Qdrant Announces Qdrant Edge: The First Vector Search Engine for Embedded AI

Qdrant, the leading provider of high-performance, open-source vector search, 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.

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“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,” said André Zayarni, CEO and Co-Founder of Qdrant.

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.

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