Azure Arc Server June Forum
Please find the recording for the monthly Azure Arc Server Forum on YouTube! During the June 2026 Azure Arc Server Forum, we discussed: Arc Server AI Agent assists with onboarding and troubleshooting through an integrated LLM with plans for surfacing the experience through Azure Copilot and in Azure Portal. Azure Arc Multicloud Connector Updates focused on both the Public Preview of the Google Cloud Platform (GCP) Connector and Public Preview of Azure Arc-enablement of detected EKS Clusters. WS 2016 ESU Updates with planned changes versus WS 2012 ESUs and expected timelines anticipating the WS 2016 end-of-support date of January 2027. Note, WS 2012 ESUs will end in October 2026. SQL 2016 ESU Updates discussed enrollment processes, licensing basics, and overall guidance with end-of-support in July 2026. To sign up for the Azure Arc Server Forum and newsletter, please register with contact details at https://aka.ms/arcserverforumsignup/. For the latest agent release notes, check out What's new with Azure Connected Machine agent - Azure Arc | Microsoft Learn. We are skipping July and August, and will resume after summer holidays with the September forum to be held on Thursday, September 17 at 9:30 AM PST / 12:30 PM EST. Finally, I wanted to thank everyone for the attendance and community over the last few years, I will no longer be leading the community calls. Please stay in touch, and Mason Torres, Yunis Hussein, and Meagan McCrory from the Arc PM team will be taking the community forward. We look forward to you joining us, thank you!How Mesh Systems Builds on Azure IoT Hub and Azure IoT Operations to Accelerate Industrial AI
Manufacturers generate vast amounts of operational data, yet its complexity and fragmentation across historians, Operational Technology (OT) systems, and cloud platforms can slow AI adoption at scale. As organizations invest in AI to enhance productivity, quality, and decision making, the ability to connect and contextualize operational data becomes critical. Azure IoT Hub, Azure IoT Operations, and Mesh address this challenge together, spanning the full path from device connectivity to actionable AI-powered insights. Mesh brings deep Azure IoT platform experience and a practical path to industrial AI, with MeshCloud built on Azure IoT Hub, an open-source .NET Akri framework for Azure IoT Operations, and MeshInsights delivering generative AI-powered operational intelligence. This expertise is backed by a long history with Azure; Mesh launched its first IoT solution on Azure in private preview in 2009 and remained an early adopter of every major Azure IoT service since. Together, Azure IoT Hub, Azure IoT Operations, and Mesh give manufacturers a streamlined way to unify operational data and apply AI where it matters most. Unlocking legacy data with Mesh's Akri Connector Industrial organizations often struggle to modernize operations because critical operational data sits isolated inside historians and legacy Operational Technology (OT) systems. Many manufacturers are also wary of integrations that create new dependencies and limit future flexibility. Azure IoT Operations addresses this through an open architecture built around Akri, connecting industrial data sources while preserving interoperability across hardware and software environments. Mesh built on this foundation with its Akri Historian Connector, bringing historian and legacy operational data into Azure IoT Operations through prebuilt connectivity rather than source-by-source integration work. The result is faster access to operational data ready for analytics, AI, and industrial automation. The key features of this connector include: Restart-safe data continuity: Manufacturers can trust that operational data keeps flowing even through outages or restarts, with no data loss and no time spent recovering or reprocessing data. Secure, flexible authentication: Modern and legacy industrial systems connect under one security model, meeting enterprise-grade authentication standards without restructuring existing infrastructure. A foundation other connectors can be built on: The underlying framework handles the heavy lifting, so teams only need to build what's unique to each new OT data source. This means faster time to value for every new data source. Built in alignment with Azure IoT Operations roadmap: The connector stays up to date automatically, as new Azure IoT Operations features become available. This means manufacturers have access to the latest capabilities as the platform evolves. Together, Mesh and Azure IoT Operations give organizations a production-ready path from the shop floor into Azure IoT Operations and onward to Microsoft Fabric. Delivering Scalable Connected Products with Azure IoT Hub and MeshCloud MeshCloud helps manufacturers move from connected product pilots to fleet scale deployments faster by providing a platform build on Azure IoT Hub and other Azure native services. Azure IoT Hub provides per-device identity, support for MQTT, AMQP, and HTTPS, and built-in capabilities like device twins, direct methods, and rules-based message routing. Azure Device Provisioning Service (DPS) extends this foundation with zero-touch, just-in-time onboarding using X.509, TPM, or symmetric key attestation. Mesh operates as one cohesive engineering practice across the full connected product lifecycle, delivering hardware, firmware, wireless, edge, and cloud integration natively for Azure IoT Hub and Azure IoT Operations. That end-to-end scope is what MeshCloud, their Azure-native connected product platform, brings together. MeshCloud embeds Azure IoT Hub and DPS into an Azure-native connected product platform, giving organizations a faster path to connected product delivery without sacrificing control, scale, or solution ownership. The platform comes together across four layers: Edge to cloud: Connected devices, from MCU controllers to tablets and phones, register and authenticate through DPS and connect securely to IoT Hub, giving organizations a direct, secure line from shop floor to enterprise systems. Messaging and command: Event Hubs and Container Apps move telemetry and commands between devices and the cloud, with asset and ontology data exposed for digital twin management and device control. IT and operations: Azure Monitor, OpenTelemetry, Microsoft Entra, and Application Gateway bring platform observability, identity management, and secure ingress together, equipping IT and operations teams with a unified way to manage and secure the environment. Analytics and visualization: Telemetry flows into Azure Data Explorer and Microsoft Fabric for processing and storage, with Grafana, Power BI, and a Device Health UI giving teams fleet-wide visibility. This architecture enables organizations to move from pilot projects to fleet-scale deployments using Azure-native services, while maintaining interoperability across devices, connectivity protocols, and analytics platforms. For manufacturers, this means less time integrating