Qdrant

What Qdrant does

Qdrant is an open-source vector search engine that deploys as an API service, providing search for the nearest high-dimensional vectors. It enables the use of embeddings or neural network encoders to build full-fledged applications for matching, searching, recommending, and more. Qdrant is engineered for real-time retrieval with the speed, accuracy, and scale demanded by modern AI, offering expansive metadata filters, native hybrid search, built-in multivector capabilities, and efficient one-stage filtering. It supports various deployment models including on-premise, hybrid, edge, and Qdrant Cloud, with enterprise-grade security and flexibility. The company focuses on building the most scalable, high-performance vector search engine to fuel the future of AI and machine learning.

Products

• Qdrant Vector Search Engine (Open-Source)An open-source vector search engine built in Rust with SIMD and a custom storage engine (Gridstore) for high-performance, scalable vector search. It allows real-time indexing and memory-efficient storage of billions of vectors.
• Qdrant CloudA fully managed vector search service with high availability and auto-sharding on AWS, GCP, or Azure. It offers Free, Standard, and Premium tiers with varying levels of features, support, and compliance.
• Qdrant Hybrid CloudAllows users to run managed Qdrant clusters on their own infrastructure using their compute, network, and storage, while being fully managed through Qdrant Cloud. Best for local data residency and regulated workloads.
• Qdrant Private CloudA dedicated, isolated deployment for maximum control, security, and compliance, suitable for large enterprises and sensitive, air-gapped workloads.
• Qdrant Edge (Beta)Lightweight, low-latency vector search designed to run close to where data is generated.

Key capabilities

• High-Performance Vector Search
• Scalability at any scale
• Multiple deployment models (on-prem, hybrid, edge, cloud)
• Expansive Metadata Filters (nested, text, geo, has_vector)
• Native Hybrid Search (Dense + Sparse, supports BM25, SPLADE++, miniCOIL)
• Built-in Multivector support
• Efficient, One-Stage Filtering
• Full-Spectrum Reranking (score boosting, late interaction models like ColBERT, Maximum Marginal Relevance (MMR))
• Real-Time Indexing
• Memory-Efficient Storage
• Asymmetric, Scalar and Binary Quantization (up to 64x memory reduction)
• Intuitive APIs and built-in tools
• SOC2 & HIPAA compliant
• GDPR-aligned Options
• Prometheus, Grafana, Datadog integration
• SSO (SAML/OIDC)
• Multitenancy & Granular RBAC
• Private Networking
• Zero-downtime upgrades
• Backups & Point-in-time restore
• Vector-scoped API Keys
• Encryption at Rest and in Transit
• Disk Encryption with Custom Key (AWS and GCP only)
• API-driven control and Terraform support
• Audit logging
• Network binding
• TLS for encrypted connections
• Product Quantization (memory footprint reduction)
• Payload storage (JSON, Integer, Float, Bool, Keyword, Geo, Datetime, UUID types)
• API key authentication (Admin, Read-Only, Granular Access API Keys)

Use cases

• AI retrieval
• Matching
• Searching
• Recommending
• Advanced Search
• Personalized Recommendation Systems
• Retrieval Augmented Generation (RAG)
• Data Analysis
• Anomaly Detection
• AI Agents
• AI Trip Planner
• Real-time, personalized responses
• Real-time context across AI-driven conversations
• Scalable vector search for AI agents
• Reducing latency and increasing throughput for AI agents
• Semantic code search
• Visual search
• Content-driven video recommendation engine
• Hybrid Graph RAG Platform
• Real-time computer vision
• AI Memory
• Multimodal similarity search
• Patent Intelligence
• AI analytics
• Intelligent AI Assistant
• AI-Driven Insights for Wealth Management
• AI-Powered Internal Service Desk
• Precision AI Solutions for Finance and Insurance
• AI Legal Assistants
• Editorial-grade visual search
• Restaurant Discovery
• Life Insurance Underwriting
• Modeling aesthetic taste
• Generative AI for Enterprise-Level Customers
• Real-Time Crypto Intelligence
• Testing Agents
• Developer Autonomy
• Government AI Services
• Enhance AI-Driven Customer Experience Solutions
• Conversational AI
• Agentic AI
• Quality control and anomaly detection in computer vision
• No-Code AI Agent Creation
• Personalized financial advice
• Customer Support with AI

AI approach

Qdrant is an open-source vector search engine built in Rust, designed for high-performance, real-time vector similarity search at scale. It allows users to turn embeddings or neural network encoders into full-fledged AI applications for matching, searching, and recommending. It supports advanced features like expansive metadata filters, native hybrid search (dense + sparse), built-in multivector, efficient one-stage filtering, and full-spectrum reranking. Qdrant focuses on optimizing each component for speed, scalability, and customization of AI retrieval and search.

Tech named: Vector Search Engine, Vector Similarity Search, Embeddings, Neural Network Encoders, Metadata Filters, Hybrid Search (Dense + Sparse), BM25, SPLADE++, miniCOIL, Multivector, HNSW traversal, Score Boosting, Late Interaction Models (e.g. ColBERT), Maximum Marginal Relevance (MMR), Rust, SIMD, Gridstore (custom storage engine), Real-Time Indexing, Memory-Efficient Storage, Asymmetric Quantization, Scalar Quantization, Binary Quantization, Product Quantization, K-means algorithm, Vector Database, Semantic Search

Industries served

• E-commerce
• Legal tech
• Healthcare
• Developer tools
• Media and entertainment
• Finance
• Insurance
• Government

What it says sets it apart

• Built entirely in Rust with SIMD and a custom storage engine (Gridstore) for highest performance
• Open-source DNA with enterprise-grade security and flexibility
• Expansive metadata filtering capabilities
• Native hybrid search blending keyword and vector search in one query
• Built-in multivector support for more expressive and multimodal retrieval
• Efficient one-stage filtering applied during HNSW traversal for high recall and low latency
• Full-spectrum reranking for infusing business logic and diversifying results
• Real-time indexing without rebuilding the entire index
• Optimized storage architecture for memory-efficient storage of billions of vectors
• Advanced quantization techniques (Asymmetric, Scalar, Binary, Product Quantization) to reduce memory usage while maintaining search quality
• Comprehensive deployment options: Qdrant Cloud (fully managed), Hybrid Cloud (bring your own Kubernetes), Private Cloud (air-gapped), and Edge (lightweight)
• Enterprise-ready tooling including SOC 2, GDPR-aligned options, SSO, RBAC, private networking, zero-downtime upgrades, backups, and point-in-time restore
• Strong community and developer focus with intuitive APIs and built-in tools
• Proven track record with numerous case studies across various industries and use cases