#44th of 8 in Vector databases
Qdrant
Rust vector database with payload filtering, REST and gRPC
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- Stars
- 35k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Oct 2026
Overview
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
What it needs
- GPU optional
- Docker
- Models: any embedding model; dense, sparse and late-interaction (ColBERT) vectors
- port 6333
Also in Vector databases
See all 8| Rank | Project | Score |
|---|---|---|
| 1 | MilvusDistributed vector database with dense, sparse and hybrid search at scale | 81 out of 100 |
| 2 | MeilisearchRust search engine API with full-text, vector and hybrid search | 69 out of 100 |
| 3 | ChromaEmbedding database with a four-function API for Python and JavaScript | 64 out of 100 |
| #5 | pgvectorPostgreSQL extension for vector similarity search with HNSW and IVFFlat | 59 out of 100 |
| #6 | WeaviateGo vector database with built-in vectorizers, hybrid search and RAG | 57 out of 100 |