Advancing sovereign AI with Foundry Local and Azure Local
The next chapter of AI is about more than powerful models. It’s about bringing AI closer to the data, applications, and systems that organizations depend on every day. For governments, regulated industries, and organizations operating critical infrastructure, this means making AI accessible in environments where data is sensitive, connectivity may be limited, and greater operational control is essential. Microsoft’s approach to sovereign AI starts with the requirements of each workload. Our vision is to give organizations the choice and flexibility to determine where AI runs, which models they use, and how their data and infrastructure are managed. Depending on the workload, this can include control over where data and models reside, who can access and operate the environment, how AI workloads are governed, and whether critical operations can continue when cloud connectivity is restricted or unavailable. The goal is to help organizations meet their security, regulatory, and operational requirements while maintaining access to modern cloud and AI capabilities. With Foundry Local, entering general availability on Azure Local at Ignite, we're bringing advanced AI capabilities to customer-controlled environments. This enables organizations to build and run AI applications closer to their data, with greater control over how AI workloads are deployed and operated, extending the possibilities of AI beyond the public cloud. Bringing the Microsoft Foundry experience to customer-controlled environments AI delivers the greatest value when organizations can connect it securely to the data, applications, and processes that power their business. Microsoft Foundry is our unified platform for building, optimizing, and governing AI applications and agents. Foundry Local extends these capabilities to customer-controlled environments, allowing organizations to deploy and run supported AI models on Azure Local infrastructure, with local inference, model management, and GPU acceleration. Built on Azure Arc-enabled Kubernetes, Foundry Local supports CPU- and GPU-backed deployments, model lifecycle management, and OpenAI-compatible APIs. Developers can select from an integrated model catalog or bring their own supported models. Models and inference workloads can run locally, including in supported disconnected environments. We're also expanding what organizations can build with Foundry Local, moving beyond standalone inference toward intelligent applications and agents that can securely interact with enterprise data. These capabilities include: Model catalog (general available): Offers a curated model catalog, making it easy to discover and deploy AI models optimized for local and edge environments. Gives organizations greater flexibility to select models based on capability, performance, latency, resource requirements, and the sovereignty needs of individual workloads. Agentic Retrieval (general available): Enables applications to ground responses in enterprise knowledge through local data ingestion, indexing, and multistep retrieval, with citations that allow users to verify the underlying sources. Agents and enterprise connectors (available in preview): Emerging capabilities for orchestrating agents and connecting them to enterprise systems, including on-premises Exchange Server, SharePoint Server, and SQL databases through Model Context Protocol (MCP). Configurable AI guardrails (available in preview): Support for applying safety and compliance controls to local AI workloads, with flexibility to select guardrail models based on security requirements and available compute resources. Together, these capabilities help organizations build AI applications where the data lives instead of requiring the data to move to the AI. From local inference to sovereign AI applications Running AI models locally is an important first step, but sovereign AI extends beyond inference. Organizations need to connect models to enterprise knowledge, build intelligent applications and agents, and maintain control over how those applications are developed, deployed, and operated. Foundry Local helps make this possible by bringing AI capabilities closer to enterprise data and applications. Developers can use familiar Microsoft tools and technologies to build AI applications across cloud and customer-controlled environments, with greater flexibility over where models run and how they interact with organizational data. Azure Local provides the infrastructure foundation for running these workloads across datacenters, distributed locations, and edge environments, including sovereign private cloud scenarios. For organizations requiring greater operational independence, Azure Local disconnected operations enables supported infrastructure and services to operate through a local control