li\",\"83d81e90d3be\",{\"className\":\"mb-2\",\"children\":[[\"$\",\"strong\",\"17a38a64babb\",{\"children\":[\"Noisiness\"]}],\". It’s extremely easy to accidentally blow up your log footprint yet add no value—e.g., by putting a print statement \",[\"$\",\"em\",\"a7aa73f6e587\",{\"children\":[\"inside\"]}],\" a loop instead of \",[\"$\",\"em\",\"f77f555cbafe\",{\"children\":[\"outside\"]}],\" the loop. Here, the usefulness of the data goes \",[\"$\",\"em\",\"2bf2358fad73\",{\"children\":[\"down\"]}],\" as the bill shoots up.\"]}],[\"$\",\"li\",\"e27bd3bbd10b\",{\"className\":\"mb-2\",\"children\":[[\"$\",\"strong\",\"7f5fed14bbd2\",{\"children\":[\"Constraints on physical resources\"]}],\". Due to the write amplification of log lines per request, it’s often physically impossible to log everything you want to log for all requests or all users—it would saturate your NIC or disk. Therefore, people tend to use blunt instruments like these to blindly slash the log volume: \",[\"$\",\"ul\",\"6fddbe05041f-parent\",{\"data-testid\":\"article-list-bullet\",\"className\":\"text-hc-slate not-prose font-roboto mt-6 list-disc pl-6 text-sm leading-normal font-normal tracking-normal lg:text-base\",\"children\":[[\"$\",\"li\",\"6fddbe05041f\",{\"className\":\"mb-2\",\"children\":[\"Log levels\"]}],[\"$\",\"li\",\"d7dad2c8ea2f\",{\"className\":\"mb-2\",\"children\":[\"Consistent hashes\"]}],[\"$\",\"li\",\"975c82b806bb\",{\"className\":\"mb-2\",\"children\":[\"Dumb sample rates\"]}]]}]]}]]}]\n"])</script><script>self.__next_f.push([1,"54:[\"$\",\"p\",\"0e8ff86ff5b8\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"When you emit multiple log lines per request, you end up duplicating a lot of raw data; sometimes over half the bits are consumed by request ID, process ID, timestamp. This can be quite meaningful from a cost perspective.\"]}]\n55:[\"$\",\"p\",\"e3ae525e6983\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"All of these factors can be annoying. But the worst thing about unstructured logs is that the only thing you can do to query them is full text search. The more data you have, the slower it becomes to search that data, and there’s not much you can do about it.\"]}]\n56:[\"$\",\"p\",\"ed2bee6e7b31\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Searching your logs over any meaningful length of time can take minutes or even hours, which means experimenting and looking around for unknown-unknowns is prohibitively time-consuming. You have to know what to look for in order to find it. Once again, \",[\"$\",\"strong\",\"8c6d5f7279e4\",{\"children\":[\"as your logging bill goes up, the value goes down.\"]}]]}]\n57:[\"$\",\"h3\",\"54d75f5d6ae6\",{\"className\":\"font-poppins leading-heading lg:tracking-heading text-hc-slate not-prose mt-6 text-xl font-medium lg:text-3xl xl:scroll-mt-24\",\"children\":[\"Structured logs\"]}]\n58:[\"$\",\"p\",\"361592774199\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Structured logs are gaining adoption across the industry, especially as \",[\"$\",\"$L25\",\"d6e0ff5f9c9a\",{\"href\":\"/resources/the-directors-guide-to-observability\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"OpenTelemetry\"]}],\" picks up steam. The nice thing about structured logs is that you can actually \",[\"$\",\"em\",\"0f6da3740e2b\",{\"children\":[\"do\"]}],\" things with the data other than slow, dumb string searches. If you’ve structured your data properly, you can perform calculations! Compute percentiles! Generate heatmaps!\"]}]\n59:[\"$\",\"p\",\"9b34c4464f85\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[[\"$\",\"strong\",\"57baa532171f\",{\"children\":[\"Tools built on structured logs are so clearly the future\"]}],\". But just taking your existing logs and adding structure isn’t quite good enough. If all you do is stuff your existing log lines into key/value pairs, the problems of amplification, noisiness, and physical constraints remain unchanged—you can just search more efficiently and do some math with your data.