Slow CI/CD builds? How to speed up GitHub Actions cache

Why your software delivery takes too long to run

When the continuous delivery pipeline, the automated process that tests and ships code to production, starts taking twenty minutes to run, the entire team's workflow hits a brick wall. Developers are left waiting for the test robot to finish just to know if their change worked, which destroys focus and daily productivity. In practice, this means that CI/CD slowness drains engineering time and delays urgent bug fixes in the production system.

Most of this wasted time does not come from complex mathematical calculations or running the tests themselves, but rather from repetitive tasks. Downloading thousands of packages from the internet, compiling libraries from scratch, and rebuilding container images that haven't changed in weeks are the primary culprits behind this delay. The secret to reversing this scenario lies in implementing intelligent caching policies, storing intermediate results for future reuse.

How the native GitHub Actions cache works

GitHub Actions provides a built-in tool called actions/cache, designed to store files or directories between different workflow executions. Simply put, it takes a compressed snapshot of folders like node_modules or vendor before wrapping up the job and instantly restores it the next time the trigger fires. This eliminates the need to download heavy libraries over and over again.

For this mechanism to perform well, the system requires a unique key based on the contents of dependency manifest files, such as package.json or go.sum. If the dependency file changes, the system notices, invalidates the old cache, and creates a new one. Otherwise, it reuses what is already saved, cutting installation time from minutes down to mere seconds. If you want to understand more about keeping your environment clean and avoiding space issues, check out our tips on how to clean Docker to prevent unexpected failures.

Optimizing Docker image builds inside the pipeline

Modern applications almost always run inside containers, isolated packages containing everything the software needs to execute. When the pipeline builds a Docker image from scratch on every single change, the process becomes extremely costly because every instruction in the configuration file runs sequentially. To get around this, modern engineering utilizes BuildKit along with the docker buildx command, enabling intermediate layers to be stored in external registries.

In practice, BuildKit can upload and download specific chunks of the image directly from a remote cache hosted in GitHub Packages or compatible services. This means that if only a single line of code changed at the tail end of the application, the system reuses all previous layers without needing to recompile the base operating system or reinstall system dependencies. To deepen your knowledge regarding the behavior of isolated environments under high load, read our article on how to use Worker Threads in Node.js to handle heavy data processing.

To configure Docker caching in GitHub Actions efficiently, use the configuration snippet below in your automation YAML file:

name: Build Docker Image
on: [push]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      - name: Login to GitHub Container Registry
        uses: docker/login-action@v3
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      - name: Build and push
        uses: docker/build-push-action@v5
        with:
          context: .
          push: true
          tags: ghcr.io/user/app:latest
          cache-from: type=gha
          cache-to: type=gha,mode=max

Avoiding common pitfalls in key creation

A frequent mistake when setting up caches is creating excessively broad or generic keys, which contaminates the test environment with corrupted or stale files. When the system stores everything indiscriminately, temporary file changes can invalidate the entire cache, forcing unnecessary downloads on every single commit. The ideal strategy is to use strict hashes tied exclusively to the project's dependency control files.

Additionally, GitHub enforces strict storage limits for caches in public and private repositories, blocking new saves if the ten-gigabyte limit is exceeded. The system automatically prunes older files when space hits the ceiling, but maintaining manual control through good cleanup practices ensures infrastructure predictability. If you need a complementary routine to free up disk space on execution servers, check out the recommendations on how to clean Docker prune automatically.

Final considerations on speed and reliability

Speeding up builds in GitHub Actions radically transforms the development routine, replacing the frustration of waiting with a fast and continuous feedback loop. By combining native dependency caching with advanced Docker layer support via BuildKit, teams eliminate operational bottlenecks and deliver value to users much faster. The time invested in properly configuring these tools pays off exponentially in productivity and stability across the entire software engineering lifecycle.