Definition
Mean Time to Recovery (MTTR)
Mean time to recovery is the average time it takes to restore service after a failure or incident — measured from when the failure begins until normal operation resumes. One of the four DORA metrics, it gauges how quickly a team detects, diagnoses, and recovers from problems. A low MTTR signals resilience: failures happen, but they're contained fast.
Key takeaways
- Mean time to recovery (MTTR) is the average time to restore service after a failure, from onset to normal operation.
- It's one of the four DORA metrics and represents the stability dimension of software delivery performance.
- Elite teams recover in under an hour, valuing fast recovery over chasing zero failures — failure in complex systems is inevitable.
- Small, reversible changes plus monitoring, automated rollback, feature flags, and blameless postmortems drive MTTR down.
MTTR is calculated by summing the recovery times of incidents over a period and dividing by the number of incidents. It captures the whole restoration path — detection, diagnosis, mitigation, and verification — so it reflects observability, on-call readiness, and the ease of rolling back or rolling forward, not just raw fix speed.
It is one of the four DORA metrics (alongside deployment frequency, lead time for changes, and change failure rate), where it represents the stability dimension of software delivery performance. Elite teams recover from failures in under an hour, treating fast recovery as more valuable than chasing zero failures — because in complex systems, failure is inevitable.
This reframes reliability around resilience rather than perfection: small, frequent, easily reversible changes lower MTTR because there's less to untangle when something breaks. Practices that drive it down include good monitoring and alerting, automated rollback, feature flags to disable bad code without a redeploy, runbooks, and blameless postmortems that turn each incident into a faster recovery next time.
Planoda's incident and audit trails record the full timeline of a disruption, so a team can measure recovery time and feed postmortem actions straight back into the backlog.
Related terms
- DORA MetricsDORA metrics are four research-backed measures of software delivery performance: deployment frequency, lead time for changes, change failure rate, and time to restore service. Identified by the DevOps Research and Assessment program, they balance speed (the first two) against stability (the last two), giving engineering teams an evidence-based scorecard for how well they ship.
- Incident ManagementIncident management is the coordinated process of detecting, responding to, and resolving unplanned disruptions to a service, then restoring normal operation as fast as possible. It defines roles (incident commander, communications lead), severity levels, escalation paths, and a status-communication cadence, with the goal of minimizing impact and learning from every failure.
- PostmortemA postmortem is a structured, blameless write-up produced after an incident that documents what happened, the timeline, the contributing factors, and the actions taken, then defines concrete follow-ups to prevent recurrence. Its purpose is organizational learning, not punishment — it treats failures as systemic, examining process and tooling rather than assigning individual blame.
- Error BudgetAn error budget is the amount of unreliability a team is allowed to spend, derived as the inverse of a service-level objective — a 99.9% SLO permits 0.1% failure. It reframes reliability as a finite resource: when budget remains, teams ship boldly; when it's exhausted, they pause risky changes and prioritize stability until it replenishes.
- Service-Level Objective (SLO)A service-level objective (SLO) is a measurable target for a system's reliability over a window — for example, 99.9% of requests succeeding in 30 days. It is set against a service-level indicator (a metric like success rate or latency) and is the internal goal that informs the externally promised SLA, giving teams a precise definition of "reliable enough."