pgvector vs Qdrant (2026): Stay in Postgres or Run a Dedicated Vector DB?

If you’re already running PostgreSQL, the pragmatic answer is usually pgvector — adding vector search is one CREATE EXTENSION vector; away, with no new service to run, back up, or monitor. You graduate to Qdrant (a dedicated, Rust-based, Apache-2.0 vector engine) when your scale, your need for native hybrid search, or filtered-search performance outgrows what a general-purpose database comfortably does. This comparison frames the decision the way it actually arrives in practice: not “which is the better vector database in the abstract,” but “do I extend the database I already have, or stand up a specialized one?”

Both are genuinely open-source and self-hostable. For the full field of options, see our best self-hosted vector databases guide.

Side-by-side comparison

	pgvector	Qdrant
What it is	A PostgreSQL extension	A dedicated vector database
License	PostgreSQL License (BSD-style)	Apache-2.0
Core language	C	Rust
GitHub stars (June 2026)	21.8k	32.4k
Hybrid search	Partial / DIY — vector + Postgres full-text in SQL	Native — dense + sparse, RRF/fusion in one query
Self-host	CREATE EXTENSION vector; on any Postgres	Single qdrant/qdrant Docker image; distributed mode available
New infrastructure	None if you already run Postgres	Yes — a separate service to operate
Managed option	Any managed Postgres (RDS, Supabase, Neon, etc.)	Qdrant Cloud (perpetual free tier; usage-based paid)

Star counts are GitHub’s rounded figures as of June 2026 and drift; license and language are the stable facts.

The real question: extend Postgres or add a service?

This comparison is a little unusual because pgvector and Qdrant aren’t quite the same kind of thing. pgvector is an extension that bolts vector search onto a database you may already operate; Qdrant is a purpose-built vector database that does one job extremely well. So the decision is really about operational architecture:

• pgvector keeps everything in one place. Your relational data, your full-text search, and your vectors all live in the same Postgres instance, joinable in a single SQL query, covered by one backup and one monitoring setup. There is no second system to learn.
• Qdrant separates concerns. You run a dedicated service optimized purely for vector workloads, with its own scaling, its own performance characteristics, and its own operational surface.

If you’re not already on Postgres, that calculus shifts — adding pgvector means adopting Postgres itself, at which point a dedicated engine is a more even comparison. But the classic pgvector pitch is: you already have Postgres, so vector search is nearly free to add.

Hybrid search

This is the clearest functional difference between the two.

• Qdrant has native hybrid search — dense + sparse vectors, multiple named vectors per point, and configurable fusion (e.g. Reciprocal Rank Fusion) in a single query. It’s a first-class feature.
• pgvector is partial / DIY on hybrid. It gives you vector search (and a sparsevec type), but there’s no single built-in hybrid operator. You achieve hybrid by combining pgvector similarity with Postgres full-text search (tsvector) in SQL and fusing the results yourself. That’s more work — but it’s also fully transparent, runs in tooling you already know, and lets you mix in arbitrary relational filters in the same query.

If hybrid search out of the box is a hard requirement, Qdrant wins cleanly. If you’re comfortable writing the SQL to blend vector and keyword results, pgvector can do it — you’re just the one assembling the fusion.

Performance and latency

The usual caveat: vector benchmarks are config-, dataset-, and hardware-dependent, and you should benchmark on your own data.

• Qdrant publishes benchmarks claiming the highest RPS and lowest latency in most scenarios, ~4× RPS on one dataset, and a notable edge on filtered search. Benchmark data was last refreshed in 2024.
• pgvector ships no first-party benchmark — it’s an extension, and its speed depends entirely on the Postgres host it runs on. Third-party numbers exist: AWS reports HNSW builds up to ~30× faster in pgvector 0.7 on Aurora, and Supabase reports its own HNSW speedups. Treat these as vendor (host) numbers, not pgvector’s own claims — absolute latency is host- and config-dependent.

The practical reading: at moderate scale on well-provisioned Postgres, pgvector is fast enough that the database is rarely your bottleneck. As your vector count climbs into the many-millions and query volume rises, a dedicated engine like Qdrant — built specifically for ANN search and filtered queries — is more likely to hold low latency without you over-provisioning your primary database. Qdrant is also the safer choice when heavy vector queries would otherwise compete for resources with your transactional Postgres workload.

