Build Queue Time
Build Queue Time measures how long a CI/CD pipeline job waits in queue before it starts executing. It reflects the availability of compute resources, the load on the automation system, and the developer’s time to feedback.
Calculation
Build Queue Time is defined as the time between when a job is submitted to the CI/CD system and when it actually begins execution.
The metric is calculated as:
build queue time = job start time – job trigger time
Goals
Build Queue Time helps teams assess how quickly their automation systems respond to developer activity. It answers questions like:
- Are developers waiting too long for builds or tests to start?
- Are our runners, agents, or infrastructure consistently saturated?
- Is CI/CD load affecting delivery pace or developer flow?
Reducing queue time improves responsiveness and shortens feedback loops—especially in fast-moving teams that depend on continuous integration.
Variations
Build Queue Time is sometimes referred to as Job Wait Time, Pipeline Latency, or Automation Scheduling Delay. Common breakdowns include:
- By pipeline type, such as build, test, or deploy
- By queue type, like shared runners vs. dedicated agents
- By service or repository, to track specific bottlenecks
- By time of day, to detect peak CI usage windows
- By triggering event, like PR creation vs. merge
Some teams report both average and 95th percentile queue time to catch spikes or high-congestion outliers.
Limitations
Build Queue Time tracks infrastructure delay—but not execution or success. It also does not explain why the job was delayed, only that it waited.
The metric may also vary based on resource allocation across teams. Without proper segmentation, it can mask disparities in CI performance.
To gain better visibility into automation health, pair this metric with:
| Complementary Metric | Why It’s Relevant |
|---|---|
| Pipeline Run Time | Shows how long builds and tests take once they begin, complementing wait time before they start |
| First-Time Pass Rate | Reveals whether jobs are passing cleanly or being retried, compounding queue congestion |
| Deployment Frequency | Highlights whether queue delays are reducing throughput of completed builds and releases |
Optimization
Reducing Build Queue Time requires scaling CI infrastructure, load-balancing demand, and improving job efficiency.
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Add more CI capacity. Use more runners or agents to handle peak load, especially for large or growing teams
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Prioritize critical jobs. Implement job prioritization or separate queues for merge-blocking and feedback-only pipelines
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Schedule non-blocking jobs off-hours. Move lower-priority builds to run asynchronously or overnight
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Consolidate redundant triggers. Ensure commits, pushes, and branch updates are not triggering unnecessary parallel jobs
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Use caching to reduce queue buildup. Faster jobs clear queues more quickly, caching dependencies and assets helps shorten build time end-to-end
Build Queue Time directly affects how fast developers get feedback. Reducing it removes invisible friction and keeps the delivery engine running at pace with the team.
