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An Azure managed device that brings the compute, storage, and intelligence of Azure to the edge.
Purpose-built hardware-as-a-service with Azure Stack Edge
Run your workloads and get quick actionable insights right at the edge where data is created using purpose-built hardware-as-a-service with Azure Stack Edge.
With easy ordering and fulfillment, you can manage your device from the cloud with standard Azure management tools
With Azure loT Edge, you can deploy and manage containers from loT Hub and integrate with Azure loT solutions at the edge with rugged options using Kubernetes with multi-node and virtual machine support
Bring hardware acceleration to a diverse set of Machine Learning workloads with NVIDIA T4 Tensor Core GPU and Intel VPU
Keep a local cache of your Azure Storage account, utilizing the storage gateway to automatically upload
Get started easily with hardware-as-a-service and a seamless cloud-to-edge experience
Simply order your appliance from the Azure portal in a hardware-as-a-service model, paid monthly via your Azure subscription.
Get a seamless cloud-to-edge experience where you configure, monitor and update your Azure Stack Edge with the same management portal and development tools that you’ve come to expect from Azure.
Run your applications at the edge close to the data
Run your containerized applications and VMs right at the edge where data is created and collected. Analyze, transform, and filter data at the edge, sending only the data you need to the cloud for further processing or storage.
Analyze your data for quick actionable insights with hardware accelerated AI/ML
Build and train Machine Learning models in Azure or use Azure Cognitive service, and then utilize the built-in NVIDIA T4 GPU or Intel VPU in the Mini R to accelerate the results locally. Upload the full dataset or a subset to the cloud to retrain your models and make your edge devices even smarter.
Transfer data efficiently and easily between the cloud and edge
Azure Stack Edge acts as a cloud storage gateway and enables eyes-off data transfers to Azure, while retaining local access to files. With its local cache capability and bandwidth throttling, to limit usage during peak business hours, Azure Stack Edge can be used to optimize your data transfers to Azure and back.
Choose the device best suited for the job
Azure Stack Edge Pro Series
Enterprise scale and performance for your edge workloads.
Pro 2
Compact form factor optimized for edge and branch locations. Flexible mounting options.
Configuration options:
- 32 vCPUs, 51 GB RAM, 720 GB
- 32 vCPUs, 102 GB RAM, 1.6 TB, 1 NVIDIA A2 GPU
- 32 vCPUs, 204 GB RAM, 2.5 TB, 2 NVIDIA A2 GPUs
All figures are customer usable capacity.
Pro
1U rack-mountable appliance, optimized for conditions inside a data center or branch location.
Configuration options:
- 40 vCPUs, 102 GB RAM, 4.2 TB, 1 NVIDIA T4 GPU
- 40 vCPUs, 102 GB RAM, 4.2 TB, 2 NVIDIA T4 GPUs
All figures are customer usable capacity.
Pro R
Ruggedized datacenter-grade power with a built-in NVIDIA T4 GPU, in a transportable case for remote locations.
Available options: With or without Uninterruptable Power Supply (UPS).
Azure Stack Edge Mini Series
Designed for edge processing on the go.
Mini R
Ruggedized, battery-operated device-small enough to fit into a backpack-designed for harsh environments and disconnected scenarios. Includes a built-in Intel VPU for edge processing.
Use cases
Machine Learning at the edge
Azure Stack Edge helps you address latency or connectivity issues by processing data close to the source. Run Machine Learning models right at the edge locations. Transfer the data set you need, either the full data set or a subset, to Azure to retrain and continue to improve your model.
Internet of Things
Process, sort, analyze your IoT or datacenter data to determine what you can act on right away, what you need to keep and store in cloud, and what you don’t.
Network data transfer from the edge to the cloud
Easily and quickly transfer data to Azure for further compute or archival purposes or to expedite your cloud migration. Return the appliance to Microsoft when you’re done.
Edge and remote site compute
Run applications at remote locations to speed transactions and address bandwidth constraints. Local applications can still work when your connectivity to cloud is limited.
Regulatory compliance
Make sure the data you’re sending back to the cloud doesn’t violate any compliance regulations by using ML models to help alert you to potentially sensitive data and take action locally.
Azure Stack Edge and Azure AI Helping Stop Animal Trafficking
Take a look at how Azure Stack Edge for AI and machine learning workloads are being piloted, combining a 3D scanner with Azure AI models, at London’s Heathrow Airport, to prevent illegal wildlife trafficking.
Comprehensive security and compliance, built in
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Microsoft invests more than $1 billion annually on cybersecurity research and development.
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We employ more than 3,500 security experts who are dedicated to data security and privacy.
Get started with an Azure free account
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After your credit, move to?pay as you go?to keep building with the same free services. Pay only if you use more than your free monthly amounts.
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See how customers are using Azure Stack Edge
"The on-premises nature of Azure Stack Edge and the Intel-based field-programmable gate arrays (FPGAs) make it really seamless for us to be able to integrate that device into part of our natural application."
T. Michael Thornton, Vice President R&D, Customer Solutions Business, Olympus
"I think an improvement of this magnitude comes roughly every 20, 30 years."
Cengiz Balkas, SVP & General Manager, Wolfspeed
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AI in edge appliances promotes Autonomous Vessel in the maritime industry.
