Edge AI Infrastructure

Qdrant Edge

Run Vector Search Inside Embedded and Edge AI Systems

Qdrant Edge is a lightweight, in-process vector search engine designed for embedded devices, autonomous systems, and mobile agents. It enables on-device retrieval with minimal memory footprint, no background services, and optional synchronization with Qdrant Cloud.

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Real-time vector retrieval for Edge AI in resource-constrained environments

Native Vector Search for Embedded & Edge AI

Runs as a lightweight, in-process library. No background threads, no services - ideal for mobile, robotic, and embedded environments.

Optimized for Low-Memory, Low-Compute Devices

Dramatically Designed for resource-constrained hardware. No idle overhead, no runtime daemons. Fits into tightly scoped edge deployments. memory usage with built-in compression options and offload data to disk.

Local by Default, Cloud-Connected When Needed

Retrieval runs fully offline. Sync with Qdrant Cloud only when required - for data transfer, tenant promotion, or coordination at scale.

Hybrid & Multimodal Search On-Device

Supports dense and multimodal vectors with structured filtering. Enables real-time retrieval from text, image, audio, or sensor-derived embeddings.

Edge-Scale Multitenancy with Native SDKs

Supports payload- and shard-based tenant isolation. Routes queries across uneven edge workloads. Native SDKs in Java (Android), Swift (Apple), and more.

Purpose-Built for On-Device AI Workloads

Robotics & Autonomy

Run multimodal retrieval from onboard sensors (like LiDAR, radar, and cameras) for real-time navigation and decision-making.

Offline Voice Assistants

Power local memory for privacy-first assistants on mobile or embedded hardware, without relying on a persistent connection.

Smart Retail & Kiosks

Enable product similarity and anomaly detection on edge terminals with limited or intermittent connectivity.

Industrial IoT

Perform local retrieval and diagnostics from sensor-derived embeddings in air-gapped or bandwidth-constrained environments.

Apply to Join the Beta

Private beta available to selected teams building embedded or edge-native AI systems.

FAQs

Who is Qdrant Edge for?

Teams building AI systems that need fast, local vector search on embedded or resource-constrained devices, such as robots, mobile apps, or IoT hardware.

Is this available to all Qdrant users?

Not yet. Qdrant Edge is in private beta. We're selecting a limited number of partners based on technical fit and active edge deployment scenarios.

What are the minimum requirements to join the beta?

You should have a clear use case for on-device or offline vector search. Preference is given to companies working with embedded hardware or deploying agents at the edge.

How do I get access?

Qdrant Edge is currently in private beta. If you're building edge-native or embedded AI systems and want early access, apply to join the beta.

Apply to Join the Beta

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