Qdrant, a leading provider of high-performance, open-source vector search, is offering a private beta of Qdrant Edge, a lightweight, embedded vector search engine designed for AI systems on devices such as robots, point of sales, home assistants, and mobile phones.
“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.”
According to the company, Qdrant Edge brings vector-based retrieval to resource-constrained environments where low latency, limited compute, and limited network access are 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.
Qdrant Edge will support in-process execution, advanced filtering, and compatibility with real-time agent workloads, the company said.
Use cases for Qdrant Edge 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 and offers full control over lifecycle, memory usage, and in-process execution without background services.
Qdrant Edge is now available through a private beta.
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