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Prompt caching can reduce the cost and processing time of repeated API requests by reusing a stable prompt prefix or saved context. It does not make new or changed input free, and a long prompt or persistent session does not guarantee a cache hit. The practical gains depend on each provider’s matching rules, cache lifetime, model, and separate charges for cache creation, reads, and storage.
How does prompt caching work?
A language model processes input into internal state used to generate its response. When a later request begins with content that matches a reusable prefix—or refers to context saved in a provider’s cache—the API may be able to reuse previously computed state instead of processing that portion from scratch.
The cache applies only to the matching, eligible content. New instructions, changed shared material, and request-specific additions still need to be processed. Providers also differ in how a cache is created, what must match, where reusable boundaries can be placed, how long entries remain available, and how usage is billed.
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Does prompt caching actually reduce an API bill?
It can, but a hit rate alone cannot answer whether it saves money. Compare the full cost of a sequence of requests: the initial cache write or creation, later cache reads, ordinary input tokens that are not cached, any storage-duration charge, and the cost of misses or rewrites. A cache-write rate may be higher than ordinary input pricing, while later reads may cost less; a stored context may also incur charges while it remains available.
#1 Best Overall
There is no reliable universal savings percentage or provider-wide “cheapest” choice. The result depends on the exact model and endpoint, workload, cache lifetime, input and output mix, and how often requests reuse the same content. Published rates and model support change, so check the provider’s current documentation and pricing before calculating a budget.
Estimate the break-even point
- Measure the stable prefix or saved context and the changing portion of representative requests.
- Record the provider’s applicable ordinary input, cache-write, cache-read, and storage rates for the chosen model and endpoint.
- Estimate how many later requests will reuse the cached material before expiry, including realistic misses and cache rewrites.
- Compare the total cost for that request sequence with the cost of sending the full prompt uncached each time. Include storage charges over the relevant period.
- Repeat the estimate using actual usage reports from a representative workload; do not treat a theoretical hit as a realized saving.
How do the major API approaches differ?
These mechanisms are provider-specific rather than interchangeable. The documentation and prices below are volatile; verify current support, billing, and exact request syntax for the model and endpoint you plan to use.
Quick Recap
Best Value
| Provider | How reuse is initiated | Billing and lifetime considerations | What to verify |
|---|---|---|---|
| OpenAI API | For supported models, prompt caching is enabled by default and reuses KV states for a matching prompt prefix. The guide describes model-dependent cacheable minimums and breakpoints, as well as cache-hit monitoring. | Uncached input, cache reads, and cache writes can have different model-dependent rates. The guide describes prompt_cache_options controls for GPT-5.6-and-later behavior and retention options for earlier models. |
Check supported model and endpoint, minimum input length, matching/breakpoint behavior, current retention controls, usage fields, and rates in the OpenAI prompt-caching guide. OpenAI cautions that keeping a session does not itself guarantee a hit. |
| Anthropic Claude API | Cache-control markers identify content for caching. | The documentation describes a default five-minute cache lifetime and an optional one-hour duration at additional cache-write cost. It separates cache creation from reads; write and read multipliers vary by model. The documentation cited here describes five-minute writes at 1.25 times base input pricing, one-hour writes at 2 times base input pricing, and cache hits at a fraction of base input price; verify current model-specific rates before using these figures. | Check marker placement, model support, current TTL options and write/read pricing in Anthropic’s prompt-caching documentation. |
| Google Gemini API | Explicit context caching: create a cached-content resource and refer to it in later requests. | Storage is billed by TTL, and usage reporting includes cached-token counts. Minimum token counts and supported models vary. | Check model-specific minimums, TTL, resource behavior and usage reporting in the Gemini context-caching guide and the caching API reference. |
How do I structure requests to reuse stable context?
- Identify what repeats. Separate shared instructions, tool definitions, and reference material from details that change with each request.
- Place reusable material where the provider expects it. Keep stable content early in a prompt prefix, or use the provider’s saved-context resource or cache-control mechanism. Follow that model’s exact rules; there is no cross-provider syntax.
- Keep variable content outside the reusable portion where possible. Put request-specific facts and frequently changing material in the uncached suffix or corresponding request field if the API permits it.
- Preserve exact matching conditions. Avoid changing content within a prefix that must match. Use documented breakpoints or markers rather than assuming any long prompt will be cached.
- Send representative repeated requests. Include realistic changes and spacing between requests so the test reflects the workload and cache lifetime you expect.
- Inspect reported usage. Confirm that cached-token, cache-read, or cache-write reporting shows reuse. Field names and availability differ by API.
- Recalculate after changes. Recheck model support, minimum lengths, cache controls, rates, and TTL whenever you change model, provider, endpoint, or deployment.
What should I check before relying on a cache?
- Model and endpoint: Confirm caching is supported for the exact model and API route, not merely by the provider in general.
- Eligibility and matching: Check minimum cacheable length, required identical content, and permitted marker or breakpoint placement.
- Lifetime and refresh: Find out how long content remains cached and whether reuse refreshes or extends that lifetime; do not assume it does.
- All billable components: Account for writes, reads, uncached input, storage duration where applicable, and misses or rewrites.
- Observed usage: Validate actual cached-token volume using the provider’s usage fields or dashboard after representative calls.
- Data handling: Review the provider’s current documentation for how cached content is handled and retained, especially if the context contains sensitive material.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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