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Maddie Duhon
2026-01-21 10:49:22 -05:00
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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: Yes. Read the [Quick Start guide](https://qdrant.tech/documentation/edge/edge-quickstart/), and [view the demo](https://github.com/qdrant/qdrant-edge-demo) in GitHub.
answer: Yes. Read the <a href="https://qdrant.tech/documentation/edge/edge-quickstart/">Quick Start guide</a>,
and view the <a href="https://github.com/qdrant/qdrant-edge-demo">demo</a> on 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.
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question: How do I get access?
answer: If you're building edge-native or embedded AI systems, apply to join the beta. Or, read the [Quick Start guide](https://qdrant.tech/documentation/edge/edge-quickstart/), and [view the demo](https://github.com/qdrant/qdrant-edge-demo) in GitHub.
answer: If you're building edge-native or embedded AI systems, apply to join the beta. Or, read the <a href="https://qdrant.tech/documentation/edge/edge-quickstart/">Quick Start guide</a>, and view the <a href="https://github.com/qdrant/qdrant-edge-demo">demo</a> on GitHub
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