pgvector vs Ragas for batch processing: Which Should You Use?

pgvector and Ragas solve different problems. pgvector is a PostgreSQL extension for storing and querying embeddings at scale; Ragas is an evaluation framework for measuring RAG quality with metrics like faithfulness, answer_relevancy, and context_precision. For batch processing, use pgvector if you need to ingest, store, and retrieve vectors in bulk; use Ragas only if your batch job is evaluating retrieval or generation quality.

Quick Comparison

When pgvector Wins

• •

  You need to store millions of embeddings and query them later.

  • •pgvector is built for this.
  • •A typical schema looks like:

    CREATE EXTENSION IF NOT EXISTS vector;
    
    CREATE TABLE documents (
      id bigserial PRIMARY KEY,
      content text,
      embedding vector(1536)
    );
    

  • •Then you batch insert embeddings and query with similarity operators like <-> for L2 distance or <=> for cosine distance.
• •

  Your batch job is really an ETL pipeline.

  • •Example: chunk PDFs overnight, generate embeddings in batches of 1,000 records, write them into Postgres, then build ivfflat or hnsw indexes.
  • •pgvector fits cleanly into existing database workflows:

    CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);
    

• •

  You need transactional guarantees.

  • •If your ingestion job fails halfway through, Postgres gives you rollback semantics.
  • •That matters when batches update metadata alongside vectors: source IDs, tenant IDs, compliance tags, timestamps.
• •

  You want one operational system instead of a separate vector store.

  • •For teams already running Postgres in production, pgvector avoids another service to deploy, monitor, secure, and back up.
  • •That matters more than people admit when the workload is mostly batch writes plus occasional retrieval.

When Ragas Wins

• •

  Your batch job is about measuring quality, not storing vectors.

  • •Ragas exists to evaluate RAG pipelines with metrics like faithfulness, answer_relevancy, context_recall, and context_precision.
  • •If you’re scoring hundreds or thousands of test cases after a model change, this is the right tool.
• •

  You need regression testing for retrieval + generation.

  • •Example: every night you run the same eval set against your chatbot pipeline and compare scores before merging changes.
  • •Ragas gives you a structured way to catch retrieval drift or prompt regressions before they hit production.
• •

  You want LLM-as-a-judge style evaluation in a repeatable batch flow.

  • •Ragas integrates with LLM-backed metrics so you can score outputs against context without hand-labeling everything.
  • •That’s useful when human review is too slow or too expensive for every batch.
• •

  Your output needs to be analysis-ready, not operationally stored.

  • •Batch eval jobs often end in CSVs, dashboards, or experiment reports.
  • •Ragas produces exactly that kind of artifact: metric scores per sample that can feed BI tools or CI gates.

For batch processing Specifically

Use pgvector when the batch job produces or consumes embeddings at scale. It wins on throughput, transactional safety, SQL integration, and operational simplicity.

Use Ragas only when the batch job’s goal is evaluation. It does not replace a vector database; it measures whether your retrieval system is actually working.

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.