The Complete Guide to Inference Caching in LLMs

Inference caching shifts execution costs from immediate API calls to predictable storage lookups. When running RAG pipelines, failing to cache intermediate prompt-response pairs can result in 30% higher inference costs using GPT-4. This is not an optimization; it is a fundamental operational necessity for managing latency.

This change directly impacts deployment workflows. Implementing a caching layer on agent calls using Haiku significantly reduces token usage. The operational metric for a high-throughput agent system with 10,000 requests drops from $500 to $150 per hour. Caching is not an optional feature; it is required for cost control in production.

Teams running RAG in production must prioritize caching the context retrieval step in their workflow. Ignore caching, and expect latency spikes when scaling beyond 100 concurrent calls. Agents hitting the OpenAI API should cache full responses with a Redis store instead of relying on immediate, redundant calls.