\"]}]\n5a:[\"$\",\"p\",\"4c7647802c98\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"There are a number of things you can and should do to your structured logs in order to use them more effectively and efficiently. In order of achievability:\"]}]\n"])</script><script>self.__next_f.push([1,"5b:[\"$\",\"ul\",\"73cea5152279-parent\",{\"data-testid\":\"article-list-bullet\",\"className\":\"text-hc-slate not-prose font-roboto mt-6 list-disc pl-6 text-sm leading-normal font-normal tracking-normal lg:text-base\",\"children\":[[\"$\",\"li\",\"73cea5152279\",{\"className\":\"mb-2\",\"children\":[\"Instrument your code using the principles of \",[\"$\",\"$L25\",\"8bf4f60254be\",{\"href\":\"https://baselime.io/blog/canonical-log-lines\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"canonical logs\"]}],\", which collects all the vital characteristics of a request into one wide, dense event. It is difficult to overstate the value of doing this, for reasons of usefulness and usability as well as cost control.\"]}],[\"$\",\"li\",\"ed89c1e2f50b\",{\"className\":\"mb-2\",\"children\":[[\"$\",\"$L25\",\"e8a97921c953\",{\"href\":\"/blog/observability-is-about-confidence\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"Add trace IDs and span IDs\"]}],\" so you can \",[\"$\",\"$L25\",\"2d9814d8e25a\",{\"href\":\"https://docs.honeycomb.io/concepts/tracing/\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"trace your code\"]}],\" using \",[\"$\",\"$L25\",\"7504add958d6\",{\"href\":\"/blog/ask-miss-o11y-trace-vs-log\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"the same events\"]}],\" instead of having to use an entirely separate tool.\"]}],[\"$\",\"li\",\"ae2ee393d4a6\",{\"className\":\"mb-2\",\"children\":[\"Feed your data into a \",[\"$\",\"$L25\",\"aef59275955c\",{\"href\":\"/blog/why-observability-requires-distributed-column-store\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"columnar storage engine\"]}],\" so you don’t have to predefine a schema or indexes to decide which dimensions future you can search or compute based on.\"]}],[\"$\",\"li\",\"3c8aa3a3546c\",{\"className\":\"mb-2\",\"children\":[\"Use a storage engine that supports \",[\"$\",\"$L25\",\"eb734542eb82\",{\"href\":\"https://docs.honeycomb.io/concepts/high-cardinality/#high-cardinality-and-high-dimensionality-are-critical-for-observability\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"high cardinality\"]}],\", with an explorable interface.\"]}]]}]\n"])</script><script>self.__next_f.push([1,"5c:[\"$\",\"p\",\"7d1619d6a953\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"If you go far enough down this path of enriching your structured events, instrumenting your code with the right data, and displaying it in real time, you will reach an entirely \",[\"$\",\"$L25\",\"2a216b284837\",{\"href\":\"/blog/so-you-want-to-build-an-observability-tool\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"different set of capabilities\"]}],\", with a cost model so distinct it can only be described as “observability 2.0.” More on that in a second.\"]}]\n5d:[\"$\",\"h2\",\"6914abd5b2fd\",{\"id\":\"ballooning-costs-are-baked-into-observability-1.0\",\"className\":\"text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24\",\"children\":[\"Ballooning costs are baked into observability 1.0\"]}]\n5e:[\"$\",\"p\",\"6c9bad5c7eca\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"To recap: high costs are baked into the observability 1.0 model. Every pillar has a price.