Vector Database

Vector databases are purpose-built storage systems optimized for managing high-dimensional vector embeddings, the numerical representations that capture semantic meaning from text, images, audio, and other data types. Unlike traditional relational databases that match on exact values, vector databases excel at finding the most similar items in a collection using distance metrics such as cosine similarity, Euclidean distance, or dot product.

At their core, vector databases use approximate nearest neighbor (ANN) algorithms like HNSW (Hierarchical Navigable Small World), IVF (Inverted File Index), or product quantization to enable sub-millisecond similarity searches across billions of vectors. These indexing strategies trade a small amount of recall accuracy for massive speed improvements over brute-force search, making real-time retrieval practical at enterprise scale.

Popular vector database solutions include Pinecone, a fully managed cloud service; Weaviate, which supports hybrid search combining vectors with keyword filtering; Milvus, an open-source option designed for massive scale; Qdrant, known for its filtering capabilities; and pgvector, a PostgreSQL extension that adds vector search to existing relational infrastructure. Each has distinct trade-offs in terms of scalability, managed versus self-hosted deployment, filtering capabilities, and integration ecosystem.

Vector databases are foundational to modern AI applications including RAG systems, recommendation engines, semantic search, anomaly detection, and image similarity matching. In production environments, key considerations include index tuning for your recall and latency requirements, metadata filtering for access control, horizontal scaling strategies, backup and disaster recovery, and monitoring of query performance as data volumes grow.