---
title: RTO vs RPO: Key Differences for Modern Disaster Recovery
source: https://launchdarkly.com/blog/rto-vs-rpo/
author: shenwei
published: 2019-01-18
created: 2025-07-26
description: Understand RTO vs. RPO: their critical differences, their impact on modern software delivery, and how to effectively achieve your disaster recovery goals.
tags: []
---



Recovery Time Objective (RTO) and Recovery Point Objective (RPO) are fundamental metrics in disaster recovery. However, many software teams struggle to translate these concepts into actionable goals for modern software delivery.

**Your app just went down. How fast can you get it back up?**

That's what RTO measures: the maximum downtime you can tolerate before your business suffers a significant impact. RPO is its counterpart: how much data loss you can accept when things go sideways.

Most teams treat RTO and RPO as abstract concepts related to disaster recovery. But if you're shipping code multiple times a day, these metrics matter for every release (not just when the data center catches fire).

The old approach was reactive: build your app, then bolt on [disaster recovery](https://launchdarkly.com/blog/designing-for-failure-to-avoid-disaster/) as an afterthought. Today's reality is different. When you're [deploying features continuously](https://launchdarkly.com/the-definitive-guide-to-feature-management/build/), your biggest risks aren't hardware failures—they're the bugs you ship to production.

Below, we’ll cover what RTO and RPO actually mean for modern development teams, and how tools like [feature flags](https://launchdarkly.com/blog/what-are-feature-flags/) can help you hit aggressive recovery targets without over-engineering your infrastructure.

## What RTO and RPO actually mean

**RTO (Recovery Time Objective)**: How long your system can stay down before you're in serious trouble. Think "we need to be back online in 15 minutes or customers start calling support."

**RPO (Recovery Point Objective)**: How much recent data you can afford to lose. If your last backup was an hour ago, can you live with losing an hour's worth of transactions?

These are no longer just disaster recovery buzzwords. When you're pushing code daily, *every* deployment is a potential RTO/RPO scenario.

Traditional disaster recovery planning focused on big, rare events, such as data centers flooding, hardware failure, power outages, and the like. But most outages today come from code changes:

- A bug in your payment flow that breaks checkout
- A database migration that locks up your app
- An AI model update that starts giving weird responses
- A new feature that tanks performance under load

Sure, your disaster recovery plan probably covers the server rack catching fire. But does it cover rolling back a feature flag when your conversion rate drops 30%?

The primary goal of a disaster recovery plan is to resume business operations quickly after a disruption, with minimal data loss. This encompasses all business functions that IT systems support to ensure that key operations can continue (or can be quickly restored) for the organization's survival.

Your RTO and RPO depend on what you're building. It’s critical to align your recovery targets with actual business impact, rather than selecting aggressive numbers simply because they sound impressive.

## RTO vs. RPO: What's the difference?

RTO and RPO aren't the same thing, but teams often confuse them. You need both to build a solid recovery strategy. **RTO is about speed:** how fast you get back online. **RPO is about data:** how much you can afford to lose.

You can recover quickly but still lose a lot of data, or vice versa.

| **Scenario** | **RTO Target** | **RPO Target** | **Why They Differ** |
| --- | --- | --- | --- |
| **E-commerce checkout** | 2 minutes | 0 seconds | Need to get back online fast, can't lose any transactions |
| **User analytics dashboard** | 30 minutes | 1 hour | Downtime hurts but isn't critical, some data loss is acceptable |
| **Internal CRM** | 4 hours | 15 minutes | Can work around downtime, but recent customer updates matter |
| **Blog/marketing site** | 2 hours | 24 hours | Visitors can wait, losing a day of comments/signups isn’t terrible |
| **Real-time chat** | 30 seconds | 5 minutes | Users expect instant messaging, but can live with losing recent messages |

**RTO is about getting back online.** It's the clock that starts ticking the moment your system goes down. Whether that's due to a failed deployment, a server crash, or a bug you've just shipped. RTO measures how long it takes for users to be able to use your app again.

**RPO is about protecting data.** It's measured backwards from the moment of failure. If your database crashes at 3 PM and your last backup was at 2 PM, you've got a 1-hour RPO. Everything that happened between 2:00 and 3:00 PM is gone.