plane without continuous connectivity to the public cloud. Sovereignty also extends to the software development lifecycle. With GitHub Enterprise Local, becoming generally available at Ignite, organizations can bring familiar GitHub development capabilities to customer-controlled infrastructure on Azure Local. Developers can manage source code, collaborate, and automate build and deployment processes using GitHub Actions and self-hosted runners while maintaining greater control over their repositories, workflows, and artifacts. Foundry Local can also provide locally hosted models for AI-assisted development scenarios in supported disconnected environments. Together, Foundry Local, Azure Local, and GitHub Enterprise Local provide a foundation for building, deploying, and operating AI applications within customer-controlled environments, extending sovereignty from the infrastructure to the models, applications, and development processes that run on it. What this means for customers The significance of these capabilities is ultimately about what they enable organizations to do. Keep sensitive data in the environment they control. Organizations can bring AI to data that may need to remain within a specific facility, geography, security boundary, or operational environment. Run AI where decisions need to happen. Manufacturers, retailers, transportation providers, healthcare organizations, governments, and others can bring intelligence closer to physical operations and distributed locations rather than depending on every interaction traveling to a remote cloud service. Support environments with limited or disconnected connectivity. Organizations operating in remote, isolated, or highly controlled environments can continue to pursue AI scenarios where constant public cloud connectivity may not be appropriate or possible. Maintain greater control over the AI stack. Customers can make deliberate choices across infrastructure, data, models, applications, operations, and governance based on the requirements of individual workloads. Turn enterprise data into useful AI experiences. Retrieval, connectors, agents, and model capabilities can help organizations build AI experiences grounded in their own information and business processes while keeping those capabilities closer to the systems they support. Most importantly, customers do not have to define a single architecture for every workload. Some AI workloads will benefit from the scale and rapid innovation of the public cloud. Others will require greater local control. Many organizations will need both. Our strategy is to give customers the flexibility to choose the right architecture for each workload while maintaining a consistent foundation across their environments. Sovereignty that enables innovation As AI becomes more deeply embedded across applications, operations, and critical infrastructure, organizations will need architecture that give them both powerful AI capabilities and meaningful control over how those capabilities are deployed. With Azure Local and Foundry Local, we’re continuing to bring Microsoft AI innovation to the places customers need it, from the cloud to customer-controlled datacenters and edge locations. That is the foundation of our approach to sovereign AI: giving every organization the control and choice to pursue its AI ambitions on its own terms. Resources Join us at Microsoft Ignite in San Francisco to explore the latest innovations across Microsoft AI, Azure Local, and sovereign cloud. Learn about AI workloads on Azure Local Explore digital sovereignty at Microsoft Learn more about Microsoft Sovereign Private CloudAutomating Arc-enabled SQL Server license type configuration with Azure Policy
Azure Arc enables customers to onboard SQL Server instances - hosted on Linux or Windows - into Azure, regardless of where they are hosted: on‑premises, in multicloud environments, or at the edge. Once onboarded, these resources can be managed through the Azure Portal using services like Azure Monitor, Azure Policy, and Microsoft Defender for Cloud. An important part of this onboarding is configuring the license type on each Arc-enabled resource to match your licensing agreement with Microsoft. For SQL Server, the LicenseType property on the Arc extension determines how the instance is licensed: Paid (you have a SQL Server license with Software Assurance or a SQL Server subscription), PAYG (you are paying for SQL Server software on a pay-as-you-go basis), or LicenseOnly (you have a perpetual SQL Server license). Setting this correctly matters for two reasons: Unlocking additional benefits: customers with Paid or PAYG license type gain access to some Azure services at no extra cost - such as Azure Update Manager and Machine Configuration - as well as exclusive capabilities like Best Practices Assessment and Remote Support Enabling