pgvector vs NeMo for AI agents: Which Should You Use?

pgvector and NeMo solve different problems, and that matters for AI agents. pgvector is a PostgreSQL extension for storing and querying embeddings with SQL; NeMo is NVIDIA’s framework for building and serving LLM workflows, especially when you want model customization, guardrails, and GPU-backed inference.

For most AI agents, start with pgvector. It gives you the simplest path to retrieval, memory, and production data access without adding a second platform to operate.

Quick Comparison

When pgvector Wins

• •

  Your agent needs retrieval more than generation infrastructure

  If the core problem is “find the right customer policy clause,” “pull the last 10 support interactions,” or “retrieve similar claims,” pgvector is the right tool. You can store embeddings in a vector column and query them directly with SQL.

• •

  You want one database for state + vectors

  Agents need more than embeddings: conversation state, tool outputs, user profiles, audit trails. With pgvector inside PostgreSQL, you keep structured data and vector search together instead of splitting them across systems.

• •

  You need strong metadata filtering

  This is where pgvector is brutally practical. A query like “top similar docs for this user’s region and product line” is just SQL with filters plus vector distance operators like <->, <=>, or <#> depending on your setup.

• •

  You are shipping fast with a normal backend stack

  If your app already uses Postgres through SQLAlchemy or Prisma, adding pgvector is low-risk work. You do not need to introduce a new serving layer just to get semantic retrieval into an agent.

Example pattern:

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE documents (
  id bigserial PRIMARY KEY,
  tenant_id bigint NOT NULL,
  content text NOT NULL,
  embedding vector(1536)
);

CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

That gets you production-grade retrieval without changing your architecture.

When NeMo Wins

• •

  You are building custom model workflows

  If the agent depends on fine-tuned domain models, prompt orchestration at scale, or specialized inference behavior, NeMo is the better fit. It is built for model-centric systems rather than just storage-backed retrieval.

• •

  GPU inference is your main bottleneck

  When latency and throughput come from generation workloads instead of search workloads, NeMo makes sense. This is where NVIDIA’s stack earns its keep: optimized deployment around large models on GPUs.

• •

  You need enterprise guardrails around LLM behavior

  For regulated environments where response control matters as much as answer quality, NeMo gives you more room to build controlled LLM pipelines than a plain vector store ever will.

• •

  You are already deep in NVIDIA infrastructure

  If your team runs CUDA-heavy workloads and already uses NVIDIA tooling across training and serving, NeMo reduces friction. In that environment it is easier to standardize on one vendor stack than stitch together separate pieces.

NeMo is not a replacement for retrieval storage like pgvector. It sits higher up the stack: model development, tuning, serving, and governance.

For AI agents Specifically

Use pgvector unless your agent project is actually an LLM platform project. Most agents need fast retrieval over business data plus structured state management; PostgreSQL with pgvector handles that cleanly with less operational drag.

Choose NeMo only when the agent’s value depends on custom model behavior or GPU-heavy inference pipelines that go beyond standard RAG. If you are building an agent that needs memory and search first, pgvector wins hard.

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.