Qdrant
Qdrant / qdrant.tech
High-performance Rust-based open source vector database with sparse vector support for hybrid search, advanced filtering, and named vectors for production AI applications.
Pricing
Free
Free plan
Yes
Category
Developer Tools
Platforms
3
Free plan
Yes
API access
Yes
Open source
Yes
Platforms
3
What is Qdrant?
Qdrant is an open source vector database written in Rust, prioritising high performance, low memory footprint, and advanced filtering. Sparse vector support alongside dense vectors enables hybrid search without separate keyword search infrastructure. Named vectors allow storing multiple vector types per data point for different embedding models. The filtering system allows complex metadata filtering conditions with minimal performance impact.
How Qdrant works
Qdrant runs as ml platform software built around text and data workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on web, python, and typescript, with API access for teams that want to embed it into their own products.
Watch Qdrant in action
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What makes it worth shortlisting
The capabilities that matter most for teams evaluating Qdrant.
Rust-based performance
High performance vector database producing low memory footprint and efficient throughput for production workloads.
Sparse vector support
Stores sparse keyword vectors alongside dense semantic vectors for native hybrid search without separate keyword search infrastructure.
Advanced filtering
Complex metadata filtering conditions on vector search queries with minimal performance impact for precise retrieval.
Best use cases
Who should use it
Pros
- Rust implementation provides strong performance for high-throughput production workloads
- Sparse vector support enables native hybrid search without separate keyword infrastructure
- Advanced filtering with minimal performance impact on vector search queries
- Named vectors support multiple embedding types per data point for multi-modal search
Cons
- More setup complexity than managed services like Pinecone
- Smaller community and fewer learning resources than Weaviate or Pinecone
- Enterprise support requires cloud or custom deployment engagement
Is it worth the price?
Open source self-hosted free. Qdrant Cloud free tier (1GB cluster). Managed cloud from $0.014/hour. Enterprise cloud custom pricing.
Model
Open Source
Starting price
Free
Free trial
No
Tools like Qdrant
Pinecone is the most adopted managed vector database with simpler setup. Weaviate provides built-in model integrations and multi-tenancy. Chroma is more accessible for development. PgVector extends PostgreSQL.
Qdrant vs Weaviate
A side-by-side look at the closest alternative in this category.
Qdrant
Qdrant
Weaviate
Weaviate
Open source self-hosted free. Qdrant Cloud free tier (1GB cluster). Managed cloud from $0.014/hour. Enterprise cloud custom pricing.
Open source self-hosted free. Cloud sandbox free. Standard cloud from $25/month. Enterprise cloud custom pricing.
Open source self-hosted provides complete data control. Qdrant Cloud: review data handling policy. Enterprise cloud includes data handling agreements.
SOC 2 Type II. GDPR compliant. Open source self-hosted provides complete data control. Enterprise cloud includes data handling agreements.
Self-hosted Qdrant provides complete data control. Qdrant Cloud processes data on Qdrant managed infrastructure.
Open source self-hosted provides full data control. Managed cloud processes data on Weaviate infrastructure. Review privacy policy for cloud deployments.
- Rust implementation provides strong performance for high-throughput production workloads
- Sparse vector support enables native hybrid search without separate keyword infrastructure
- Advanced filtering with minimal performance impact on vector search queries
- Named vectors support multiple embedding types per data point for multi-modal search
- Open source with self-hosting option provides complete data control
- Built-in ML model integrations simplify vectorisation pipeline
- Hybrid search produces better retrieval quality than pure semantic search
- Multi-tenancy supports SaaS applications with multiple customers
- More setup complexity than managed services like Pinecone
- Smaller community and fewer learning resources than Weaviate or Pinecone
- Enterprise support requires cloud or custom deployment engagement
- More complex to configure than Pinecone managed service
- Self-hosted requires infrastructure management expertise
- Cloud pricing less predictable than Pinecone transparent per-index model
Technical & deployment info
Key facts about model providers, platforms, and team support.
Model Provider
Agnostic
Platforms
Web, Python, TypeScript
Deployment
Open Source, SaaS, Self-hosted
Integrations
LangChain, LlamaIndex, OpenAI, Anthropic, Hugging Face, Any embedding model
Team Collaboration
No
Launch Year
2021
Security & privacy
Compliance signals and data-handling notes as reported by the vendor.
Open source self-hosted provides complete data control. Qdrant Cloud: review data handling policy. Enterprise cloud includes data handling agreements.
Self-hosted Qdrant provides complete data control. Qdrant Cloud processes data on Qdrant managed infrastructure.
What users are saying
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Common questions about Qdrant
Editorial Verdict
Should you use Qdrant?
Qdrant is the best choice for performance-sensitive AI applications needing high-throughput vector search with advanced filtering and hybrid search capabilities.
Last verified July 24, 2026.