If your data already lives in Postgres, pgvector often means you don't need a separate vector database at all — one less system to run, back up, and keep in sync. It's not free performance, though: indexing strategy and query design decide whether it holds up past a demo. We handle that part.
Adding a vector column is easy — indexing it correctly at scale is the real work.
Vector search added to a database you already run, back up, and monitor — no separate vector store to keep in sync with your source of truth.
HNSW or IVFFlat index configuration matched to your data size and recall requirements, not a default that degrades under real load.
Queries that join vector similarity with your existing relational filters in a single query — something a separate vector store can't do as cleanly.
Honest guidance on when pgvector stops being enough and a dedicated vector database becomes worth the added operational cost.
Scoped to your existing Postgres setup.
Vector column design and HNSW/IVFFlat index tuning for your embedding dimensionality and dataset size.
Combined relational and similarity queries optimized so vector search doesn't slow down the rest of your database.
Connecting pgvector-backed retrieval to your LLM application for grounded, retrieval-augmented answers.
Clear signals for when to stay on pgvector versus move to a dedicated vector database as scale or query complexity grows.
If your data already lives in Postgres and your vector volume is moderate, pgvector avoids running a second system entirely. If you need very high query throughput, billions of vectors, or advanced hybrid-search features, a dedicated store like Qdrant or Weaviate scales further. We look at your actual data volume before recommending either.
It depends on how much of your schema and query layer already exists — adding vector search to an existing Postgres database is typically cheaper than standing up a new vector store. We give a fixed estimate after a short scoping call.
RAG and semantic search for products already on Postgres, recommendation features that need to join vector similarity with relational data, and any case where avoiding a second database outweighs the ceiling on raw vector-search throughput.
Tell us what you're building and what's in your current Postgres setup — we'll scope an approach and a fixed estimate.