What is prompt caching? LLM speed & cost guide

Prompt caching is a technique used in large language models (LLMs) to reduce latency and costs by storing computational states from an LLM's attention layers, allowing the model to skip redundant processing on repeated prompt prefixes. This approach decreases time-to-first-token (TTFT) and input costs for requests that share a prefix, although output token costs remain unchanged. Prompt caching differs from regular and semantic caching, as it focuses on input-side computation, while regular caching stores full LLM responses and semantic caching uses vector embeddings for similar queries. Effective use of prompt caching involves structuring prompts with stable content first and variable content last, maximizing cache hit rates in RAG pipelines, chatbots, and agentic systems. Combining prompt caching with other caching methods, such as exact-match and semantic caching, offers comprehensive cost and latency reductions for LLM applications. Redis supports these caching strategies, providing a platform that integrates vector search, semantic caching, and in-memory data structures for efficient query handling.

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