What is Vector Database?
A vector database is a specialized database designed to store, index, and query high-dimensional vector embeddings efficiently.
A vector database is a specialized database designed to store, index, and query high-dimensional vector embeddings efficiently. Unlike traditional databases that search by exact matches or keywords, vector databases perform similarity search — finding the vectors closest to a query vector in high-dimensional space.
Popular vector databases include: Pinecone (managed cloud-native), Weaviate (open-source), Qdrant (open-source, Rust), Chroma (lightweight, developer-friendly), Milvus (enterprise-scale), and pgvector (PostgreSQL extension).
Vector databases are the backbone of RAG pipelines. When a user asks a question, the question is embedded into a vector, the vector database finds the most similar document vectors, and those documents are provided as context to the LLM.
Key performance metrics: query latency (milliseconds to return results), recall (% of truly relevant results returned), and throughput (queries per second at scale).
Why It Matters
Vector databases determine the speed, accuracy, and cost of your RAG pipeline. Choosing the right vector database and optimizing its configuration directly affects AI feature quality and unit economics.
Frequently Asked Questions
What is a vector database?
A vector database stores and queries high-dimensional vector embeddings, enabling similarity search — finding items most similar to a query based on meaning rather than exact keywords.
Which vector database should I use?
Pinecone for managed simplicity, pgvector for PostgreSQL users, Weaviate or Qdrant for open-source, and Milvus for enterprise scale. Choice depends on scale, budget, and operational complexity tolerance.
Related Terms
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