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A prompt-cache write does not guarantee a later cache hit. Check the response’s usage fields first: cache_creation_input_tokens shows tokens written, while cache_read_input_tokens shows tokens read. If reads stay at zero, verify that the prompt qualifies, the reusable prefix through the cache breakpoint is unchanged, the next request arrives before the cache expires, and your provider supports the caching mode you selected.
What a cache write—and a missing read—actually means
Prompt caching can store an eligible portion of a prompt so a later request can reuse it. A nonzero cache_creation_input_tokens indicates that tokens were written to the cache; a nonzero cache_read_input_tokens indicates that tokens were read from it. These are separate outcomes, so seeing a write on one response does not prove a later response used that entry.
Anthropic says that if both fields are zero, the request was not cached; one likely reason is that the prompt did not meet the model’s minimum-length requirement. A cache_control marker does not make an otherwise ineligible prompt cacheable. See Anthropic’s prompt-caching documentation.
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The cacheable prefix is different
Caching applies to a prompt prefix through a cache breakpoint. A later request needs to preserve the relevant content and ordering up to that breakpoint. If earlier content changes or is rearranged, the later request may no longer have the same reusable prefix. Where your request structure allows, keep stable instructions and other shared content before the breakpoint, and place per-request or changing content after it.
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The prompt is below the model’s minimum
Minimum prompt lengths vary by model and platform. A request that is too short may be processed normally without being cached or returning an error, even when it includes cache_control. Check the applicable minimum for the exact model and platform you are using.
The cache expired before the next request
The default ephemeral cache lifetime is five minutes. A request arriving after expiration may create a new cache entry rather than read the earlier one. Anthropic also documents a one-hour option; confirm that your model and platform support it and check the applicable pricing in the prompt-caching documentation.
Your provider does not support that caching mode
Provider behavior can differ. Anthropic’s documentation for Claude on Amazon Bedrock says automatic caching through the top-level cache_control field is unsupported there and recommends explicit breakpoints. If you use Bedrock, do not assume that automatic caching behavior documented for another platform applies; follow the Bedrock guidance.
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Diagnose the issue from response data
- Record the context for each call. Capture the model, provider or platform, request timestamp, cache mode, TTL, and response
usage. - Compare writes and reads across requests. Check
cache_creation_input_tokenson the request that should create the entry andcache_read_input_tokenson the subsequent request. A write followed by a zero read means creation occurred, but reuse did not. - Check eligibility for the exact setup. Confirm that the model and platform support the caching mode, and that the cacheable prefix meets the applicable minimum length. The marker alone cannot make a short prompt eligible.
- Compare each request through the breakpoint. Look for changes to the prefix’s content or ordering. Keep shared content stable there and, where the structure allows, put changing content after the breakpoint.
- Check elapsed time against the TTL. Compare the next request’s timestamp with the cache lifetime. A request after expiration can cause another write instead of a read.
- For Bedrock, verify breakpoint behavior. Use explicit breakpoints as Anthropic recommends rather than relying on top-level automatic caching.
- Review token usage and current prices. Compare measured token counts and the prices for the model you use before concluding why a bill changed.
These checks cover documented causes, but the usage fields alone cannot identify the individual root cause. That requires the request payloads, model, platform, timing, and usage records.
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How cache behavior affects cost
Anthropic’s pricing page lists cache writes with these multipliers against the base input-token price: 5-minute writes cost 1.25×, one-hour writes cost 2×, and cache reads cost 0.1×. These are pricing multipliers, not fixed charges; actual spend depends on the model, token counts, and current pricing. Repeated writes can therefore affect the bill, particularly when requests keep creating entries instead of reusing them. Check the current Anthropic pricing page for the model and rates that apply to your use.
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