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

pgvector and DeepEval solve different problems, and that’s the first thing to get straight.

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 like AnswerRelevancyMetric, FaithfulnessMetric, and test cases built around LLMTestCase.

For production AI, use pgvector for retrieval, and DeepEval for evaluation. If you force one tool to do the other’s job, you’ll build the wrong system.

Quick Comparison

When pgvector Wins

• •

  You need retrieval in the same database as your app data.
  If your product already runs on PostgreSQL, pgvector keeps embeddings next to customer records, tickets, policies, or claims. That means simpler joins, fewer services, and less operational drag.

• •

  You need hard filters plus semantic search.
  pgvector works well when you want queries like “find similar documents for this tenant where status = active and region = EU.” SQL plus WHERE clauses beats bolting a separate vector store onto a relational system.

• •

  You want predictable production behavior.
  Postgres gives you mature backups, replication, access control, monitoring, and schema management. pgvector inherits that stability instead of introducing a new datastore just for embeddings.

• •

  You need hybrid search without overengineering.
  A common pattern is full-text search plus vector similarity in one query path. pgvector fits cleanly into systems where lexical ranking and semantic ranking both matter.

Example pattern:

CREATE EXTENSION IF NOT EXISTS vector;

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

CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

That is production-friendly because it stays inside the database team’s existing operational model.

When DeepEval Wins

• •

  You need to measure whether your LLM system is actually good.
  DeepEval is built for evaluation workflows. If you’re shipping a chatbot or agent, you need repeatable tests around relevance, faithfulness, toxicity, and task success. That’s where evaluate()-style runs and metric objects matter.

• •

  You want regression tests in CI/CD for prompts and chains.
  A prompt change can silently break answer quality even if your code still passes unit tests. DeepEval lets you encode expected behavior using LLMTestCase so bad prompt edits fail before they hit production.

• •

  You are debugging hallucinations or retrieval quality.
  Metrics like FaithfulnessMetric are useful when your RAG system starts inventing facts or ignoring context. DeepEval gives you a structured way to catch those failures instead of relying on manual spot checks.

• •

  You need evals across multiple dimensions of output quality.
  Production AI rarely fails in one way only. It can be correct but incomplete, grounded but verbose, or safe but useless. DeepEval lets you score those dimensions separately instead of treating “looks okay” as a metric.

Example pattern:

from deepeval import evaluate
from deepeval.test_case import LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric

test_case = LLMTestCase(
    input="What is our refund policy?",
    actual_output="Refunds are available within 30 days with receipt.",
    expected_output="Refunds are available within 30 days."
)

metric = AnswerRelevancyMetric(threshold=0.8)
evaluate([test_case], [metric])

That belongs in test pipelines, not in your online request path.

For production AI Specifically

Use pgvector when the problem is storage and retrieval. Use DeepEval when the problem is proving your AI system works before users see it. They are not substitutes; they sit on different sides of the production boundary.

If I had to choose one first for a production AI stack: start with pgvector if you are building RAG or semantic search infrastructure; add DeepEval immediately after to keep that system honest. Retrieval without evals ships blind. Evals without retrieval infrastructure have nothing real to measure.

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.