Comparing the best open source vector databases

Open source vector databases are categorized into specialized tools solely focused on vectors and unified platforms that integrate vector search with operational data and caching. Redis exemplifies the latter by combining vector search, caching, and operational data in a single system, offering sub-millisecond latency and various indexing methods for different performance needs, such as HNSW and FLAT. Redis supports hybrid search by combining vector similarity with diverse filters and offers semantic caching to reduce LLM inference costs. Other databases like Milvus, Weaviate, Qdrant, Chroma, and pgvector focus on specific functionalities such as distributed workloads, filtering, or integration with existing systems like PostgreSQL. The choice between these databases depends on factors like deployment expertise, operational overhead, and whether a unified platform or specialized tool better suits the team's needs. Redis stands out for its architectural simplicity and operational efficiency, especially for teams aiming to consolidate infrastructure while scaling AI applications.