SRE

MTTR Calculator (SRE)

Paste a timestamped incident log (start, detected, resolved) to compute Mean Time To Repair, Mean Time To Detect, Mean Time To Failure, and an availability percentage estimate. All calculation runs in your browser.

No data is transmitted โ€” everything runs locally

Example โ€” 5 incidents: 12, 15, 22, 45, 60 minutes

MTTR (mean)
30.8 min
Median
22 min
P90
60 min
Incidents
5

MTTR Calculator (SRE)

The MTTR Calculator takes a timestamped incident log and returns MTTR, MTTD, MTTF, MTBF, and an availability percentage estimate, with SRE-specific framing throughout.

โ€ข Compute MTTR for a quarterly SRE review from raw incident timestamps

โ€ข Separate MTTD from MTTR to find whether detection or resolution is the bottleneck

โ€ข Estimate service availability from incident history to validate against SLO targets

โ€ข Build a reliability scorecard for engineering team planning sessions

Uptime, incident, and on-call management. Better Stack provides status pages, incident management, and on-call scheduling for engineering teams.View options with Better Stack
What does this tool tell you?
The MTTR Calculator takes a timestamped incident log and returns MTTR, MTTD, MTTF, MTBF, and an availability percentage estimate, with SRE-specific framing throughout.
What affects the result most?
MTTR (Mean Time To Repair/Resolve) = average time from incident detection to full resolution. MTTD (Mean Time To Detect) = average time from incident start to detection/alert fire. MTTF (Mean Time To Failure) = average time between end of one incident and start of next.
How should I use the result?
The calculation is deterministic โ€” the same inputs always produce the same output โ€” so the most useful workflow is to vary one input at a time and see which factor moves the result most. That tells you where to focus your attention before committing to a decision.
MTTR is a symptom. The pattern behind it is the problem. The DevOps Health Pack benchmarks MTTR across your incident history โ€” shows which services are slow to recover and why.
View recovery pattern analysis โ†’
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