A vector database stores data as high-dimensional numerical vectors (embeddings) and enables fast similarity searches — finding the most semantically similar items to a query. Unlike traditional relational databases that match exact values, vector databases return results based on conceptual closeness. They are a core component of RAG systems, AI-powered search, and recommendation engines. Popular vector databases include Pinecone, Weaviate, Chroma, and pgvector. They enable AI applications to access and retrieve relevant information efficiently at scale.