infrastructure and more time delivering operational insights, connected services, and AI-powered workflows. Advancing Industrial Intelligence with MeshInsights As organizations connect more assets and operational systems, the next challenge becomes turning information into consistent actions and decisions. Microsoft Azure provides the cloud, data, and AI foundation for intelligent operational workflows, while giving organizations control over their data and business processes. Mesh extends this foundation through MeshInsights. MeshInsights is Mesh's AI agent offering for connected-product manufacturers. Mesh works with organizations to define a specific operational decision worth automating, such as classifying an alert or determining the right service response, and builds an evaluation standard from real telemetry, service history, and expert-validated examples. AI agents are then developed and measured against that standard, acting automatically on high-confidence cases and routing the rest to the organization's own experts. This extends connected systems beyond monitoring and reporting into trusted, auditable operational decisions. By combining Azure IoT platform services with MeshInsights, Mesh helps organizations move from connected infrastructure to autonomous, AI-driven action without changing where their data lives or who owns the architecture. Why This Matters Industrial transformation increasingly depends on strong collaboration between hyperscale cloud platforms and ecosystem partners who bring operational expertise, deployment acceleration, and industry-specific engineering capabilities. Mesh Systems demonstrates how partners can build differentiated value on top of Azure IoT platform services while helping organizations accelerate deployment timelines, standardize industrial data architectures, and operationalize AI across connected environments. Organizations are already putting this value to work in everyday operations. BUNN's cloud-connected coffee machines now give technicians a head start before every service call. As Kurt Powell, Executive Vice President at BUNN, put it: "With this solution, we know exactly which component to fix before we get there." WLS Lighting Systems has turned that same visibility into measurable savings at scale. Built on MeshCloud and Azure IoT, WLS's netLiNK gives property owners remote monitoring and control over individual light fixtures. Kevin Fletcher, President National Accounts at WLS, shared that the company has saved customers a little over $50 million in electrical costs since bringing netLiNK to market. Together, Azure IoT Hub, Azure IoT Operations, and Mesh Systems help manufacturers reduce integration complexity and operationalize industrial data, creating a foundation for AI driven operations spanning plant, edge, and cloud. The result: manufacturers spend less time on integration and more time improving productivity, resiliency, and decision making across their operations. Learn More Explore Mesh Systems solutions on Azure Marketplace: Microsoft Marketplace | cloud solutions, AI apps, and agents Read customer success stories: https://meshsystems.com/case-study-eaton-1/ Learn more about Azure IoT Operations: https://azure.microsoft.com/products/iot-operations/Build, deploy, and govern sovereign AI with Foundry Local on Azure Local
Not every AI workload can run in the cloud. For many of our customers, data needs to stay within defined boundaries, connectivity may be limited or absent, and latency, governance, and auditability are non-negotiable. With Foundry Local on Azure Local, you can use the same model catalog, developer workflows, and governance capabilities you know from Azure, while running AI entirely within your own environment where your data resides. Foundry Local provides the model catalog and developer experience. Azure Local provides the customer-managed infrastructure. Azure Arc provides unified policy, governance, and lifecycle management across cloud and local environments. This gives developers a consistent way to build, deploy, and operate AI. The same az commands, the same model catalog, the same Arc policies, all running on hardware you control. Expansion of Foundry Local on Azure Local We're expanding the Foundry Local model offering on Azure Local, with support for multi-node deployments and new agents and tools that run locally, in preview. Deploy and run AI models locally. Run models with Foundry Local in customer-managed environments on Azure Local, across sovereign, private, and edge scenarios, including fully disconnected operation. Choose from a flexible, high-performance model catalog. Access proprietary and community models through Foundry Local, now expanded with vLLM-optimized models alongside ONNX-based offerings. You explore and deploy through the same catalog API experience, then operate locally on Azure Local. Build for production realities. Bring governance, identity, and auditability into your applications while keeping execution inside your controlled boundary. See what’s new in Foundry Local on Azure Local in the Tech Community blog. From intelligence to action: agents and tools inside the enterprise boundary Most production AI use cases need two things: grounded answers and the ability to act on them, without sending data outside the environment. Here's how we're enabling that locally. Preview: Agentic retrieval with Foundry Local: Ground agents in enterprise data using retrieval-augmented generation across local Microsoft 365 services, including Exchange and SharePoint. Read the Tech Community blog to learn more. Preview: Agents and tools with Foundry Local: Build AI systems that reason, retrieve information, and take action within customer-controlled environments. Learn more. Preview: Developer acceleration templates: Jump-start local AI application development with new Foundry solution templates, including local chat experiences and video agents, powered by Azure AI Video Indexer. Read the Tech Community to learn more. GitHub Enterprise Local: Now available in public preview Sovereign AI is also about how systems are built and secured, not just where they run. With GitHub Enterprise Local on Azure Local, you can bring your full software development lifecycle on-premises: Source control and repositories CI/CD pipelines Security and DevSecOps workflows GitHub Enterprise Local deploys entirely within customer-owned infrastructure, so teams get the developer tools they expect without compromising on data residency or operational control. This extends modern DevSecOps practice into sovereign environments and pairs naturally with the AI development workflows above: build, secure, and ship your AI applications within the same boundary where they run. Read the tech community blog to learn more about GitHub Enterprise Local and how to join the preview. Accelerating High-performance AI at the Edge with NVIDIA We are expanding our collaboration with NVIDIA to deliver high-performance AI capabilities directly at the edge. At Build, we are bringing: