How prompt caching changes cost
Prompt caching can reduce the cost and latency of repeated input, but only when the reusable portion of a request is structured deliberately.
What prompt caching means
Many AI workflows send the same system instructions, policy text, examples, or document prefix repeatedly. When a provider supports prompt caching, eligible repeated input may be billed differently from fresh input and can be processed faster.
Caching behavior, minimum sizes, lifetime, and pricing vary by provider and model. Treat the provider documentation and returned usage fields as authoritative.
Build a stable prefix
Place reusable instructions and shared context before rapidly changing user content when the API’s caching design rewards stable prefixes. Avoid timestamps, random IDs, or reordered examples inside the reusable section because small changes can prevent a cache match.
- Keep system instructions consistent.
- Order examples and tools deterministically.
- Separate stable context from user-specific data.
- Track cache creation and cache-read usage.
When caching does not help
Short requests may not qualify or may save too little to justify added complexity. Workloads with unique context on every call also have limited reuse. Calculate savings from your actual cache hit rate rather than assuming every repeated-looking request is cached.
Measure total task cost
Compare uncached input, cache creation, cache reads, output, retries, and request volume. A cheaper cached prefix is valuable only if the workflow still produces reliable answers. Monitor cost per completed task alongside token-level metrics.