Prompt caching: the easiest 30%+ cut to your LLM bill
7 min read · June 17, 2026 · TurboFinOps
Most production LLM calls send the same large block of context every time — a system prompt, a set of few-shot examples, a retrieved-document preamble — followed by a small unique question. You pay full input price for all of it on every call. Prompt caching lets the provider charge a fraction for the repeated part.
Where the money goes
For many features, input tokens dwarf output tokens, and most of those input tokens are identical call to call. OpenAI, Anthropic and Bedrock all expose a cached-input price that is a fraction of the standard input rate.
If you are not caching, every repeated system prompt and example set is billed at full input price, thousands of times a day. That is the single biggest avoidable cost in a token-heavy workload.
When caching pays off
Three conditions make caching worth it: high input-token volume, a low current cache-hit rate, and a model that offers a cached tier. If all three hold, the savings on the reusable portion of your input are immediate.
Structure prompts so the stable content comes first and the variable content last — that maximizes the cacheable prefix. Keep the system prompt and examples byte-identical across calls; even small changes invalidate the cache.
Measure before you assume
Estimate conservatively: take your uncached input tokens, assume a realistic reusable share, and price the delta between the standard and cached input rate. That gives a defensible floor, not a fantasy number.
TurboFinOps surfaces this automatically — it looks at real input and cached token counts per model and flags prompt-caching opportunities with an estimated monthly saving when the model has a cached tier.
Frequently asked questions
- Does caching change the model’s output?
- No. Caching only affects how the input prefix is billed and processed; the model produces the same result. It is a pricing and latency optimization, not a quality trade-off.
- Which providers support it?
- OpenAI (prompt caching), Anthropic (prompt cache) and Bedrock all expose a cached-input rate. The opportunity is model-agnostic: any model with a cached tier and repeated context qualifies.
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