Prompt Caching with Claude: Cut API Costs by 90% on Repeated Context
This article explains how prompt caching in the Anthropic Claude API can reduce API costs by up to 90% on repeated context. It demonstrates how to mark content for caching and check cache usage.
Why it matters
Prompt caching can help developers significantly reduce API costs for applications that require sending large context with each request to language models like Claude.
Key Points
- 1Prompt caching stores large context (system prompt, documents, tool definitions) once and charges a smaller fee on subsequent calls
- 2First call incurs full input token billing plus a small cache write fee, while subsequent calls only pay a cache read fee (90% cheaper)
- 3Developers can mark content for caching using the 'cache_control' parameter in the API request
- 4The API response provides details on input tokens, cache creation tokens, and cache read tokens to track usage
Details
Prompt caching is a feature of the Anthropic Claude API that allows developers to reduce API costs when sending the same large context with every request. Normally, the full input tokens are billed for each API call, but with prompt caching, the context is stored on the first request and only a small cache read fee is charged on subsequent calls - around 10% of the full token cost. This can lead to significant savings, especially for applications that require sending large system prompts, documents, or tool definitions with each request. The article provides sample code demonstrating how to mark content for caching using the 'cache_control' parameter, as well as how to check the cache usage metrics in the API response. Prompt caching is a useful optimization technique for developers building applications on top of large language models like Claude.
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