# The real cost of prompt caching (and when it actually pays off)

> The break-even math on prompt caching is non-obvious. We benchmarked 14 production workloads to find when caching saves money — and when it costs more.

- Source: https://spendtensor.com/blog/the-real-cost-of-prompt-caching
- Author: Spend Tensor
- Published: Jun 18, 2026
- Category: Engineering
- Reading time: 12 min
- Topics: Caching, Anthropic, OpenAI

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Prompt caching looks like free money on the pricing page: write tokens cost 1.25× normal, but cached reads are 0.1×. The catch is in the access pattern.

We instrumented 14 production workloads across our customer base. For workloads with >3 reads per cached prefix per 5-minute TTL, caching delivered 62–78% savings on input tokens. For workloads under that threshold — and there were more of them than we expected — caching was a net loss of 15–25% because the write premium never paid back.

The single best predictor of cache ROI was prompt-prefix entropy. Long, stable system prompts with rotating short user turns are the textbook win. Few-shot examples that change per request are the textbook trap.

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Published by SpendTensor — AI spend management and LLM FinOps. Quote with attribution and a link to https://spendtensor.com/blog/the-real-cost-of-prompt-caching.