Qdrant
Product information, use cases, and access for Qdrant.
Pricing information
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What is Qdrant?
Qdrant is a vector search engine for building AI retrieval systems. It stores vector data and supports real-time similarity search across deployment models, making it relevant to retrieval-augmented generation, recommendation, semantic search, and data analysis applications. It serves teams that want either self-managed infrastructure or a managed cloud-based retrieval layer.
Developers can write data and query vectors through REST, gRPC, or official clients, then refine results with metadata filters, hybrid retrieval, and reranking. Qdrant also provides a web interface for exploring collections, testing queries, and inspecting results, while its cloud offering can handle text and image embeddings. This combination suits builders creating RAG systems, AI agents, recommendation features, or anomaly-detection workflows that need a configurable retrieval component.
Key features of Qdrant
Vector similarity search
Qdrant stores object vectors in collections and retrieves nearby results for a query vector, providing a foundation for semantic retrieval, recommendations, and content matching. Developers can ingest data and run searches through APIs or official clients, which fits applications that need vector search inside an existing workflow.
Hybrid retrieval and reranking
Qdrant can combine dense and sparse vectors in a single retrieval flow and supports keyword-oriented approaches alongside multiple vectors per object. Results can then be refined with score boosting, late-interaction models, or diversity-oriented ranking, making the feature useful when search quality depends on semantics, keywords, and application-specific rules together.
Metadata filtering and live updates
Qdrant lets developers store JSON metadata with vectors and apply filters such as nested, text, geographic, and vector-presence conditions during search. Filtering is applied during index traversal, while newly added data can become searchable immediately, which suits retrieval systems with changing content and attribute-based query constraints.
Deployment and developer tooling
Qdrant is available through self-managed, cloud, hybrid-cloud, private, and edge-oriented deployment options, allowing teams to match the setup to their infrastructure and data-control needs. Developers can inspect collections and query results in a web interface, and cloud inference can generate text or image embeddings as part of the retrieval workflow.