--- title: "Remove caches that cost more than they save" description: "Measure cache restore and upload time against the work avoided. Remove caches whose transfer cost outweighs their benefit." url: https://starsling.dev/ci-playbooks/techniques/remove-expensive-caches canonicalUrl: https://starsling.dev/ci-playbooks/techniques/remove-expensive-caches --- # Remove caches that cost more than they save Measure cache restore and upload time against the work avoided. Remove caches whose transfer cost outweighs their benefit. ## 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. - **[Linear](https://starsling.dev/ci-playbooks/linear#drop-a-cache-that-costs-more)**: Use it when Comparable measurements show restore and save overhead outweighing the work avoided. Check before adopting: First try a package-manager store or a narrower cache, and separate miss-rate problems from an expensive successful restore. - **[OpenAI](https://starsling.dev/ci-playbooks/openai#costly-cache)**: Use it when Cache save and restore overhead is large and reuse is low. Check before adopting: Check key stability and restore fallbacks first. Removing a cache shortens saves and makes the occasional warm run cold. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Remove caches that cost more than they save. Measure cache restore and upload time against the work avoided. Remove caches whose transfer cost outweighs their benefit. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/remove-expensive-caches.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. ### Linear: Remove a cache when restoring it costs more than rebuilding Linear measured a node_modules cache hit at about 28 seconds to restore, against roughly 7.5 seconds for a filtered install. The cache key depended on a frequently changing lockfile, so Linear removed the cache. Evidence: Documented technique Use it when: Comparable measurements show restore and save overhead outweighing the work avoided. Before applying it: First try a package-manager store or a narrower cache, and separate miss-rate problems from an expensive successful restore. [Read more in the Linear playbook](https://starsling.dev/ci-playbooks/linear#drop-a-cache-that-costs-more) [Primary source](https://linear.app/now/ci-bottleneck-reworked#don%27t-cache-when-it%27s-faster-to-rebuild) ### OpenAI: Drop a cache that costs more than it saves Release-tag runs saved Cargo caches that later tagged runs rarely used. Codex removed the cache step from both release workflows and kept compilation, signing, packaging and publishing as they were. Evidence: Source PR/commit evidence Across 289 release-tag runs: 66,295s spent saving caches against 1,515s restoring them ([Source](https://github.com/openai/codex/pull/11540)) Use it when: Cache save and restore overhead is large and reuse is low. Before applying it: Check key stability and restore fallbacks first. Removing a cache shortens saves and makes the occasional warm run cold. [Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#costly-cache) [Primary source](https://github.com/openai/codex/pull/11540) ## 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 ### [Reduce the setup floor before increasing shard count](https://starsling.dev/ci-playbooks/techniques/setup-cost-limits-sharding) Each extra shard repeats checkout, dependencies and container setup. 1 implementation · 1 company (Linear) ### [Stop writing caches that no later run restores](https://starsling.dev/ci-playbooks/techniques/disable-unused-caches) Remove cache writes and archives that have no consumer, eliminating work without changing the build result. 1 implementation · 1 company (DoorDash) ### [Keep unrelated environment changes out of cache keys](https://starsling.dev/ci-playbooks/techniques/content-based-cache-keys) Include inputs that can change the output while excluding branch identity and unrelated environment churn. 3 implementations · 2 companies (Mastra, OpenAI) ## 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. ### [Bound runner jobs with timeouts](https://starsling.dev/best-practices/github-actions/bound-job-timeouts) Set job and step timeouts using expected durations and failure behavior.