Qdrant vs lancedb
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
lancedb
Embedded vector database built on the Lance columnar format
Verdict
Qdrant ranks higher, 73 to 69. Of the 5 score parts we measure for both, Qdrant leads on 2, lancedb on 2 and 1 is even.
- AdoptionQdrant leads by 46 points, 80 to 34: a wide gap.
- FreshnessEven at 100.
- Maintenancelancedb leads by 6 points, 88 to 82.
- Easy to runlancedb leads by 17 points, 67 to 50: a wide gap.
- Agent-readyQdrant leads by 10 points, 40 to 30.
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 lancedb if you are
Developers needing an embedded vector store for multimodal AI apps
- Runs embedded and locally, with no separate server required per the README
- Combines vector search, full-text search and SQL in one store
| What we compare | Qdrant | lancedb |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 80, popular | 34, known |
| Freshness | 100, active | 100, active |
| Maintenance | 82, healthy | 88, healthy |
| Easy to run | 50, easy | 67, easy |
| Agent-ready | 40, minimal | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 35.2k | 11.7k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Rust | Rust |
| Docker | Yes | Yes |
| GPU | Optional | Optional |
| 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
lancedb
LanceDB is a vector database written in Rust on top of the Lance columnar format. It stores vectors, metadata and multimodal data (text, images, video, point clouds) and supports vector similarity search, full-text search and SQL filtering. It runs locally or in your own cloud, with Python, TypeScript and Rust SDKs plus a REST API.
Who it is for: Developers needing an embedded vector store for multimodal AI apps
Strengths
- Runs embedded and locally, with no separate server required per the README
- Combines vector search, full-text search and SQL in one store
- Automatic data versioning and zero-copy access without extra infrastructure
- Python, TypeScript and Rust SDKs; integrates with LangChain, LlamaIndex, Pandas, Polars, DuckDB
Weaknesses
- README gives no benchmarks or hardware figures to back the billion-vector claims
- Managed Cloud and Enterprise tiers exist; feature split with open source is not detailed
- README has little setup detail and defers to external docs
- RAM and GPU requirements are not stated
- GPU optional
- Docker + Compose
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