Azure Local and Foundry Local on NVIDIA-powered GPUs, including NVIDIA RTX PRO 6000 Blackwell Server Edition, with expanded GPU support coming soon Integration with Nemotron models, optimized for enterprise performance A scalable foundation for data-intensive, low-latency workloads This partnership ensures that organizations can run advanced AI workloads where data is generated - without dependency on centralized cloud infrastructure. Hardware options: AI factory configurations are available now in the catalog Alongside our hardware partners, we’re bringing integrated solutions to customers building AI within sovereign environments. The Azure Local hardware catalog now includes AI factory configurations from our OEM partners, including NVIDIA-certified 8xH100 systems, with options from DataON, Dell, HPE, and Lenovo. These configurations are sized for the performance that model serving and agentic workloads require on customer-managed infrastructure. Together with Microsoft, we are advancing sovereign AI by bringing the open NVIDIA Nemotron model family to Microsoft Foundry Local on Azure Local. This collaboration gives organizations a production-ready AI platform that enables them to deploy AI where their data resides while maintaining the governance, control, and performance needed to scale AI across the enterprise.” Kari Briski, VP Generative AI Software Products, NVIDIA ”Sovereign AI is becoming increasingly important for governments, regulated industries, and enterprises that want to use AI while maintaining control of their data, location, and operations. Lenovo’s ThinkAgile MX Series delivers trusted, enterprise-grade infrastructure with global deployment expertise to help customers run AI wherever their data resides. Co-engineered with Foundry Local and Azure Local, this solution provides an optimized platform to deploy, run, and scale AI locally with greater simplicity, consistency, and control, while helping meet strict data residency, security, and compliance requirements." Scott Patti - VP Infrastructure Solutions Group (ISG), Lenovo From AI models to trusted, mission-critical systems: what this unlocks for developers and operators AI is evolving from systems that answer questions to systems that plan, reason, and take action across workloads. These capabilities move AI from a cloud-only assumption to something you can deploy where sensitive work actually happens, with governance and operational controls intact. For our customers, this means you can now: Keep data, identities, and audit trails inside your sovereign boundary. Run AI inference and agentic workloads in connected, intermittently connected, or fully disconnected modes. Apply consistent policy and governance across cloud and local environments through Azure Arc. Use the same Foundry catalog and developer experience you already know, on infrastructure you own. Build, secure, and ship your AI applications with GitHub Enterprise Local, keeping source control, CI/CD, and DevSecOps workflows inside the same sovereign boundary. Resources Join us at Build OD837 Shipping physical AI to the edge with Azure Local and Foundry Local https://github.com/microsoft/build26-OD837 OD839 Foundry Local: AI solutions for industrial and sovereign needs https://github.com/microsoft/build26-OD839 LTG425 Expanding horizons: Foundry Local for devices and on-prem https://build.microsoft.com/en-US/sessions/LTG425 Request to join the Foundry Local on Azure Local preview Hands-on walkthrough: Your first model deployment on Foundry Local on Azure Local: from catalog to inference in 10 minutes | Microsoft Community Hub Read our Tech Community blogs: Foundry Local announcing multi-node and vLLM support Agentic Retrival with Foundry Local blog: https://aka.ms/AgentsAndToolsBuildBlog2026 Code sample / model catalog blog: https://aka.ms/foundry-local-model-catalog-blog For more details on the expanded capabilities of Foundry Local for highly secure environments, contact your Microsoft account team Discover Microsoft Sovereign Cloud Explore product documentation at: Foundry Local models on Azure Local: https://aka.ms/FoundryLocalonAzureLocal_documentation Local Agentic retrieval with Foundry Local: https://aka.ms/edge-agentic-retrieval-docsPlan for Upcoming Changes to Extended Security Updates on Azure Local
Beginning April 1 2026, Microsoft introduced a consistent pricing model for Extended Security Updates (ESU) for SQL Server and Windows products, including SQL Server 2016, Windows 10 Enterprise LTSB 2016 and Windows Server 2016. This update aims to simplify the Extended Security Update pricing so that customers pay the same list price for ESUs regardless of deployment location (Azure, on-premises, or other public clouds) or purchasing channel (Microsoft Customer Agreement, Enterprise agreements, Cloud Solution Provider program, or other licensing programs). ESUs on Azure Local This pricing change affects any new Extended Security Update offerings starting on or after April 1, 2026, including Windows 10 Enterprise LTSB 2016 (reaching end of support October 13, 2026) and Windows Server 2016 (reaching end of support January 12, 2027). Existing ESU offerings, including Windows Server 2012 or Windows 10 version 22H2, are not affected by this pricing change. This means that customers who already leverage ESUs will continue to have them available on Azure Local at no cost through Azure Verification for VMs. Next Steps As products reach end of support, it is recommended to upgrade your servers to the latest release available. For customers needing to remain on older versions after the end of support date has passed, further guidance on pricing and availability of ESUs will be shared in the coming months. Keep an eye on Extended Security Updates on Azure Local for more details. For More Information Microsoft Services: Pricing Consistency Update | Microsoft Licensing Resources Plan for Windows Server 2016 and Windows 10 2016 LTSB end of support - Windows IT Pro BlogAzure Arc Server May 2026 Forum
Please find the recording for the monthly Azure Arc Server Forum on YouTube! During the May 2026 Azure Arc Server Forum, we discussed: Private Preview of Windows Server 2016 Extended Security Updates (ESUs): Customers with Windows Server 2016 Servers with an NDA and willingness to provide feedback can sign up at https://aka.ms/WS2016ESU-PrivatePreview Special Announcement: Windows Server Hotpatch enabled by Azure Arc is now available at no additional cost for Windows Server 2025 machines connected to Azure Arc Feedback opportunity to product group on Azure Arc + RDP Capability: Customers can sign up at https://aka.ms/arc-rdp To sign up for the Azure Arc Server Forum and newsletter, please register with contact details at https://aka.ms/arcserverforumsignup/. For the latest agent release notes, check out What's new with Azure Connected Machine agent - Azure Arc | Microsoft Learn. Our June 2026 forum will be held on Thursday, June 18 at 9:30 AM PST / 12:30 PM EST. We look forward to you joining us, thank you!
