Pipeline Throughput Calculator
Calculate how many pipeline runs per hour your CI/CD system can sustain.
About Pipeline Throughput Calculator
Pipeline throughput is simply completed runs divided by the elapsed measurement period, giving a rate that's easy to track over time and compare across teams or pipelines. Because throughput for a single-pipeline lane is ultimately capped by the slowest stage in the sequence (the classic theory-of-constraints bottleneck), comparing average stage durations pinpoints exactly where to focus optimization effort to raise the ceiling on how fast pipelines can complete.
Formula
throughput = completedPipelines / timePeriodHours
- C
- Completed pipeline runs
- T
- Measurement period in hours
- \Theta
- Pipeline throughput in runs per hour
Worked example
With the default values below, Pipeline Throughput Calculator returns Pipeline Throughput of 7.50. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Completed Pipeline Runsaccepts 0–10,000,000 runs
- 180 runs
- Measurement Periodaccepts 0.01–1,000,000 hr
- 24 hr
- Build Stage Avg Durationaccepts 0–86,400 sec
- 60 sec
- Test Stage Avg Durationaccepts 0–86,400 sec
- 240 sec
- Deploy Stage Avg Durationaccepts 0–86,400 sec
- 50 sec
Results
- Pipeline Throughput
- 7.50 runs/hr
- Bottleneck Stage
- test
- Bottleneck Share of Stage Time
- 68.6%
Step by step
- 1Throughput = completed runs ÷ time period= 7.50 runs/hr
- 2Bottleneck stage = longest average duration= test (240s)
Assumptions and limits
- The result is derived only from the 5 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
Is throughput the same as builds-per-hour capacity?
Not quite — this throughput reflects completed runs actually observed in a period, which is shaped by both pipeline stage durations and available executor capacity, whereas the executor/agent capacity calculators model theoretical maximum capacity independent of observed demand.
Why focus optimization on the bottleneck stage specifically?
In a sequential pipeline, speeding up a non-bottleneck stage doesn't reduce total run time or raise throughput — the bottleneck stage sets the pace, so per theory of constraints, the fastest path to higher throughput is reducing that specific stage's duration or parallelizing it.
How does test parallelism interact with this bottleneck analysis?
If the test stage is the bottleneck (commonly the case), applying the parallel-stage or test-execution-time calculators to split that specific stage across more workers directly attacks the identified bottleneck rather than optimizing stages that aren't limiting throughput.