What is MTTR (Mean Time to Resolution)?
Mean time to resolution measures the average time from incident detection through complete resolution, encompassing root cause identification and corrective measures. It serves as a concrete benchmark for tracking incident response effectiveness.
Understanding Mean Time to Resolution
The acronym MTTR carries multiple meanings: Mean Time to Resolution (full lifecycle from detection to complete resolution), Mean Time to Recovery (time for system return to normal after failure), Mean Time to Repair (active repair work duration only), and Mean Time to Respond (speed of initial response acknowledgment). The formula: MTTR = Total resolution time across all incidents / Number of incidents. Teams typically calculate MTTR using rolling windows (weekly/monthly) segmented by severity level.
MTTR as a DORA Metric
DORA (DevOps Research and Assessment) performance benchmarks: Elite (less than one hour), High (less than one day), Medium (one day to one week), Low (more than one week). Four distinct incident resolution phases exist: Detection (noticing issues), Triage (severity assessment and assignment), Diagnosis (root cause identification), Fix and verification (repair implementation and validation). The diagnosis phase typically consumes 60–80% of total resolution time across incidents.
Common Pitfalls and How AI Reduces MTTR
Common pitfalls when tracking MTTR include inconsistent clock starts across teams, mixing severity levels in calculations, gaming metrics through early ticket closure, ignoring outliers that skew averages, and failing to track resolution phases separately. AI-driven investigation compresses the diagnosis phase by simultaneously correlating signals across multiple tools. Platforms leverage context engineering to assemble relevant information automatically rather than requiring manual dashboard navigation.
What to remember
- 1MTTR measures average time from detection to resolution; clarify which variant you're tracking
- 2Formula: total resolution time / number of incidents; segment by severity level
- 3Diagnosis typically dominates resolution time; track phases separately for actionable insights
- 4Avoid inconsistent definitions, severity mixing, metric gaming, and outlier dismissal
- 5AI investigation tools compress diagnosis from hours to minutes, directly reducing MTTR
Frequently asked questions
What does MTTR stand for?
MTTR can represent Mean Time to Resolution, Recovery, Repair, or Respond, with Mean Time to Resolution being most common in SRE/DevOps contexts.
How is MTTR calculated?
Total resolution time across incidents divided by incident count. Example: 5 incidents totaling 12.5 hours = 2.5 hours MTTR.
What is a good MTTR benchmark?
Elite teams restore service in under one hour; high performers within one day; medium performers between one day and one week; low performers exceed one week.
What's the difference between MTTR and MTTM?
MTTR covers full resolution lifecycle; MTTM measures only user impact mitigation time.
Why is my MTTR getting worse?
Common causes include system complexity growth, alert fatigue, observability gaps, on-call burden, and accumulated technical debt.
Can AI actually reduce MTTR?
Yes; AI compresses diagnosis phase (typically 40–60% of MTTR) by automating correlation across metrics, logs, traces, and deployment history.
Should MTTR be tied to engineer performance reviews?
Generally no; individual metrics create gaming incentives; track as team/system metric instead.
What is the difference between MTTR and MTBF?
MTBF measures reliability (time between failures); MTTR measures recoverability (time to restore service after failure).
Is MTTR a KPI?
Yes; widely tracked operational KPI included in the four DORA metrics for software delivery performance.
See it in action. No slides.
NeuBird AI compresses incident investigation from hours to minutes: autonomous root cause analysis, with zero manual triage.