Optimize LLM response costs and latency with effective caching

This article explains how to optimize LLM costs and latency through effective caching strategies, potentially reducing costs by up to 90% and response times to milliseconds.

• Caching stores and reuses previous embeddings, tokens, outputs, or prompts to reduce inference costs and latency
• Prompt caching reduces latency by 85% and input token costs by 90% for repeated prompt prefixes
• Request-response caching stores identical request-response pairs for quick retrieval without reprocessing
• In-memory caches like Amazon MemoryDB provide persistent semantic caching with vector search capabilities
• External database caches (Redis, OpenSearch, DynamoDB) support distributed applications with high concurrent writes
• TTL-based invalidation automatically removes stale cache entries after specified periods
• Proactive invalidation allows manual deletion of specific cache entries when data updates occur
• Implement guardrails to prevent caching PII or protected data; maintain context-specific cache segregation
• Only implement caching if it applies to at least 60% of system calls to justify added complexity

Effective caching transforms LLM deployments by dramatically reducing costs, improving response times, enabling greater scale, and ensuring consistency for production applications.