pay-as-you-go billing: customers who do not have Software Assurance can pay for SQL Server software only when they use it via their Azure subscription by setting the license type to PAYG Configure the license types at scale using Azure Policy Configuring the license type on each Arc-enabled SQL Server instance can be done manually in the Azure Portal, but for large scale operations, automation is essential. One way to implement automation is via PowerShell, as explained here: Configure SQL Server - SQL Server enabled by Azure Arc | Microsoft Learn. But here we will focus on how this can be automated using Azure Policy. An existing article, written by Jeff Pigott, explains this process for Windows Server, which inspired extending the same approach to SQL Server. How to deploy the policy? Deployment has two steps: Create/update the Azure Policy definition and assignment Start a remediation task so existing Arc-enabled SQL Server extensions are brought into compliance You can deploy Azure Policy in multiple ways. In this article, we use PowerShell. See also: Tutorial: Build policies to enforce compliance - Azure Policy | Microsoft Learn. Source code: microsoft/sql-server-samples/.../arc-sql-license-type-compliance. Personal repository: claestom/sql-arc-policy-license-config. Definition and assignment creation Download the required files: # Optional: create and enter a local working directory mkdir sql-arc-lt-compliance cd sql-arc-lt-compliance $baseUrl = "https://raw.githubusercontent.com/microsoft/sql-server-samples/master/samples/manage/azure-arc-enabled-sql-server/compliance/arc-sql-license-type-compliance" New-Item -ItemType Directory -Path policy, scripts -Force | Out-Null curl -sLo policy/azurepolicy.json "$baseUrl/policy/azurepolicy.json" curl -sLo scripts/deployment.ps1 "$baseUrl/scripts/deployment.ps1" curl -sLo scripts/start-remediation.ps1 "$baseUrl/scripts/start-remediation.ps1" Note: On Windows PowerShell 5.1, curl is an alias for Invoke-WebRequest . Use curl.exe instead, or run the commands in PowerShell 7+. Authenticate to Azure: Connect-AzAccount Set your variables. Only TargetLicenseType is required - all others are optional: # Required $TargetLicenseType = "PAYG" # "Paid" or "PAYG" # Optional (uncomment to override defaults) # $ManagementGroupId = "<management-group-id>" # Default: tenant root management group # $SubscriptionId = "<subscription-id>" # Default: policy assigned at management group scope # $ExtensionType = "Both" # "Windows", "Linux", or "Both" (default) # $LicenseTypesToOverwrite = @("Unspecified","Paid","PAYG","LicenseOnly") # Default: all Run the deployment script: # Minimal: uses defaults for management group, platform, and overwrite targets .\scripts\deployment.ps1 -TargetLicenseType $TargetLicenseType # With subscription scope .\scripts\deployment.ps1 -TargetLicenseType $TargetLicenseType -SubscriptionId $SubscriptionId # With all options .\scripts\deployment.ps1 ` -ManagementGroupId $ManagementGroupId ` -SubscriptionId $SubscriptionId ` -ExtensionType $ExtensionType ` -TargetLicenseType $TargetLicenseType ` -LicenseTypesToOverwrite $LicenseTypesToOverwrite Parameter notes: ManagementGroupId (optional): management group where the policy definition is created. Defaults to the tenant root management group when not specified ExtensionType (optional, default Both ): Windows , Linux , or Both . When Both , a single policy definition and assignment covers both platforms SubscriptionId (optional): if provided, assignment scope is subscription (otherwise management group scope) TargetLicenseType (required): Paid or PAYG LicenseTypesToOverwrite (optional, default all): controls which current states are eligible for update Unspecified = no current LicenseType Paid , PAYG , LicenseOnly = explicit current values The script also creates a system-assigned managed identity on the policy assignment and assigns required roles automatically. Role assignments include retry logic (5 attempts, 10-second delay) to handle managed identity replication delays, which helps prevent common PolicyAuthorizationFailed errors. Remediation task creation After deployment, allow a few minutes for Azure Policy to run a compliance scan for the selected scope. You can monitor this in Azure Policy → Compliance. More info: Get policy compliance data - Azure Policy | Microsoft Learn. Set your variables. TargetLicenseType is required and must match the value used during deployment: # Required $TargetLicenseType = "PAYG" # Must match the deployment target # Optional (uncomment to override defaults) # $ManagementGroupId = "<management-group-id>" # Default: tenant root management group # $SubscriptionId = "<subscription-id>" # Default: remediation runs at management group scope # $ExtensionType = "Both" # Must match the platform used for deployment Then start remediation: # Minimal: uses defaults for management group and platform .