pgvector & RAG Infrastructure: Your PostgreSQL Already Powers AI
Before you add Pinecone, Qdrant, or a dedicated vector database—talk to us. Most teams with under 10M vectors can run semantic search, RAG pipelines, and AI features directly on the PostgreSQL they already operate.
This is for you if:
What is AI Data Infrastructure?
AI data infrastructure is the data layer that makes AI products work: where you store embeddings, how you retrieve relevant context, how you feed that context to an LLM, and how you keep the whole system performant as your data grows.
Most teams over-engineer this. They add Pinecone because they saw it in a tutorial, then discover they need complex sync logic to keep it aligned with their PostgreSQL database. They pay for two systems, operate two systems, and debug two systems—when one would have done the job.
pgvector is a PostgreSQL extension that adds vector similarity search to your existing database. With properly tuned HNSW indexes, it delivers 471 queries per second at 99% recall accuracy—competitive with dedicated vector databases for datasets under 10 million vectors. And unlike Pinecone, it lets you filter by user, date, category, and semantic similarity in a single SQL query.
We help you decide whether pgvector is right for your workload, set it up correctly, and build the RAG pipeline that connects your data to your AI product.
Why pgvector Over a Dedicated Vector Database
For most production workloads under 10M vectors, pgvector wins on cost, simplicity, and query flexibility.
No New Database to Manage
pgvector runs inside your existing PostgreSQL. No new infrastructure, no new operational burden, no new backup strategy. Your team already knows how to run it.
Competitive Vector Search Performance
Properly tuned HNSW indexes on pgvector deliver strong throughput at high recall accuracy. For very large datasets (50M+ vectors), pgvectorscale's StreamingDiskANN index consistently outperforms standalone Qdrant in published benchmarks—without adding a separate database.
SQL Joins on Vector Data
Unlike Pinecone or Qdrant, pgvector lets you join vector similarity results directly with your relational data. Filter by user, date, category, and semantic similarity in a single query.
What We Build
Four common AI data infrastructure patterns we implement on PostgreSQL.
Document Q&A / Chatbot
Let users ask questions about your PDFs, knowledge base, or internal docs. Retrieve relevant chunks via pgvector, pass to LLM, get accurate answers.
Semantic Product Search
Find "wireless headphones good for gym" when the product is listed as "sport earbuds with Bluetooth." Vector search finds meaning, not just keywords.
Recommendation Engine
Recommend similar articles, products, or content using embedding similarity. No collaborative filtering required—works even with sparse user history.
LLM Context & Memory
Store conversation history and retrieve relevant past exchanges to give your AI product long-term memory without stuffing the entire history into the context window.
How a pgvector Engagement Works
Architecture Review
We assess your PostgreSQL version, instance sizing, current extensions, and data model to determine what's possible and what needs upgrading.
Embedding Strategy
We select the right embedding model for your use case, design the chunking strategy for your documents, and build the embedding pipeline.
pgvector Setup & Index Tuning
We enable pgvector, create optimized HNSW indexes with the right `m` and `ef_construction` parameters for your dataset size and query latency target.
RAG Pipeline Build
We build the retrieval layer—query embedding, similarity search, result re-ranking, and LLM context assembly—with evaluation metrics to track accuracy.
Handoff & Monitoring
We document the architecture, set up query performance monitoring, and train your team on how to maintain and extend the system.
Frequently Asked Questions
Find Out If pgvector Is Right for Your AI Product
Share your use case, data volume, and query latency requirements. We'll tell you whether pgvector fits—and if it doesn't, exactly what does. No sales pitch, just an honest assessment.
Stop Paying for a Vector Database You Don't Need
Most teams that come to us are mid-way through a Pinecone or Qdrant integration and realize the complexity isn't worth it. We help you evaluate the right architecture before you're committed to the wrong one.