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To measure engineering efficiency at scale, use DORA’s five software delivery performance metrics as service-level diagnostic signals, then add the product, reliability, developer-experience, and team-health context needed to understand what those signals mean. Track each application or service over time; do not turn unlike teams into a leaderboard or treat one number as a universal target.
What are DORA metrics?
DORA’s current software delivery performance model has five measures. It groups change lead time, deployment frequency, and failed deployment recovery time under throughput, and change fail rate and deployment rework rate under instability. The definitions and guidance are set out in DORA’s software delivery performance metrics guide.
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| Metric | What it measures | DORA grouping |
|---|---|---|
| Change lead time | Elapsed time from a change being committed to version control until it is deployed to production. | Throughput |
| Deployment frequency | How often the service is deployed, measured as deployments over a period or the time between deployments. | Throughput |
| Failed deployment recovery time | How long it takes to recover from a deployment failure that requires immediate intervention. | Throughput |
| Change fail rate | The proportion of deployments that require immediate intervention after deployment, often through a rollback or hotfix. | Instability |
| Deployment rework rate | The proportion of deployments that are unplanned work triggered by a production incident. | Instability |
These measures describe delivery outcomes; they do not, by themselves, explain why those outcomes occur or capture the full effectiveness of an engineering organization. A change in a metric is a prompt to investigate the system, not a complete diagnosis.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallHow the current model differs from the historic Four Keys
The earlier Four Keys set used deployment frequency, lead time for changes, time to restore services, and change failure rate. DORA’s model has evolved: the current guide names five metrics, including failed deployment recovery time and deployment rework rate. The Four Keys open-source implementation is marked as not currently maintained, so treat it as historical implementation material rather than the current definition of DORA metrics. Its original design also does not fit every kind of project—for example, some libraries release versions without deploying them to production.
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What is the right unit of analysis at scale?
Start with one application or service, and interpret its results in the context of its users, architecture, deployment model, and team workflow. Compare that service with its own results over time. If you roll everything up to an organization-wide number, differences between systems can disappear behind an average.
A mobile application and a mainframe service may face very different release processes, constraints, and risks. Ranking their teams against a shared deployment-frequency target would not make those conditions equivalent. DORA cautions against comparing materially different systems or teams and against setting a universal deployment target.
Use an organization-wide view to spot questions worth investigating, not to declare which team is best. Before comparing results, confirm that the services use consistent metric definitions and that the data actually represents comparable delivery events. Keep the service-level context visible alongside any summary.
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How do you measure engineering efficiency at scale without creating a leaderboard?
Run measurement as a recurring improvement conversation, not as a performance scoring exercise. The people who prioritize, build, deliver, and operate a service need a shared view of where work gets stuck and what could improve it. A practical cycle is:
- Choose the service and define the measures. Agree what counts as a production deployment, an intervention, an incident-related unplanned deployment, and recovery. Use definitions consistently before interpreting trends.
- Establish a baseline. Record the service’s current results and relevant context. The baseline is for noticing change, not judging the team against another service.
- Bring the workflow together. Include the people involved in prioritizing, building, delivering, and operating the service. Discuss friction across the path rather than assigning ownership of a single metric to one function.
- Choose a meaningful constraint. Select a bottleneck the team can address, such as work waiting for review or a slow feedback loop. DORA suggests using leading indicators such as code review time or test quality when they are tied to a specific improvement plan.
- Make a change and review the result. Check whether the intervention helped the constraint without worsening reliability, user outcomes, or team sustainability. If it did not, use what the team learned to choose the next experiment.
Smaller batches are one common improvement to consider: they are easier to reason about as they move through delivery and easier to recover from if something goes wrong. The point is not to maximize deployment count at any cost, but to improve the system’s ability to deliver useful changes safely.
Avoid making metric collection an end in itself. DORA cautions that instrumentation can consume more effort than improvement, and that turning metrics into targets can invite gaming. A metric becomes useful when it helps a team make and evaluate a concrete change.
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What should engineering leaders measure beyond DORA metrics?
There is no universal combined scorecard prescribed by DORA. Instead, add measures that help answer the questions the delivery metrics leave open. DORA’s 2025 report says simple delivery metrics show what is happening, not why, and describes seven team archetypes that connect performance and stability with team well-being. Use a small set of measures suited to the service and the decision at hand.