\scripts\start-remediation.ps1 -TargetLicenseType $TargetLicenseType -GrantMissingPermissions # With subscription scope .\scripts\start-remediation.ps1 -TargetLicenseType $TargetLicenseType -SubscriptionId $SubscriptionId -GrantMissingPermissions # With all options .\scripts\start-remediation.ps1 ` -ManagementGroupId $ManagementGroupId ` -ExtensionType $ExtensionType ` -SubscriptionId $SubscriptionId ` -TargetLicenseType $TargetLicenseType ` -GrantMissingPermissions Parameter notes: ManagementGroupId (optional): defaults to tenant root management group ExtensionType (optional, default Both ): must match the platform used for the assignment SubscriptionId (optional): run remediation at subscription scope TargetLicenseType (required): must match the assignment target GrantMissingPermissions (optional switch): checks and assigns missing required roles before remediation starts You can track remediation progress in Azure Policy → Remediation → Remediation tasks. It can take a few minutes to complete, depending on scope and resource count. Recurring Billing Consent (PAYG) When TargetLicenseType is set to PAYG , the policy automatically includes ConsentToRecurringPAYG in the extension settings with Consented: true and a UTC timestamp. For details of this requirement see: Move SQL Server license agreement to pay-as-you-go subscription - SQL Server enabled by Azure Arc | Microsoft Learn. The policy also checks for ConsentToRecurringPAYG in its compliance evaluation - resources with LicenseType: PAYG but missing the consent property are flagged as non-compliant and remediated. This applies both when transitioning to PAYG and for existing PAYG extensions that predate the consent requirement (backward compatibility). Note: Once ConsentToRecurringPAYG is set on an extension, it cannot be removed - this is enforced by the Azure resource provider. When transitioning away from PAYG, the policy changes LicenseType but leaves the consent property in place. RBAC When .\scripts\deployment.ps1 creates the policy assignment, it uses -IdentityType SystemAssigned . Azure then creates a managed identity for that assignment. The assignment identity needs these roles at assignment scope (or inherited scope): Azure Extension for SQL Server Deployment: allows updating Arc SQL extension settings, including LicenseType Reader: allows reading resource and extension state for policy evaluation Resource Policy Contributor: allows policy-driven template deployments required by DeployIfNotExists This identity is used whenever DeployIfNotExists applies changes, both during regular compliance evaluation and during remediation runs. By default, the deployment script assigns these roles automatically with built-in retry logic to handle managed identity replication delays, which helps prevent common PolicyAuthorizationFailed errors. Brownfield and Greenfield Scenarios This policy is useful in both brownfield and greenfield Azure Arc environments. Brownfield: existing Arc SQL inventory In a brownfield environment, you already have Arc-enabled SQL Server resources in inventory and the current LicenseType values might be mixed, incorrect, or missing. This is where Azure Policy is especially useful, because it gives you a controlled way to remediate the current estate at scale. Depending on how you configure targetLicenseType and licenseTypesToOverwrite, you can use the policy to: standardize all in-scope resources on a single value set LicenseType only when it is missing migrate a specific subset, such as Paid to PAYG preserve selected states while correcting only the resources that need attention Examples: Standardize everything to Paid targetLicenseType: Paid licenseTypesToOverwrite: ['Unspecified','Paid','PAYG','LicenseOnly'] Result: every in-scope Arc SQL extension is converged to LicenseType == Paid. Backfill only missing values targetLicenseType: Paid licenseTypesToOverwrite: ['Unspecified'] Result: only resources without a configured LicenseType are updated; existing Paid, PAYG, and LicenseOnly values remain unchanged. Migrate only Paid to PAYG targetLicenseType: PAYG licenseTypesToOverwrite: ['Paid'] Result: only resources currently set to Paid are updated to PAYG; missing, PAYG, and LicenseOnly remain unchanged. When transitioning to PAYG, the policy also automatically sets ConsentToRecurringPAYG with Consented: true and a UTC timestamp, as required for recurring pay-as-you-go billing. Protect existing PAYG, fix only missing or LicenseOnly targetLicenseType: Paid licenseTypesToOverwrite: ['Unspecified','LicenseOnly'] Result: resources with no LicenseType or with LicenseOnly are updated to Paid, while existing PAYG stays untouched. Greenfield: newly onboarded SQL Servers In a greenfield scenario, the main value of Azure Policy is ongoing enforcement. Once new SQL Servers are onboarded to