Ultimately, you can't just optimize for one. Having [backups](https://launchdarkly.com/docs/sdk/concepts/data-stores) every 30 seconds (a great RPO) doesn't help if it takes you 6 hours to restore from those backups (a terrible RTO). Similarly, being able to spin up a new server in 5 minutes (great RTO) is useless if you lost the last 4 hours of customer data (terrible RPO).

**The best approach is to build both into your deployment process. Feature flags enable you to resolve issues in seconds (a great RTO) while preserving user state and data integrity (a great RPO).**

## How to align RTO and RPO with application criticality

Your internal employee directory doesn't need the same recovery targets as your payment processing system. However, figuring out what each app actually needs requires having an honest conversation about business impact.

### How to prioritize your apps

Skip the formal "Business Impact Analysis" and just ask these questions:

**What happens if this goes down for an hour?**

- Lost revenue? How much?
- Angry customers? How many?
- Blocked employees? Can they work around it?
- Regulatory issues? Legal problems?

**What happens if we lose the last hour of data?**

- Can we recreate it?
- Does it contain money/transactions?
- Will users notice?
- Is it required for compliance?

### An example tiering system

| **Tier** | **Examples** | **RTO Target** | **RPO Target** | **Reality Check** |
| --- | --- | --- | --- | --- |
| **(1) Critical** | Payment processing, user auth, core product features | < 5 minutes | < 1 minute | Your business stops without these |
| **(2) Important** | Admin dashboards, reporting, customer support tools | < 1 hour | < 15 minutes | Work slows down, but doesn't stop |
| **(3) Nice-to-have** | Internal tools, dev environments, documentation sites | < 4 hours | < 1 hour | Annoying but not business-critical |

- **Tier 1 apps (where to start):** These get feature flags, automated rollbacks, and monitoring that wakes people up at 3 AM. Invest in making these bulletproof.
- **Tier 2 gets basic protection:** Feature flags for major releases, monitoring during business hours, and documented rollback procedures.
- **Tier 3 gets best effort:** Basic monitoring, manual recovery procedures, backups that actually work.

Most teams try to give everything Tier 1 treatment, which can lead to burnout. Be ruthless about what actually matters to your business. You can’t do *everything*.

## Stop fighting fires, start preventing them

[Proactive risk mitigation](https://launchdarkly.com/blog/risk-mitigation-strategies-software-releases/) in software delivery involves using strategies, practices, and tools to prevent issues or minimize their impact *before* they escalate. Most teams spend their time reacting to outages instead of preventing them. But the best way to reach aggressive RTO and RPO targets isn't building a better disaster recovery plan— *it's shipping code that doesn't break in the first place.*

### Deploy!= Release (and why that matters)

Traditional deployments are all-or-nothing: you push code and everyone gets it immediately. This is why deployments are scary and why teams deploy at 2 AM "just in case."

Feature flags change this. You can deploy code to production without releasing it to users:

```javascript
if (featureFlag.enabled('new-checkout-flow')) {
  return newCheckoutProcess();
} else {
  return oldCheckoutProcess(); 
}
```

Now, deployment and release are separate events. Deploy whenever you want, release when you're ready.

### Progressive rollouts: limit the area of impact

Instead of flipping the switch for everyone simultaneously, roll out gradually:

- **1% of users** → watch error rates, performance metrics
- **5% of users** → monitor conversion rates, user feedback
- **25% of users** → check load on downstream systems
- **100% of users** → full rollout

If something breaks at the 5% mark, you've contained the damage. Your RTO is measured in seconds (flip the flag off) instead of hours (emergency rollback deployment).

### Kill switches: your RTO insurance policy

Feature flags aren't just for new releases; they're instant [kill switches](https://launchdarkly.com/blog/what-is-a-kill-switch-software-development/) for anything going wrong:

- Payment processor acting up? Route to backup provider
- Search results looking weird? Fall back to the old algorithm
- New AI model hallucinating? Switch back to the previous version

Instead of debugging under pressure while users suffer, you flip a switch and fix the problem properly later. Everybody wins.

### The result: prevention beats cure

This approach shifts your focus from "how fast can we recover?" to "how do we avoid breaking things?" You still need traditional disaster recovery, but most of your incidents become non-events because you caught and contained them early.

