How to zero Docker build time using remote cache

Why Docker builds take so long in CI/CD

When writing a Dockerfile, every command like RUN or COPY creates an isolated layer in the file system. Docker is smart: if the command and copied files haven't changed since last time, it simply reuses the result stored in memory. On your local machine, this works perfectly and subsequent builds feel instantaneous because everything is already saved on your disk.

However, this magic disappears when we send code to continuous integration and continuous delivery servers, known as CI/CD. Environments like GitHub Actions or GitLab CI typically run each task in a clean, brand new virtual machine. In practice, this means the server starts working from scratch, knowing absolutely nothing about previous builds, throwing away all layer history and forcing the computer to compile everything again.

The invisible bottleneck of lost context

Imagine you updated just a single line of text in your web application interface. Logically, the container engine should update only this small detail and finish the process in a few seconds. Yet, without saved history, the CI/CD server needs to download heavy dependencies, reinstall libraries, and reprocess entire blocks of code that suffered no changes at all.

This daily waste consumes precious minutes from dozens of developers while inflating cloud infrastructure costs. To mitigate this problem and ensure the same agility we have on our workstation, we need to teach the remote server where to fetch layer history from a centralized location. As we discussed in our guide on how to design high resilience in modern infrastructures, planning state and cache storage is the secret to avoiding operational bottlenecks and maintaining predictable workflows.

How remote caching works in practice

The concept of remote caching consists of separating building intelligence from the data generated by it. Instead of storing results only on the local hard drive of the ephemeral virtual machine, we configure the build tool to send and fetch compiled layer pieces from an internet-accessible repository, such as an image registry or a dedicated object storage.

When the CI/CD pipeline starts, it downloads only the metadata and references needed to validate what changed. If the file tree matches the previous one, Docker pulls the ready layer from the remote server instead of running the command again. In practice, this turns a sluggish ten-minute process into a routine taking only seconds, considerably accelerating the delivery cycle for end users.

Configuring BuildKit to export and import cache

To leverage this technology, we need to enable BuildKit, the modern Docker build mechanism that replaces the old builder and brings native support for advanced caching features. BuildKit allows defining clearly where cache should be stored through specific command-line parameters.

Below is a practical example of how to trigger builds using GitHub Actions with registry-based cache export and import:

name: Build Docker Image
on: [push]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      - name: Login to Docker Hub
        uses: docker/login-action@v3
        with:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_TOKEN }}
      - name: Build and push
        uses: docker/build-push-action@v5
        with:
          push: true
          tags: user/my-app:latest
          cache-from: type=registry,ref=user/my-app:cache
          cache-to: type=registry,ref=user/my-app:cache,mode=max

In this configuration snippet, cache parameters inform the system where to look for previous layers and where to send new blocks generated after the successful completion of the compilation task.

Choosing the ideal backend for your scenario

There are different ways to store remote cache, and the correct choice depends on the infrastructure your team already uses daily. The registry-based driver, known as type=registry, is the most popular because it leverages the same image repository where final software will be published, simplifying credential and permission management.

Another alternative widely used in corporate environments demanding high performance is storage in cloud object services, like Amazon S3 or compatible equivalents. This approach avoids overloading the image registry with temporary compilation data and offers granular control over retention and automatic cleanup of old files no longer accessed by development teams.

Final thoughts on speed gains and maintenance

Implementing remote cache in CI/CD pipelines is a game changer for teams seeking operational efficiency and fast delivery cycles. Although it requires careful initial configuration of credentials and BuildKit parameters, return on investment appears immediately through drastic reductions in developer wait times and savings on cloud computing resources.

Maintaining this optimized workflow ensures that infrastructure keeps pace with codebase growth without becoming an obstacle to innovation. By eliminating idle compilation times, your organization gains the agility to push fixes and new features into production with absolute safety and predictability.