You need more than a vector database

Running Large Language Models (LLMs) in production can be costly, and while vector databases are useful for semantic search, they alone are insufficient for a complete AI infrastructure. Vector databases facilitate the retrieval of relevant document chunks, but production AI systems require additional capabilities like session management, semantic caching, security, and agent memory. The field of information retrieval has long relied on algorithms like TF-IDF and BM25, but today's hybrid search solutions blend vector and lexical signals for improved precision. Semantic caching helps reduce LLM costs by matching query meanings instead of exact strings, allowing for significant savings by avoiding redundant calls. Redis offers a comprehensive platform that integrates these functionalities, including semantic caching, session management, and real-time coordination, making it a robust solution for managing production AI workloads efficiently. As AI systems grow in complexity, patterns such as AI gateways, semantic routing, and embedding caching are essential for managing cost, latency, and reliability, demonstrating why more than just a vector database is needed for effective production AI systems.