MTBF Calculator
Calculate mean time between failures from total uptime and failure count.
About MTBF Calculator
MTBF = total_uptime ÷ number_of_failures — the average time the system runs between failure events during the observed period. Annualizing it (8760 hours ÷ MTBF) projects expected failures per year at the same failure rate, and applying the exponential reliability model (assuming failures follow a Poisson process) gives the probability of experiencing at least one failure within a year, which is a more intuitive number for risk conversations than raw MTBF hours.
Formula
MTBF_hours = total_operational_hours / number_of_failures
- total_operational_hours
- Total hours the system was operational in the period
- number_of_failures
- Number of failures/incidents in the period
Worked example
With the default values below, MTBF Calculator returns MTBF of 730.0. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Total Uptime Observedaccepts 0.01–10,000,000 hours
- 4,380 hours
- Number of Failuresaccepts 1–1,000,000 failures
- 6 failures
Results
- MTBF
- 730.0 hours
- MTBF
- 30.42 days
- Expected Failures per Year
- 12.00 failures/year
- Annualized Failure Probability
- 100.00%
Step by step
- 1MTBF: total uptime ÷ number of failures= 730.0h (30.42 days)
- 2Expected failures per year= 12.00 failures/year
- 3Annualized failure probability= 100.00%
Assumptions and limits
- The result is derived only from the 2 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax or fees or charges into account.
- Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
How is MTBF different from MTTR?
MTBF measures how often failures happen (time between them), while MTTR measures how long it takes to recover once a failure occurs — a system can have excellent MTTR (fast recovery) but poor MTBF (frequent failures), or vice versa, and both matter for overall availability.
Does higher MTBF always mean higher availability?
Not necessarily on its own — availability depends on both MTBF and MTTR: availability ≈ MTBF / (MTBF + MTTR), so a system with high MTBF but very slow recovery can still have worse availability than one with more frequent but much faster-to-fix failures.
Why use an exponential model for annualized failure probability?
It's the standard assumption in reliability engineering when failures are memoryless and roughly independent (a Poisson process) — it's an approximation, and real systems can deviate (e.g. wear-out failures cluster over time), but it's a reasonable default absent more specific failure-distribution data.
What's a good MTBF for a production service?
There's no universal target — it depends entirely on your availability SLO. Back-calculate the MTBF needed to hit your SLO given your typical MTTR, rather than chasing an arbitrary MTBF number in isolation.