First embeddable vector database optimized for on-device AI in robotics, mobile agents, and offline intelligent systems
Qdrant, the leading provider of high-performance, open-source vector search, today announced the private beta of Qdrant Edge, a lightweight, embedded vector search engine designed for AI systems running on devices such as robots, point of sales, home assistants, and mobile phones.
Qdrant Edge brings vector-based retrieval to resource-constrained environments where low latency, limited compute, and limited network access are fundamental constraints. It enables developers to run hybrid and multimodal search locally, on edge, without a server process or background threads, using the same core capabilities that power Qdrant in cloud-native deployments.
"AI is moving beyond the cloud. Developers need infrastructure that runs where many decisions are made on the device itself," said André Zayarni, CEO and Co-Founder of Qdrant. "Qdrant Edge is a clean-slate vector search engine designed for Embedded AI. It brings local search, deterministic performance, and multimodal support into a minimal runtime footprint."
Qdrant Edge will support in-process execution, advanced filtering, and compatibility with real-time agent workloads. Use cases include robotic navigation with multimodal sensor inputs, local retrieval on smart retail kiosks and point-of-sale systems, and privacy-first assistants running on mobile or embedded hardware. It shares architectural principles with Qdrant OSS and Qdrant Cloud, but extends them for embeddability, offering full control over lifecycle, memory usage, and in-process execution without background services.
Qdrant Edge is now available through a private beta. Teams building robotics, on-device assistants, or embedded inference pipelines are encouraged to learn more at: qdrant.tech/blog/qdrant-edge.
About Qdrant
Qdrant is the leading high-performance, scalable, open-source vector database, essential for building the next generation of AI/ML applications. Qdrant is able to handle billions of vectors and is implemented in Rust for performance, memory safety, and scale. Recently, Qdrant's open-source project surpassed 250 million installs across all open-source packages and earned a place in The Forrester Wave: Vector Databases, Q3 2024. The company was also recognized as one of Europe's top 10 startups in Sifted's 2025 B2B SaaS Rising 100, an annual ranking of the most promising B2B SaaS companies valued under $1 billion. Today, Qdrant powers real-time Agentic RAG applications at scale in enterprises like Tripadvisor, HubSpot, and Deutsche Telekom.
For more information, please visit qdrant.tech or contact press@qdrant.com.
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