pgvector vs Qdrant for RAG: Which Should You Use?

pgvector is not a vector database product in the same sense as Qdrant. It is a PostgreSQL extension that adds vector search to your existing relational database, which makes it great when your app already lives in Postgres. Qdrant is a purpose-built vector database with filtering, payload indexing, and retrieval features designed around semantic search and RAG.

For RAG, use Qdrant unless you have a strong reason to keep everything inside Postgres.

Quick Comparison

When pgvector Wins

• •

  You already run PostgreSQL as your source of truth.

  If your documents, users, permissions, and metadata already live in Postgres, pgvector keeps everything in one place. That means fewer moving parts and simpler joins between embeddings and business data.

• •

  Your RAG workload is small to medium scale.

  If you're indexing thousands to low millions of chunks and query volume is modest, pgvector is enough. A well-tuned hnsw index or ivfflat index will get you production-grade retrieval without introducing another service.

• •

  You need transactional consistency with metadata updates.

  When chunk records change alongside app data, Postgres gives you ACID semantics across the whole flow. That matters when document visibility depends on tenant IDs, roles, or approval states.

• •

  Your team is SQL-first.

  If your engineers are comfortable with SELECT ... ORDER BY embedding <-> $1 LIMIT 10, they will move faster with pgvector than learning a new platform. The operational model is also familiar: backups, replicas, migrations, monitoring.

Example: simple RAG schema in pgvector

CREATE EXTENSION IF NOT EXISTS vector;

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

CREATE INDEX ON chunks USING hnsw (embedding vector_cosine_ops);
CREATE INDEX ON chunks (tenant_id);

That setup works well when retrieval is tightly coupled to relational filters like tenant isolation or document status.

When Qdrant Wins

• •

  Retrieval is the core product feature.

  If semantic search or RAG drives the application value, Qdrant is the right tool. It is built for fast approximate nearest neighbor search with payload filtering as a first-class feature.

• •

  You need strong filtering at scale.

  Qdrant’s payload model lets you attach metadata directly to points and filter efficiently during search using structured conditions. For RAG systems that need tenant isolation, document type filters, access control tags, or freshness windows, this matters a lot.

• •

  You expect growth in corpus size and query load.

  Qdrant handles larger vector datasets more naturally than forcing Postgres to do double duty as both OLTP store and ANN engine. This separation keeps retrieval latency predictable as usage grows.

• •

  You want cleaner RAG-specific APIs.

  With Qdrant you work with collections and points through APIs like upsert, search, scroll, and delete. That maps cleanly to ingestion pipelines and retrieval services without pretending vectors are just another column type.

Example: typical Qdrant collection setup

from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance

client = QdrantClient(url="http://localhost:6333")

client.create_collection(
    collection_name="chunks",
    vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
)

And then retrieve with filters:

from qdrant_client.models import Filter, FieldCondition, MatchValue

results = client.search(
    collection_name="chunks",
    query_vector=[0.1] * 1536,
    limit=5,
    query_filter=Filter(
        must=[
            FieldCondition(key="tenant_id", match=MatchValue(value=42))
        ]
    )
)

That is exactly the shape you want for RAG: embed once, filter hard, retrieve fast.

For RAG Specifically

Pick Qdrant by default. RAG systems are retrieval systems first; Qdrant was built for that job with better filtering semantics, cleaner operational separation from your transactional database, and more predictable performance as your corpus grows.

Use pgvector only when Postgres already owns the data model and the retrieval layer is small enough that simplicity beats specialization. If you are building serious RAG infrastructure from scratch, start with Qdrant and keep PostgreSQL focused on being PostgreSQL.

Keep learning

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By Cyprian Aarons, AI Consultant at Topiax.