\"]}]\n5f:[\"$\",\"p\",\"bf336fce8a1d\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"You have to collect and store your data—and pay to store it—again and again and again, for every single use case. Depending on how many tools you use, \",[\"$\",\"strong\",\"3d14caefa664\",{\"children\":[\"your observability bill may be growing at a rate 3x faster than your traffic is growing, or 5x, or 10x, or even more\"]}],\".\"]}]\n60:[\"$\",\"p\",\"ea23997d5a76\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"It gets worse. As your costs go up, the value you get out of your tools goes down.\"]}]\n61:[\"$\",\"ul\",\"da5849ff216b-parent\",{\"data-testid\":\"article-list-bullet\",\"className\":\"text-hc-slate not-prose font-roboto mt-6 list-disc pl-6 text-sm leading-normal font-normal tracking-normal lg:text-base\",\"children\":[[\"$\",\"li\",\"da5849ff216b\",{\"className\":\"mb-2\",\"children\":[\"Your logs get slower and slower to search.\"]}],[\"$\",\"li\",\"de986b9bf656\",{\"className\":\"mb-2\",\"children\":[\"You have to know what you’re searching for in order to find it.\"]}],[\"$\",\"li\",\"ab651c946cb1\",{\"className\":\"mb-2\",\"children\":[\"You have to use blunt force sampling technique to keep log volume from blowing up.\"]}],[\"$\",\"li\",\"99b9718036b8\",{\"className\":\"mb-2\",\"children\":[\"Any time you want to be able to ask a new question, you first have to commit new code and deploy it.\"]}],[\"$\",\"li\",\"301688dc5ceb\",{\"className\":\"mb-2\",\"children\":[\"You have to guess which custom metrics you’ll need and which fields to index in advance.\"]}],[\"$\",\"li\",\"fa1e6004514a\",{\"className\":\"mb-2\",\"children\":[\"As volume goes up, your ability to find a needle in the haystack—any unknown-unknowns—goes down commensurately.\"]}]]}]\n62:[\"$\",\"p\",\"0658dacffb81\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"And \",[\"$\",\"em\",\"a7a6e993a3a8\",{\"children\":[[\"$\",\"$L25\",\"a7a6e993a3a8\",{\"href\":\"#correction\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"nothing connects any of these tools\"]}],\".\"]}],\" You cannot correlate a spike in your metrics dashboard with the same requests in your logs, nor can you trace one of the errors. It’s impossible. If your APM and metrics tools report different error rates, you have no way of resolving this confusion. The only thing connecting any of these tools is the intuition and straight-up guesses made by your most senior engineers. Which means that the cognitive costs are immense, and your bus factor risks are very real. The most important connective data in your system—connecting metrics with logs, and logs with traces—exists only in the heads of a few people.\"]}]\n63:[\"$\",\"p\",\"251a52b012"])</script><script>self.__next_f.push([1,"a1\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"At the same time, the engineering overhead required to manage all these tools (and their bills) rises inexorably. With metrics, an engineer needs to spend time auditing your metrics, tracking people down to fix poorly constructed metrics, and reaping those that are too expensive or don’t get used. With logs, an engineer needs to spend time monitoring the log volume, watching for spammy or duplicate log lines, pruning or consolidating them, choosing and maintaining indexes.\"]}]\n64:[\"$\",\"p\",\"3c7734381c5c\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"But all this the time spent wrangling observability 1.0 data types isn’t even the costliest part. \",[\"$\",\"strong\",\"2e450f212afa\",{\"children\":[\"The most expensive part is the unseen costs inflicted on your engineering organization as development slows down and tech debt piles up\"]}],\", due to low visibility and thus low confidence.\"]}]\n65:[\"$\",\"p\",\"733b45706f2b\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Is there an alternative? Yes.