DevOps Metrics and KPIs: Measure Success | DevOpsNess

DevOps Metrics and KPIs: Measuring Success

Tracking the right metrics helps improve DevOps performance. This guide covers essential metrics.

DORA Metrics #

Deployment Frequency #

How often you deploy to production:

Elite: Multiple per day
High: Once per day to once per week
Medium: Once per month to once per 6 months
Low: Less than once per 6 months

Lead Time #

Time from code commit to production:

Elite: Less than 1 hour
High: 1 day to 1 week
Medium: 1 month to 6 months
Low: More than 6 months

Mean Time to Recovery (MTTR)#

Time to recover from failures:

Elite: Less than 1 hour
High: Less than 1 day
Medium: Less than 1 week
Low: More than 1 week

Change Failure Rate #

Percentage of deployments causing failures:

Elite: 0-15%
High: 16-30%
Medium: 31-45%
Low: 46-60%

Additional Metrics #

Build success rate
Test coverage
Code review time
Infrastructure cost per deployment

Tracking #

python.python

def calculate_metrics():
    return {
        "deployment_frequency": get_deployment_frequency(),
        "lead_time": get_lead_time(),
        "mttr": get_mttr(),
        "change_failure_rate": get_failure_rate()
    }

Conclusion #

Track DORA metrics to measure DevOps performance. Focus on improving deployment frequency and reducing lead time.

Production Notes 1 #

For DevOps Metrics and KPIs: Measuring Success, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.

Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.

Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.

Production Notes 2 #

For DevOps Metrics and KPIs: Measuring Success, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.

Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.

Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.

Production Notes 3 #

For DevOps Metrics and KPIs: Measuring Success, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.

Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.

Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.