At-Scale Failure Reporting for Azure Update Manager
Introduction Azure Update Manager simplifies patching across Azure virtual machines and Azure Arc-enabled servers by providing a centralized platform for patch assessment and installation. However, as environments scale, a key challenge emerges—efficiently identifying and troubleshooting patch failures across large fleets of machines. While Azure Update Manager surfaces detailed error messages in the Azure portal, this information is typically available only at an individual machine level. In enterprise environments managing hundreds or thousands of systems, drilling into each VM to find error details quickly becomes impractical. In this article, we walk through a real-world use case and demonstrate how to leverage Azure Resource Graph (ARG) to extract failed machines along with their error details for a specific maintenance run—using a single query. The Challenge: Scaling Patch Failure Visibility In a large enterprise deployment, Azure Update Manager was configured to manage patching across: Windows and Linux virtual machines Azure cloud VMs and Arc-enabled on‑premises servers Multiple regions and subscriptions While patching operations were largely successful, a subset of machines experienced failures. The key challenges faced by the operations team were: Error messages were visible only by drilling into each failed VM in the portal No built‑in way to aggregate failures across all machines Lack of a simple mechanism to export: Failed VMs Error codes Error messages The team needed a scalable, query‑driven approach to analyze failures across an entire maintenance run. Key Insight: Where Azure Update Manager Stores Data Azure Update Manager does not rely on Log Analytics to store operational results. Instead: Patch assessment and installation results are stored in Azure Resource Graph Azure Resource Graph acts as a centralized, queryable store for update operations This design enables powerful querying without requiring additional ingestion, configuration, or cost overhead. Understanding Maintenance Runs and Correlation IDs Each Azure Update Manager maintenance run generates a unique identifier: properties.correlationId represents the maintenance (schedule) run ID All machines involved in the same patch cycle share this ID This allows all machines within a single patch execution to be correlated and queried collectively. The Solution: Query Failed VMs with Error Messages Azure Resource Graph allows querying failures at scale using the maintenanceresources dataset. Core Query (Kusto Query Language) 1 maintenanceresources 2 | where type =~ "microsoft.maintenance/applyupdates" 3 | where tostring(properties.correlationId) contains "<YourMaintenanceRunID>" 4 | where tostring(properties.status) =~ "Failed" 5 | project properties.resourceId, properties.errorCode, properties.errorMessage What This Query Delivers All machines that failed in a specific maintenance run Error codes for troubleshooting Full error messages that are otherwise visible only in the Azure portal Note: Property names for error information can vary by environment. Validate available fields using Azure Resource Graph Explorer and adjust the project clause if required. Sample Output (Conceptual) Resource ID Error Code Error Message vm-01 0x80244007 Windows Update API failed vm-02 0x80072f8f Connectivity issue vm-03 1C WSUS configuration issue Advanced Scenario: Automatically Detecting the Latest Failed Maintenance Run In real-world scenarios, you may not always know the maintenance run ID. The following query dynamically identifies the most recent maintenance run that had failures, and then retrieves all failed machines from that run. 1 // Step 1: Identify the latest maintenance run ID with failures 2 let lastFailedRun = toscalar( 3 maintenanceresources 4 | extend runId = extract(@"applyupdates/(\d+)$", 1, properties.correlationId) 5 | where type =~ "microsoft.maintenance/applyupdates" 6 | where tostring(properties.status) =~ "Failed" 7 | order by tostring(properties.startDateTime) desc 8 | take 1 9 | project runId 10 ); 11 // Step 2: Query all failed VMs from that run 12 maintenanceresources 13 | where type =~ "microsoft.maintenance/applyupdates" 14 | where tostring(properties.correlationId) contains lastFailedRun 15 | where tostring(properties.status) =~ "Failed" 16 | project properties.resourceId, properties.errorCode, properties.errorMessage This approach is ideal for automation, scheduled reporting, and dashboard scenarios. Why This Approach Matters Operational Efficiency Eliminates manual portal navigation Provides consolidated failure insights in seconds Scalability Works across large, distributed environments Supports both Azure and hybrid (Arc‑enabled) machines Automation Ready Can be integrated into scripts, dashboards, and reporting pipelines Enables proactive monitoring and alerting scenarios Best Practices for Enterprise Patch Reporting To maximize the value of this approach: Capture and track maintenance run IDs Use Azure Resource Graph as the primary reporting layer Build reusable queries for different patch scenarios Export reports for compliance and auditing Correlate failures with root‑cause trends over time Conclusion As organizations scale patching operations with Azure Update Manager, visibility, speed, and automation become essential. While the Azure portal is effective for per‑machine troubleshooting, it is not optimized for fleet‑level analysis. Azure Resource Graph fills this gap by enabling a shift from manual troubleshooting to automated, query‑driven failure analysis at scale. By adopting this approach, teams can significantly improve operational efficiency, reduce mean time to resolution, and build a more mature patch management strategy. Final takeaway: Don’t rely only on the portal Leverage Azure Resource Graph to operationalize patch insights at enterprise scale References Azure Update Manager – Query resources with Azure Resource Graph https://learn.microsoft.com/azure/update-manager/query-logs Azure Update Manager – Troubleshooting guide https://learn.microsoft.com/azure/update-manager/troubleshoot Sample Azure Resource Graph queries for Azure Update Manager https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/update-manager/sample-query-logs.mdUnlocking the Human Telemetry Layer for Safer Industrial Operations
