Vector databases: what you need to know before production

Vector databases are specialized systems designed to handle and search high-dimensional vector embeddings, which represent unstructured data like text, images, and audio, by semantic similarity rather than exact matches. They are essential for AI-driven applications that require efficient similarity searches across large datasets, offering sub-100ms query latency for millions of items. These databases differ from traditional ones by focusing on embedding storage and similarity searches, which are crucial for applications like recommendation systems, image and video retrieval, NLP tasks, and fraud detection. Architectural choices between standalone vector databases, embedded extensions, or unified platforms can significantly impact operational complexity, performance, and costs. Various indexing and similarity metrics, such as HNSW and cosine similarity, are used to optimize search performance based on the specific requirements of the dataset and application. Redis, a unified platform, integrates vector search with caching and operational data management, supporting both HNSW and FLAT indexing and offering sub-100ms search latency at billion-scale with features like semantic caching and Active-Active Geo Distribution for high availability.