How does prompt caching save money?

Providers cache the leading, unchanging portion of a prompt and bill cached input tokens at a steep discount on subsequent requests. Anthropic charges roughly 10% of the input rate for cache reads; OpenAI applies about a 50% discount on cached input.

Why must static context come first?

Caches match on an exact prefix. Any change near the start of the prompt invalidates everything after it, so putting the unchanging system and reference context first, before dynamic content, keeps the cacheable prefix as large as possible.

What is the minimum cacheable size?

There is a floor — Anthropic requires roughly 1,024 tokens in the cached prefix, and OpenAI caches automatically above about 1,024 tokens. Below that, caching does not apply, which the tool flags.

Does caching help one-off requests?

No. Caching pays off only when the same prefix is reused before it expires (a few minutes by default). High-volume, repetitive workloads with a shared system prompt benefit most; sporadic unique prompts see little gain.

What hurts my hit rate?

Putting a timestamp, user ID, or any per-request value near the top of the prompt breaks the prefix and zeroes out cache hits. Keep volatile data after the static block and you preserve the cache across requests.

Context Caching Strategy Planner

Context caching strategy planner

Prompt caching can cut input costs by 50–90% on repetitive workloads, but only if your prompt is structured so the cache can match a large, unchanging prefix. This planner models your prompt as static context, dynamic context, and a per-request user message, then compares the cost of no caching against caching just the static block and against a fully front-loaded layout — so you can see exactly how much prompt order is worth.

How it works

You enter token counts for the static portion (system prompt, reference docs), the dynamic portion (retrieved chunks that change per request), and the user message, plus daily volume and your provider. The tool applies the provider’s cache-read discount — roughly a 90% reduction for Anthropic cache reads, about 50% for OpenAI cached input — to whatever sits in the cacheable prefix. It checks the minimum cacheable size, computes cost under each layout, and reports the monthly savings and recommended structure.

Tips and notes

Front-load everything stable. System prompt, tools, and reference material go first; volatile data goes last.
Never put a timestamp up top. A single changing token near the start invalidates the whole prefix.
Mind the minimum. Below ~1,024 prefix tokens, caching does not engage — the tool warns you.
Caching expires fast. It only helps when the same prefix recurs within the cache window, so it favors steady high-volume traffic.