Your RPO stays low because you're not losing data during rollbacks (you're just changing which code path executes). Your RTO drops to seconds because fixing issues becomes a configuration change, not a code deployment.

## How to choose the right disaster recovery tools

Most disaster recovery (DR) solutions focus on traditional scenarios: server crashes, data corruption, and hardware failures. But if you're shipping code frequently, you need tools that handle software-induced incidents, too. Look for:

- **Speed matters more than features.** Can you recover in minutes, not hours? Can you test recovery procedures without taking systems offline? Can you automate the common failure scenarios?
- **Integration with your deployment pipeline.** Your DR solution should work with how you actually ship code. If you're using feature flags, canary deployments, or progressive rollouts, make sure that your recovery tools comprehend and support these patterns.
- **Cost vs. benefit reality check.** Enterprise DR solutions (with licensing, training, and maintenance fees) can cost more than the downtime they prevent. Be honest about what you actually need vs. what vendors want to sell you.

Companies like Veeam and Acronis handle the traditional stuff well: database backups, server imaging, and cross-region replication. Cloud providers (AWS, Azure, GCP) offer solid infrastructure-level recovery.

However, for code-related incidents, feature management platforms like LaunchDarkly can be more effective:

- **HP** reduced rollback times [from hours to minutes with feature flags](https://launchdarkly.com/case-studies/hp/)
- **Christian Dior** went from [15-minute rollbacks to instant toggles](https://launchdarkly.com/case-studies/dior/)
- **86% of surveyed LaunchDarkly customers** [recover from incidents within a day](https://launchdarkly.com/blog/2024-survey-impact-launchdarkly-customer-outcomes/#:~:text=%E2%80%9CWhen%20a%20software%20incident%20occurs,an%20hour%2C%20if%20not%20minutes.)
- 42% of surveyed LaunchDarkly customers [recover in hours (if not minutes)](https://launchdarkly.com/blog/2024-survey-impact-launchdarkly-customer-outcomes/#:~:text=%E2%80%9CWhen%20a%20software%20incident%20occurs,an%20hour%2C%20if%20not%20minutes.)

Don't trust demos or datasheets. Run a proof of concept with your actual systems and realistic failure scenarios. Simulate a bad deployment during peak traffic. Test your recovery procedures when you're stressed and the CEO is asking for updates every 5 minutes. The best disaster recovery solution is the one you'll actually use when things go wrong.

Here are some additional criteria to consider:

- **Supported Environments:** Does the solution cover all necessary environments? This includes physical servers, virtual machines (VMs), cloud services (IaaS, PaaS, SaaS), endpoints, and critical applications.
- **RPO Capabilities:** What backup frequencies and replication options does it offer (e.g., continuous data protection (CDP), snapshots, synchronous/asynchronous replication) to meet your RPOs?
- **RTO Capabilities:** What recovery methods and automation features are available (e.g., instant recovery, bare-metal restore, VM/granular restore, automated failover/failback) to achieve your RTOs?
- **Consistency:** Does the solution guarantee application-consistent and crash-consistent backups? For distributed systems, can it handle feature state consistency?
- **Testing and Verification:** Does it facilitate easy, non-disruptive DR testing? Regular testing is key for validating that RTO and RPO targets are achievable.
- **Scalability and Performance:** Can the solution scale to handle current and future data volumes while meeting required recovery speeds?
- **Management and Reporting:** Does it offer centralized management and clear reports on backup status, RPOs, recovery readiness, and test results?

## RTO/RPO for continuous delivery

Traditional disaster recovery plans for server crashes and natural disasters, but when you're deploying multiple times per day, your biggest risks are the bugs you ship yourself.

**Software incidents happen more often.** A broken login flow, a payment bug, or a database migration gone wrong can take down your app just as effectively as a hardware failure. The difference is that these happen weekly, not yearly.

**Speed expectations have changed.** When you're shipping daily, users expect problems to be fixed quickly. A 4-hour RTO for a deployment bug feels like an eternity when your CI/CD pipeline normally moves in minutes.

**Feature flags change the game.** Instead of rolling back entire deployments, you can disable specific features instantly:

- Payment processing breaks? Route to backup provider in seconds
- New search algorithm returning weird results? Switch back to the old one
- Database migration causing slowdowns? Roll back just that change

**Protecting data integrity.** Quick feature toggles also prevent data corruption. If a bug is actively corrupting transactions, disabling it immediately protects your RPO better than waiting for a full rollback deployment.

