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Supported CI Providers

GitHub Actions

GitLab CI

Buildkite

If your provider is not listed here, please open an issue or contact us on Discord.

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Benefits of CI Performance Testing

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Catch Regressions Early

Performance issues are detected immediately when code is committed, not weeks later in production.
Integrating performance tests into your CI pipeline creates an automated safety net that catches performance regressions as soon as they’re introduced. Instead of discovering slowdowns after deployment, your team gets immediate feedback during code review.

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Maintain Performance Standards

Set performance thresholds to automatically fail builds that introduce significant regressions.
CI-based performance testing enforces consistent performance standards across your codebase. You can configure performance checks to automatically block merges when benchmarks exceed acceptable regression thresholds, ensuring your application maintains its performance characteristics over time.

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Streamlined Development Workflow

Performance testing in CI eliminates the friction of manual performance validation:
  • Automated execution: Benchmarks run automatically on every pull request
  • Consistent environment: Tests execute in standardized CI environments, reducing variability
  • Zero maintenance overhead: No need to remember to run performance tests manually
  • Team visibility: Performance results are shared directly in pull request comments

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Historical Performance Tracking

CodSpeed maintains a complete history of your application’s performance evolution across all commits and branches.
Your CI pipeline becomes a continuous performance monitoring system, building a comprehensive history of how your application’s performance changes over time. This historical data helps identify performance trends and understand the impact of specific changes.

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Data-Driven Optimization

CI performance testing provides the metrics needed to make informed optimization decisions:
  • Compare performance across different implementations
  • Validate that optimizations actually improve performance
  • Identify which changes have the most significant performance impact
  • Track performance improvements over multiple iterations