pgvector vs Qdrant for startups: Which Should You Use?

pgvector is the “keep it in Postgres” choice: simple, boring, and easy to ship when your app already lives on PostgreSQL. Qdrant is the purpose-built vector database: more features, better retrieval tooling, and a cleaner path once vector search becomes a core product requirement. For most startups, start with pgvector unless vector search is already central to the product or you expect serious scale soon.

Quick Comparison

When pgvector Wins

• •

  Your product already runs on Postgres

  If your app uses PostgreSQL for users, billing, permissions, and content metadata, adding pgvector is the lowest-friction move. You avoid introducing another datastore and keep joins in SQL instead of stitching data across systems.

• •

  You need embeddings tied tightly to relational records

  A common startup pattern is “search over documents linked to customers/projects/tickets.” With pgvector, you can store embeddings in the same table or a related table and query with normal SQL filters plus similarity search.

  CREATE EXTENSION IF NOT EXISTS vector;
  
  CREATE TABLE documents (
    id bigserial PRIMARY KEY,
    tenant_id bigint NOT NULL,
    title text NOT NULL,
    embedding vector(1536)
  );
  
  CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);
  

• •

  You want one operational surface area

  Startups die from too many moving parts. If your team can manage one Postgres instance well but not a separate vector service with its own backup strategy and scaling model, pgvector is the correct call.

• •

  Your vector workload is real but not dominant

  If embeddings are supporting search or RAG features rather than defining the product itself, pgvector gives you enough performance without overengineering the stack.

When Qdrant Wins

• •

  Vector search is the product

  If your app lives or dies on retrieval quality and latency — semantic search, RAG platform, recommendation engine — use Qdrant. It is built for this problem instead of adapting a relational database to do it.

• •

  You need powerful metadata filtering

  Qdrant’s payload filtering is one of its biggest advantages. You can combine similarity search with structured constraints cleanly through its filter API instead of forcing complex SQL plans around ANN indexes.

  Example shape:

  {
    "vector": [0.12, 0.98],
    "filter": {
      "must": [
        { "key": "tenant_id", "match": { "value": 42 } },
        { "key": "status", "match": { "value": "active" } }
      ]
    },
    "limit": 10
  }
  

• •

  You expect high write/read volume on vectors

  Qdrant handles large-scale approximate nearest neighbor workloads better than pgvector once you start pushing serious throughput and dataset size. It gives you a cleaner scaling story when embeddings become a first-class data type.

• •

  You want dedicated vector-native features

  Qdrant gives you things like named vectors, payload indexes, collection-level tuning knobs like HNSW configuration and quantization options depending on deployment/versioning choices. Those are useful when you need control over recall/latency/cost tradeoffs.

For startups Specifically

Use pgvector first unless you have a strong reason not to. It wins on simplicity: fewer services, lower cost, easier debugging, and no new operational category to learn while your team is still finding product-market fit.

Pick Qdrant when vector search is not just a feature but a core subsystem that will grow fast and needs dedicated tuning from day one. If you’re building an AI-first product where retrieval quality directly affects revenue or retention, go straight to Qdrant and skip the “Postgres can do it too” phase.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
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• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.