What is MTBF?

MTBF, or Mean Time Between Failures, is the average operational time a repairable system runs between one unplanned failure and the next. It is the primary reliability metric used in software engineering, IT infrastructure, and systems operations to measure how stable a system is under normal operating conditions.

Note: Mean time before failure is a widely used variant of the same term. Both phrases describe the same calculation. MTBF applies to repairable systems specifically; for non-repairable components such as ephemeral cloud instances or hardware chips, the equivalent metric is Mean Time to Failure (MTTF).

MTBF does not measure how long a system lasts in total. It measures how reliably it operates between incidents. A higher MTBF indicates fewer failures per unit of time.

How to Measure MTBF

MTBF is calculated by dividing total operational uptime by the number of failures during the same period. Planned maintenance windows are excluded; only unplanned failures count toward this calculation.

MTBF = Total Operational Uptime (hours) / Number of Unplanned Failures

Example: a service runs 4,320 hours over six months and experiences three production incidents. MTBF = 4,320 / 3 = 1,440 hours. That average interval is the baseline to track over time.

Data inputs: incident timestamps from your observability stack (Datadog, PagerDuty), deployment records, and continuous uptime data from your monitoring tool.

How Hivel measures MTBF

Hivel tracks MTBF-related signals in the context of software delivery stability, correlating incident data with deployment events and PR activity. The DORA Metrics screen surfaces Time to Restore Service alongside deployment frequency and change failure rate, giving engineering leaders a complete reliability picture. For broader incident context, Hivel's engineering analytics dashboard correlates failure spikes with deployment patterns and team workload signals.

How to validate your MTBF signals in Hivel

1. Open the DORA Metrics screen and filter by team or repository.

2. Cross-reference Time to Restore incidents with your SCM deployment log.

3. Compare the MTBF trend line against Change Failure Rate to determine whether instability is deployment-driven or infrastructure-driven.

See how Hivel tracks reliability metrics across your engineering org →

MTBF vs MTTR

MTBF and MTTR are the two core axes of system reliability. MTBF tells you how often things break; MTTR tells you how fast you fix them. A team can post a high MTBF (few failures) and still miss SLA targets if MTTR is slow. Availability, the metric that appears in customer-facing SLAs, is derived from both:

Availability = MTBF / (MTBF + MTTR)

Treat them as a pair. Optimizing one without tracking the other gives a false picture of reliability health.

Why MTBF Matters for Engineering Teams

Most engineering teams monitor deployment frequency and cycle time closely. Reliability metrics like MTBF surface only when something breaks. That is the wrong sequence. MTBF is a leading indicator of architecture health.

A declining MTBF trend over 90 days tells you that deployment velocity is outpacing system stability long before a major incident forces the conversation.

Engineering leaders use MTBF to schedule maintenance proactively, justify infrastructure investment, and set SLAs with product and business stakeholders from a position of data rather than guesswork. Teams tracking MTBF alongside change failure rate are far better positioned to identify whether instability is deployment-driven, infrastructure-driven, or linked to team cognitive load.

Hivel surface MTBF-related signals alongside DORA metrics so engineering leaders can see reliability trends in context, not just isolated incident counts.