pgvector vs Ragas for fintech: Which Should You Use?

pgvector and Ragas solve different problems. pgvector is a PostgreSQL extension for storing and querying embeddings with vector, ivfflat, and hnsw; Ragas is an evaluation framework for measuring RAG quality with metrics like faithfulness, answer relevancy, context precision, and context recall.

For fintech, use pgvector in production data paths and Ragas in your evaluation pipeline. They are not substitutes.

Quick Comparison

When pgvector Wins

• •

  You need vector search inside the same database that stores customer records.

  In fintech, that matters. If your KYC notes, dispute cases, policy docs, and audit metadata already live in Postgres, adding pgvector avoids another datastore and keeps joins simple.

• •

  You care about transactional consistency.

  A support agent should not retrieve embeddings for a document that hasn’t been committed yet. With pgvector inside PostgreSQL, your embeddings sit next to the source record under the same transaction model.

• •

  You need strict access control.

  Fintech teams often rely on PostgreSQL roles, row-level security, and existing audit controls. pgvector inherits those controls instead of forcing you to rebuild authorization around a separate vector service.

• •

  You want a simpler operational footprint.

  One database backup strategy. One replication story. One monitoring stack. For regulated environments, fewer moving parts usually wins.

Example: storing embeddings in Postgres

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE policy_chunks (
  id bigserial PRIMARY KEY,
  policy_id bigint NOT NULL,
  chunk ტექst text NOT NULL,
  embedding vector(1536)
);

CREATE INDEX ON policy_chunks USING hnsw (embedding vector_cosine_ops);

That is the right shape when your app needs semantic retrieval over internal documents without introducing another system.

When Ragas Wins

• •

  You need to prove your RAG system is not hallucinating.

  Fintech does not tolerate vague answers. If your assistant explains fee reversals or card chargeback rules incorrectly, that becomes a customer complaint or compliance issue. Ragas gives you metrics like faithfulness and answer_relevancy so you can measure that failure mode.

• •

  You are comparing retrievers or prompts before rollout.

  If you are tuning chunk sizes, rerankers, or prompt templates for loan policy Q&A or fraud investigation copilots, Ragas is the tool that tells you which setup actually improves retrieval quality.

• •

  You need regression tests for knowledge assistants.

  When product or compliance updates change source documents every week, you want automated evals that catch degraded retrieval or unsupported answers before deployment. Ragas fits directly into CI pipelines for this.

• •

  You already have retrieval infrastructure and need measurement.

  If your stack uses Pinecone, OpenSearch, Elasticsearch, Weaviate, or even pgvector underneath, Ragas stays useful because it sits above storage. It evaluates the output of the system rather than replacing it.

Example: evaluating a RAG pipeline

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy
from datasets import Dataset

data = Dataset.from_dict({
    "question": ["What is the chargeback window?"],
    "answer": ["The chargeback window is 120 days."],
    "contexts": [["Chargebacks must be filed within 90 days unless..." ]],
    "ground_truth": ["Chargebacks must be filed within 90 days unless..."]
})

result = evaluate(data, metrics=[faithfulness, answer_relevancy])
print(result)

That is what you use when the question is not “where do I store vectors?” but “can I trust this assistant’s answer?”

For fintech Specifically

Use pgvector when building the retrieval layer behind customer support search, internal policy lookup, fraud ops assistants, or compliance document discovery. Use Ragas when validating that those systems stay accurate under change and do not produce unsupported claims.

If I had to pick one first for a fintech team shipping production features: pgvector. It solves an infrastructure problem cleanly; Ragas solves an evaluation problem after the system exists.

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.