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Qdrant
Qdrant is a high-performance, open-source vector database and search engine designed specifically for vector similarity search. Written in Rust, Qdrant offers fast and reliable performance even under high load, making it an ideal choice for applications that demand speed and scalability. With Qdrant, you can turn your embeddings or neural network encoders into powerful, full-fledged applications for a wide range of use cases including matching, search, recommendation, and complex operations on large datasets.
Qdrant's comprehensive API and extended filtering support make it well-suited for various applications, including faceted search, semantic-based matching, and hybrid search combining vector similarity with keyword matching. The platform offers both self-hosted and managed cloud solutions, providing flexibility for different deployment scenarios.
| General | |
|---|---|
| Release Date | October 29, 2021 |
| Author | Qdrant |
| Type | Vector Database & Search Engine |
| Language | Rust |
| License | Apache License 2.0 |
| Latest Version | 1.15.x (July 2025) |
| GitHub Stars | 19,000+ (as of 2025) |
Core Features
High-Performance Vector Search
- HNSW (Hierarchical Navigable Small World) indexing algorithm
- Sub-millisecond search latency for millions of vectors
- Handles billions of vectors with horizontal scaling
- SIMD hardware acceleration for optimal CPU utilization
Advanced Quantization Techniques
- Scalar Quantization: Reduces memory usage by 4x with minimal accuracy loss
- Binary Quantization: Up to 32x compression with 40x speed improvement
- Product Quantization: Higher compression ratios for high-dimensional vectors
- 1.5-bit and 2-bit Quantization: New compression options balancing efficiency and accuracy
- Asymmetric Quantization: Different encoding for stored vectors vs. queries
Hybrid Search Capabilities
- Dense Vectors: Traditional semantic similarity search using neural embeddings
- Sparse Vectors: Keyword-based search with neural network weighting (BM25, SPLADE)
- Combined Search: Unified queries across both dense and sparse vectors in single collections
- BM42 Algorithm: Proprietary hybrid search combining vector and keyword methods
Comprehensive Filtering & Payload Support
- Rich JSON payload attachment to vectors
- Advanced filtering conditions (must, should, must_not clauses)
- Geo-location, numerical ranges, and full-text filtering
- Custom business logic implementation on top of similarity matching
Latest Enhancements (2024-2025)
Qdrant 1.15.x (July 2025)
- Smarter Quantization: Enhanced 1.5-bit and 2-bit quantization options
- Advanced Full-Text Filtering: Improved text search capabilities
- Performance Optimizations: Better resource utilization and faster processing
Qdrant 1.14.x (April 2025)
- Score-Boosting Reranker: Blend vector similarity with custom rules and context
- Incremental HNSW Indexing: Build indexes gradually as data arrives
- Optimized Batch Search: Parallel processing for large query batches
- Memory Optimization: Reduced RAM usage for large datasets
Qdrant Cloud Inference (July 2025)
- Unified Embedding Generation: Generate, store, and index embeddings in single API calls
- Multimodal Support: Text and image embedding models integrated natively
- Supported Models: MiniLM, SPLADE, BM25, Mixedbread Embed-Large, CLIP
- Free Tier: 5 million tokens per model monthly, unlimited BM25 tokens
Deployment Options
Self-Hosted (Open Source)
- Full control over infrastructure and data
- Horizontal scaling through sharding and replication
- Zero-downtime rolling updates and dynamic scaling
- Available on GitHub with comprehensive documentation
Qdrant Cloud (Managed Service)
- Fully managed SaaS solution across AWS, Google Cloud, and Azure
- Automatic scaling, monitoring, and maintenance
- Built-in backups and disaster recovery
- Simple setup with production-ready clusters in minutes
Hybrid Cloud
- Deploy across diverse environments for performance and compliance
- Integrate on-premise and cloud infrastructure
- Maintain data residency requirements while leveraging cloud benefits
Private Cloud
- Full Kubernetes-based deployment on any infrastructure
- Complete control and customization for enterprise requirements
- Air-gapped deployments for maximum security
Key Use Cases
Retrieval-Augmented Generation (RAG)
- Semantic search over knowledge bases and documents
- Context retrieval for large language models
- Enterprise knowledge management and Q&A systems
Recommendation Systems
- Product recommendations based on user behavior
- Content discovery and personalization
- Similar item matching across catalogs
Multimodal Search
- Combined text and image search capabilities
- Video content indexing and retrieval
- Cross-modal similarity matching
Enterprise Search
- Semantic document search with filtering
- Compliance and regulatory document retrieval
- Customer support knowledge bases
Anomaly Detection
- Outlier detection in high-dimensional data
- Fraud detection and security monitoring
- Quality control and process optimization
Technical Specifications
Performance Characteristics
- Throughput: Up to 10,000+ queries per second
- Latency: Sub-millisecond search response times
- Scale: Billions of vectors with horizontal scaling
- Memory Efficiency: Up to 97% RAM reduction with quantization
Supported Distances
- Cosine similarity
- Euclidean (L2) distance
- Dot product
- Manhattan (L1) distance
Integration Ecosystem
- Embeddings: OpenAI, Cohere, Hugging Face, Sentence Transformers
- Frameworks: LangChain, LlamaIndex, Haystack
- Languages: Python, Rust, JavaScript, Go, .NET
- Platforms: Docker, Kubernetes, cloud providers
Recent Achievements
- 250+ million installs across all open-source packages
- Featured in The Forrester Waveâ„¢: Vector Databases, Q3 2024
- Named one of Europe's top 10 startups in Sifted's 2025 B2B SaaS Rising 100
- Powers AI applications at Tripadvisor, HubSpot, and Deutsche Telekom
- Active community of 6,000+ members on Discord
Tutorials
Great tutorials on how to build with Qdrant
👉 Discover more Qdrant Tutorials on lablab.ai
Resources
Getting Started
- Qdrant Documentation - Comprehensive guides and API reference
- Qdrant Cloud - Managed cloud service with free tier
- GitHub Repository - Open-source codebase and examples
Community & Support
- Discord Community - Active developer community
- Qdrant Blog - Latest features, tutorials, and best practices
- Benchmarks - Performance comparisons with other vector databases
Qdrant represents the cutting edge of vector database technology, combining high performance with developer-friendly APIs and comprehensive feature sets. Whether building semantic search, recommendation systems, or complex AI applications, Qdrant provides the infrastructure needed to scale from prototype to production with confidence.
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