DORA Metrics: Measuring Software Delivery Performance

How do you know if your engineering organization is getting better? The DORA (DevOps Research and Assessment) team identified four key metrics that differentiate high-performing teams from low-performing ones.

The Four Key Metrics

1. Deployment Frequency: How often does your organization successfully release to production? (Velocity)
2. Lead Time for Changes: How long does it take to go from code committed to code running in production? (Agility)
3. Change Failure Rate: What percentage of deployments cause a failure in production? (Quality)
4. Time to Restore Service: How long does it take to recover from a failure in production? (Stability)

Why they work

DORA metrics balance speed and stability. If you only measure speed (Frequency), quality might drop. If you only measure stability (Change Failure Rate), speed might drop. Measuring all four ensures a healthy delivery chain.

How to use them well

• Measure trends per team/service, not a single global number.
• Automate collection from CI/CD and incident tooling to avoid manual reporting.
• Pair DORA with context metrics (availability/SLOs, rework rate) so you can explain changes.

Turn DORA into a platform feedback loop

DORA becomes much more useful when it is tied to daily workflows (not quarterly reporting):

1. Expose the metrics per service in your portal (Backstage) or dashboards.
2. Use a consistent definition (what counts as “production”, what is a “deployment”, how to measure incidents).
3. Add drill-downs: from the DORA number to the pipeline step, repo, or incident that explains it.

This turns DORA into a continuous improvement loop for the platform team and product teams.

What to improve first

If you want quick impact, focus on the constraints that usually dominate:

• Lead time: CI duration, review queues, environment provisioning, flaky tests.
• Change failure rate: progressive delivery, feature flags, automated rollback.
• Time to restore: better alerts, runbooks-as-code, fast rollback paths.

Common mistakes when implementing DORA

• Mixing team and platform signals: a slow pipeline is often a platform problem, not a team problem.
• Ignoring reliability outcomes: pair DORA with SLOs so “faster” doesn’t mean “riskier”.
• Over-normalizing: teams with different risk profiles can improve, but comparisons should be careful.

What to measure alongside DORA

• pipeline duration (p50/p95) and flaky test rate
• change size (PR size) and review time
• error budget burn rate for critical services

Conclusion

DORA metrics provide a data-driven way to track the success of your DevOps and Platform Engineering initiatives. By focusing on these outcomes, you move away from vanity metrics and toward real business impact.