Qdrant
High-performance open-source vector database written in Rust for AI applications.
How the 8.2 was reached
- Quality of output8/10
34,871 GitHub stars, Apache-2.0, active; hybrid search and filtering features; no independent benchmark in the facts. github.com
- Ease of use8/10
One Docker command starts a local server; clients in five languages; docs page not found. github.com
- Pricing value8/10
Free forever cloud tier, open-source Apache-2.0 code, usage-based Standard tier; prices not detailed. qdrant.tech
- API and integration quality9/10
REST and gRPC APIs plus clients for Python, Go, Rust, JavaScript/TypeScript and.NET. github.com
- Solves the problem it claims to solve8/10
Vector search with filtering and hybrid retrieval delivered; 34,871 stars; 'at scale' claim not independently tested. github.com
8.2/10 is the mean of the 5 criteria that apply. Scored on the five-criteria rubric v1, 29 September 2026. How scoring works
Pricing
- 1GB storage
- Community support
- Open source
- 8GB storage
- 100k requests/mo
- Basic support
- 32GB storage
- 1M requests/mo
- Priority support
- Unlimited scale
- Hybrid deployment
- SOC2 & HIPAA compliant
Key Features
- Vector similarity search
- Hybrid search (dense + sparse)
- Advanced metadata filtering
- Real-time indexing
- Multi-tenancy support
Pros & Cons
Pros
- Rust-based performance
- Comprehensive API
- Strong filtering capabilities
- Active open-source community
- Multiple deployment options
Cons
- Requires vector knowledge to optimize
- Limited built-in ML models
- Steeper learning curve than simpler alternatives
- Rust ecosystem dependencies
Qdrant is a robust, production-ready vector database that excels in performance and flexibility. While it requires more technical expertise than plug-and-play solutions, it offers superior control and scalability for serious AI applications.
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Qdrant Comparisons
Head-to-head comparisons featuring Qdrant.
Chroma vs Qdrant: Open Source Vector DB for RAG (2026)
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