Qdrant vs pgvector
Two of the top projects in Vector databases, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
Qdrant
Rust vector database with payload filtering, REST and gRPC
pgvector
PostgreSQL extension for vector similarity search with HNSW and IVFFlat
Verdict
Qdrant ranks higher, 83 to 68. Of the 4 score parts we measure for both, Qdrant leads on 3, pgvector on none and 1 is even.
- AdoptionQdrant leads by 12 points, 80 to 68.
- FreshnessEven at 100.
- MaintenanceQdrant leads by 23 points, 83 to 60: a wide gap.
- Agent-readyQdrant leads by 40 points, 40 to 0: a wide gap.
Pick Qdrant if you are
Developers wanting a filter-heavy vector store with gRPC
- Dense, sparse and multivector (ColBERT) search with RRF and DBSF fusion
- Quantization cuts RAM up to 97 percent; on-disk storage and io_uring
Pick pgvector if you are
Teams already on Postgres who want vectors without a new database
- Vectors live next to relational data with ACID, joins and point-in-time recovery
- HNSW and IVFFlat indexes with six distance operators
| What we compare | Qdrant | pgvector |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 80, widely used | 68, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 83, healthy | 60, fair |
| Agent-ready | 40, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 35.4k | 23.6k |
| License | Apache-2.0 (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | None published |
| Language | Rust | C |
| Docker | Yes | Yes |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
Qdrant
Qdrant is a Rust vector database exposing REST (OpenAPI 3.0) and gRPC on port 6333 for storing points (vectors plus JSON payload) and searching with dense, sparse and multivector (ColBERT) embeddings, rich payload filters and hybrid fusion (RRF, DBSF). It adds quantization, on-disk storage, sharding and replication, multitenancy, GPU-accelerated indexing and a web UI. Qdrant Edge runs the same engine embedded in-process.
Who it is for: developers wanting a filter-heavy vector store with gRPC
Strengths
- Dense, sparse and multivector (ColBERT) search with RRF and DBSF fusion
- Quantization cuts RAM up to 97 percent; on-disk storage and io_uring
- REST with OpenAPI 3.0 spec plus gRPC; six official clients
- Sharding and replication with zero-downtime collection resize
Weaknesses
- Default docker run has no auth and binds all interfaces
- GPU acceleration covers indexing only; search runs on CPU
- Qdrant Edge embedded mode is Python and Rust only
- Sharding and tenant isolation require upfront design
- GPU optional
- Docker
- Models: any embedding model; dense, sparse and late-interaction (ColBERT) vectors
- port 6333
pgvector
pgvector is a PostgreSQL extension (Postgres 13+) that adds vector, halfvec, bit and sparsevec column types with L2, inner product, cosine, L1, Hamming and Jaccard distance operators, exact search by default and HNSW or IVFFlat indexes for approximate search. Vectors sit beside ordinary rows with ACID, joins and backups, and Postgres full-text search can be combined for hybrid retrieval. It installs via make, Docker or OS packages.
Who it is for: teams already on Postgres who want vectors without a new database
Strengths
- Vectors live next to relational data with ACID, joins and point-in-time recovery
- HNSW and IVFFlat indexes with six distance operators
- Half-precision, binary and sparse vector types plus binary quantization
- Installs via make, Docker, Homebrew, APT, Yum; preinstalled on many hosted Postgres
Weaknesses
- vector type capped at 2,000 dimensions (halfvec 4,000)
- Approximate indexes filter after scanning; filtered recall needs iterative scan tuning
- HNSW builds slow down sharply once the graph exceeds maintenance_work_mem
- No server of its own; capacity depends on your Postgres tuning
- no GPU
- Docker
- Needs PostgreSQL 13+
- Models: any embedding model; stores precomputed vectors
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