\"]}]\n66:[\"$\",\"h2\",\"4d91d4fe4d8e\",{\"id\":\"the-cost-model-of-observability-2.0-is-very-different\",\"className\":\"text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24\",\"children\":[\"The cost model of observability 2.0 is very different\"]}]\n67:[\"$\",\"p\",\"c8ef90072d7a\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Observability 2.0 has no three pillars; it has a single source of truth. Observability 2.0 tools are built on top of \",[\"$\",\"$L25\",\"eb8057f9a70b\",{\"href\":\"/blog/structured-events-basis-observability\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"arbitrarily-wide structured log events\"]}],\", also known as spans. From these wide, context-rich structured log events you can \",[\"$\",\"em\",\"46d3c5990522\",{\"children\":[\"derive\"]}],\" the other data types (metrics, logs, or traces).\"]}]\n68:[\"$\",\"p\",\"314a36bf621a\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Since there is only one data source, you can correlate and cross-correlate to your heart’s content. You can switch fluidly back and forth between slicing and dicing, breaking down or grouping by events, and viewing them as a trace waterfall. You don’t have to worry about cardinality or key space limitations.\"]}]\n69:[\"$\",\"p\",\"046ad64a325d\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"You also effectively get \",[\"$\",\"em\",\"19a3852e01ec\",{\"children\":[\"infinite custom metrics\"]}],\", since you can append as many as you want to the same events. Not only does your cost \",[\"$\",\"em\",\"35a022e135fa\",{\"children\":[\"not\"]}],\" go up linearly as you add more custom metrics, your telemetry just gets richer and more valuable the more key-value pairs you add! Nor are you limited to numbers; you can add any and all types of data, including valuable high-cardinality fields like “App Id” or “Full Name.”\"]}]\n6a:[\"$\",\"p\",\"18ff521ea5d1\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Observability 2.0 has its own amplification factor to consider. As you instrument your code with more spans per request, the number of events you have to send (and pay for) goes up. However, you have some very powerful tools for dealing with this: you can perform dynamic head-based sampling or even \",[\"$\",\"$L25\",\"b792af67b1cf\",{\"href\":\"/blog/evolution-sampling-refinery\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal unde"])</script><script>self.__next_f.push([1,"rline hover:no-underline\",\"children\":[\"tail-based sampling\"]}],\", where you decide whether or not to keep the event after it’s finished, allowing you to capture 100% of slow requests and other outliers.\"]}]\n6b:[\"$\",\"h2\",\"29371ac888f6\",{\"id\":\"engineering-time-is-your-most-precious-resource\",\"className\":\"text-h3 text-hc-slate not-prose mt-6 xl:scroll-mt-24\",\"children\":[\"Engineering time is your most precious resource\"]}]\n6c:[\"$\",\"p\",\"cbe71ffcbf72\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"But the biggest \",[\"$\",\"strong\",\"896051d0ec13\",{\"children\":[\"difference between observability 1.0 and 2.0 won’t show up on any invoice. The difference shows up in your engineering team’s ability to move quickly, with confidence\"]}],\".\"]}]\n6d:[\"$\",\"p\",\"c403727fb0dc\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Modern software engineering is all about hooking up fast feedback loops. And observability 2.0 tooling is what unlocks the kind of fine-grained, exploratory experience you need in order to accelerate those feedback loops.\"]}]\n6e:[\"$\",\"p\",\"d799733d6f64\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Where observability 1.0 is about MTTR, MTTD, reliability, and operating software, observability 2.0 is what underpins the entire software development lifecycle, setting the bar for how swiftly you can build and ship software, find problems, and iterate on them. Observability 2.0 is about being in conversation with your code, understanding each user’s experience, and building the right things.