Research over thousands of companies and our experience working with dozens of engineering teams shows that you should care about these four metrics to achieve sustainable success in software delivery. These aren't just vanity metrics, they are predictive indicators of organizational performance. Teams that excel in these areas consistently deliver better business outcomes, have happier developers, and outperform their competition.
Throughput Metrics
Deployment Frequency
How often do you ship to production?
Deployment Frequency measures how often your team successfully releases to production. It's a leading indicator of your team's ability to deliver value continuously and respond to market changes.
Why it matters: Frequent deployments mean smaller batches, which are easier to test, easier to debug, and less risky. They also enable faster feedback loops with customers and quicker time-to-market for new features.
Elite performers target:
Multiple deploys per day
Low performers may only deploy monthly or quarterly
Lead Time for Changes
How fast can you go from commit to production?
Lead Time for Changes measures the time it takes for a code commit to reach production. This includes code review, testing, staging, and deployment processes—everything between writing code and customers seeing the change.
Why it matters: Short lead times mean your team can respond quickly to bugs, security issues, and customer feedback. Long lead times often indicate bottlenecks in your process—whether manual approvals, slow tests, or deployment friction.
Elite performers target:
Less than 1 hour
Low performers may take weeks or months
Mean Time to Repair (MTTR)
How quickly can you recover from failures?
Mean Time to Repair measures the average time it takes to restore service when an incident occurs. This includes the time from when an issue is detected to when it's fully resolved and the system is back to normal operation.
Why it matters: Failures are inevitable in complex systems. What separates high-performing teams isn't the absence of failures—it's their ability to detect and recover from them quickly. A low MTTR means less downtime, less revenue loss, and less stress on your team.
Elite performers target:
Less than 1 hour
Low performers can take days or even weeks to recover
Change Failure Rate
How often do deployments cause problems?
Change Failure Rate is the percentage of deployments that result in a degraded service or require immediate remediation (rollback, hotfix, or patch). It measures the quality and reliability of your deployment process.
Why it matters: A high failure rate creates a vicious cycle: teams become afraid to deploy, which leads to larger batches, which increases risk, which leads to more failures. Reducing this rate builds confidence and enables faster iteration.
Elite performers target:
0-15%
Low performers often see failure rates of 46-60%
Why These Metrics Work Together
The power of these four metrics lies in their balance. You might think that deploying faster would lead to more failures, but the research shows the opposite: speed and stability reinforce each other.
Teams that deploy frequently build the automation, testing, and monitoring infrastructure that also reduces failure rates. When failures do occur, the same practices that enable frequent deployment also enable rapid recovery.
"There is no tradeoff between speed and stability. Teams that practice continuous delivery achieve both high throughput and high stability."
— Accelerate: The Science of Lean Software and DevOps
The following table offers a comprehensive comparison of all metrics. By analyzing these together, organizations can identify current state and create a plan for improvement to achieve elite status.
| Software delivery performance metric | Elite | High | Medium | Low |
|---|---|---|---|---|
Deployment frequency How often does your organization deploy code to production? | On-demand (multiple deploys per day) | Between once per week and once per month | Between once per month and once every 6 months | Fewer than once per six months |
Lead time for changes How long does it take to go from code committed to code running in production? | Less than one hour | Between one day and one week | Between one month and six months | More than six months |
Time to restore service How long does it generally take to restore service when an incident occurs? | Less than one hour | Less than one day | Between one day and one week | More than six months |
Change failure rate What percentage of changes to production result in degraded service? | 0%–15% | 16%–30% | 16%–30% | 16%–30% |
How to Improve Your Metrics
Invest in Automation
CI/CD pipelines, automated testing, and infrastructure as code are foundational. They reduce manual work, catch issues early, and make deployments routine rather than risky events.
Embrace Small Batches
Smaller changes are easier to test, review, and debug. Break work into independently deployable pieces and ship them as soon as they're ready.
Build Observability
You can't improve what you can't measure. Invest in monitoring, logging, and alerting so you can detect issues quickly and understand their impact.
Practice Blameless Postmortems
When failures happen, focus on learning and improvement rather than blame. This creates psychological safety and encourages teams to take smart risks.