Here's a cost leak hiding in plain sight: most AI applications send the same big chunk of context on every request — a long system prompt, a document, a set of examples — and pay full price to process it each time. Prompt caching stops that waste.
What it does
When a model processes your prompt, it does expensive work to "understand" the tokens. Prompt caching saves the result of that work for the unchanging prefix of your prompt. On the next request that starts with the same prefix, the model reuses the cached computation instead of redoing it — at a small fraction of the cost (often ~10% of the normal price for the cached part) and with lower latency.
If the first thousand tokens of every request are identical, you shouldn't pay to process them a thousand times. Caching means you pay once.
Where it pays off
Caching wins whenever a large, stable context repeats across requests:
- A long system prompt or instruction set used on every call.
- A document you ask many questions about.
- Few-shot examples included in every request.
- A conversation history that grows turn by turn.
The one rule
Caching is a prefix match — it works only for the part of the prompt that stays byte-for-byte identical from the start. So put the stable content first (system prompt, documents) and the variable content (the user's actual question) last. Slip a timestamp or a per-request ID into the beginning and you invalidate the cache for everything after it.
For high-volume applications, prompt caching is one of the simplest, biggest cost savings available — often cutting bills severalfold with a small change to how you order your prompt.
