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

pgvector is a PostgreSQL extension for vector similarity search. Ragas is an evaluation framework for LLM and retrieval quality. They solve different problems, and for real-time apps the default choice is pgvector for serving, with Ragas used offline to measure whether your retrieval pipeline is actually good.

Quick Comparison

When pgvector Wins

• •

  You need sub-100ms retrieval inside a transactional app

  If your app already uses PostgreSQL for users, orders, claims, or tickets, pgvector keeps vector search in the same database. That means one connection pool, one consistency model, and no extra network hop to a separate vector service.

• •

  You need filtering and vector search in one query

  This is where pgvector earns its keep. You can combine metadata filters with similarity search directly in SQL:

  SELECT id, title
  FROM documents
  WHERE tenant_id = $1
    AND status = 'active'
  ORDER BY embedding <-> $2
  LIMIT 10;
  

  For real systems, this matters more than benchmark vanity numbers.

• •

  You want operational simplicity

  pgvector fits cleanly into existing Postgres backup, replication, monitoring, and access control. If your team already knows how to run Postgres safely in production, adding vectors is a small step instead of introducing a new platform.

• •

  You are building the retrieval layer of an agent

  Real-time agents need fast candidate selection before generation. pgvector gives you cosine, L2, or inner product search through standard SQL patterns without forcing your app into an eval-centric framework.

When Ragas Wins

• •

  You need to know if your retrieval pipeline is actually good

  pgvector stores embeddings. It does not tell you whether your chunks are relevant or whether your retriever returns evidence that supports the answer. Ragas gives you metrics like context precision, context recall, faithfulness, and answer relevancy.

• •

  You are running regression tests on an LLM app

  If you changed chunking strategy, embedding model, reranker logic, or prompt templates, Ragas helps you compare before and after. That makes it useful in CI pipelines where you want to catch quality drops before they hit production.

• •

  You are tuning a RAG system with multiple components

  Real apps fail because of bad chunking, weak retrievers, noisy contexts, or hallucinated answers. Ragas helps isolate which part is broken by evaluating retrieved context against ground truth and generated answers against references.

• •

  You need human-readable quality signals for stakeholders

  Product teams do not care that your ANN index returns vectors quickly if the answer is wrong. Ragas produces metrics that map closer to business quality: did the model answer correctly, was the context relevant, did it stay grounded?

For real-time apps Specifically

Use pgvector in the request path. It is built for serving: fast similarity search through SQL, tight Postgres integration with ivfflat or HNSW indexing patterns, and no extra service boundary to slow down your response time.

Use Ragas outside the request path to validate that pgvector-backed retrieval is good enough. The right architecture is not either/or: pgvector serves users in real time; Ragas tells you whether your retrieval setup deserves to be shipped at all.

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.