Qdrant
Open-source vector database for semantic search and AI applications
What it does well
- Exceptional query performance with sub-millisecond latencies at scale
- Rich filtering and hybrid search combining vectors with metadata/text search
- Flexible deployment: self-hosted, cloud, or fully managed options
- Strong production features including clustering, replication, and high availability
Where it falls short
- Smaller ecosystem and community compared to established vector databases
- Managed cloud pricing can be costly for very large-scale deployments
- Steeper learning curve for teams new to vector database concepts and operations
Core Features
| Vector Search | Yes |
| Similarity Search | Yes |
| HNSW Indexing | Yes |
| Filtering & Metadata | Yes |
| Multi-Vector Support | Yes |
| Payload Indexing | Yes |
| Cloud Deployment | Yes |
| Snapshot & Recovery | Yes |
| Batch Operations | Yes |
Integrations
| REST API | Yes |
| gRPC API | Yes |
Security
| Role-Based Access Control | Yes |
Open Source
Free
- Self-hosted vector database
- Full API access
- Semantic search capabilities
- Community support
- Unlimited collections and vectors
Qdrant Cloud Starter
$25/mo
$250/yr billed annually
- Managed cloud hosting
- Up to 50GB storage
- High availability
- API access
- Community support
Qdrant Cloud Professional
$299/mo
$2990/yr billed annually
- Everything in Starter
- Up to 500GB storage
- Advanced monitoring and analytics
- Priority support
- Custom configurations
Qdrant Cloud Enterprise
Custom
- Custom storage and resources
- Dedicated support
- SLA guarantees
- Custom integrations
- On-premise deployment options
Comparisons with Qdrant
Guides recommending Qdrant
ToolAudit may earn a commission when you visit a tool through our links. This never affects our scores or rankings. How we make money