What if we could track human health & safety conditions as precisely as we do with machines, and take immediate actions to protect our greatest asset, our people? Many industrial organizations still lack visibility into real-time human conditions, even as worker safety and operational risk remain major investment priorities. One of the most important operational signals has largely remained outside the industrial data estate: the human telemetry. VOORMI and Microsoft have joined forces to fill this gap in understanding real human conditions. Through the Mij™ platform, VOORMI brings human telemetry into Azure IoT, enabling enterprises to integrate worker conditions such as heat stress and fatigue into the same operational architecture already used for machines and industrial systems. VOORMI, SWNR’s performance apparel brand, is among the first to bring this technology into garments designed for real industrial field conditions. This integration brings their proprietary wearable technology directly into high-impact worker safety and field operations scenarios. The partnership helps establish a new telemetry layer for industrial operations, allowing human, machine, and environmental signals to converge and drive safer operations, real-time awareness, and adaptive AI workflows. Bringing the Human Signal into Industrial AI with Azure Industrial organizations increasingly recognize that many safety, productivity, and operational challenges occur at the intersection of people and machines. Workers operate in high-heat environments, hazardous conditions, remote sites, and physically demanding field scenarios where situational awareness matters in real time. Historically, worker telemetry has remained fragmented across proprietary wearable platforms and disconnected safety systems, creating governance and operational challenges for enterprise IT and OT teams. Mij™ is designed differently, integrating directly into customer-controlled Azure environments through Azure IoT Operations running at the edge or Azure IoT Hub in the cloud rather than introducing another isolated platform. Running intelligence at the edge enables virtual safety agents and operational workflows to execute closer to the worker, supporting low-latency responses, local interaction with OT systems, and operational resilience even in disconnected or bandwidth-constrained environments. This gives enterprises flexibility to support real-time worker safety responses at the edge while also enabling long-term analytics, reporting, and operational intelligence through Microsoft Fabric. Telemetry from garment-integrated sensors flows through edge gateways into Azure services including Azure IoT Operations, Azure Data Explorer, Azure Managed Grafana, and Microsoft Fabric. The result is a unified operational environment where worker telemetry can live beside machine, site, and environmental data under the customer’s existing identity, security, governance, and analytics model. The vision is simple and transformative: make human telemetry a trusted, first-class industrial data source. Azure Digital Operations as the Intelligence Layer The reference architecture demonstrates how Azure IoT Operations can serve as a scalable operational intelligence layer for worker safety and connected operations scenarios across manufacturing, energy, and field environments. Mij™-enabled garments broadcast Bluetooth Low Energy (BLE) telemetry that can be processed locally through edge gateways and routed into Azure IoT Operations using MQTT and dataflows. Data is then operationalized through Azure Data Explorer and visualized using Azure Managed Grafana dashboards for field operations, worker safety, fleet health, gateway monitoring, and operational readiness scenarios. Telemetry can also be made available to Foundry Local-hosted GenAI agents to support real-time, context aware safety guidance, such as prompting workers operating in high-heat conditions to hydrate or seek cooler environments. While Mij™-enabled garments are the initial implementation, the edge device-to-cloud architecture creates a broader onboarding point for additional wearable, sensor, and field telemetry scenarios over time. This allows enterprises to bring more human and operational signals into a unified Azure-native operational environment. The architecture also supports flexible ingestion patterns for environments where dedicated edge gateways are not practical. Using Microsoft Entra External ID, Azure Container Apps, and Azure IoT Hub, telemetry can securely flow into Azure services without exposing operational infrastructure credentials to client devices. This pattern aligns with the broader Azure adaptive cloud approach: enabling customers to run distributed edge-native services on Arc-enabled Kubernetes infrastructure while maintaining centralized security, governance, and analytics capabilities across the enterprise. Depending on customer architecture preferences, telemetry can be processed through Azure IoT Operations at the edge or ingested directly through Azure IoT Hub for cloud-first analytics and downstream processing in services such as Microsoft Fabric. Edge processing also enables real-time sensor fusion across worker telemetry, ambient environmental conditions, machine parameters, and site-level operational signals, supporting faster safety interventions and more context-aware operational decisions. This gives enterprises flexibility in how they balance edge processing, operational responsiveness, governance and privacy requirements. Enabling the Next Generation of Industrial Workflows The long-term opportunity extends well beyond visualization dashboards. As worker telemetry becomes part of the operational fabric, enterprises can begin building more adaptive and intelligent workflows across worker safety, field readiness, incident response, compliance, environmental monitoring, and industrial AI systems. Human telemetry can provide critical real-time context that complements machine and environmental signals enabling more responsive operations and eventually more autonomous decision-support experiences. By bringing human telemetry into enterprise AI and analytics workflows, organizations can build more adaptive operational systems that improve worker safety, situational awareness, and real-time decision making at scale. This partnership reflects a broader industry shift: industrial transformation is no longer only about connected machines. It is about connected operations where people, equipment, environments, and AI systems participate in a shared operational intelligence layer. With SWNR’s Mij™platform and Azure IoT Operations, Microsoft and VOORMI are helping unlock that future. Learn more: Mij™ product page: https://swnrtechnologies.com/pages/mij Learn more about Azure IoT Operations: Documentation & Getting Started See what’s new with Azure IoT Hub: Preview Documentation To get started with a pilot, contact: pilots@swnrtechnologies.comAnsible + Azure Arc: Use Ansible modules to deploy and manage Azure Arc machine extensions at scale
