Vector Databases: What They Are and How to Implement Them?

▤ Summary

This article explains what vector databases are and why they matter for modern AI stacks. It covers embeddings, similarity search, ANN algorithms such as HNSW and IVF, and the trade-offs between vector, relational, and NoSQL databases. It also reviews major market options like pgvector, Milvus, Qdrant, and Pinecone, and shows where each fits in production. The piece highlights common use cases such as RAG, semantic search, recommendations, and fraud detection. It closes by explaining when not to use a vector database and what operators should monitor in production.

▦ Linked Companies

OpServices

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Opus Labs

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