Caching for Agentic Java Systems: Internal, Distributed, and Semantic

Caching strategies in software engineering range from simple internal memory stores to complex distributed and semantic architectures. Internal caching, utilizing libraries like Caffeine, offers nanosecond response times but lacks cross-server consistency and persistence. Distributed solutions such as Redis and Valkey provide shared state, durability, and advanced features like rate limiting, though they introduce network latency. Semantic caching represents a sophisticated evolution, leveraging vector similarity search to compare input meanings rather than exact keys. By vectorizing prompts and storing them in a vector database, systems can retrieve cached LLM responses for semantically similar queries, significantly reducing expensive inference costs. Implementing these strategies requires balancing memory usage, particularly with high-dimensional vectors, and configuring similarity thresholds to maintain accuracy while optimizing performance and cost-efficiency in agentic architectures.