Vector database for similarity search
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12 reviews trackedThe Bottom Line
Entry price
Free plan available, paid tiers above
Biggest pro
Fast performance
Biggest con
Smaller community
TL;DR - Qdrant
- Qdrant is a vector database for AI similarity search applications
- It stores and queries high-dimensional vectors with filtering support
- Free and open-source, Cloud version available
Pricing: Free plan available
Best for: Growing teams
4.5/5 across review platforms
What is Qdrant?
Qdrant is an open-source vector similarity search engine. Features horizontal scaling, filtering, and high availability for production AI applications.
Available on: Web
Pros & Cons
Pros
- Fast performance
- Rust-based
- Good filtering
- Open source
- Self-hostable
Cons
- Smaller community
- Cloud newer
- Documentation improving
- Fewer integrations
- Less known
Ratings Across the Web
4.5(12 reviews)
Ratings aggregated from independent review platforms. Learn more
Key Features
Vector search engineHorizontal scalingFilteringHigh availabilityAuto-healingBackup and recoveryOpen-sourceOn-premise optionAir-gapped deploymentMulti-cloud support
Pricing Plans
Pricing checked Jul 5, 2026
Managed Cloud (Free)
Free
- 1 GB free forever cluster
- No credit card required
- AWS/GCP/Azure regions
- Horizontal and vertical scaling
- Monitoring and alerting
- High availability
- Backup and disaster recovery
- Zero-downtime upgrades
Managed Cloud (Paid)
null
- Usage-based pricing
- All free features
- Standard support and uptime SLAs
- Premium support available
Hybrid Cloud
null
- All Managed Cloud benefits
- Security and data isolation
- Optimal latency
- Standard or Premium support
Private Cloud
null
- All Hybrid benefits
- On-premise deployment
- Maximum data sovereignty
- Air-gapped environments
- Premium Support included
Reviews
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4.5/5
Across 12 verified user reviews on G2
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Qdrant FAQ
How does Qdrant facilitate AI application development?
Qdrant serves as a vector database specifically designed for similarity search, providing the necessary infrastructure for production AI applications. It offers features like horizontal scaling and high availability to support demanding AI workloads.
What kind of user benefits most from Qdrant?
Qdrant is best suited for developers and organizations building AI applications that require efficient and scalable similarity search capabilities. Its open-source nature and self-hostable option appeal to those who need control over their data infrastructure.
How does Qdrant compare to Milvus?
Qdrant and Milvus both offer vector similarity search capabilities, but Qdrant is built with Rust, which contributes to its fast performance and good filtering. Qdrant is also open-source and self-hostable, providing flexibility for deployment.
What are the main trade-offs when choosing Qdrant?
A primary trade-off with Qdrant is its relatively smaller community and fewer integrations compared to more established solutions. Its cloud offering is also newer, and the documentation is still in the process of improving.
How is Qdrant priced?
Qdrant is available on a free tier, allowing users to get started without initial cost. For more extensive usage and additional features, paid plans are offered.
Can Qdrant be self-hosted?
Yes, Qdrant is an open-source vector database that can be self-hosted. This provides users with flexibility and control over their deployment environment.
Which features support high-performance AI applications in Qdrant?
Qdrant supports high-performance AI applications through its fast performance, horizontal scaling, and high availability features. It also offers robust filtering capabilities for precise similarity searches.
Source: qdrant.tech