Prompt Caching: Cut Latency and Cost Without Cutting Quality

Prompt caching stores the unchanging prefix of a request—system instructions, style guides, long reference documents—so the model can skip reprocessing them on the next call. On supported APIs a cache write costs slightly more than a normal token, but cache reads are typically about 10% of the base price, and time-to-first-token drops sharply because the heavy prefill work is reused.

The trick is ordering: put everything stable at the top (persona, rules, retrieved context) and everything variable—the user's actual message—at the bottom. Mark the cache breakpoint at the end of the static block. A five-minute rolling TTL means chatty sessions and tight agent loops hit the cache repeatedly, while one-off requests simply pay the normal rate. Cache boundaries must match byte-for-byte, so never interleave timestamps or random IDs into the prefix.

On CinderHub we apply this across chat, image, video, and storyboard pipelines: a shared brand brief or character sheet is cached once, then every downstream generation reads it cheaply. Audit your cache hit rate weekly—if it is low, your prompts are probably shuffling their static content, and a small reorder can pay for itself within a day.