Measure prompt cache hits to verify cost savings
Anthropic prompt caching silently fails in four ways (misplaced breakpoints, prefix drift, TTL expiration, unmeasured hit rates); wrap the SDK with explicit cache metrics to catch regressions.
May 27, 2026
Summary
Cache setup looks successful on first call but regresses silently—without measuring cache_read_input_tokens per request, you're likely paying full price on stale cached prefixes. Visibility prevents unexpected billing spikes.
Why it matters
Cache setup looks successful on first call but regresses silently—without measuring cache_read_input_tokens per request, you're likely paying full price on stale cached prefixes. Visibility prevents unexpected billing spikes.
Implementation verdict
Replaces manual response parsing and guesswork with a drop-in SDK wrapper that tracks hit rate, cost savings, and emits passive warnings on cache failures. Zero dependencies, ~50KB. Worth installing immediately if you're using Anthropic's cache API.
Sources
- 1.the only way to verify a call had hit the cache was to manually parse cache_read_input_tokens from the response usage on every request
- 2.The cache only hits if the cacheable prefix is byte-identical to what was cached
- 3.Anthropic recently dropped the default cache TTL from 1 hour to 5 minutes
- 4.A chatbot answering 1000 questions/day with a 10K-token system prompt easily hits 70%+ cost reductions
Dev Signal
Get briefs like this in your inbox — free, 3x a week.
100+ sources compressed into one 4-minute read. Ranked, cited, implementation-ready.