Every online service has one job: stay available. When something goes wrong, users feel it right away. Mean time between failures (MTBF) is a metric that measures the duration of time a service remains available between failures. It provides developers, team leads, and SREs with a single, trackable number that links code quality to user trust and revenue.
What “Failure” Means in Software
Hardware fails when, for example, a motor seizes. Software fails when it stops meeting its service-level objective (SLO). Typical software failures include:
- An unhandled exception that kills a container
- A bad deploy that forces a rollback
- A data store outage that prevents writes
- A full region loss when multi-region traffic cannot fail over
Since most services can be restarted or redeployed after a crash, they’re treated as repairable assets. For repairable assets, MTBF is the proper reliability metric. (MTTF [mean time to failure] is for non-repairable items that are discarded after a single failure).
The Core Mean Time Between Failure Calculation
MTBF = Number of failures / Total uptime
Uptime refers to every minute that the service is meeting its SLO, from the moment the last incident was completely resolved. Leave out:
- time spent restoring the service
- any post-mortem or follow-up work
- planned maintenance windows
Those periods do not reflect normal, healthy operation, so they should not be included in the uptime total.
How to Calculate Mean Time Between Failure in Practice
- Clearly define each failure. Decide what crosses the line: breached latency SLO, HTTP 5xx spike, pager alert, or a full outage. Be consistent.
- Collect clean incident data. Pull start and end times from your monitoring or incident-tracking tool. Tag each entry with the root cause if known.
- Sum the uptime periods. A small script or query can add the healthy windows between incidents.
- Divide by the failure count. The result is the MTBF, expressed in minutes, hours, or days, whatever best fits your release cadence.
Quick illustration
Assume five blocking incidents occurred over a month, with healthy runs of about six days each. Add those healthy windows (roughly 720 hours) and divide by five incidents. In this case, the MTBF is 144 hours, so the team can expect a major outage approximately once every six days unless they make improvements.
Linking MTBF, MTTR, and Availability
MTBF tells you how often things break. Mean time to repair (MTTR) tells you how fast you fix them. Put them together to calculate availability:
Availability = MTBF / (MTTR + MTBF)
For example, if MTBF is 144 hours and MTTR is 0.33 hours, availability is ~99.77%. In this case, increasing MTBF yields the greatest return because MTTR is already minimal.
Common Pitfalls that Skew MTBF
- Counting slow but working systems as failures. Determine whether a degraded state constitutes a failure or merely a warning.
- Mixing staging and production data. MTBF should reflect real user traffic, not test environments.
- Too few data points. Two incidents rarely provide a stable metric; aim for double-digit samples.
- Time-sync drift. If servers disagree on the clock, uptime windows can be wrong by hours.
Strategies to push MTBF higher
- Root-cause fixes. Patch memory leaks, race conditions, and configuration drift that cause repeat incidents.
- Automated testing and static analysis. Catch regressions before they ship.
- Progressive delivery. Use canary, feature flags, or blue-green deploys to limit the blast radius.
- Chaos engineering. Inject controlled faults (node loss, latency spikes) to expose weak recovery paths.
Automate the metric itself. Most stacks stream logs to Prometheus, Datadog, or similar. A short job can pull incident windows, run the mean time between failure calculation, and post the current figure to a dashboard or Slack channel every day.
Setting MTBF Targets That Matter
Tie every MTBF goal to the real cost of downtime. If one minute offline burns $5,000, extending MTBF from six to eighteen days saves about $1.5 million each quarter, enough to justify more tests, deeper observability, or an extra failover region.
Keeping the Data Clean
MTBF is only as reliable as the incident log behind it. Make start and end times mandatory in every ticket, and let your monitoring stack stamp them automatically to avoid typos. Once a month, sweep the log: merge duplicate alerts, link related events into a single failure, and fill any missing fields. Clean data keeps the MTBF trend honest and prevents teams from chasing ghost improvements.
MTBF vs. Mean Time to Failure
The mean time to failure formula (MTTF) applies when an item cannot be repaired. For example, a one-time-use sensor or a fuse. It is simply lifespan divided by unit count. Because microservices, databases, and queues can be restarted, software teams almost always use MTBF instead.
Final Takeaway
For software teams, mean time between failures is more than a historical statistic. It serves as an early warning signal for code quality, deployment safety, and user satisfaction. By following a clear guide on how to calculate mean time between failures, automating that workflow, and focusing improvement work where it matters, you turn MTBF from a passive metric into an active driver of reliability. Keep the number visible, revisit it in retros, and watch it rise as disciplined engineering pays off.
