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.

Apply to Join the Beta

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.

In filling out the Qdrant Edge private beta form, I can confirm that:

We are building or deploying AI systems on embedded or edge devices (e.g. robots, mobile hardware, IoT, or offline agents)
We require local vector search as part of our product or infrastructure
We are able to test and provide feedback within the next 60 days
We understand this is a research beta

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Last Name*

Business Email*

Company name*

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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