If you build an AI application where the system prompt is the same with every request, you pay every time for processing that same text. Prompt caching solves that: repeated input is processed more cheaply, and response time decreases.
Prompt caching is a feature that several major AI providers now offer, including Anthropic and OpenAI. It reduces costs and latency for applications where a large part of the input is the same with every request. For business AI applications at scale, this can deliver significant savings.
When you send a request to the API, the model processes the entire input again: the system prompt, the knowledge base context and the user message. If the system prompt is long and identical with every request, that is waste: you pay every time for the same thing.
With prompt caching, a portion of the input is stored in the provider's server memory. With the next request, that cached part is not processed again. You pay a reduced rate for the cached tokens: with Anthropic this is typically 90% less than the standard input rate. Response time also drops because processing the cached part is skipped.
Prompt caching is most valuable when:
If your system prompt is only a few hundred tokens, savings are limited. Caching only pays off when the cached section is substantial: several thousand tokens or more.
With Anthropic, you enable caching by adding a cache_control parameter to the sections you want to cache in your API request. You explicitly mark which parts of the input may be cached. That gives you control: you choose which sections are stable enough to cache.
The cache has a limited lifespan (typically five minutes with Anthropic). If the interval between requests is longer than that lifespan, the cache is rebuilt. Plan your request frequency with that lifespan in mind.
OpenAI's prompt caching works automatically for long, repeated inputs. You do not need to configure anything extra: the system recognises cached parts and processes them more cheaply. The saving is visible in your usage overview.
The downside of the automatic approach is less control over what is and is not cached. With Anthropic's explicit approach you have more control.
Suppose you have a chatbot with a system prompt of 5,000 tokens and a knowledge base context of 10,000 tokens per request. That is 15,000 input tokens processed with every request. At 10,000 requests per day and an input rate of $3 per million tokens, that costs $450 per day.
With prompt caching, where 90% of cached tokens are reduced, you pay for 1,500 tokens at normal rate and 13,500 tokens at 10% of the rate. Daily costs drop to approximately $45 for the cached parts, plus the normal rate for the rest.
Caching only works for identical input. If the system prompt or knowledge base context varies slightly per request, the cache is not hit. Variable parts of your prompt must not sit in the cached section.
Caching is also less useful at low volumes. The savings only become significant at hundreds or thousands of requests per day.
Prompt caching is a practical way to lower API costs for AI applications at scale. It requires limited technical implementation but can considerably reduce costs at high usage. Mach8 applies prompt caching as standard for production clients where it is applicable.
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