## Feature-level recovery targets

Don't treat your entire app like one big system. Different features have different risks and business impacts, so they should have different recovery targets.

- **Micro-recoveries with feature flags.** Instead of rolling back your entire deployment when a single feature breaks, simply toggle off that feature. Your checkout flow has a bug? Disable the new version and fall back to the old one in seconds. Users might not even notice.
- **Different features, different targets:**
- **Core payment processing**: RTO of seconds, RPO of zero
- **New recommendation engine**: RTO of 5 minutes, RPO of 15 minutes
- **Beta dashboard features**: RTO of 30 minutes, RPO of an hour
- **Targeted rollbacks.** If a feature only affects mobile users in Europe, you can disable it just for that segment while leaving everyone else unaffected. This gives you localized recovery without global disruption.

The goal is to match your recovery strategy to the actual business impact rather than applying blanket policies across features that have wildly different importance to your users and revenue.

## RTO/RPO across your tech stack

Your recovery strategy needs to work everywhere your code runs, but the approach varies by environment.

- **Cloud-first applications** get the most options. AWS, Azure, and GCP offer a range of options, from basic backups (cheaper but slower) to active-active setups (more expensive but instant). Most teams start with automated backups and add a hot standby for critical services.
- **On-premises/physical servers** are harder to recover quickly. Replacing hardware takes time, so focus on preventing issues rather than rushing for a quick recovery. Legacy systems often get longer RTOs because the alternative is expensive.
- **Mobile apps** have a unique challenge—you can't instantly deploy fixes like web apps. Feature flags solve this by letting you disable broken features without waiting for app store approval.
- **Databases and stateful services** need special attention. You can't just restore from backup and lose transactions. Utilize read replicas, point-in-time recovery, and careful migration strategies.
- **The practical reality:** Most incidents happen in your application code, not your infrastructure. A bug in your payment flow is more likely than a data center failure. Focus your RTO/RPO planning on software-induced problems first, then worry about hardware disasters.

Feature flags work across all these environments to give you consistent recovery capabilities, whether users are on mobile, web, or hitting your APIs directly.

## How to balance criticality, cost, and RTO/RPO

Aggressive RTO/RPO targets can become expensive quickly. Near-zero downtime requires redundant everything: servers, databases, networks, and entire data centers. Most teams simply can't justify the cost.

**Do the math honestly.** What does an hour of downtime actually cost your business? If it's $10K, don't spend $100K/year on infrastructure to prevent it. You're better off accepting some downtime and investing in faster recovery.

**Software-first approach wins.** Feature flags and progressive delivery often deliver better ROI than traditional disaster recovery infrastructure. Instead of spending millions on hot standby servers, spend thousands on tools that prevent incidents.

**Tier your investments:**

- **Critical systems**: Get the expensive stuff - redundancy, monitoring, instant rollback
- **Important systems**: Get feature flags, automated alerts, and documented procedures
- **Everything else**: Get basic backups and hope for the best

Think about these numbers from our *[2024 Survey: Impact of LaunchDarkly on Customer Outcomes](https://launchdarkly.com/blog/2024-survey-impact-launchdarkly-customer-outcomes/)*:

- 8% of customers say LaunchDarkly has reduced their operational costs by over 50%.
- 59% say LaunchDarkly has reduced their operational costs between 11% and 50%.
- 26% say LaunchDarkly has reduced their operational costs up to 10%.

Ultimately, prevention is almost always cheaper than elaborate recovery systems.

## Start preventing problems instead of just fixing them faster

RTO and RPO are daily realities when you're shipping code continuously. Every deployment is a potential incident, and traditional recovery methods aren't fast enough for modern development cycles.

LaunchDarkly provides the tools to achieve aggressive RTO/RPO targets without over-engineering your infrastructure. Deploy with confidence, recover instantly, and focus on building features instead of fixing outages. Instead of building elaborate disaster recovery systems, embed resilience directly into your development workflow. Explore the LaunchDarkly platform with a [free trial](https://app.launchdarkly.com/signup) to see how its control mechanisms can help your teams meet and exceed RTO/RPO targets.