Why cache-hit pricing, not model cost, drives agentic AI billing

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In multi-step AI agent workflows, the dominant cost driver is not the base input price of a language model but the repeated billing for context tokens already sent in prior steps. Each loop iteration re-sends the same system prompt and conversation history alongside a small amount of new content, creating a compounding 'repeat tax' across dozens of calls. Cache-hit pricing, offered by some providers at a steep discount to standard input rates, charges less when a previously sent token prefix is already stored by the provider. However, this pricing advantage is only actionable when developers have full observability into which tokens were cache hits versus fresh charges on every request. Optimizing for cached-read rates rather than headline model prices is where analysts say the largest cost reductions in agentic systems — potentially over 70% — actually originate.

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