Find the repeated work
An AI application may send the same instructions and reference document with every request. Prompt caching can make that repetition cheaper to process, depending on the provider or serving engine. The starting question is concrete: which portion of the actual serialized input remains identical across requests? Similar meaning is a different property from an identical prefix.
Anthropic's prompt-caching documentation describes exact matching for the prompt segments covered by its cache boundary. It separately reports cache reads, cache creation and other input tokens. Those details support measuring actual reuse. They do not justify applying a published maximum discount to every token in a monthly forecast.
Distinguish reading from generating
The vLLM Automatic Prefix Caching guide describes reusing key-value cache blocks for a shared prefix. Its stated performance boundary is important: the optimization avoids repeated prefix processing and does not accelerate the generation of new output tokens. A workload dominated by long answers can therefore experience a different benefit from one dominated by repeated document reading.
Our suggested measurement separates arrival-to-first-token time, completion time, input reuse and output length. Preserve cold requests alongside warm ones. Include the first cache population and any relevant expiration behavior in the workload. Replaying only the favorable warm sequence would answer a narrower question than the operating team needs.
Make stable material stable for a reason
Put genuinely reusable instructions and versioned reference material where the chosen engine can reuse them. Place request-specific content after that stable section when the application's semantics allow it. Avoid putting a changing request identifier at the front merely for logging convenience; carry operational metadata through an appropriate separate field when supported.
At the same time, do not keep an outdated policy or stale document in a prompt to protect a hit rate. A source update should produce a deliberate version change and invalidate the relevant reuse. Cache efficiency has value only when the application still supplies the evidence and instructions that the current request requires.
A useful experiment can begin with recorded, authorized request shapes rather than live customer data. Group them by repeated prefix, document version and output length. Compare complete runs with and without the supported cache feature under the same model and serving configuration. Record the exact configuration; compatibility and behavior can vary across architectures and engine versions.
Treat isolation as part of the design
The vLLM cache-design documentation describes optional per-request salting that limits reuse to requests with the same salt. That is a specific mechanism for cache isolation. It is not a substitute for authenticating callers or deciding which documents they are allowed to access.
For a shared service, decide which trust group may reuse cached computation and how that group is assigned. Keep that decision outside user-controlled text. Check the deployed engine's current security guidance before enabling cross-user sharing. A higher hit rate is an incomplete operating objective when its authorization boundary is undefined.
Finally, calculate savings from observed request accounting and the applicable rates for the actual service. Include cache creation, misses and the remaining generation work. This produces a workload-specific estimate that can be revised, rather than a promise borrowed from a feature announcement.