- Product performance: Are users receiving useful product outcomes? Choose product measures that reflect the service’s intended value rather than assuming more deployments automatically mean better results.
- Reliability and user impact: Are changes and incidents affecting service reliability or users? Delivery instability measures signal part of the picture; operational and product context helps explain its significance.
- Flow and friction: Where does work wait between prioritization, development, review, testing, and release? A leading measure such as review time can help test a particular improvement, but is not a standalone performance score.
- Developer experience: Can developers complete work without avoidable friction? Look for relevant workflow obstacles and feedback from the people doing the work rather than assuming delivery outcomes explain their causes.
- Team well-being: Can the team sustain its pace? Interpret delivery performance alongside indicators of team health so that faster output does not conceal an unsustainable working pattern.
DORA’s capabilities material points to technical, process, and cultural conditions that can shape outcomes, including continuous integration, continuous delivery, maintainable code, cloud infrastructure, and transformational leadership. These are useful areas to investigate when a result needs explanation; they are not a checklist that every team must optimize in the same way.
What do the DORA reports say about AI and delivery?
The findings are year-specific and should be read as survey results and reported relationships, not proof that AI caused a particular outcome. Nathen Harvey and Derek DeBellis’s Google Cloud announcement of the 2025 DORA report, dated September 23, 2025, says it draws on more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals around the world.
- In the 2025 report, 90% of respondents said they used AI at work, more than 80% believed AI had increased their productivity, and 30% reported little or no trust in AI-generated code.
- The 2025 announcement reports positive relationships between AI adoption and software delivery throughput and product performance, alongside a negative relationship with delivery stability.
- The 2024 DORA announcement reported that more than 75% of respondents relied on AI for at least one daily professional responsibility, and more than one-third reported moderate to extreme productivity increases.
- In its 2024 estimates, a 25% increase in AI adoption was associated with a 7.5% increase in documentation quality, a 3.4% increase in code quality, and a 3.1% increase in code review speed. The same announcement estimated a 1.5% decrease in delivery throughput and a 7.2% reduction in delivery stability as AI adoption increased; 39% of respondents reported little or no trust in AI-generated code.
The 2024 and 2025 findings are not interchangeable, and the announcements do not establish that the different figures were produced using identical methods. Treat them as evidence about reported patterns, not forecasts for an individual organization. If AI appears to make work faster, measure downstream product outcomes and stability as well as individual productivity claims.
The 2025 report also describes high-quality internal platforms, clear workflows, team alignment, automated testing, mature version control, and fast feedback as relevant conditions for realizing AI benefits. It reports that 90% of organizations had adopted at least one platform and describes a direct correlation between platform quality and the ability to unlock AI value. That is not evidence that buying a platform alone will cause better results. As Nathen Harvey and Derek DeBellis put it in the 2025 announcement: “AI doesn’t fix a team; it amplifies what’s already there.”
How should you choose measurement tools and integrations?
Choose an approach that makes trustworthy, service-level improvement conversations easier—not one that produces the most impressive dashboard. Compare options on five practical dimensions:
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- Outcome coverage: Can you see throughput and instability alongside relevant product results and team-health context?
- Level of analysis: Can you inspect an application or service without losing the context behind an organization-wide rollup?
- Definitions and provenance: Can teams understand where the data came from and how events map to each metric?
- Ongoing effort: What will it take to integrate, validate, and maintain the data?
- Use in practice: Does the approach help teams identify constraints and review improvements, or does it mainly enable rankings?
DORA notes that prebuilt integrations may be preferable to building a costly custom data pipeline at the outset. The historic Four Keys repository describes an implementation using development events, Cloud Run, Pub/Sub, BigQuery, and a Grafana dashboard, but it is marked as not currently maintained. Treat it as reference material, not a maintained product or a requirement to copy its architecture.
What a useful measurement practice looks like
A useful practice keeps each service’s delivery signals connected to the people and outcomes behind them. It uses consistent definitions, reviews trends over time, investigates friction with the whole workflow in view, and checks whether an improvement helped without trading away reliability, product value, or team well-being. DORA metrics provide a starting point for that conversation—not a universal verdict on engineering efficiency.
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