We are making Azure Arc extensible and increasing the flexibility of the tooling you can use to operate your machines using Azure’s control plane. We are excited to announce new modules in Ansible Galaxy that make it easier to manage Azure Arc machine extensions at scale. With the latest updates to the azure.azcollection on Ansible Galaxy, you no longer need to switch between existing tools. You can now deploy and manage Azure Arc extensions using familiar, declarative Ansible workflows. These new modules include: Azure Arc machine extensions module Azure Arc extensions info module Together, they enable infrastructure and platform teams to automate extension lifecycle management across their hybrid estate—bringing consistency, security, and efficiency to Azure Arc-enabled servers. Why this matters Azure Arc machine extensions power critical scenarios such as security, monitoring, update management, configuration and compliance. Until now, managing these Azure Arc extensions across hybrid estates often required Azure CLI scripts, ARM templates, or manual operations. With these new Ansible modules, you can: Integrate Azure Arc extension management into existing Ansible playbooks Enforce consistent configuration across hybrid servers Reduce operational overhead through declarative automation Align extension deployment with broader configuration management workflows What’s included azure_rm_arcmachineextensions This module allows you to manage the full lifecycle of Azure Arc machine extensions, including: Creating and deploying extensions Updating extension settings Removing extensions when no longer needed You can define extension state declaratively, ensuring consistent enforcement across your Azure Arc-enabled servers. azure_rm_arcmachineextensions_info This module provides visibility into extension state by retrieving: Installed extensions on Azure Arc-enabled machines Provisioning status and configuration details Extension metadata for reporting and validation This is useful for compliance validation, auditing, and conditional automation in playbooks. Scenario: Enforcing identity-based SSH access across a hybrid fleet Consider a regulated enterprise that must ensure all Linux servers—whether on-premises or in a multicloud environment—use Microsoft Entra ID for SSH access. The organization wants to: Eliminate local SSH credentials Enforce centralized identity and access controls Audit access consistently across all environments By combining Azure Arc with Ansible, the organization can deploy the Microsoft Entra SSH for Linux extension across all Azure Arc-enabled servers as part of a standardized playbook, ensuring compliance and reducing operational overhead. Example: Deploy Microsoft Entra SSH for Linux extension Below is an example of using Ansible to deploy the Microsoft Entra SSH extension to an Azure Arc-enabled server: - name: Deploy Entra SSH extension to Arc server hosts: localhost connection: local tasks: - name: Install Entra SSH extension for Linux azure_rm_arcmachineextensions: resource_group: myResourceGroup machine_name: myArcServer name: AADSSHLoginForLinux publisher: Microsoft.Azure.ActiveDirectory type: AADSSHLoginForLinux type_handler_version: "1.0" settings: {} state: present Example: Retrieve extension information Below is an example of using Ansible to retrieve details about your Azure Arc extensions: - name: Get Arc machine extension details hosts: localhost connection: local tasks: - name: Fetch extensions azure_rm_arcmachineextensions_info: resource_group: myResourceGroup machine_name: myArcServer Integrating with existing Ansible workflows If you’re already using Ansible for: OS configuration Patch and update management Application deployment You can now extend those workflows to include Azure Arc extension management—without introducing new tools or processes. This allows you to manage on-premises servers, Edge infrastructure and multicloud environments through a unified automation approach powered by Azure Arc and Ansible. Read more at Enable VM Extensions Using Red Hat Ansible - Azure Arc | Microsoft Learn What’s next These modules are part of our continued investment in making Azure Arc a first-class platform for managing Windows and Linux machines in hybrid and multicloud infrastructure. By bringing extension lifecycle management into Ansible, we’re enabling teams to enforce security, compliance, and operational consistency at scale—using the tools they already trust. Stay connected Join the Azure Arc Monthly Forum here: aka.ms/ArcServerForumSignup Let us know what you’d like to see next in the comments!Resource Guide: Making Physical AI Practical for Real‑World Industrial Operations
Microsoft’s adaptive cloud approach enables organizations to turn operational technology (OT) data into intelligent actions, autonomously, without requiring everything to live in the cloud by unifying cloud-to-edge management plane, data plane, and intelligence platform. At the center of this approach are key foundational technologies: Key Purpose Offering Direct-to-cloud device management + telemetry ingestion Azure IoT Hub Industrial connectivity + edge data plane Azure IoT Operations Unified analytics + real-time intelligence Microsoft Fabric On-device AI inferencing runtime Foundry Local Microsoft Azure IoT Gartner winner: Microsoft named a Leader in the 2025 Gartner® Magic Quadrant™ for Global Industrial IoT Platforms See it all come together Before diving into each component, watch this end-to-end demo showing how Azure IoT Operations, Azure IoT Hub, Microsoft Fabric, and Foundry Local work as one stack across the edge-to-cloud lifecycle - Making industrial AI practical for real-world operations with adaptive cloud. How these components work together Azure IoT Operations and Azure IoT Hub collect real-time data from operational assets and send semantically-ready, modeled data to Microsoft Fabric, where it's contextualized with enterprise data for downstream analytics. Microsoft Foundry extends to the edge through Foundry Local, so the same tooling used to deploy and manage AI models in the cloud applies to edge use cases. All of it integrates into Azure Resource Manager, bringing OT devices, assets, and edge AI models into the same management and security paradigm as every other Azure-managed resource. This blog walks through where to get started with each product capability: 1. Manage Cloud-Connected Devices and Telemetry with Azure IoT Hub Azure IoT Hub is a fully managed cloud service that enables secure bidirectional communication, device-to-cloud telemetry ingestion, cloud-to-device command execution, per-device authentication, remote management and more. Telemetry from IoT Hub can also be routed downstream into analytics platforms like Microsoft Fabric for visualization or AI modeling. Recommended Usage: Devices that utilize IoT Hub are distributed, stand-alone devices with fixed-functions. These devices typically do not require cloud-managed containerized workloads or cloud-managed proximal industrial protocol connectivity. Examples of appropriate device-to-cloud IoT Hub endpoint devices include water monitoring stations, vehicle telematics, distributed fluid level sensors, etc. Resources Current in-market services overview: IoT Hub: What is Azure IoT Hub? - Azure IoT Hub DPS: Overview of Azure IoT Hub Device Provisioning Service - Azure IoT Hub Device Provisioning Service ADU: Introduction to Device Update for Azure IoT Hub Building scalable solutions with Azure IoT platform: Best practices for large-scale IoT deployments - Azure IoT Hub Device Provisioning