pgvector vs DeepEval for real-time apps: Which Should You Use?

pgvector and DeepEval solve different problems. pgvector is a PostgreSQL extension for storing and querying embeddings with vector, ivfflat, and hnsw; DeepEval is a framework for evaluating LLM outputs with metrics, test cases, and synthetic datasets. For real-time apps, use pgvector in the request path and DeepEval in your offline evaluation pipeline.

Quick Comparison

When pgvector Wins

• •

  You need retrieval inside the same transaction boundary as your app data.
  If your customer record lives in Postgres, keeping embeddings there avoids dual writes and consistency bugs.

• •

  You need deterministic low-latency query paths.
  A query like this is simple to reason about and fast enough for real-time lookup:

  SELECT id, content
  FROM documents
  ORDER BY embedding <-> $1
  LIMIT 5;
  

  Add an index with either:

  CREATE INDEX ON documents USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);
  

  or:

  CREATE INDEX ON documents USING hnsw (embedding vector_l2_ops);
  

• •

  Your app already runs on PostgreSQL and you want fewer moving parts.
  One database means simpler ops, simpler backups, simpler observability, and fewer network hops.

• •

  You need hybrid filtering plus vector search.
  pgvector plays well with normal SQL filters:

  SELECT id
  FROM tickets
  WHERE tenant_id = $1 AND status = 'open'
  ORDER BY embedding <-> $2
  LIMIT 10;
  

  That pattern matters in multi-tenant real-time systems.

When DeepEval Wins

• •

  You care about whether your LLM output is actually good.
  pgvector can retrieve context; it cannot tell you if the answer is grounded, relevant, or hallucinated. DeepEval gives you metrics for that.

• •

  You need regression tests for prompts and chains.
  DeepEval lets you define test cases and run them against metrics like AnswerRelevancyMetric, FaithfulnessMetric, and ContextualPrecisionMetric. That is what catches prompt drift before production does.

• •

  You want a repeatable evaluation harness for model changes.
  If you swap prompts, retrievers, or models weekly, DeepEval gives you a scorecard instead of gut feel.

• •

  You are validating RAG quality offline before shipping.
  Use it to compare retrieval strategies, chunking schemes, or prompt templates against a labeled dataset. That is the right place to measure quality.

For real-time apps Specifically

Use pgvector in the live request path every time. Real-time apps need fast retrieval with predictable latency, and pgvector gives you that directly inside Postgres without adding another service hop.

Use DeepEval around that system to prove the answers are good before they hit users. The winning architecture is not either/or: pgvector serves context at runtime, DeepEval guards quality during development and release gates.

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.