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Tool Comparison
Qdrant
High-performance Rust-based vector database with advanced filtering and sparse vectors.
VS
At a Glance
| Attribute | Qdrant | Weaviate |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.5/5 | 4.5/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 5 listed | 5 listed |
| Categories | Vector Database | Vector Database |
Key Features
Qdrant
- Written in Rust — extremely high throughput and low latency
- Sparse + dense vector hybrid search
- Advanced payload filtering with complex conditions
- On-disk indexing for large datasets beyond RAM
- Multitenancy with collections and named vectors
- gRPC and REST API with Python, TypeScript, Go clients
Weaviate
- Built-in vectorization modules (OpenAI, Cohere, HuggingFace)
- Hybrid search combining BM25 keyword and vector search
- GraphQL and REST APIs
- Multi-tenancy for SaaS applications
- Generative search — query + LLM response in one call
- Cloud-managed or self-hosted with Docker/Kubernetes
Real-World Use Cases
Qdrant
High-throughput recommendation system
Embed user and item data into vectors
Weaviate
Build generative search over your knowledge base
Start Weaviate with docker-compose (includes vectorizer module)
Integrations
Qdrant
langchainllamaindexopenaihuggingfacedspy
Weaviate
langchainllamaindexopenaihuggingfacedspy
🏆 Which should you choose?
Choose Qdrant if…
- → you're already in the Vector Database ecosystem and prefer Qdrant's workflow
Choose Weaviate if…
- → you're already in the Vector Database ecosystem and prefer Weaviate's workflow