What are Accelerate Metrics? Latest 2024 Guide

What separates elite software teams from the rest? According to extensive research across thousands of organizations, it comes down to four key metrics that predict both delivery performance and business success. These accelerate metrics have become the industry standard for measuring software delivery excellence, with elite teams deploying code 182 times more frequently than their low-performing counterparts.

In this article, we'll explore the four accelerate metrics that matter most, how they predict organizational success, and practical steps for improvement.

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Accelerate metrics (also known as DORA metrics) are key performance indicators that measure software delivery performance and operational excellence.

Based on research spanning over 36,000 professionals across organizations of all sizes and industries, these metrics have proven to be reliable predictors of organizational performance. Over time, these metrics have evolved, leading to updates and the introduction of a fifth metric in 2024:

1. Change Lead Time: The time it takes to go from first commit to code successfully running in production.
2. Deployment Frequency: How often an organization deploys code to production or releases it to end users.
3. Failed Deployment Recovery Time (Formerly Mean Time to Recovery): The time it takes to restore service when a deployment causes an outage or service failure in production (whereas previously Mean Time to Recovery also included uncontrollable failure events such as an earthquake disrupting service).
4. Change Failure Rate: The percentage of changes that result in degraded service or require remediation (e.g., that lead to service impairment or outage, and require a hotfix, rollback, fix forward, or patch).
5. Rework rate: This fifth metric was introduced later in 2024, and together with Change Failure Rate provide an indicator of software delivery stability. Since it's a newer addition, there aren’t yet established quantifiable benchmarks and so this metric tends to receive less focus.

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When discussing software delivery performance metrics, you'll often hear both "Accelerate metrics" and "DORA metrics" mentioned — these terms refer to the same set of four key performance indicators.

DORA publishes performance levels, which are accepted as an industry benchmark for what elite engineering performance looks like against Accelerate metrics. This is because  DORA's 10+ years of research and related book Accelerate consistently show a direct link between high-performing tech teams , psychological safety, and financial performance.  While the DORA benchmarks are useful guides, "elite" level performance may not be suitable for every team.

Let's explore each Accelerate metric and examine what excellence looks like according to the latest research:

Change Lead Time

The duration between a developer's first code commit and when that code is successfully running in production. It's a fundamental measure of how quickly your delivery pipeline can move code from development to serving users.

The 2024 DORA Report shows that elite performing teams consistently achieve Change Lead Times of less than one day, setting a clear benchmark for delivery excellence.

Deployment Frequency

Deployment frequency captures how often your organization deploys code to production or releases it to end users. This straightforward metric provides clear insight into the velocity of your software delivery process.

Elite performing teams, as identified in the latest DORA research, achieve multiple deployments per day, operating with on-demand deployment capabilities.

Change Failure Rate

Change failure rate measures the percentage of deployments that result in degraded service or require remediation. This includes changes that lead to service impairment or outages, requiring intervention through hotfixes, rollbacks, fix forwards, or patches.

The 2024 DORA Report indicates that elite teams maintain a change failure rate between 0-5%, demonstrating that high velocity and high stability can coexist.

Failed Deployment Recovery Time (Formerly Mean Time to Recovery)

Failed Deployment Recovery Time measures how long it takes to restore service when a deployment causes an outage or service failure in production. This metric has evolved from its previous incarnation as Mean Time to Recovery to focus specifically on deployment-related failures, rather than including uncontrollable events like infrastructure outages due to natural disasters.

According to the 2024 DORA Report, elite performing teams restore service in less than one hour when failures occur.

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The significance of these metrics stems from years of rigorous research conducted across thousands of global organizations, as documented in the annual State of DevOps Reports. This extensive research demonstrates that teams who excel in these metrics consistently achieve superior performance outcomes.

These four metrics provide valuable insights into both the strengths and challenges within a team's software development process. They offer a balanced view of delivery performance by measuring two critical aspects:

1. Throughput — Speed of making updates of any kind, both for normal changes and changes in response to a failure
2. Stability — Whether deployments unintentionally lead to immediate, additional work

Teams that demonstrate high performance across these metrics aren't just delivering faster - they're delivering better, so having a grasp on these metrics can help improve your performance.

While these metrics provide valuable data points, remember they're just one part of measuring team health and success. Elite performance numbers might not be the right target for every team - what matters most is steady improvement and sustainable practices that work for your specific context. For example, a team working on critical financial infrastructure may need to optimize for stability over speed, while a team building a new consumer app might prioritize rapid iteration.

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Before jumping in, remember that a high-trust environment needs to be created first. Without trust, these metrics can be gamed and misused, leading to fear and uncertainty among team members.

Picture this: developers splitting their work into smaller, more frequent deployments just to improve their Accelerate metrics. It’s Goodhart’s law in action: “When a measure becomes a target, it ceases to be a good measure”. However, a key strength with the DORA metrics is that they're designed to be in tension with each other. This tension can create a natural guardrail  in preventing the over-optimization of a single metric.  For example:

• Speed vs Stability: Deployment Frequency and Change Lead Time measure speed, Change Failure Rate and Failed Deployment Recovery Time ensure this speed doesn't come at the expense of quality.

So how can we implement Accelerate in thoughtful way? Here's our approach:

1. Be Clear about the Business Goal: What are the bigger outcomes we're hoping to support? Make sure your chosen metrics align with the value creation (e.g., shipping a new release, retaining key talent). And don't keep it a secret – let everyone know why these metrics matter.
2. Establish a Baseline: Take a snapshot of your current performance against the selected metrics. This gives you a reference point for seeing how you compare to benchmarks and tracking progress as you run experiments.
3. Integrate with Existing Tools and Processes: Getting metrics should help you, not slow you down. This can look like:
   • Incorporating tracking into your git tooling, CI/CD pipelines, issue tracking, and IMS
   • Weaving metrics into your team practices like stand-ups, retros and 1:1s to stay across bottlenecks and issues early. Most dashboards are built and then get forgotten; you don't want that to happen here!
   • Supporting team members on the metrics and encouraging open communication and feedback during the integration process.
4. Run an experiment: By now your team has live access to key metrics, woven the metrics into your team practices, and are having rich discussions about what's going well and where you can improve. So its time to run an experiment! Pick one metric you want to improve, come up with a hypothesis, and then give it a go!
5. Track Progress and Celebrate Successes: Tooling like Multitudes can help track you check in how things are going. Whether the experiment worked or it’s time to try a new approach — the most important part is to celebrate every win or learning, no matter how small. And remember, spread the knowledge! Sharing builds a culture of continuous improvement by making failed experiments a common and celebrated part of work.
6. Repeat steps 4 - 6

This approach will help build a culture of continuous improvement and blameless experimentation. Getting into a habit of experimentation lowers the barriers for teams to change their ways of working, and builds the muscle of tackling new challenges and shifting priorities.

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Tracking and analyzing accelerate metrics effectively requires the right tools. Multitudes is an engineering insights platform that makes this process seamless by integrating with your existing development tools like GitHub and Jira, providing a comprehensive view of your team's delivery performance.

With Multitudes, you can:

• Automatically track accelerate metrics, including Change Lead Time and Deployment Frequency, without manual data collection
• Gain clear visibility into work patterns, helping you distinguish between feature development and bug fixing activities
• Receive timely, automated Slack nudges with evidence-backed suggestions for improvement

By implementing Multitudes, teams can improve their accelerate metrics while spending less time on measurement and more time on meaningful improvements.

Our clients ship 25% faster without sacrificing code quality.

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