Service Scale Out an Azure IoT Hub-based Solution to Support Millions of Devices - Azure Architecture Center Azure IoT Hub scaling Try out our preview of new IoT Hub capabilities (integration with Azure Device Registry and Certificate Management) Learn more about these capabilities on our blog post: Azure IoT Hub + Azure Device Registry (Preview Refresh): Device Trust and Management at Fleet Scale… Integration with Azure Device Registry (preview): Integration with Azure Device Registry (preview) - Azure IoT Hub Microsoft-backed X.509 certificate management (preview): What is Microsoft-backed X.509 Certificate Management (Preview)? - Azure IoT Hub How to start with the preview: Deploy IoT Hub with ADR integration and certificate management (Preview) - Azure IoT Hub 2. Connect Industrial Assets with Azure IoT Operations Azure IoT Operations provides a unified data plane for the edge that runs on Azure Arc–enabled Kubernetes clusters and supports open industrial standards. It allows organizations to connect and capture equipment telemetry, normalize OT data locally, route hot-path signals to real-time analytics, securely manage layered industrial networks, and more. Edge‑processed data can then be sent upstream to Microsoft Fabric for AI‑driven analysis. Recommended Usage: Azure IoT Operations is intended to be the data plane for an adaptive cloud deployment extending the management, data, and AI capabilities of the Microsoft cloud to an on-prem device. This device binds to these cloud planes providing a platform for local data processing and intermittent connectivity. The target for these devices range from a small-gateway-style PC to a full data center. Azure IoT Operations endpoints enable cloud-managed containerized workloads and cloud-managed proximal industrial protocol connectivity. Examples of appropriate adaptive cloud and Azure IoT Operations endpoints include, on-robot computers, industrial machine controllers, retail store sensor/vision processing, and top-of-factory site infrastructure for line of business applications. Resources Azure IoT Operations Overview Azure IoT Operations Documentation Hub Quickstart: explore-iot-operations/quickstart at main · Azure-Samples/explore-iot-operations Open-source framework for scaling robotics from simulation to production on Azure + NVIDIA: microsoft/physical-ai-toolchain Demo video showcasing this in action: Making industrial AI practical for real-world operations with adaptive cloud How we built the demo: explore-iot-operations/quickstart at main · Azure-Samples/explore-iot-operations Edge-AI: microsoft/edge-ai: Production-ready Infrastructure as Code, applications, pluggable components, and… Latest Announcements & Blogs Making Physical AI Practical for Real-World Industrial Operations: Part 1 | Microsoft Community Hub Making Physical AI Practical for Real-World Industrial Operations: Part 2 | Microsoft Community Hub Unlock Industrial Intelligence | Microsoft Hannover Messe 2026 From pilots to production: How Microsoft and partners are accelerating intelligent operations 3. Advanced Analytics with Microsoft Fabric Microsoft Fabric delivers a unified, end‑to‑end analytics platform that transforms streaming OT telemetry into real‑time insights and live dashboards. Fabric Operations Agents monitor industrial signals to recommend targeted actions, while Fabric IQ provides a shared semantic foundation that enables AI agents to reason over enterprise data with business context. Together, Fabric turns live industrial data into AI‑powered operational intelligence. Resources Get Started with Microsoft Fabric Learning Path Fabric Real-Time Intelligence documentation - Microsoft Fabric | Microsoft Learn Create and Configure Operations Agents - Microsoft Fabric | Microsoft Learn Fabric IQ documentation - Microsoft Fabric | Microsoft Learn 4.Run AI Models On‑Device with Foundry Local Foundry Local extends on‑device AI to Arc‑enabled Kubernetes edge clusters, providing a Microsoft‑validated inferencing layer for running AI models in industrial, disconnected or sovereign environments. Resources Foundry Local on Azure Local Documentation Participate in Foundry Local on Azure Local preview form Foundry Local on Azure Local: HELM deployment Demo Customer Stories Chevron: Chevron plans facilities of the future with Azure IoT Operations Husqvarna: Husqvarna Group Boosts Operational Efficiency with Azure Adaptive Cloud Ecopetrol: Azure IoT Operations and Azure IoT for energy help Ecopetrol optimize energy distribution while lowering operational costs P&G: Procter & Gamble cuts model deployment time up to 90% with Azure IoT Operations Toyota: Toyota Industries innovates its paint shop processes with Azure industrial AI and Azure IoT Hub
Your first model deployment on Foundry Local on Azure Local: from catalog to inference in 10 minutes
Foundry Local on Azure Local lets you run open-source models directly on your own Azure Local cluster, behind an OpenAI-compatible AP. It's the same experience you've gotten used to in the cloud, but the inference runs on hardware you own. Foundry Local on Azure Local is in public preview at the time of this writing. You've installed Foundry Local on your Azure Local cluster. The operator's pods are running, the CRDs are registered, you've checked it twice with kubectl get pods . Now what? This blog covers the part that comes right before all of that - the lifecycle pattern you'll use to deploy any model on Foundry Local on Azure Local. Our recent announcement covers the bigger picture: multi-node inference, vLLM as a first-class runtime alongside ONNX-GenAI, and an expanded catalog. We'll keep this walkthrough single-node for clarity, but the same ModelDeployment pattern scales without changes to your client code or workflow. By the end of this walkthrough, you'll have gone from an empty kubectl prompt to a working, OpenAI-compatible inference endpoint serving Phi-4. All in about ten minutes, using nothing but kubectl , Python, and a small sample script. We'll also show you how to switch that same flow to the new vLLM runtime by changing roughly five lines of YAML. All the code lives in Azure-Samples/foundry-local-model-catalog. Clone it and follow along. What you'll build The sample walks through five steps, each driven by the same Python script with different flags: Query the model catalog - read the ConfigMap the operator syncs from the Microsoft Foundry catalog API. Deploy a model - create a ModelDeployment custom resource pointing at one catalog entry. Wait for ready - the operator pulls the model image, schedules pods, and reports state. Run inference - call the OpenAI-compatible /v1/chat/completions endpoint with an API key the operator generates for you. Clean up - delete the deployment. The same five steps apply whether you're serving an ONNX model on a CPU node or a vLLM model on a GPU node. We'll start with the simpler path: Phi-4 on CPU on the ONNX runtime, then show you the vLLM variant at the end. Before you start You'll