pgvector vs Qdrant for real-time apps: Which Should You Use?

pgvector is a PostgreSQL extension for vector similarity search. Qdrant is a purpose-built vector database with filtering, indexing, and payload-aware retrieval built in.

For real-time apps, use Qdrant unless your vector workload is small and already lives inside Postgres.

Quick Comparison

When pgvector Wins

Use pgvector when the vector layer should not become a separate system.

• •

  Your app already runs on PostgreSQL

  • •If your users, orders, events, or tickets are already in Postgres, adding pgvector keeps everything in one place.
  • •You can join embeddings with relational data in one query instead of syncing between systems.
• •

  You need transactional consistency

  • •If you insert a record and its embedding together, Postgres gives you ACID semantics.
  • •That matters for workflows like support case triage or compliance review where stale metadata is a bug.
• •

  Your scale is moderate

  • •If you are doing thousands of vector queries per minute rather than tens of thousands per second, pgvector is usually enough.
  • •With hnsw indexes it can be fast enough for many production workloads without another database.
• •

  You want simpler ops

  • •One backup strategy.
  • •One auth model.
  • •One observability stack.
  • •For small teams shipping internal tools or early-stage products, that matters more than theoretical retrieval performance.

A typical pattern looks like this:

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE documents (
  id bigserial PRIMARY KEY,
  tenant_id bigint NOT NULL,
  content text NOT NULL,
  embedding vector(1536)
);

CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

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

That is clean when your app logic already lives in SQL.

When Qdrant Wins

Use Qdrant when retrieval latency and concurrency are part of the product requirement.

• •

  You need fast filtered search at scale

  • •Qdrant’s payload filtering is built for this.
  • •You can combine vector similarity with structured filters using must, should, and must_not without turning your database into a bottleneck.
• •

  You have real-time personalization or recommendation

  • •For feed ranking, “similar items,” session memory, or next-best-action systems, Qdrant handles high-frequency upserts and searches better than forcing Postgres into that role.
  • •The API is designed around point-level updates and retrieval speed.
• •

  Your vectors change often

  • •Real-time apps frequently ingest new events: clicks, messages, document updates, profile changes.
  • •Qdrant’s upsert flow fits that pattern better than treating embeddings as just another row in a relational table.
• •

  You want vector-native features

  • •Collections are explicit.
  • •Payload indexes are explicit.
  • •Search APIs are explicit.
  • •That clarity pays off when you need to tune recall/latency tradeoffs instead of fighting SQL abstractions.

A common Qdrant write path looks like this:

POST /collections/products/points?wait=true
{
  "points": [
    {
      "id": "sku_123",
      "vector": [0.12, 0.98, ...],
      "payload": {
        "tenant_id": 42,
        "category": "shoes",
        "price": 89
      }
    }
  ]
}

And the query side stays focused on retrieval:

POST /collections/products/points/search
{
  "vector": [0.11, 0.95, ...],
  "limit": 10,
  "filter": {
    "must": [
      { "key": "tenant_id", "match": { "value": 42 } },
      { "key": "category", "match": { "value": "shoes" } }
    ]
  }
}

That structure is exactly what real-time systems need: quick writes, fast filtered reads, no SQL gymnastics.

For real-time apps Specifically

Pick Qdrant if the app needs low-latency retrieval under concurrent writes and reads. Real-time systems fail when the database starts acting like a general-purpose compromise engine; Qdrant avoids that trap by being opinionated about vectors from day one.

Pick pgvector only when real-time is secondary to simplicity and your existing Postgres setup can absorb the load comfortably. If you expect growth in traffic or retrieval complexity, start with Qdrant now instead of migrating later under pressure.

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.