Self-hosting and operations

• pgvector — the lightest possible footprint if you already run Postgres. It’s CREATE EXTENSION vector; on an existing database. No new container, no new service, no separate backup story. If you don’t already run Postgres, you’re now operating Postgres — well-understood, but real work.
• Qdrant — a clean dedicated service. A single official qdrant/qdrant Docker image, single-node out of the box, scaling to distributed/clustered. It’s about as easy as a standalone vector DB gets — but it is, by definition, one more service to run, secure, back up, and monitor alongside whatever database you already have.

There’s a subtle operational risk worth naming with pgvector: large vector indexes and heavy ANN queries consume memory and CPU on the same instance serving your application’s transactional traffic. At small-to-medium scale that’s fine and convenient; at large scale it can mean your vector workload and your primary workload start fighting for resources — which is exactly the point where splitting Qdrant out becomes worth the extra service.

Cost and pricing

Self-hosting either is free software on hardware you control, and both fit on a ~$20–30/mo VPS at small-to-medium scale.

• pgvector has no standalone cloud — your cost is simply whatever your Postgres costs, whether that’s a box you run yourself or a managed Postgres (AWS RDS/Aurora, Google Cloud SQL/AlloyDB, Supabase, Neon, etc.). If you’re already paying for Postgres, the marginal cost of vector search is close to zero until your index gets big enough to need more RAM.
• Qdrant self-hosted is free; Qdrant Cloud offers a perpetual free tier (1-node, 0.5 vCPU / 1 GB RAM / 4 GB disk) and usage-based paid Standard via calculator. Running it yourself is a second box (or a second container on an existing one), which is a small additional infrastructure line.

So pgvector’s cost edge is real but specific: it’s cheapest because you’re amortizing infrastructure you already pay for. Once vectors demand their own dedicated, well-provisioned hardware, that edge narrows and Qdrant’s specialization starts paying off.

When to pick which

Stay on pgvector if:

• You already run PostgreSQL and want zero new infrastructure.
• Your vector count is small-to-medium and queries don’t dominate your database load.
• You value keeping relational data, full-text, and vectors joinable in one SQL query.
• You’re fine assembling hybrid search yourself from pgvector + Postgres full-text.

Move to Qdrant if:

• You need native hybrid search out of the box, not hand-rolled in SQL.
• You’re scaling into the many-millions of vectors and/or high query volume.
• Filtered-search performance is critical to your product.
• You want to keep heavy vector workloads off your primary transactional database.

Verdict

If you’re already on Postgres, start with pgvector — it’s the lowest-friction, lowest-cost way to add vector search, and for a great many applications it’s genuinely all you need. Reach for Qdrant when you hit one of the clear triggers: native hybrid search as a requirement, scale into the many-millions of vectors, demanding filtered-search latency, or the need to isolate vector load from your transactional database. The decision isn’t “which is better” — it’s “is vector search a feature of my existing database, or a workload big enough to deserve its own engine?” pgvector answers the first; Qdrant answers the second.

FAQ

Should I use pgvector or Qdrant? If you already run PostgreSQL and your scale is small-to-medium, use pgvector — it adds vector search with no new infrastructure. Switch to Qdrant when you need native hybrid search, are scaling to many millions of vectors, need strong filtered-search performance, or want to keep heavy vector queries off your primary database.

Is pgvector good enough for production? Yes, for a wide range of production workloads — especially when vectors are a feature of an app whose data already lives in Postgres. It becomes the wrong tool when vector volume or query load grows large enough to compete with your transactional traffic, at which point a dedicated engine like Qdrant is the better architecture.

Does pgvector support hybrid search? Partially. pgvector handles the vector side (and offers a sparsevec type), but there’s no single built-in hybrid operator. You combine pgvector with Postgres full-text search (tsvector) in SQL and fuse the results yourself. Qdrant, by contrast, does hybrid natively in one query.

Is Qdrant faster than pgvector? Qdrant is purpose-built for vector search and publishes benchmarks claiming high RPS and an edge on filtered search (last refreshed 2024). pgvector publishes no first-party benchmarks; its speed depends on the Postgres host. At moderate scale on good hardware, pgvector is often fast enough; at large scale, a dedicated engine like Qdrant is more likely to hold low latency. Benchmark on your own data.

What licenses do pgvector and Qdrant use? pgvector uses the PostgreSQL License (a permissive, BSD-style license); Qdrant is Apache-2.0. Both are permissive with no copyleft, so either is safe to embed in commercial software.

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