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Maddie Duhon
2026-01-21 10:19:59 -05:00
parent 9e42f735ee
commit 707157e288
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qdrant-landing/content/edge-beta/edge-beta-faq.md
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answer: Teams building AI systems that need fast, local vector search on embedded or resource-constrained devices, such as robots, mobile apps, or IoT hardware.
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question: Is this available to all Qdrant users?
answer: 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.
answer: Yes. Read the Quick Start guide, and view the demo in GitHub.
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question: What are the minimum requirements to join the beta?
answer: 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.
qdrant-landing/content/edge-beta/edge-beta-hero.md
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subtitle: Run Vector Search Inside Embedded and Edge AI Systems
description: 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.
startFree:
text: Apply to Join the Beta
text: Join the Beta
url: "#form"
image:
src: /img/qdrant-edge-scheme.svg