CI/CD & Pipelines
CI Cache Hit Rate Calculator
Enter cache hit count, dependency install time, and cache restore time to compute hit rate, effective time savings, and cache ROI.
No data is transmitted — everything runs locallyTool
Example — 82% hit rate · 8-min install · 0.5-min restore
Cache hit rate
82%
of last 100 runs
Avg time saved/run
6.15 min
hit rate × (install − restore)
Miss rate
18%
full install runs
Recommendation
Good
target >80%
About this tool
CI Cache Hit Rate Calculator
The CI Cache Hit Rate Calculator computes cache hit rate, effective time savings per run, and monthly saved compute minutes from hit count and install duration.
• Measure cache effectiveness after changing a GitHub Actions cache key strategy
• Calculate time saved per month from a caching configuration change
• Determine whether a 72% cache hit rate justifies the restore overhead
• Model cache savings improvement from switching to content-hash cache keys
Affiliate disclosure
Uptime, incident, and on-call management. Better Stack provides status pages, incident management, and on-call scheduling for engineering teams.
View pipeline performance with Better Stack
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FAQ
What does this tool tell you?
The CI Cache Hit Rate Calculator computes cache hit rate, effective time savings per run, and monthly saved compute minutes from hit count and install duration.
What affects the result most?
Cache hit rate = cache_hits / total_runs — low hit rate means cache key is too specific. GitHub Actions cache key: hashFiles('**/package-lock.json') — invalidated when lockfile changes. Cache size limit: GitHub 10GB per repo, evicts LRU after 7 days of no access.
How should I use the result?
The calculation is deterministic — the same inputs always produce the same output — so the most useful workflow is to vary one input at a time and see which factor moves the result most. That tells you where to focus your attention before committing to a decision.
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