pgvector vs DeepEval for startups: Which Should You Use?

pgvector and DeepEval solve different problems. pgvector is a PostgreSQL extension for storing and searching embeddings with SQL; DeepEval is an evaluation framework for testing LLM apps with metrics like AnswerRelevancy, Faithfulness, and GEval. For startups, use pgvector first if you need retrieval in production, then add DeepEval when you need to measure whether your AI app is actually good.

Quick Comparison

When pgvector Wins

• •

  You need vector search in production now

  If your startup is building a chatbot over internal docs or customer records, pgvector gets you from zero to working retrieval fast. Store embeddings in a vector(1536) column, index them with HNSW, and query with:

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

  That is enough for a real RAG pipeline.

• •

  You already run PostgreSQL

  Startups should avoid adding another datastore unless there is a hard reason. With pgvector, you keep embeddings next to users, tenants, permissions, audit logs, and business data in one database.

• •

  You need transactional guarantees

  If embedding updates must stay in sync with document inserts or deletes, Postgres gives you ACID semantics that dedicated vector stores often complicate. That matters when stale chunks or orphaned embeddings create broken answers.

• •

  You want the cheapest operational path

  pgvector avoids introducing a separate vector database early on. For seed-stage teams with one backend engineer wearing five hats, fewer moving parts beats theoretical scale.

When DeepEval Wins

• •

  You need to know if your prompts are getting worse

  DeepEval is built for regression testing LLM behavior. If you ship prompt changes weekly and want a scorecard on output quality before production deploys, metrics like AnswerRelevancy and Faithfulness are the right tool.

• •

  You are comparing models or prompt versions

  Startups often swap between GPT-4-class models, smaller hosted models, or fine-tuned variants. DeepEval gives you a repeatable way to run the same test cases against each version and compare results instead of relying on gut feel.

• •

  Your product depends on agent behavior

  If your app uses multi-step tools or function calls where failure modes are subtle—wrong tool choice, hallucinated facts, bad reasoning traces—DeepEval helps you build evaluation gates around those behaviors.

• •

  You need CI for AI quality

  Put DeepEval into your pipeline so every PR can run eval suites before merge. That is how you stop prompt edits from silently breaking production responses.

Example pattern:

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

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

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

For startups Specifically

Pick pgvector first if you are building any product that needs semantic retrieval in the request path. It solves the core infrastructure problem: find the right context quickly and cheaply inside a stack your team already understands.

Add DeepEval second once the system works end-to-end and you need discipline around quality. Startups fail more often from shipping unmeasured AI behavior than from choosing the wrong vector store; pgvector gets you live faster, DeepEval keeps you honest after launch.

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.