How to zero Docker build time using remote cache

Why Docker builds take so long in the cloud

When setting up a continuous integration (CI/CD) environment—the automated systems responsible for testing and packaging our software—Docker is usually the core engine. However, if the CI server lacks the local history of previous images, it has to download everything from scratch or rebuild every single line of your instructions file, the famous Dockerfile. In practice, this means wasting precious minutes just installing basic libraries that haven't changed in months.

To understand the severity of this, think of a construction site where workers have to manufacture their own bricks every time they arrive, instead of using the ones already stacked in the yard. In the container universe, local cache acts like this stack of ready-made bricks. When the server loses this local state—which happens frequently in ephemeral cloud environments—wait times skyrocket and team productivity plummets.

The main villain of this story is the lack of shared persistence between execution nodes. In platforms like GitHub Actions or GitLab CI, each build execution frequently happens on a clean, isolated machine. Because this machine doesn't know what happened in the previous run, it treats everything as an absolute novelty. This is precisely where the remote cache strategy comes in, allowing previously built artifacts to be saved in a location accessible to any machine in your infrastructure.

How layer caching works in BuildKit

BuildKit is Docker's modern image building engine, designed to be faster, safer, and more efficient than the legacy builder. When it processes a configuration file, it divides the process into steps called layers, evaluating whether the code or command of each line has changed compared to what was processed before. If nothing has changed, BuildKit simply reuses the previous result instantly.

To take this intelligence beyond the local machine, BuildKit introduced support for remote cache exporters and importers. This means that instead of saving the result only on the local hard drive, the system can send these compressed layers directly to a container registry, such as Docker Hub, GitHub Packages, or a private repository like Harbor. Thus, the next build can download only the missing pieces over the network, saving bandwidth and processing time.

To ensure the process runs without bottlenecks, it's worth integrating these improvements with complementary delivery strategies, such as those discussed in our analysis on how to achieve zero downtime deployments with Blue-Green. After all, there's no use in speeding up image creation if putting it live still causes disruptions for end users.

Configuring Docker Buildx with registry cache

To get your hands dirty and activate this magic in your pipeline, we need to use the docker buildx command, which enables advanced BuildKit features. The first step involves creating and starting a dedicated builder that supports multiple storage drivers and data export over the network, ensuring the cache is correctly sent to your remote repository.

Below is a practical example of a command that initializes the builder and triggers the image creation process, sending the generated cache directly to a compatible registry:

docker buildx create --name my-builder --use
docker buildx build \
  --builder my-builder \
  --cache-from type=registry,ref=myregistry.com/my-app:cache \
  --cache-to type=registry,ref=myregistry.com/my-app:cache,mode=max \
  --tag myregistry.com/my-app:latest \
  --push .

In this example, the --cache-from parameter tells Docker where to look for existing layers before starting the heavy lifting, while --cache-to defines where to send the new layers generated during the process. Using mode=max ensures that all intermediate steps are stored, maximizing reuse in future runs.

Best practices for organizing your Dockerfile

Configuring the remote registry is only half the battle; the other half depends on how you write your Dockerfile. Docker evaluates cache sequentially: if one line of the file changes, all subsequent lines lose their right to the cache and must be executed again. Therefore, the order of commands matters greatly for the overall efficiency of your continuous delivery pipeline.

A fundamental practice is to separate dependency file copying from the rest of the application source code. In Node.js projects, for example, you should copy only the package.json and package-lock.json files before running the package installation command, leaving the main code copy for a later moment. Thus, if you change only a text file in your application, Docker won't need to download and reinstall all external libraries again.

Furthermore, keeping your development and infrastructure tools organized prevents operational bottlenecks on other fronts, ensuring the workflow remains fluid and predictable. In complex development environments, similar speed and performance optimizations, such as those applied in the article on slow database and how to find disk IOPS bottlenecks in Linux, show that every system layer needs close monitoring to prevent hidden productivity losses.

Final considerations on speed gains in CI/CD

Zeroing build time in containers is no longer a luxury but a basic requirement for teams seeking high delivery frequency and fast feedback. The combination of BuildKit with remote cache registries completely transforms the dynamics of continuous integration servers, eliminating unnecessary waiting and optimizing computational resource consumption in the cloud.

By applying intelligent structuring to your Dockerfile and correctly configuring cache export parameters, your team gains agility to focus on what truly matters: writing quality code and delivering value to the end user with safety and predictability.