Tech Stacks · Data
pgvector
Category
Data
At the studio
Since 2023
Projects shipped
1 case study
Status
Active
(01) Our take
pgvector is how we avoid standing up a second database for most AI features. For applications that already run Postgres — which is most of ours — pgvector lets us add semantic search, retrieval-augmented generation, and embedding-based features without adding Pinecone or Qdrant to the operational surface area.
The performance ceiling is lower than specialised vector databases, but we've yet to hit a client use case where the gap mattered. For scale beyond 10 million vectors with high QPS, we'd consider a dedicated store — below that, pgvector is almost always the right call.
(02) What we build with it
Typical work
- RAG (retrieval-augmented generation) systems
- Semantic search over documents, products, or content
- Recommendation systems that combine text and behaviour signals
- Duplicate detection and content clustering
(03) How we engage
Engagement shape
Usually part of an AI feature build. 4–8 weeks to production for a well-scoped RAG application running on pgvector.
(04) In production
Where we've shipped it
(05) Pairs with
Works well with
Technologies we reach for alongside pgvector in most engagements.
Thinking about a pgvector project?