Prompt Caching for Developers: How It Works and When It Saves Money

Understanding that prompt caching exists and saves money is one thing, actually structuring an application to get real benefit from it is a separate, more specific engineering problem. This is the implementation-level version of that explanation.

The core requirement: a stable prefix

Caching only helps for the portion of a prompt that's byte-for-byte identical across requests, typically the beginning: system instructions, static reference documents, few-shot examples. The part of your prompt that changes with every request, the user's latest message, needs to come after the stable, cacheable portion, not interleaved with it. Getting this ordering wrong, putting anything that changes early in the prompt, silently invalidates caching for everything after it, without necessarily throwing any error.

Structuring an agent's context for cache efficiency

For an agent maintaining a large amount of context, a loaded codebase, a long tool-call history, structure matters directly: keep genuinely static content (system prompt, unchanging reference material) at the front, and append new information (the latest tool result, the newest message) at the end rather than inserting it mid-context. An agent that re-orders or re-summarizes earlier context on each turn defeats caching entirely, since the "stable" prefix is no longer actually stable between requests.

Understanding cache TTL in practice

Cached prefixes expire after a period of inactivity, commonly on the order of minutes for many providers, not hours. An application with bursty traffic, long gaps between requests in the same session, will see the cache expire and pay full price again more often than the theoretical savings might suggest. If your usage pattern involves genuinely long gaps between messages in a session, don't assume caching alone solves your cost problem, model the actual expected cache hit rate given your real traffic pattern.

Measuring whether it's actually working

Most providers that support caching report cache hit information in the response metadata, cached versus non-cached token counts. Actually checking this in production, rather than assuming caching is working because you structured the prompt the way the documentation suggested, catches configuration mistakes, an unexpectedly changing prefix, a TTL shorter than your actual session gaps, that would otherwise silently cost more than expected.

Where caching interacts with other cost-optimization choices

If you're already truncating or summarizing older context to manage context window size, be aware that summarization changes the prefix and invalidates the cache for everything after that point, there's a real trade-off between context-window management strategies and cache efficiency that's easy to miss if the two are handled by different parts of a codebase without coordination.

The realistic expectation

For applications with a large, stable system prompt and short, frequent follow-up messages within a session, genuinely substantial cost and latency savings are achievable. For applications with highly variable prompts or long gaps between requests, the benefit is real but smaller than the headline numbers in provider marketing material suggest, measure your own actual hit rate rather than assuming the best case.