Azure Arc and fall within the assignment scope, the policy can act as a governance control to keep LicenseType aligned with your business model. This means Azure Policy is not only a remediation mechanism for existing inventory, but also a way to continuously enforce the intended license configuration for future Arc-enabled SQL Server resources. Azure Policy vs tagging By default, Microsoft manages automatic deployment of SQL Server extension for Azure. It include an option to enforce the LicenseType setting via tags. See Manage Automatic Connection - SQL Server enabled by Azure Arc | Microsoft Learn for details. This way all newly onboarded SQL Server instance are set to the desired LicenceType from day one. The deployment of the Azure Policy is still important to ensure that the changes of the extension properties or ad-hoc additions of the SQL Server instances stay compliant to our business model. A practical way to think about it: Tagging ensures the initial compliance of the newly connected Arc-enabled SQL servers Azure Policy enforces ongoing compliance of the existing Arc-enabled SQL servers Tools Interested in gaining better visibility into LicenseType configurations across your estate? Below you'll find an insightful KQL query and an accompanying workbook to help track compliance. KQL Query resources | where type == "microsoft.hybridcompute/machines" | where properties.detectedProperties.mssqldiscovered == "true" | extend machineIdHasSQLServerDiscovered = id | project name, machineIdHasSQLServerDiscovered, resourceGroup, subscriptionId | join kind= leftouter ( resources | where type == "microsoft.hybridcompute/machines/extensions" | where properties.type in ("WindowsAgent.SqlServer","LinuxAgent.SqlServer") | extend machineIdHasSQLServerExtensionInstalled = iff(id contains "/extensions/WindowsAgent.SqlServer" or id contains "/extensions/LinuxAgent.SqlServer", substring(id, 0, indexof(id, "/extensions/")), "") | project License_Type = properties.settings.LicenseType, machineIdHasSQLServerExtensionInstalled)on $left.machineIdHasSQLServerDiscovered == $right.machineIdHasSQLServerExtensionInstalled | where isnotempty(machineIdHasSQLServerExtensionInstalled) | project-away machineIdHasSQLServerDiscovered, machineIdHasSQLServerExtensionInstalled Source: Configure SQL Server - SQL Server enabled by Azure Arc | Microsoft Learn. Azure Workbook claestom/azure-arc-sa-workbook: Azure Workbook for monitoring Software Assurance compliance across Arc-enabled servers and SQL Server instances. Resources Configure SQL Server - SQL Server enabled by Azure Arc | Microsoft Learn Azure Policy documentation | Microsoft Learn Automating Windows Server Licensing Benefits with Azure Arc Policy | Microsoft Community Hub Recurring billing consent - SQL Server enabled by Azure Arc | Microsoft Learn claestom/azure-arc-sa-workbook: Azure Workbook for monitoring Software Assurance compliance across Arc-enabled servers and SQL Server instances microsoft/sql-server-samples/.../arc-sql-license-type-compliance claestom/sql-arc-policy-license-config Thank you!Accelerate your cloud migration journey with Azure Arc resource discovery in Azure Migrate (preview)
With Azure Migrate's new Arc-based discovery (preview), you can leverage your existing Arc-enabled servers and Arc-enabled SQL Server instances to quickly gain insights into: Migration readiness for Azure targets such as Azure VMs, Azure SQL Database, and Azure SQL Managed Instance. Savings potential for different migration strategies—all without deploying new on-premises infrastructure.Empowering the Physical World with AI
Unlocking AI at the Edge with Azure Arc The integration of AI into the physical environment is revolutionizing ways we interact with and navigate the world around us. By embedding intelligence into edge devices, AI is not just processing data—it is defining how machines perceive, reason, and act autonomously in real-world scenarios. AI at the edge is transforming how we interact with our environment, driven by critical factors such as data sensitivity, local regulations, compliance, low latency requirements, limited network connectivity, and cost considerations. Added to this, the emergence of new, powerful agentic AI capabilities enables autonomous and adaptive real-time operations, making AI an indispensable tool in reshaping the physical world. Customers’ Use Cases By embedding AI into edge operations, industries are unlocking transformative efficiencies and innovations. In manufacturing, edge-powered AI enables real-time quality control and predictive maintenance, minimizing downtime and maximizing productivity. In retail, AI enhances customer experiences with personalized recommendations and streamlined inventory management. Similarly, finance leverages AI's capabilities for robust fraud detection and advanced risk management. Moreover, sectors