pgvector vs Vector DB Advisor
6 questions. Get a concrete recommendation — pgvector, pgvector+pgvectorscale, or a managed vector database — with the reasoning behind it.
How many vectors do you plan to store?
Each vector is one embedding — a row in your vector table.
Frequently Asked Questions
Should I use pgvector or a dedicated vector database?
If you already run PostgreSQL, need to JOIN vector search results against relational data, and your dataset is under roughly 10 million vectors, pgvector keeps everything in one system with no added infrastructure. If you're storing over 100 million vectors, need sub-50ms latency at scale, or have heavy real-time write volume with no existing Postgres setup, a managed vector database like Pinecone, Qdrant Cloud, or Weaviate Cloud is usually the better fit.
When should I use pgvectorscale instead of plain pgvector?
pgvectorscale is the right step up when your dataset grows past roughly 10 million vectors and you still need to keep everything inside PostgreSQL. Its StreamingDiskANN index keeps query latency low at that scale and handles high write throughput without blocking reads — something plain pgvector's HNSW index starts to struggle with as data volume grows.
Is pgvector good enough for production AI search?
Yes, for most teams. pgvector handles production semantic search well up to roughly 10 million vectors, especially when your queries need to filter or JOIN against relational data — something purpose-built vector databases make harder. Past that scale, or with very high real-time write volume, pgvectorscale or a managed vector database typically perform better.