pgvector vs Helicone for RAG: Which Should You Use?

pgvector and Helicone solve different problems, and mixing them up leads to bad architecture decisions.

pgvector is a PostgreSQL extension for storing and querying embeddings with vector, ivfflat, and hnsw. Helicone is an observability and gateway layer for LLM traffic, with request logging, prompt tracking, cost analytics, and debugging. For RAG: use pgvector for retrieval storage, and use Helicone around your LLM calls if you need observability.

Quick Comparison

When pgvector Wins

• •

  You need retrieval inside your database.

  • •If your app already lives on PostgreSQL, pgvector keeps embeddings next to the source data.
  • •That means one transaction boundary, one backup strategy, one permission model.
• •

  You need real filtering with vector search.

  • •pgvector plays well with SQL filters like tenant IDs, document types, timestamps, or ACL checks.
  • •This matters in enterprise RAG where retrieval must respect authorization before generation.
• •

  You want production control over indexing strategy.

  • •ivfflat works when you want a simpler approximate nearest neighbor setup.
  • •hnsw is the better choice when you care about fast recall at scale.
  • •You control index creation directly in SQL:

    CREATE EXTENSION IF NOT EXISTS vector;
    
    CREATE INDEX ON documents
    USING hnsw (embedding vector_cosine_ops);
    

• •

  You need a durable system of record for embeddings.

  • •For regulated environments, embeddings are not just cache entries.
  • •Storing them in Postgres gives you auditability, backups, replication, and standard operational tooling.

When Helicone Wins

• •

  You need visibility into every LLM call in the RAG pipeline.

  • •Helicone gives you request logs, latency breakdowns, token usage, cost tracking, and prompt history.
  • •That is what you need when users complain that the assistant “got worse” after a deployment.
• •

  You are debugging prompt quality or model drift.

  • •RAG failures are often not retrieval failures.
  • •Sometimes the retriever is fine and the issue is prompt formatting, context truncation, or model selection. Helicone makes that obvious by showing actual requests and responses.
• •

  You run multiple models or providers behind one interface.

  • •Helicone sits well as a gateway for OpenAI-style traffic and helps normalize observability across providers.
  • •If your stack includes retries, fallbacks, or A/B tests across models, this becomes useful immediately.
• •

  You care about operations more than storage.

  • •Helicone is not where embeddings live.
  • •It is where you watch costs spike when chunk sizes explode or when a bad prompt causes token usage to double overnight.

For RAG Specifically

Use pgvector as the retrieval layer. That is the right tool for embedding storage plus similarity search plus metadata filtering inside a system that already understands your data model.

Use Helicone on top of the generation layer if you need observability into prompts, responses, latency, and spend. In a real RAG stack, pgvector answers “what documents should I retrieve?” while Helicone answers “what did the model do with that context?”

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.