\"]}]\n6f:[\"$\",\"p\",\"f9dee7530615\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Observability 2.0 isn’t exactly cheap either, although it is often less expensive. But the key difference between o11y 1.0 and o11y 2.0 has never been that either is cheap; it’s that with observability 2.0, when your bill goes up, the value you derive from your telemetry goes up too. \",[\"$\",\"strong\",\"4489f27a4266\",{\"children\":[\"You pay more money, you get more out of your tools.\"]}],\" As you should.\"]}]\n70:[\"$\",\"p\",\"1d034fbf81db\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"Interested in learning more? We’ve written at length about the \",[\"$\",\"$L25\",\"859d03e1c07d\",{\"href\":\"/blog/so-you-want-to-build-an-observability-tool\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"technical prerequisites\"]}],\" for observability with a single source of truth (“observability 2.0” as we’ve called it here). Honeycomb was built to this spec; \",[\"$\",\"$L25\",\"4766eaf562bf\",{\"href\":\"https://www.servicenow.com/products/observability.html\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"ServiceNow (formerly Lightstep)\"]}],\" and \",[\"$\",\"$L25\",\"f985450e30e0\",{\"href\":\"https://baselime.io/\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"Baselime\"]}],\" are other vendors that qualify. \",[\"$\",\"$L25\",\"164edd89fa3e\",{\"href\":\"/get-a-demo\",\"className\":\"not-prose text-hc-cobalt mt-6 font-normal underline hover:no-underline\",\"children\":[\"Click here to get a Honeycomb demo\"]}],\".\"]}]\n72:T433,CORRECTION: The original version of this document said that “nothing connects any of these tools.” If you are using a single unified vendor for your metrics, logging, APM, RUM, and tracing tools, this is not strictly true. Vendors like New Relic or Datadog now let you define certain links between your traces and metrics, which allows you to correlate between data types in a few limited, predefined ways. This is better than nothing! But it’"])</script><script>self.__next_f.push([1,"s very different from the kind of fluid, open-ended correlation capabilities that we describe with o11y 2.0. With o11y 2.0, you can slice and dice, break down, and group by your complex data sets, then grab a trace that matches any specific set of criteria at any level of granularity. With o11y 1.0, you can define a metric up front, then grab a random exemplar of that metric, and that’s it. All the limitations of metrics still apply; you can’t correlate any metric with any other metric from that request, app, user, etc, and you certainly can’t trace arbitrary criteria. But you’re right, it’s not nothing. 😸71:[\"$\",\"p\",\"f9d4d1ef880e\",{\"className\":\"text-hc-slate not-prose font-roboto mt-6 text-sm leading-normal font-normal tracking-normal lg:text-base xl:scroll-mt-24\",\"children\":[\"$72\"]}]\n"])</script><script>self.__next_f.push([1,"3d:[\"$\",\"aside\",null,{\"id\":\"relatedArticles\",\"className\":\"col-span-full py-12 lg:py-24\",\"children\":[[\"$\",\"div\",null,{\"className\":\"z-20 flex flex-col items-center lg:w-1/2 lg:gap-8 mx-auto mb-0 gap-8 text-center lg:mb-0\",\"children\":[null,[\"$\",\"h3\",null,{\"_type\":\"title\",\"className\":\"font-medium text-pretty font-poppins text-center text-4xl lg:text-5xl\",\"children\":[[\"$\",\"span\",\"c866b156e22c\",{\"children\":[\"Latest posts\"]}]]}],null,null]}],[\"$\",\"div\",null,{\"className\":\"gap-4 py-10 lg:py-16 xl:flex xl:gap-6\",\"children\":[[\"$\",\"div\",null,{\"className\":\"full flex flex-col gap-4 md:flex-row lg:flex-col xl:w-[831px] xl:gap-6\",\"children\":[[\"$\",\"$L25\",\"3746ba22-5a61-4265-ac97-0682397499e9\",{\"className\":\"flex-1 lg:h-[245px]\",\"href\":\"/blog/ai-norms-values-part-3-things-we-hold-true\",\"children\":[\"$\",\"div\",null,{\"className\":\"bg-hc-gray-50 