pgvector vs Qdrant — which vector database wins for your brief, in 2026
Two vector engines, side by side. pgvector is vector search inside postgres. the default for teams already on postgres or supabase. Qdrant is rust-fast open-source vector engine. cleaner api than weaviate, smaller footprint. The verdict, the criteria, and the honest take below.
ALL VECTOR-DB COMPARISONS →Verdict in one paragraph
In-Postgres vs purpose-built self-hosted. pgvector wins on simplicity (no second service), cost, and integration with relational data. Qdrant wins on raw performance, filter-then-vector latency, and the ability to scale past Postgres's ceiling. For most teams at most scales, pgvector. For latency-critical filtered queries at high QPS, Qdrant.
Score across the criteria: pgvector 4 · Qdrant 2
Side by side
Decision criteria
-
Which has lower operational overhead?
pgvector
pgvector is just Postgres. Qdrant is a separate service to run.
-
Which has better filter-then-vector latency?
Qdrant
Qdrant's filterable HNSW outperforms pgvector at filter-heavy workloads.
-
Which lets you join vector + relational data?
pgvector
Single SQL query. Qdrant requires a second store + app-level join.
-
Which scales further on raw QPS?
Qdrant
Qdrant's Rust + dedicated architecture beats Postgres at very high vector-query QPS.
-
Which is cheaper?
pgvector
pgvector inherits Postgres cost (often already paid). Qdrant adds its own infra.
-
Which is the safer 5-year bet?
pgvector
Postgres has decades of production. Qdrant is a real project but younger.
What pgvector is best for
- RAG apps already running on Postgres / Supabase
- Workloads under 50 million vectors where horizontal sharding is not yet the constraint
- Teams that want vector + relational data in one query (vector + JOIN)
- Cost-sensitive deployments — the bill is whatever Postgres costs
Read the full pgvector entry: /vector-databases/pgvector/
What Qdrant is best for
- Latency-critical RAG workloads
- Apps with heavy filtered search (filter by user_id, tenant, time, then vector-rank)
- Self-host-first teams who value Rust's performance and operational simplicity
Read the full Qdrant entry: /vector-databases/qdrant/
The vector-store choice is the easy half — your retrieval design is the hard one
The hard half is your chunking, your hybrid retrieval, your reranking, your eval loop. The 30-min call is where you describe your corpus and your constraints; I tell you whether pgvector or Qdrant (or something else) is your fit.