Qdrant is an open-source vector database optimized for high-performance similarity search in AI and machine learning applications. Designed for handling dense vector embeddings from models like OpenAI, Hugging Face, and Cohere, it powers use cases such as semantic search, recommendation engines, anomaly detection, and image or audio retrieval.

Built for developers and ML engineers, Qdrant emphasizes reliability, filtering precision, and real-time indexing. It supports billions of vectors and provides strong filtering mechanisms that enable hybrid search, combining semantic relevance with structured metadata conditions.
Key benefits include:

• High-accuracy vector search with filtering

• Real-time ingestion and immediate searchability

• Lightweight, production-ready deployment

What are the main features of Qdrant?

Efficient similarity search at scale

Qdrant is optimized for fast and accurate nearest neighbor search on dense vector data.

• Supports cosine, dot product, and Euclidean distance

• Built-in HNSW (Hierarchical Navigable Small World) indexing

• Low-latency top-k search on large datasets

Advanced filtering and hybrid search

Combining vector search with metadata filters, Qdrant enables more refined and context-aware results.

• Supports structured filters (numeric, text, tags)

• Combines filtering with similarity ranking

• Useful for multi-attribute search scenarios (e.g., location + embedding)

Real-time data ingestion and updates

Qdrant is designed for production use cases that require frequent data updates or streaming input.

• Vectors become searchable immediately after insertion

• Supports insert, update, and delete operations

• Consistent performance with frequent write operations

Flexible deployment and integration

Qdrant is easy to deploy and integrates well into modern ML pipelines.

• Runs as a standalone binary or in Docker/Kubernetes

• REST and gRPC APIs available

• SDKs for Python, JavaScript, Go, and Rust

Storage and persistence optimized

The engine is designed for efficient memory and disk usage to ensure reliable long-term storage.

• On-disk persistence with memory-mapped file access

• Optimized for high-load and low-resource environments

• Supports snapshots and recovery features

Why choose Qdrant?

• Fast and accurate vector search: Built to deliver high recall and performance for dense embeddings.

• Hybrid search by default: Combines semantic relevance with structured metadata filters.

• Real-time indexing: Ideal for applications where data changes frequently or streams continuously.

• Developer-friendly: Simple deployment, REST/gRPC APIs, and active community support.

• Optimized for resource efficiency: Performs well even in constrained environments and edge deployments.