A database optimized for storing and querying high-dimensional vector embeddings at scale.
A vector database is a specialized database management system designed to store, index, and query high-dimensional vector embeddings efficiently. Unlike traditional databases that excel at exact-match lookups and range queries, vector databases are optimized for approximate nearest-neighbor (ANN) search — finding the vectors most similar to a query vector across millions or billions of stored vectors. They are the storage and retrieval backbone of modern AI applications.
Vector databases have become essential infrastructure for every enterprise AI deployment. RAG systems, semantic search engines, recommendation systems, and multimodal AI all require the ability to store and retrieve embeddings at scale. The vector database market has exploded, with dedicated solutions like Pinecone, Weaviate, Qdrant, and Milvus competing with vector extensions to existing databases like pgvector (PostgreSQL) and Snowflake Cortex.
Vector databases use specialized indexing algorithms — HNSW (Hierarchical Navigable Small World), IVF (Inverted File Index), or LSH (Locality-Sensitive Hashing) — to enable fast approximate nearest-neighbor search. These algorithms trade a small amount of accuracy for dramatic speed improvements, enabling sub-millisecond similarity search across billions of vectors. Most vector databases also support metadata filtering, allowing hybrid queries that combine vector similarity with traditional attribute filters.
A media company stores embeddings of 50 million news articles in a vector database. When a user reads an article about climate policy, the recommendation system queries the vector database for the 20 most semantically similar articles — returning related pieces about carbon markets, renewable energy legislation, and international climate agreements — even if they share no keywords with the original article.