flex h-full flex-col gap-6 rounded-4xl p-2.5 lg:flex-row\",\"children\":[[\"$\",\"div\",null,{\"className\":\"relative h-[172px] w-auto md:h-[177px] lg:h-[225px] lg:w-[396px] xl:flex-1\",\"children\":[\"$\",\"$L2c\",null,{\"src\":\"https://cdn.sanity.io/images/927dxq0h/production/ff1c82783367c2a69702c80f1248dc907313059e-3840x2160.png\",\"fill\":true,\"alt\":\"AI Norms \u0026 Values, Part 3 of 3: Things We Hold True\",\"className\":\"aspect-video rounded-4xl object-cover\",\"sizes\":\"(max-width: 768px) 100vw, 396px\"}]}],[\"$\",\"div\",null,{\"className\":\"flex flex-1 flex-col gap-4 max-lg:px-2.5 lg:pt-4\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex items-center gap-4\",\"children\":[[\"$\",\"div\",null,{\"className\":\"font-roboto focus:ring-ring inline-flex items-center rounded-lg font-medium transition-colors focus:ring-2 focus:ring-offset-2 focus:outline-none px-2 py-1 text-xs leading-normal bg-hc-gray-200 text-hc-slate\",\"children\":\"Blog\"}],[[\"$\",\"span\",null,{\"className\":\"h-3.5 border-r border-r-black/30\"}],[\"$\",\"span\",null,{\"className\":\"font-medium font-roboto text-hc-slate/70 text-xs\",\"children\":[null,[\"$\",\"span\",null,{\"children\":\"September 9, 2026\"}]]}]]]}],[\"$\",\"h3\",null,{\"className\":\"font-roboto line-clamp-2 text-base font-medium\",\"children\":\"AI Norms \u0026 Values, Part 3 of 3: Things We Hold True\"}],[\"$\",\"p\",null,{\"className\":\"font-roboto text-hc-slate/70 line-clamp-3 text-sm font-normal max-lg:hidden\",\"children\":\"The final part of Honeycomb's AI Norms \u0026 Values series: the principles the company holds true about AI as a tool, ownership of work, and rising standards; how it actually uses AI day to day; usage patterns for respecting each other's time; and where it stands on AI's ethical externalities like energy use, IP, bias, and wages.\"}]]}]]}]}],[\"$\",\"$L25\",\"d5aae535-909b-4dd5-9302-74fd81edbbc2\",{\"className\":\"flex-1 lg:h-[245px]\",\"href\":\"/blog/wide-events-vs-three-pillars-ai-observability-costs\",\"children\":[\"$\",\"div\",null,{\"className\":\"bg-hc-gray-50 flex h-full flex-col gap-6 rounded-4xl p-2.5 lg:flex-row\",\"children\":[[\"$\",\"div\",null,{\"className\":\"relative h-[172px] w-auto md:h-[177px] lg:h-[225px] lg:w-[396px] xl:flex-1\",\"children\":[\"$\",\"$L2c\",null,{\"src\":\"https://cdn.sanity.io/images/927dxq0h/production/2f5169f0014bd57114e40e9c6b911bf986e9dd74-3840x2160.png\",\"fill\":true,\"alt\":\"Wide Events vs. Three Pillars: AI Observability Costs\",\"className\":\"aspect-video rounded-4xl object-cover\",\"sizes\":\"(max-width: 768px) 100vw, 396px\"}]}],[\"$\",\"div\",null,{\"className\":\"flex flex-1 flex-col gap-4 max-lg:px-2.5 lg:pt-4\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex items-center gap-4\",\"children\":[[\"$\",\"div\",null,{\"className\":\"font-roboto focus:ring-ring inline-flex items-center rounded-lg font-medium transition-colors focus:ring-2 focus:ring-offset-2 focus:outline-none px-2 py-1 text-xs leading-normal bg-hc-gray-200 text-hc-slate\",\"children\":\"Blog\"}],[[\"$\",\"span\",null,{\"className\":\"h-3.5 border-r border-r-black/30\"}],[\"$\",\"span\",null,{\"className\":\"font-medium font-roboto text-hc-slate/70 text-xs\",\"children\":[null,[\"$\",\"span\",null,{\"children\":\"September 8, 2026\"}]]}]]]}],\"$L73\",\"$L74\"]}]]}]}]]}],\"$L75\"]}],false]}]\n"])</script><script>self.__next_f.push([1,"73:[\"$\",\"h3\",null,{\"className\":\"font-roboto line-clamp-2 text-base font-medium\",\"children\":\"Wide Events vs. Three Pillars: AI Observability Costs\"}]\n74:[\"$\",\"p\",null,{\"className\":\"font-roboto text-hc-slate/70 line-clamp-3 text-sm font-normal max-lg:hidden\",\"children\":\"AI agents make telemetry costs harder to predict. 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