What is Vector Database?
Vector Database — A specialized database that stores data as high-dimensional vectors, enabling fast similarity search for AI applications.
Vector databases store information as mathematical representations called embeddings. When you search a vector database, it finds results based on meaning rather than exact keyword matches. This is what powers semantic search, recommendation engines, and the retrieval step in RAG architectures.
Frequently Asked Questions
How is a vector database different from a SQL database?
SQL databases match exact values and keywords. Vector databases find similar meanings — so a search for ‘automobile’ would also return results about ‘car’ and ‘vehicle’ even if those exact words were not used.
Which vector databases are most popular?
Pinecone, Weaviate, Milvus, Chroma, and pgvector (a PostgreSQL extension) are the most widely adopted. Each has different trade-offs for scale, hosting, and cost.
Do I need a separate vector database?
Not always. PostgreSQL with pgvector can handle moderate workloads. Dedicated vector databases become necessary when you need to search across millions of documents with low latency.