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

pgvector and DeepEval solve different problems, and treating them as substitutes is how teams waste time.

pgvector is a PostgreSQL extension for vector similarity search. DeepEval is an evaluation framework for testing LLM outputs, RAG pipelines, and agent behavior. For AI agents: use pgvector for retrieval storage, and use DeepEval to prove the agent actually works.

Quick Comparison

When pgvector Wins

• •

  You need retrieval inside your existing Postgres stack.

  • •If your agent already uses PostgreSQL for users, sessions, tickets, or transactions, adding vector columns is the cleanest move.
  • •You avoid introducing a separate vector database just to store embeddings.
• •

  You need hard filtering alongside semantic search.

  • •pgvector works well when you need WHERE tenant_id = ... AND status = 'open' plus nearest-neighbor search.
  • •That matters in enterprise agents where permissions and tenant isolation are non-negotiable.
• •

  You want one operational surface area.

  • •One database means one backup strategy, one auth model, one monitoring setup.
  • •For internal AI agents in banks and insurance companies, that simplicity beats tool sprawl every time.
• •

  Your team is already strong in SQL.

  • •pgvector uses normal Postgres patterns: CREATE EXTENSION vector;, ORDER BY embedding <-> query_embedding, indexes like ivfflat or hnsw.
  • •Your engineers do not need to learn a new datastore just to ship retrieval.

When DeepEval Wins

• •

  You need to test whether the agent is actually good.

  • •DeepEval is built for evaluation with metrics like AnswerRelevancyMetric, FaithfulnessMetric, ContextualPrecisionMetric, and HallucinationMetric.
  • •That’s what you use when stakeholders ask, “Did the new prompt make the agent worse?”
• •

  You are shipping RAG or multi-step agents into production.

  • •Retrieval quality alone does not tell you if the final answer is correct.
  • •DeepEval lets you build test cases around full agent behavior: input, retrieved context, output, and expected outcome.
• •

  You want regression testing in CI.

  • •DeepEval fits into automated checks so prompt changes, retriever changes, or model swaps do not silently break your system.
  • •This is the difference between “it worked in staging” and “we caught the failure before deployment.”
• •

  You need LLM-as-judge style scoring without building it yourself.

  • •DeepEval gives you a framework to define tests instead of wiring custom evaluation logic from scratch.
  • •That saves weeks when you are validating conversational agents across dozens of scenarios.

For AI agents Specifically

Use pgvector as part of the agent’s memory and retrieval layer. Use DeepEval as the quality gate before anything reaches users. If you have to choose one first, pick pgvector only if you are still building retrieval; pick DeepEval if the agent already exists and you need to measure whether it deserves production traffic.

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.