pgvector vs NeMo for RAG: Which Should You Use?

pgvector is a Postgres extension for storing and querying embeddings with SQL. NeMo is NVIDIA’s AI stack for building and serving generative AI systems, including retrieval and inference pipelines. For RAG, start with pgvector unless you already need NVIDIA’s full enterprise AI stack.

Quick Comparison

When pgvector Wins

• •

  You already run PostgreSQL in production.

  • •This is the biggest one. If your app data lives in Postgres, adding pgvector means you keep retrieval next to the source of truth.
  • •You can filter by tenant, status, region, or document type using normal SQL before or during vector search.
• •

  You need hybrid retrieval without extra infrastructure.

  • •pgvector works well when you combine semantic search with structured predicates.
  • •Example: WHERE tenant_id = $1 AND published_at > now() - interval '90 days' ORDER BY embedding <-> $query_embedding LIMIT 10.
  • •That pattern is hard to beat for production RAG on business data.
• •

  Your team is small and wants one datastore.

  • •One backup strategy.
  • •One access control model.
  • •One operational surface area.
  • •That matters more than raw benchmark numbers when you’re shipping an internal assistant or customer support bot.
• •

  Your scale is moderate.

  • •If you’re dealing with thousands to low millions of chunks per tenant, pgvector is usually enough.
  • •With proper indexing (HNSW or IVFFlat) and sane chunking, it performs well without introducing a separate vector database tier.

When NeMo Wins

• •

  You are already standardized on NVIDIA infrastructure.

  • •If your platform team runs GPUs everywhere and expects NVIDIA tooling, NeMo fits cleanly.
  • •NeMo Retriever and NIM microservices are built for teams that want managed model serving patterns rather than hand-rolled glue code.
• •

  You need high-throughput inference at scale.

  • •NeMo makes sense when the bottleneck is not just retrieval but the full generation pipeline.
  • •If your RAG system needs aggressive batching, GPU acceleration, and model-serving controls, NeMo gives you a better path than trying to force Postgres into that role.
• •

  You want an enterprise AI platform, not just retrieval storage.

  • •pgvector solves one problem: vector similarity search inside Postgres.
  • •NeMo covers more of the stack: embedding generation workflows, retrieval components, guardrails-adjacent enterprise patterns, and deployment primitives.
• •

  You have compliance and platform requirements that favor vendor-backed architecture.

  • •Some organizations want a single commercial support path for AI infrastructure.
  • •In those environments, NeMo is easier to justify to ops and security teams than stitching together open-source pieces yourself.

For RAG Specifically

Use pgvector unless your RAG system is clearly becoming a GPU-backed platform problem. Most RAG apps need tight metadata filtering, simple operations, low cost, and fast iteration — that is exactly where pgvector inside PostgreSQL shines.

Choose NeMo only when retrieval is part of a larger NVIDIA-centered AI architecture or when you need serious inference throughput at enterprise scale. For most developers building RAG over documents, tickets, policies, or knowledge bases, pgvector is the correct default.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.