Pipeline Run Time
Pipeline Run Time measures how long it takes for a CI/CD pipeline to complete from start to finish. It reflects the duration of automated build, test, and deployment stages and is a key indicator of delivery speed and feedback efficiency.
Calculation
Pipeline Run Time is defined as the elapsed time from the start of a CI/CD job to its successful or failed completion. This includes time spent in all configured stages, such as build, test, artifact creation, and deployment.
The metric is calculated as:
pipeline run time = pipeline end time – pipeline start time
Goals
Pipeline Run Time helps teams assess how quickly changes are validated and delivered through automation. It answers questions like:
- Are our feedback loops fast enough to support rapid iteration?
- Are pipelines delaying review, merge, or deployment decisions?
- Which stages are causing the most execution time?
Shorter run times support faster cycles, tighter feedback, and more confident shipping. Long pipelines create friction and reduce the likelihood of continuous delivery.
Variations
Pipeline Run Time may also be referred to as Build Duration, CI Run Time, or Automation Execution Time. Common segmentations include:
- By stage, such as build, unit test, integration test, or deploy
- By service or repository, to isolate slow pipelines
- By team or trigger type, e.g., pull request vs. scheduled job
- By environment, such as test, staging, or production
- By commit size, to examine correlation between scope and execution time
Some teams report both average and 95th percentile run time to understand normal and long-tail durations.
Limitations
Pipeline Run Time shows how long automation takes—but not whether the time is being used efficiently. A fast pipeline may skip important checks, while a long one may include redundant or overly granular stages.
It also doesn’t reveal success rate or test quality. Use alongside stability metrics to ensure performance gains don’t reduce safety.
To better understand pipeline performance, use this metric with:
| Complementary Metric | Why It’s Relevant |
|---|---|
| Pipeline Success Rate | Shows how often pipelines complete without failure, not just how long they take |
| First-Time Pass Rate | Reveals whether failed runs are forcing developers to re-trigger long processes |
| Lead Time for Changes | Highlights how much pipeline time contributes to overall delivery duration |
Optimization
Improving Pipeline Run Time focuses on reducing waste, parallelizing slow steps, and balancing speed with coverage.
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Parallelize test and build stages. Split large suites into smaller jobs that can run concurrently, especially for integration or end-to-end tests
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Cache dependencies and build artifacts. Avoid rebuilding unchanged assets between runs to eliminate unnecessary overhead
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Break up monolithic pipelines. Modularize jobs so that each run only includes the relevant parts of the workflow
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Fail fast. Add quick validations (like linting or syntax checks) early to stop invalid changes before long jobs run
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Monitor slowest steps over time. Track trends in stage duration and optimize the longest-running or most variable steps
Pipeline Run Time affects how fast teams can test, iterate, and deliver. A fast, reliable pipeline unlocks continuous delivery without slowing teams down—or burning them out.
