Mean time between failures

Unplanned outages cost software teams far more than the downtime itself: they drain engineering capacity, erode stakeholder trust, and push squads into reactive firefighting instead of planned delivery. Mean time between failures (MTBF) is the metric that tells you how stable your systems actually are, measured as the average time a system operates before the next failure occurs. For engineering leaders, a declining MTBF is an early warning sign that technical debt, deployment practices, or infrastructure quality are quietly undermining your team's output. This page covers the definition, formula, benchmarks, how to improve it, and how to connect MTBF to the rest of your engineering health data.

Key takeaways

• Mean time between failures measures the average time a system runs without a failure, giving engineering leaders a quantitative signal of system reliability. Teams with low MTBF spend disproportionate time on incidents rather than planned work, which degrades delivery velocity and squad morale over time.
• The formula is MTBF = Total operational time ÷ Number of failures. No universally published DORA benchmark exists for MTBF specifically, but elite teams typically see MTBF measured in weeks or months rather than days, while struggling teams see failures occurring multiple times per week.
• The most common mistake teams make is treating MTBF in isolation. A rising MTBF only tells part of the story if your mean time to recovery (MTTR) is also climbing. Both metrics need to move in the right direction together for your reliability posture to genuinely improve.
• DevStats surfaces deployment frequency, change failure rate, and related reliability signals through its DORA metrics feature, giving you the process-level context needed to interpret your MTBF trend. Connect your tools in under two minutes and start a free trial to see your numbers against benchmarks from 1,000+ engineering teams.

Mean time between failures definition

Mean time between failures is the average amount of time a system operates between one failure and the next. It is a reliability metric: a higher MTBF means your systems fail less often, and your squad spends less time responding to incidents instead of building.

Technically, MTBF = Total operational time ÷ Number of failures, where operational time counts only the time the system is running, not time spent in recovery. It is worth noting that MTBF applies to repairable systems; for non-repairable components, the equivalent metric is mean time to failure (MTTF). In a software context, a low MTBF translates directly to higher incident costs, missed sprint commitments, and compounding pressure on your team's productivity.

Why mean time between failures matters for engineering teams

When squads do not track MTBF, reliability problems tend to stay invisible until they become crises. A squad shipping features at pace while quietly accumulating system instability will eventually hit a wall: incident volume spikes, on-call rotations become unsustainable, and players burn out managing fires instead of doing planned work. By the time the problem is visible to stakeholders, the engineering investment required to fix it has multiplied.

MTBF connects directly to the KPIs most engineering leaders are held to: uptime commitments, sprint predictability, and the ratio of reactive to planned work. If your squad is spending more than 20% of capacity on unplanned work, MTBF is one of the first metrics to interrogate. Teams that track DORA metrics alongside MTBF get a fuller picture, since change failure rate and deployment frequency are leading indicators of where failures are likely to originate. Measurement is the starting point; what your team does with the data is what drives improvement.

How to measure mean time between failures

To calculate MTBF, sum the total time your system was operational during a defined period, then divide by the number of failures recorded in that same period. For example, if your service ran for 720 hours in a month and experienced 6 failures, your MTBF is 120 hours. The data sources you need are your incident management tool (PagerDuty, Opsgenie, or similar), your deployment pipeline for correlating failures to deploys, and your uptime monitoring system.

No single published DORA benchmark exists for MTBF as a standalone metric, since DORA focuses on change failure rate and MTTR as its reliability proxies. The table below reflects qualitative industry patterns rather than fabricated numbers. Benchmarks vary significantly by system criticality, team size, and release model. For context on how your deployment patterns relate to failure rates, the DevStats benchmarks feature lets you compare your team's signals against peers.

Mean time between failures in practice: a real example

A VP of Engineering at a 45-person SaaS company noticed that her platform squad's sprint completion rate had dropped from 82% to 61% over two months. She pulled incident data and calculated MTBF for their core API service: it had dropped from roughly 10 days between failures to under 3 days. Cross-referencing with deployment data, she found that a shift to larger, less frequent releases had increased the blast radius of each deploy. She made the call to move the squad to smaller, more frequent deployments with mandatory automated smoke tests before each release.

Six weeks later, MTBF had climbed back to 8 days and sprint completion had recovered to 78%. She also started reviewing deployment frequency data weekly alongside MTBF, so she could catch the correlation between deploy size and failure rate before it became a pattern again. The data told her where to look; the decision to change the release process was hers.

How to improve mean time between failures

1. Reduce deployment batch size. Smaller, more frequent deployments reduce the number of changes in each release, making failures easier to isolate and roll back. Track your deployment frequency as a leading indicator: if deploys are infrequent, batch sizes are almost certainly large.
2. Invest in automated testing coverage at the integration layer. Most production failures in software systems originate at integration points, not unit-tested components. Add integration and contract tests to your CI pipeline and measure what percentage of failures were caught pre-production versus post-deploy.
3. Run post-incident reviews focused on process, not people. After each failure, document the contributing process factors: gaps in test coverage, missing feature flags, inadequate monitoring. Use the output to build a backlog item in the next sprint. Review your sprint allocation to ensure reliability work is getting scheduled alongside feature delivery.
4. Instrument your system before you need the data. Teams that discover MTBF problems late are usually teams with insufficient observability. Add structured logging, error rate alerting, and latency thresholds to critical paths before the next incident, not after.
5. Track change failure rate alongside MTBF. Change failure rate, one of the four DORA metrics, measures the percentage of deployments that cause a failure in production. If your MTBF is low and your change failure rate is high, the deployment process itself is the primary driver to address.

Mean time between failures vs. mean time to recovery

MTBF and MTTR are both reliability metrics, but they measure different things: MTBF tells you how often failures happen, while MTTR tells you how quickly your squad recovers from them.

Use MTBF to understand whether your systems are getting more or less stable over time, and use MTTR to understand whether your squad's ability to respond when things go wrong is improving. Both metrics together give you a complete reliability picture, and both are reflected in the DORA framework's reliability dimension.