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Find what is making the workflow slow
Start with representative workflow runs. Work out whether elapsed time is dominated by dependency installation, compilation, tests, serial job dependencies, or waiting for a runner. Caching chiefly targets repeated downloads or regeneration; parallel jobs help when independent work is being run one task at a time. Neither guarantees a fixed time saving: measure run durations and cache hits in your repository.
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Cache dependencies with keys tied to lockfiles
Use a cache for package-manager data or other expensive-to-recreate files that can be reused across runs. GitHub-hosted runners start from clean runner images, so repeatedly downloading dependencies can add runtime and network use. GitHub’s dependency caching guide covers package managers such as npm, Yarn, Maven, and Gradle.
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Build the key from the operating system or other relevant tool context and dependency-defining inputs, especially the lockfile. When those inputs change, the key changes, so the workflow can create a cache for the new dependency set. Add restore-key prefixes from most specific to least specific: an exact-key miss can then fall back to a related cache. GitHub documents matching, restore keys, and branch scope in its dependency caching reference.
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A cache is an optimization, not the source of truth. Configure installation so the job downloads or regenerates required files normally if no cache is available. A miss, eviction, or unavailable cache should not fail the build.
Check cache size and churn
As documented by GitHub in 2026, the default total cache storage is 10 GB per repository, and entries not accessed for more than seven days are removed. Organization settings can change the configured limit or retention, so check the settings that apply to your repository. GitHub’s Actions limits page also lists cache-operation rate limits; these limits and defaults can change.
Choose a cache or an artifact based on the job
Caches and artifacts have different purposes. A cache is for reusable dependencies or intermediate files; an artifact preserves a job’s output or makes it available to another job. For example, cache downloaded packages between runs, but upload a build product or test report as an artifact when it needs to be retained or handed off. See GitHub’s documentation on dependency caching and workflow artifacts.
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|---|---|---|
| Dependency cache | Reusable packages or intermediate files across workflow runs | May miss, be scoped, or be evicted; the job must be able to recover without it. |
| Artifact | Preserving a job’s outputs or passing files between jobs | Represents outputs to retain or share, rather than a reusable dependency cache. |
| Parallel jobs or matrix | Independent builds, tests, or platform/version combinations | Can reduce elapsed time when runners are available, while using more concurrent resources. |
| Concurrency group | Preventing conflicting runs or dropping stale work | Can serialize work or cancel pending runs; it is not a way to increase parallelism. |
Run independent jobs in parallel
GitHub Actions jobs run in parallel by default. Separate independent test suites, platform builds, or version checks into jobs, or use a matrix when the same job needs to run across combinations. Add needs only where one job genuinely requires another job’s result; unnecessary dependencies create a serial chain. GitHub’s workflow syntax reference explains job dependencies and matrix behavior.
Control matrix fan-out
A matrix expands a job across configured values, such as operating systems or runtime versions. Set max-parallel when launching every combination at once would exceed useful runner capacity, burden a service, or use more resources than the team intends. Matrix jobs are not guaranteed to execute in a particular order. GitHub also documents how to run variations of jobs.
GitHub’s limits documentation, checked in 2026, caps a matrix expansion at 256 jobs per workflow run. The same page lists standard GitHub-hosted runner concurrent-job totals by plan: Free 20, Pro 40, Team 60, and Enterprise 500, with separate macOS caps. These are service limits, not a guarantee that a repository can use all slots simultaneously; account plan, runner type, and availability matter. Check the current limits page before planning a large matrix.
Use concurrency groups to prevent waste, not to speed up work
The concurrency setting controls which matching jobs or workflow runs may overlap. It is useful when deployments must not conflict or when a newer commit makes an older check obsolete. It does not create parallelism: a concurrency group can serialize work. Under the default behavior, one run may be pending in a group; a newly queued run cancels the earlier pending run. Where runs need to wait in order instead of replacing pending work, use GitHub’s documented queue option when it fits the workflow. See GitHub’s concurrency guidance.
Protect cache contents and permissions
Never put secrets, tokens, or credentials in a cache. A workflow that can read an entry receives its contents, and cache scope is based on branches and tags rather than job identity. Treat restored files as untrusted, particularly when a lower-trust event can access cache data. GitHub describes read-only defaults for less-trusted triggers; granting cache write access where it is not needed can increase cache-poisoning risk. Review the cache security guidance in Dependency caching before changing permissions.
Measure whether the changes help
- Compare representative run durations before and after changes, rather than assuming a cache or a larger matrix will make runs faster.
- Check whether the intended cache keys are producing useful hits and whether changing dependencies correctly produces a new key.
- Look for work that can overlap without violating prerequisites, then encode only genuine ordering with
needs. - Balance elapsed time against runner availability, Actions minutes, external service capacity, cache storage, and cache churn.
GitHub’s documentation describes mechanisms and service limits, not a guaranteed speedup for a particular repository. The best configuration depends on where that workflow spends time and which resources its account can use.
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