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

pgvector and DeepEval solve different problems, and that matters a lot for batch jobs. pgvector is a PostgreSQL extension for storing and querying embeddings with vector, ivfflat, and hnsw; DeepEval is an evaluation framework for scoring LLM outputs with metrics like GEval, AnswerRelevancyMetric, and FaithfulnessMetric.

For batch processing, use pgvector when the job is about retrieval, indexing, or similarity search at scale. Use DeepEval when the job is about scoring model outputs or running offline eval pipelines.

Quick Comparison

When pgvector Wins

• •

  You need to process millions of embeddings in batches.

  • •Example: ingest documents nightly, generate embeddings in chunks, then bulk load into a table with a vector(1536) column.
  • •pgvector handles this cleanly with standard Postgres tooling: COPY, transactions, indexes, and SQL filters.
• •

  Your batch job is really a retrieval pipeline.

  • •If the work is “embed → store → query top-k similar rows,” pgvector is the right tool.
  • •You can run queries like:

    SELECT id
    FROM chunks
    ORDER BY embedding <-> '[0.12, 0.34, ...]'::vector
    LIMIT 10;
    

  • •That’s a real batch-friendly pattern because the database owns persistence and retrieval.
• •

  You need operational simplicity.

  • •One system does storage, filtering, joins, metadata lookup, and vector search.
  • •For banks and insurance teams, that matters because batch workflows usually need auditability and deterministic SQL semantics.
• •

  You want to combine vector search with business rules.

  • •Example: only compare active policies from the last 90 days or only retrieve claims from a specific region.
  • •pgvector lets you do this in one query instead of shuttling data between a vector store and an analytics layer.

When DeepEval Wins

• •

  Your batch job is evaluating model quality, not searching vectors.

  • •DeepEval is built for scoring outputs against references or expectations.
  • •A typical run looks like testing hundreds of generated answers with metrics such as:

    from deepeval import evaluate
    from deepeval.metrics import AnswerRelevancyMetric
    
    evaluate(test_cases=test_cases, metrics=[AnswerRelevancyMetric()])
    

• •

  You need batch QA for RAG or agent pipelines.

  • •DeepEval gives you metrics like FaithfulnessMetric, ContextualRecallMetric, and GEval so you can score whether retrieved context actually supports the answer.
  • •That’s exactly what you want when reviewing a nightly batch of prompt/version changes.
• •

  You care about regression testing across releases.

  • •If your team ships prompt changes weekly or retrains models monthly, DeepEval lets you compare runs over time.
  • •This is better than eyeballing samples in a notebook.
• •

  You need human-readable evaluation reports more than infrastructure.

  • •DeepEval is designed to tell you whether outputs are good or bad according to criteria you define.
  • •It’s an eval harness first; it’s not trying to be your storage layer.

For batch processing Specifically

If the batch job touches embeddings at rest or needs high-throughput similarity search, pick pgvector. It belongs in the data plane: ingest in bulk, index once with hnsw or ivfflat, query fast forever.

If the batch job scores generations after the fact — especially RAG answers or agent traces — pick DeepEval. It belongs in the evaluation plane: run metrics over a dataset of test cases and produce quality signals you can gate on before release.

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.