If you’re running AI models in production and haven’t looked at caching yet, you’re very likely overpaying — by a lot. It’s one of the few optimizations in AI infrastructure that’s almost pure upside: lower cost, lower latency, no meaningful tradeoff in output quality. Here’s what it actually is, and why it’s arguably the highest-return change you can make to an AI-powered workflow.
The Problem: You’re Paying to Re-Read the Same Text, Over and Over
Every time you call an AI model’s API, you’re charged for the tokens it has to process — and that includes everything in the prompt, not just your new question. If your system prompt, your reference documents, or your conversation history stay largely the same between calls, the model is re-processing that same unchanged text again and again, and you’re paying full price for it every single time.
For a simple one-off chatbot, that’s a rounding error. For an AI agent working through a long document, a customer-support system replaying conversation history, or an automation workflow (like the ones built in n8n — see our piece on how those platforms are built) that calls the same model hundreds of times a day, it adds up to a genuinely large chunk of your AI bill.
The Fix: Prompt Caching
Prompt caching lets the model provider store the “already processed” version of a chunk of your prompt, so the next time you send that same chunk, it doesn’t need to be reprocessed from scratch — just read from cache, at a steep discount. The mechanics differ slightly by provider, but the shape is the same:
- Anthropic’s Claude lets you explicitly mark reusable sections of a prompt (a system prompt, a reference document, tool definitions) for caching. A cache hit is billed at roughly a tenth of the normal input price — about a 90% discount on that portion of the prompt.
- OpenAI applies caching automatically on longer prompts across its GPT model line, with no configuration required, typically cutting the cost of the cached portion roughly in half.
In practice, teams that restructure their prompts to put static content first (so it’s reliably cached) and dynamic content last report cutting total spend by 50–90%, depending on how much of their prompt is actually reusable. One widely cited example: an AI agent’s monthly bill dropping from around $720 to $72 after adding just a few cache markers — a 90% reduction from a change that took an afternoon, not a re-architecture.
There’s a Second Kind of Caching, Too
Semantic caching works differently: instead of caching parts of a prompt, it caches entire responses, and reuses one when a new query is similar enough in meaning to a past one — even if the wording is different. Ask “How do I reset my password?” and “I forgot my password, help” back to back, and a semantic cache can recognize those as close enough to answer the second one instantly, with no model call at all. It’s a bigger engineering lift than prompt caching, but for high-volume, repetitive use cases like customer support, it can eliminate a large share of API calls entirely.
Getting Started
You don’t need a research team to benefit from this. The practical starting point for most businesses:
- Put anything static — system instructions, reference documents, tool definitions — at the start of your prompt, and anything that changes per request at the end.
- Turn on prompt caching for whichever model provider you use (check their current docs, since exact mechanics and TTLs vary and change over time).
- Watch your cache hit rate. A workflow with a high hit rate is a workflow that’s genuinely cheap to run at scale — one real-world team reported raising their hit rate from 7% to 84% and cutting total spend by well over half in the process.
Why This Matters More Than It Sounds Like It Should
Most conversations about AI cost focus on which model is cheapest per token. That’s the wrong lever. How much of your prompt gets re-processed unnecessarily has a bigger effect on your actual bill than which model you picked in the first place. If you’re building anything that calls an AI model repeatedly — including AI-powered automations — caching isn’t an optional optimization. It’s close to free money.