LLM Caching Strategies: Prompt Caching, Semantic Caching, and When to Use Each

LLM caching involves storing processed prompt representations or full model responses to reduce the cost and latency of subsequent requests without affecting model quality. There are two main forms: prompt caching, which operates at the provider level and discounts stable prefix tokens, and semantic caching, which functions at the application level and returns stored responses for semantically similar queries. By using LLM caching, enterprises can significantly cut costs and improve efficiency, as demonstrated by ProjectDiscovery, which achieved a cache hit rate increase from 7% to 84%, resulting in a 59-70% reduction in total LLM spend. Prompt caching can save up to 90% on token costs while semantic caching can eliminate some model calls altogether, making these strategies particularly beneficial for high-volume workloads such as customer support and FAQ systems. Implementing caching requires structuring prompts with stable content first to maximize hit rates and avoid common mistakes that reduce effectiveness.

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