pgvector vs Qdrant for AI agents: Which Should You Use?

pgvector is PostgreSQL with vector search bolted on. Qdrant is a purpose-built vector database with filtering, payload indexing, and retrieval APIs designed for similarity workloads.

If you’re building AI agents, start with Qdrant unless your agent already lives inside Postgres and your retrieval layer is small.

Quick Comparison

When pgvector Wins

Use pgvector when the vector layer should stay inside your relational database.

• •

  Your app already runs on PostgreSQL

  • •If your users table, conversations table, documents table, and embeddings all live in one place, pgvector keeps things simple.
  • •You can join retrieved chunks directly against business data without shipping records between systems.
• •

  You need SQL-first retrieval

  • •pgvector fits teams that want to write one query instead of coordinating a database plus a vector engine.
  • •Example: retrieve chunks and filter by tenant in one statement:

    SELECT id, content
    FROM chunks
    WHERE tenant_id = $1
    ORDER BY embedding <-> $2
    LIMIT 10;
    

• •

  Your workload is moderate

  • •For thousands to low millions of vectors with sensible indexing, pgvector is enough.
  • •Use ivfflat when recall tradeoffs are acceptable or hnsw when you want better search quality without leaving Postgres.
• •

  You care more about operational simplicity than retrieval specialization

  • •One backup strategy.
  • •One auth model.
  • •One migration path.
  • •One observability stack.

pgvector wins on boring infrastructure. If the vector search is just one feature in a larger transactional system, it’s the right call.

When Qdrant Wins

Use Qdrant when retrieval is a first-class workload.

• •

  Your agent depends on fast semantic recall

  • •Qdrant is built for nearest-neighbor search under real load.
  • •Collections are optimized for vector similarity plus payload filtering without forcing everything through a relational engine.
• •

  You need strong metadata filtering

  • •Agents usually need more than “closest embedding.” They need filters like tenant, source type, time range, document state, or access control.
  • •Qdrant’s payload model makes this natural:

    {
      "filter": {
        "must": [
          { "key": "tenant_id", "match": { "value": "acme" } },
          { "key": "doc_type", "match": { "value": "policy" } }
        ]
      }
    }
    

• •

  You want cleaner retrieval APIs

  • •Qdrant gives you explicit primitives like upsert, search, scroll, retrieve, and collection-level configuration.
  • •That maps well to agent workflows where ingestion and retrieval are separate concerns.
• •

  You expect the system to grow

  • •More documents.
  • •More tenants.
  • •More filters.
  • •More concurrent searches.

  That’s where a dedicated vector store pays off fast.

Qdrant wins when vector search stops being an add-on and becomes part of the product’s core path.

For AI agents Specifically

Pick Qdrant for most AI agents. Agents need high-quality top-k retrieval, aggressive metadata filtering, and predictable behavior as the corpus grows; Qdrant was built for that job.

Pick pgvector only if your agent is tightly coupled to an existing PostgreSQL application and your retrieval needs are modest. Otherwise you’ll end up turning Postgres into a search engine because it was convenient on day one.

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.