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GitHub Actions Cache Calculator

Estimate cache hit savings, CI time reduction, and storage costs. Enter your workflow parameters to see the full monthly value breakdown.

✓ Time saved per day & month ✓ Storage cost estimate ✓ Real-time calculation

Workflow Configuration

0%50%100%

First 10 GB free. $0.008/GB/month thereafter.

Monthly Summary

Total Monthly Value (time + storage)

$0.00

Time Saved / Day

0 min

Time Saved / Month

0 hrs

Cache Hits / Month

0

Cache Misses / Month

0

Cost Breakdown

Cache storage (monthly) $0.00
Minutes saved × compute rate $0.00 saved
Effective runs (with cache) 0 min avg
Storage over free tier 0 GB

Cache Efficiency

Hit rate 70%

Good cache efficiency. Most runs skip full dependency install.

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Frequently Asked Questions

How does GitHub Actions cache pricing work?▼

GitHub Actions provides 10 GB of cache storage per repository for free. Beyond 10 GB, GitHub charges $0.008 per GB per month. Cache entries are evicted automatically after 7 days of no access, or when the repository cache storage exceeds 10 GB (oldest entries removed first).

How is cache hit rate calculated?▼

Cache hit rate is the percentage of workflow runs that successfully restore a previously saved cache. A 70% hit rate means 70 out of 100 runs skip the full rebuild and use cached dependencies. Higher hit rates reduce both CI time and compute costs. Typical Node.js/Python projects achieve 60–85% hit rates.

What counts toward GitHub Actions cache storage?▼

All saved caches across all branches count toward the 10 GB limit. Each cache entry has a unique key (e.g., based on lockfile hash). Multiple branches maintain separate caches. When the total exceeds 10 GB, the least recently used caches are evicted automatically.

How do concurrent jobs affect cache savings?▼

With concurrent jobs, each job independently restores cache at the start of its run. If all jobs have a cache hit, you multiply the time savings by the number of concurrent jobs. However, only one job can save a new cache entry for a given key — others write to a new key or are skipped.

What is the best strategy to maximize cache hit rate?▼

Use deterministic cache keys based on lockfiles (package-lock.json, Pipfile.lock, etc.) rather than timestamps. Use restore-keys as a fallback for partial matches. Cache dependency directories (node_modules, .pip, .gradle) rather than build artifacts. Split caches by OS and Node/Python version.