need: An Azure Local cluster (or any Arc-enabled Kubernetes cluster) with the Foundry Local extension installed. If you haven't set this up yet, the Foundry Local on Azure Local install guide walks through the cluster, extension, and resource requirements. kubectl configured against that cluster, with permissions to read ConfigMaps and Secrets and to create ModelDeployment resources in the foundry-local-operator namespace. Python 3.9 or later. Verify the operator is alive before you go further: kubectl get pods -n foundry-local-operator kubectl get crd | grep foundry You should see operator pods in Running state and at least one CRD named modeldeployments.foundrylocal.azure.com . If you don't, the install docs are the right place to back up to. Then clone and install: git clone https://github.com/Azure-Samples/foundry-local-model-catalog.git && cd foundry-local-model-catalog && python3 -m venv .venv && source .venv/bin/activate && pip install -r requirements.txt Step 1 - See what's in your catalog Start with the lowest-risk command in the sample, it talks to the cluster but changes nothing: python catalog_sample.py --catalog-only A few things to notice in that table: Most models appear more than once. The same model gets packaged for different runtime/hardware combinations, and the operator picks the right container image based on which entry you reference. There's a RUNTIME column. We'll come back to vLLM at the end of this post; for now, the default Phi-4-generic-cpu ONNX entry is what we'll deploy. The catalog itself is just a ConfigMap. No magic, no hidden registry. The operator syncs it from the Microsoft Foundry catalog API on a CronJob, and the sample reads it the same way you would: kubectl get configmap foundry-local-catalog -n foundry-local-operator -o yaml If you ever wonder what's actually available on your cluster, that's the source of truth. Step 2 - Deploy a model Now the side-effecting part. We're going to ask the operator to deploy Phi-4 on CPU: python catalog_sample.py --deploy-only Under the hood, the sample builds and applies a ModelDeployment manifest that looks like this: apiVersion: foundrylocal.azure.com/v1 kind: ModelDeployment metadata: name: phi-4-generic-cpu namespace: foundry-local-operator spec: model: catalog: name: Phi-4-generic-cpu workloadType: generative compute: cpu replicas: 1 port: 5000 The operator takes it from there. It pulls the model container image, schedules a pod, generates an API-key Secret named phi-4-generic-cpu-api-keys , and walks the deployment through Pending → Creating → Running states. The sample polls until both status.state == Running and status.deploymentReady == true . Step 3 - Run inference The endpoint is up. Time to actually use it. If you're running this script from inside the cluster (say, from a debug pod), the endpoint lives at the in-cluster service DNS and the sample picks that up automatically. Most readers will be running from a laptop, though, so we'll cover that path explicitly. In one terminal, port-forward the deployment's service: kubectl port-forward svc/phi-4-generic-cpu 5000:5000 -n foundry-local-operator In another, run the sample's inference-only mode against the forwarded endpoint: python catalog_sample.py --infer-only --endpoint https://localhost:5000 --insecure The sample reads the API key from the auto-generated Secret and sends it as Authorization: Bearer <key>. same pattern as cloud OpenAI. The request body is the standard OpenAI chat-completions shape: { "model": "Phi-4-generic-cpu:1.0.0", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What is the capital of France? Reply in one sentence."} ], "max_tokens": 256 } That response just traveled from your terminal, through kubectl port-forward , into a model serving inside your Azure Local cluster, and back. Step 4 - Clean up The sample's default flow deletes the deployment on its way out, so if you ran the full python catalog_sample.py (no flags) you're already clean. If you used --deploy-only or --skip-cleanup , drop it explicitly: kubectl delete modeldeployment phi-4-generic-cpu -n foundry-local-operator The operator garbage-collects the pod, service, and API-key Secret. The cached model image stays on its PersistentVolume, so your next deploy of the same model skips the image pull. Going beyond CPU: vLLM in 5 lines Now for the variant we've been pointing at. The same ModelDeployment CR, the same OpenAI-compatible endpoint, switched to a runtime built for concurrent users, demonstrated on a popular open-source model. The diff against the manifest from Step 2: spec: - compute: cpu + compute: gpu + runtime: vllm model: catalog: - name: Phi-4-generic-cpu + name: Mistral-7B-v0.2 The same sample script handles it: python catalog_sample.py \ --model Mistral-7B-v0.2 \ --compute gpu \ --runtime vllm What you get back is the same OpenAI-compatible endpoint your client code already knows, but now backed by vLLM's PagedAttention, continuous batching, and automatic planner-tuned configuration. Your application code doesn't need to know any of that; it's still POST /v1/chat/completions with a Bearer token. That's the point of the lifecycle pattern: ONNX or vLLM, CPU or GPU, the platform engineer's deployment loop looks the same. For the architectural "why" behind multi-node, vLLM, and the expanded catalog, read the announcement. What you've built Ten minutes ago you had an installed-but-empty Foundry Local cluster. You now have: A working OpenAI-compatible chat endpoint serving Phi-4. A clear sense of what the operator manages for you ( ModelDeployment CR, model image cache, API-key Secret) and what stays in your hands (which model, which runtime, which compute target). A small, modular script you can wire into three things: A smoke test for every new cluster you stand up ( --catalog-only → --deploy-only → --infer-only is a one-line CI step). An internal demo when you need to show a team that Foundry Local on Azure Local is real and reachable. The foundation for your team's own deployment automation - copy the manifest builder, drop the CLI, and wire it into your existing GitOps or platform tooling. From endpoint to chat surface The model deployment above exposes a standard OpenAI-compatible API - enough for any existing chat client to point at. If you'd like to see exactly that, Azure-Samples/local-chat-with-foundry-local picks up where this walkthrough ends: it wires the running endpoint into the Sovereign Chat Experience starter UI. About ten more minutes from working endpoint to working chat in the browser. Where to go from here Try the rest of the sample: --catalog-only , --deploy-only , --infer-only , and --skip-cleanup compose into whichever workflow you're testing. Read the announcement for the architectural why behind multi-node, vLLM, and the expanded catalog. Read the docs for the full operator and CRD reference. Feel free to share your feedback with us at FoundryLocalOnAzure@microsoft.com. The product is in public preview, so your feedback shapes what ships next.
