Why Your CI Is Slow (and 9 Practical Fixes to Speed It Up)

Dec 15, 2025

Feeling stuck waiting on builds? Practical, hands-on fixes to diagnose slow CI builds and shave minutes — or hours — off your pipeline without heroic rewrites.

Written by: Arjun Malhotra

Waiting on a build again? You’re not alone.

You push a small change, go make tea, and come back to find your CI job is still crawling. It’s the kind of friction that quietly chips away at momentum: context switching, lost flow, and a growing backlog of PRs. Slow CI builds are usually not a single villain — they’re a pile-up: cache misses, oversized test suites, noisy setup steps, and surprising network gotchas.

I’ve seen teams shave 50–90% off pipeline times with focused changes that don’t require re-architecting everything. Here’s a practical guide to diagnosing and fixing slow CI builds — real steps you can try today and the trade-offs to expect.

Where the time actually goes

Before changing anything, get curious about where your pipeline spends time. CI runs break down into a few common buckets:

• Provisioning runners and installing dependencies. Many CI systems spin up fresh containers or VMs per job — that setup cost adds up.
• Build and compile steps. Languages and compilers behave differently; incremental builds help if your toolchain supports them.
• Tests (unit, integration, end-to-end). E2E tests are great feedback but often the slowest.
• Test and build artifacts: uploading caches, artifacts, and logs over the network.
• Flaky retries and sequential jobs. Parallelism that’s not used, or retries for flaky tests, inflate end-to-end time.

Run a few recent builds and annotate timestamps. Most CI providers show step timings; export a few JSON logs if you can and scan for consistently long steps. That simple inventory — a list of the top 3 slowest steps — will guide everything that follows.

Quick fixes that cut minutes (do these first)

These four changes are low effort and high impact. Try them before touching architecture.

1. Cache dependencies effectively

   • Cache package managers (node_modules, pip cache, go/pkg/mod) between builds. Use a stable cache key pattern: language-version + lockfile hash.
   • Avoid caching fragile directories that change every run (build outputs with timestamps).

2. Use a warmed-up base image

   • If your jobs repeatedly install the same system packages, consider a custom Docker image with those preinstalled. The first push takes work, but every build after is faster.
   • For hosted CI, use the provider’s recommended images — they’re optimized for speed.

3. Parallelize independent steps

   • Split linting, unit tests, and static checks into separate jobs that run concurrently. You’ll reduce wall-clock time without touching test code.
   • Be mindful of parallelism quotas on your CI plan; move expensive E2E tests to scheduled runs if needed.

4. Fail fast and run the right tests

   • Run quick checks (lint, unit) early and block merge if they fail. No need to run slow integration suites on a trivial formatting change.
   • Use changed-file detection to run a subset of tests when possible — a focused test matrix often covers 80% of quick feedback needs.

Use the keyword naturally: when teams focus on these cabinets, many common slow CI builds shrink dramatically.

Deeper changes for consistent speed

If you still see long runs after quick wins, the next layer is process and tooling.

1. Split the pipeline by intent

   • Create “fast” pipelines for PR feedback (lint, unit tests) and “full” pipelines for merge or nightly runs (integration, e2e, security scans).
   • This reduces the waiting time for reviewers and keeps CI costs predictable.

2. Optimize test suites

   • Parallelize tests across multiple workers when tests are CPU-bound. Many frameworks support shardable test runners.
   • Identify and fix slow tests. Add a test-time budget: any unit test over N ms gets flagged for review.
   • Mock external dependencies where appropriate. Real network calls are slow and brittle.

3. Use artifact caching smartly

   • Persist build outputs between stages (e.g., compiled bytecode) to avoid rebuilding the same assets.
   • Store large, infrequently changing artifacts in an object store and reference them by checksum.

4. Embrace incremental builds

   • Tools like Bazel, Gradle’s configuration caching, or Makefiles with proper dependency graphs let you rebuild only changed pieces.
   • Incremental build systems have a learning curve, but they pay off for monorepos or large codebases.

These changes require discipline: tests must be stable, caching keys predictable, and build artifacts reproducible. But they move you from ad-hoc speedups to durable, repeatable results.

How to actually start (a lightweight plan to get momentum)

If this feels like a lot, follow these concrete first 10 steps — you can complete this in a few hours to a couple of days and see immediate gains:

1. Pick one slow CI workflow (the one that blocks most PRs).
2. Record recent run times and identify the top 3 slowest steps.
3. Add timestamps to your pipeline steps if they don’t already show them.
4. Implement caching for your package manager with a cache key that includes the lockfile hash.
5. Move linting and unit tests into separate parallel jobs.
6. Replace any network calls in unit tests with lightweight mocks.
7. Introduce a warmed Docker image for the base environment if you install OS-level packages repeatedly.
8. Split the pipeline: fast PR checks vs full merge checks.
9. Run the full test suite on a nightly job to catch integration regressions early.
10. Measure again — compare median build times, not just a single run.

Small wins compound. I often see teams reduce PR feedback time from 20–30 minutes to under 5 minutes with these changes.

Measuring wins and avoiding regressions

Metrics are how you keep the problem solved. Track these over time:

• Median and 90th percentile pipeline time for PR builds.
• Time-to-first-failure (how long before a failing build surfaces).
• Queue time (waiting for runners due to concurrency limits).
• Cache hit rate for critical caches.

Add an automated alert if median PR build time climbs above your target. A simple Slack report or a dashboard that shows pipeline trends is enough. This keeps speed as an ongoing objective rather than a one-time project.

Common pitfalls and how to avoid them

• Over-caching: burying bugs behind stale cached dependencies. Use conservative cache invalidation strategies and include lockfile/hashes in keys.
• Flaky tests masquerading as slowness: flaky tests often cause retries or long waits. Invest in stability before optimizing for speed.
• Premature parallelism: spreading tests across many workers without considering cost can blow budgets. Start small and measure cost vs time saved.
• Ignoring developer experience: don’t sacrifice diagnostic logs or reproducibility for raw speed. Keep artifacts and logs accessible when a build fails.

Wrapping up

Slow CI builds don’t have to be the constant drag on your team. Start with measurement, apply friction-light fixes (caching, parallel jobs, warmed images), then move to deeper optimizations like splitting pipelines and improving test suites. Aim for predictable, incremental improvements — shave minutes off the common paths first, and let that momentum fund larger changes.

If you want, tell me what your current median PR build time is and the slowest step you see — I can suggest the most impactful next move for your exact setup.