A vector database is specialized storage optimized for indexing and retrieving high-dimensional vectors (embeddings) efficiently. Unlike traditional databases that match exact values, vector databases find the nearest neighbors in embedding space, enabling semantic search, similarity matching, and RAG systems. Popular options include Pinecone, Weaviate, Qdrant, and Milvus. They are the backbone of AI applications that need to search by meaning rather than keywords.