Engineering#Caching#Anthropic#OpenAI

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.

ST
Spend Tensor
Spend Tensor
Jun 18, 2026 12 min read
The real cost of prompt caching (and when it actually pays off)

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.

Topics#Caching#Anthropic#OpenAI
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Spend Tensor
Spend Tensor

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