Context caching is a way to avoid paying for the same work twice when you talk to an AI model. Many prompts have a large fixed part, like a detailed system prompt or a reference document, followed by a small changing part, like the user’s actual question. Caching stores that fixed part after the first request so the model can reuse it instead of reading it from scratch every time.
Picture a busy café where one regular always orders the same elaborate base drink with a different syrup each visit. Instead of remaking the base every single time, the barista keeps it ready and just adds the new syrup. The base is the cached context; the syrup is the new question. The result is faster service and less wasted effort. Because most AI providers charge by the token, reusing cached input is also noticeably cheaper.
This matters most for chatbots and document tools that send the same big instructions on every message. Without caching, a 20-page manual gets re-read on every single question. With it, you pay full price once and a reduced rate after that. The numbers add up quickly. A support bot fielding a thousand questions a day against the same handbook is re-reading that handbook a thousand times, and caching turns most of those reads into a cheap lookup. The trade-off is timing. Because the cache expires after minutes, it helps a burst of related questions far more than a quiet trickle spread across the day.
At TopDevs we turn on context caching for client tools that repeatedly send the same large prompts, which keeps running costs down without changing how the feature behaves.
