1 tools tracked
Avg 7d gain: +16
Vector Databases
Store, index, and query high-dimensional embeddings for AI-powered semantic search, RAG pipelines, and recommendation systems.
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Frequently asked questions
What are vector databases used for?
Vector databases store and query embeddings — the numerical representations behind semantic search, retrieval-augmented generation, and recommendations. This category tracks the options, from pgvector, which adds vector search to Postgres, through dedicated engines built for large-scale similarity queries.
How do I choose a vector database?
Weigh scale against operational simplicity. If your data already lives in Postgres and your vector count is modest, an extension like pgvector keeps everything in one system you already run; at very high volume or query rate, a purpose-built engine earns its extra moving part. Index choice and cost per million vectors matter more than raw benchmarks — the pgvector vs Pinecone guide lays out the thresholds where the answer flips.