Hey everyone! I've been wrestling with Docker build times in our marketing automation pipelines lately, and it's really pushed me to dig into the caching specifics of these two platforms. We're a mid-sized team (~20 devs + marketing ops) with a monorepo that's getting pretty hefty. Our pipelines build and push a bunch of Docker images for different services.
I've used both GitHub Actions and GitLab CI for this, and my *experience* is that GitLab's caching feels more robust for this specific use case, but I'd love to hear if others have different benchmarks.
Here’s what I’ve observed:
* **GitLab CI** has that `docker buildx`-based **cache-from/cache-to** pattern using the GitLab Container Registry. You can push layered cache as an image, which subsequent builds can pull from. The cache feels "sticky" and survives across pipelines well.
* **GitHub Actions** relies heavily on the `actions/cache` strategy for the Docker build cache directory. It's simpler to set up, but I've had more issues with cache misses, especially when multiple branches are active. The cache is also scoped to the branch by default, which can be good or bad.
My rough benchmark for our main service image:
* **GitLab CI (with layered caching):** ~2.5 minutes on a cache hit, ~8 minutes on a full miss.
* **GitHub Actions (using actions/cache):** ~4 minutes on a good hit, but it's inconsistent—sometimes it balloons back to ~9 minutes for unclear reasons.
Has anyone run a similar comparison? I'm particularly curious if you've found ways to make GitHub Actions caching more reliable for Docker, or if you've tweaked GitLab's settings for even better performance. The integration with the rest of our sales engagement tools also plays a role, so the caching efficiency really impacts our whole deployment rhythm.
Less hype, more data.
I'm a senior platform engineer at a 350-person SaaS company in the adtech space; our entire CI/CD for a polyglot monorepo (Node, Go, Python, Docker) runs on GitLab CI, with over 150,000 monthly pipeline jobs, so caching performance is a direct cost and velocity driver.
1. **Cache Persistence and Scope:** GitLab's registry-based cache is an artifact you can explicitly name and retain, surviving pipeline failures and even being shared across project forks. GitHub's `actions/cache` uses a key-based lifecycle tied to the workflow; its branch-scoping and 7-day default eviction on cache hits led to more frequent cold builds in our multi-branch development model.
2. **Multi-arch and Buildx Integration:** GitLab CI's documentation directly integrates the `docker buildx` cache-to/from pattern with its container registry. You can push and pull cache for both `linux/amd64` and `linux/arm64` in the same step. With GitHub Actions, achieving this requires more manual `docker/config.json` setup and reliance on third-party actions, which introduced flakiness for us.
3. **Network Performance and Location:** GitLab's cache is stored as a regular image layer within the same registry as your final images, so pull performance is identical. In my last shop using GitHub, we observed cache pull times from `actions/cache` storage (hosted on Azure) could vary by 30-40 seconds depending on the runner's region, while the final image push to GitHub Container Registry was consistently faster.
4. **Cost Structure and Eviction:** GitHub Actions' free tier offers 10 GB of cache storage per repository, after which you must manually manage eviction. GitLab.com's free tier imposes a 5 GB per *project* limit on registry storage (which includes your cache images and any deployed images), but paid tiers start at $19/user/month and provide significant capacity. For a team of 20, the cost delta is material.
Given your monorepo and team size, I'd recommend GitLab CI for this specific Docker caching use case, provided you are already using or willing to use GitLab's container registry. If your deployment targets are locked into GitHub's ecosystem or you heavily utilize GitHub's Actions marketplace for other tasks, then GitHub Actions is viable, but you should tell us your monthly pipeline execution count and whether you use a single registry or multiple external ones.
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