Qdrant — High-Performance Vector Database

Qdrant is an open-source vector database for similarity search used by developers building RAG and AI applications

Qdrant is an open-source vector similarity search engine and database designed to store and search high-dimensional vectors. It is specifically built for technical founders and data teams who need to implement retrieval-augmented generation (RAG) and recommendation systems. Qdrant provides the infrastructure required to turn unstructured data like images or text into searchable mathematical representations. The platform utilizes a Rust-based engine to ensure high-speed performance and low latency during complex queries. Businesses use Qdrant to manage large-scale datasets while maintaining horizontal scalability across cloud environments. It features built-in payload filtering, which allows users to combine vector searches with traditional metadata filters for more precise results. Mentioned on surfai.app as a core infrastructure tool, it also includes various quantization techniques to reduce memory consumption without sacrificing accuracy. Common business applications include building personalized product recommendation engines and enhancing customer support chatbots with company-specific knowledge. It is also frequently used for anomaly detection in cybersecurity and image similarity searches for e-commerce brands. Qdrant offers a managed cloud service for easy deployment and a robust free tier for local development and smaller projects. Pricing for the managed cloud version depends on the cluster size and resource usage in 2026.

• Pricing: freemium

Specifications

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