Qdrant vs Weaviate
Two of the top vector databases, side by side: score, setup, license, activity and what each review found.
Qdrant
Rust vector database with payload filtering, REST and gRPC
Weaviate
Go vector database with built-in vectorizers, hybrid search and RAG
| What we compare | Qdrant | Weaviate |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 72, popular | 42, known |
| Freshness | 100, active | 100, active |
| Maintenance | 81, healthy | 71, fair |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 0, none | 40, minimal |
| Facts from GitHub and the README | ||
| Stars | 35k | 16.9k |
| License | Apache-2.0 (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Rust | Go |
| 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
Weaviate
Weaviate is a Go vector database that stores objects with their vectors and serves hybrid BM25 plus semantic search, filtering, built-in RAG and reranking through REST, gRPC and GraphQL APIs. It can vectorize data at import using modules for OpenAI, Cohere, HuggingFace, Google or a local model2vec image, or accept precomputed vectors. Docker Compose runs it on ports 8080 and 50051; production adds multi-tenancy, replication and RBAC.
Who it is for: teams wanting built-in vectorization, hybrid search and RAG in the database
Strengths
- Vectorizes at import with OpenAI, Cohere, HuggingFace, Google or a local model2vec container
- Hybrid BM25 plus vector, image search, filtering, RAG and reranking in one query
- Multi-tenancy, replication, RBAC, horizontal scaling and vector compression
- REST, gRPC and GraphQL with Python, TypeScript, Java, Go and C# clients
Weaknesses
- Enterprise features in wl/ need a commercial license key; one image mixes both
- Vectorization needs a module container or external API keys
- No RAM or sizing guidance in the README
- Both REST 8080 and gRPC 50051 must be exposed
- no GPU
- Docker + Compose
- Needs optional embedding inference container (e.g. model2vec) or external embedding APIs
- Models: OpenAI, Cohere, HuggingFace, Google and other integrated model providers, local model2vec (minishlab/potion-base-32M), precomputed vectors
- port 8080
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