--- title: "Reuse Docker build layers across runners" description: "Restore image layers from a persistent cache so new runners can avoid rebuilding unchanged stages." url: https://starsling.dev/ci-playbooks/techniques/reuse-docker-layers canonicalUrl: https://starsling.dev/ci-playbooks/techniques/reuse-docker-layers --- # Reuse Docker build layers across runners Restore image layers from a persistent cache so new runners can avoid rebuilding unchanged stages. ## How to choose an approach Compare the examples by repository setup, prerequisites, and risks before adopting one. Each company implemented this in its own environment; measure each change against your own baseline. - **[Spotify](https://starsling.dev/ci-playbooks/spotify#registry-layer-cache)**: Use it when One workflow builds images with a layer cache, and another builds from the same Dockerfile without one. Check before adopting: Release builds read the cache that default-branch CI writes, so keep write access to trusted builds. - **[Stripe](https://starsling.dev/ci-playbooks/stripe#cache-docker-layers)**: Use it when Container dependency layers rebuild on every ephemeral runner. Check before adopting: Keep registry credentials and cache writes inside the trusted boundary, and verify cold and warm builds produce equivalent images. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Reuse Docker build layers across runners. Restore image layers from a persistent cache so new runners can avoid rebuilding unchanged stages. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/reuse-docker-layers.md Inspect this repository’s CI workflows, build and test commands, dependencies, and recent run timings. Identify where this technique applies and explain the expected benefit using this repository’s evidence. Measure a baseline from recent comparable runs: elapsed time, queue time, runner minutes and failures where available. If data is unavailable, say what cannot be measured. Compare relevant company approaches, checking prerequisites, platform differences and tradeoffs. Rank applicable options by evidence, fit and measurable opportunity; do not present company results as predictions for this repo. Recommend the smallest safe change, show what checks and fallback paths remain, and ASK ME WHICH CHANGE TO APPROVE BEFORE EDITING ANY FILES. Only after approval, work on a new feature branch, make one reversible change, and open a reviewable pull request. Never merge or push to a default or protected branch. Keep required checks, test coverage, deployment and release behavior intact. Validate the change and compare equivalent runs where possible; report limitations and what should be monitored after merge. ```` ## How companies used this technique Read what each company changed and follow the original sources. ### Spotify: Restore image layers from the registry cache Confidence's CI build on pushes to main writes its Docker layer cache to the container registry. The change builds the release deployer image from a target in the shared Dockerfile and adds cache-from for that registry cache, so the release build restores the layers CI already built. Evidence: Source PR/commit evidence Use it when: One workflow builds images with a layer cache, and another builds from the same Dockerfile without one. Before applying it: Release builds read the cache that default-branch CI writes, so keep write access to trusted builds. [Read more in the Spotify playbook](https://starsling.dev/ci-playbooks/spotify#registry-layer-cache) [Primary source](https://github.com/spotify/confidence-resolver/pull/132) ### Stripe: Persist Docker build layers across runners Stripe sync-engine added BuildKit registry caches for its image builds, separated by architecture, so the dependency install layer is reused and only the source copy layer rebuilds. Evidence: Source PR/commit evidence Use it when: Container dependency layers rebuild on every ephemeral runner. Before applying it: Keep registry credentials and cache writes inside the trusted boundary, and verify cold and warm builds produce equivalent images. [Read more in the Stripe playbook](https://starsling.dev/ci-playbooks/stripe#cache-docker-layers) [Primary source](https://github.com/stripe/sync-engine-fork/pull/223) ## CI optimization is continuous work. Apply this technique yourself, or use StarSling Runners for faster GitHub Actions and optional agent-driven optimization PRs, which teams on eligible paid plans can turn on. [Get self-improving CI](https://starsling.dev/products/runners) ## Related techniques ### [Stop downloading the test image on every build](https://starsling.dev/ci-playbooks/techniques/keep-image-on-runner) Cache the container image the tests run in, so each build reuses the copy it already has. 1 implementation · 1 company (Ramp) ### [Bake stable dependencies into runner images](https://starsling.dev/ci-playbooks/techniques/prebuild-runner-images) Prepare versioned system dependencies and build downloads once in the runner image instead of installing them on every job. 2 implementations · 2 companies (Anthropic, Linear) ### [Cache package downloads and dependency stores](https://starsling.dev/ci-playbooks/techniques/reuse-package-downloads) Restore package-manager downloads or dependency stores while keeping a locked install that validates the requested dependency graph. 7 implementations · 6 companies (DoorDash, Linear, Shopify, Spotify, Stripe, Uber) ## Company Playbooks ### [Anthropic](https://starsling.dev/ci-playbooks/anthropic) Run only affected tests ### [DoorDash](https://starsling.dev/ci-playbooks/doordash) Run build and test in parallel, sharded across CI agents ### [Linear](https://starsling.dev/ci-playbooks/linear) Benchmark the same job on faster hardware ### [Mastra](https://starsling.dev/ci-playbooks/mastra) StarSling runners for the slowest test workflow ### [OpenAI](https://starsling.dev/ci-playbooks/openai) Rank CI fixes by the minutes they save ### [Ramp](https://starsling.dev/ci-playbooks/ramp) Move a CPU-bound test suite to a faster CPU ### [Shopify](https://starsling.dev/ci-playbooks/shopify) Let an agent iterate against one CI metric ### [Spotify](https://starsling.dev/ci-playbooks/spotify) Build arm64 on native arm64 runners ### [Stripe](https://starsling.dev/ci-playbooks/stripe) Run only the tests a change can reach ### [Uber](https://starsling.dev/ci-playbooks/uber) Build the merge-queue speculations most likely to be needed ## Related guides Implementation guides for this technique. ### [Cache workflow dependencies](https://starsling.dev/best-practices/github-actions/cache-dependencies) Choose dependency cache paths and keys for your package manager. ### [Cut repeated test setup](https://starsling.dev/best-practices/github-actions/cut-repeated-test-setup) Share expensive setup at the scope your tests need. ### [Share setup steps across jobs](https://starsling.dev/github-actions/optimizations/avoid-duplicated-setup) Centralize repeated setup definitions and remove duplicate or unused steps. ### [Optimize npm installs](https://starsling.dev/github-actions/optimizations/optimize-npm-install) Cache npm dependencies and large downloads and remove unused tool installs. ### [Optimize pnpm installs](https://starsling.dev/github-actions/optimizations/optimize-pnpm-install) Set up the pnpm store cache in the right order and scope recursive commands.