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Prompt caching discounts repeated input tokens but still runs the model. A semantic cache embeds each query, searches past query-response pairs, and returns a stored answer on a close-enough match, skipping the call. Here is how it works, how it differs from prompt caching, and
The batch API is the same model and the same output at half the token price, as long as nobody is waiting on the response. Here is which jobs belong on batch, the reproducible cost math, how Anthropic and OpenAI differ, and the exact n8n submit-and-poll pattern.
To reduce AI API costs you need levers that change the bill by a verifiable mechanism, not vague advice. This hub names all seven, right-size the model, prompt caching, the Batch API, routing and fallback, local versus API, token discipline, and RAG over long-context, with a
Now that 1M-token windows ship at flat pricing, should you stuff the whole corpus in one prompt or build a retrieval pipeline? This breaks long context vs RAG into reproducible per-query cost math, the recall limits of big windows, and four variables that decide it.
Prompt caching stores the unchanging part of your Claude prompt once and bills every later read at a tenth of the price, same model, same output. Here is the token math, the break-even point, and how to wire it into an n8n agent loop.
