MTTR / MTTA / MTBF Calculator
Calculate Mean Time to Recovery, Mean Time to Acknowledge, and Mean Time Between Failures from your incident data. Compare your performance against DORA industry benchmarks.
Incident Data
MTTR
Mean Time to Recovery
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MTTA
Mean Time to Acknowledge
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MTBF
Mean Time Between Failures
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Industry Benchmarks (DORA Metrics)
| Performance Tier | MTTR | MTTA |
|---|---|---|
| Elite (DORA) | <60 min | <5 min |
| High | 1-4 hours | 5-15 min |
| Medium | 4-24 hours | 15-60 min |
| Low | >24 hours | >60 min |
Automate your incident response
Reduce MTTR by 90% with AI-powered root cause analysis. Free to start.
Understanding Reliability Metrics
MTTR, MTTA, and MTBF are the three core metrics that define your incident response maturity. Together, they tell a complete story: MTTA shows how quickly you detect and respond to problems, MTTR shows how quickly you resolve them, and MTBF shows how often they occur in the first place.
According to the DORA State of DevOps report, elite performing teams recover from incidents in under one hour. These teams also deploy more frequently, have lower change failure rates, and shorter lead times. Improving MTTR is often the highest-leverage investment an engineering organization can make.
The Anatomy of MTTR
MTTR can be broken down into four phases: (1) Detection time — how long before the issue is noticed (improved by monitoring and alerting), (2) Triage time — how long to determine severity and assign responders, (3) Diagnosis time — how long to identify the root cause (typically the longest phase), and (4) Resolution time — how long to implement and verify the fix.
Most teams focus on reducing resolution time, but the biggest gains often come from reducing diagnosis time. This is where AI-powered root cause analysis delivers the most value — automating the investigation that typically takes senior engineers 30-60 minutes of manual log and metric analysis.
How Uptimes.ai Transforms Your MTTR
Uptimes.ai reduces MTTR by 90% by automating the most time-consuming phase: diagnosis. When an incident occurs, our AI agent immediately investigates — checking Kubernetes pod states, querying metrics from Datadog and Prometheus, reviewing recent deployments via GitLab, and analyzing service dependencies. The result is a structured root cause analysis delivered to your team within minutes, not hours.