like government and defense are increasingly adopting edge AI for safety-critical applications, enabling autonomous, real-time surveillance and response solutions that are both efficient and resilient. These advancements are paving the way for scalable, adaptive solutions that meet the unique demands of diverse operational environments. Azure’s Adaptive Cloud Approach enabling AI from cloud to edge Building on the promise to unify cloud and edge, Azure’s adaptive cloud approach is empowering teams to develop and scale AI workloads seamlessly across diverse environments. By enabling a unified suite of services tailored for modern AI applications, whether deployed in public clouds or distributed locations, Azure Arc enables streamlined operations with enhanced security and resilience. Central to extending AI services to the edge is our commitment to adaptive, scalable, and efficient solutions tailored to diverse operational needs. Azure Arc plays a key role in this vision by facilitating seamless deployment and management of AI workloads across various environments. This week, we’re excited to share that a subset of Microsoft Azure AI Foundry models, such as Phi and Mistral have been rigorously validated to run on Azure Local enabled by Azure Arc. Our investments are reflected in two primary areas: Foundational tools for MLOps and developer frameworks, which empower teams to build robust AI applications Intuitive, end-to-end low-code experiences designed for data analysts and solution developers. These low-code tools prioritize user-friendly interfaces and rapid deployment, enabling the creation of solutions with just a few clicks. This dual focus ensures enterprises can fully harness the potential of edge AI while maintaining flexibility and operational efficiency. Image 1: This high-level diagram illustrates our vision for the cloud to edge AI workloads, enabled by Azure Arc. Some components (agents and integration with AI Foundry and Foundry Local) are still under development, while others are more advanced and have been released to the market. Build 2025: New Capabilities and Releases This strategic vision is now being realized through a wave of new capabilities unveiled at Build 2025. These innovations are designed to accelerate edge AI adoption and simplify the developer experience—making it easier than ever to build, deploy, and manage intelligent applications across hybrid environments. Announcements related to developer Building blocks: Kubernetes AI Toolchain Orchestrator (KAITO), enabled by Azure Arc (public preview) Foundry Local (public preview) for Windows apps to be deployed on any client device read more here. Workload orchestration (public preview) Application development tools for Kubernetes enabled by Arc (public preview) Refer to this blog to read more: https://aka.ms/AdaptiveCloudBuild2025 Announcements related to End-to-end experiences: Edge RAG, enabled by Azure Arc is now available in public preview. Azure AI Video Indexer for recorded files, enabled by Arc is generally available since April 2025. Azure AI Video Indexer for live video analysis, enabled by Arc is available in private preview, for limited set of customers Customer scenarios: enabling search and retrieval for on-premises data on Azure Local Edge RAG targets customers who have data that needs to stay on premises due to data gravity, security and compliance, or latency requirements. We have observed significant and consistent interest from highly regulated sectors. These entities are exploring the use of RAG capabilities in disconnected environments through Azure Local. DataON is a hybrid cloud computing company for enterprises of all sizes, with a focus on educational institutions and local government agencies. Recently, they have worked with the their customers to successfully deploy our RAG solution on CPU and GPU clusters and begin testing with sample end-customer data. “DataON has been actively exploring how Edge RAG can enhance our Microsoft Azure Local solutions by providing more efficient data retrieval and decision-making capabilities. It’s exciting to be part of the private preview program and see firsthand how Edge RAG is shaping the future of data-driven insights.” Howard Lo | VP, Sales & Marketing | DataON This capability brings generative AI and RAG to on-premises data. Edge RAG was validated on AKS running on Azure Local. Based on DataON and other customer feedback, we have expanded the version to include new features: Model Updates: Ability to use any model compatible with OpenAI Inferencing standard APIs Multi-lingual support: 100+ common languages for document ingestion and question-answer sessions Multi-modal support: Support for image ingestion & retrieval during question-answer sessions Search Types: Support for Text, Vector, Hybrid Text & Hybrid Text+Image searches Ingestion Scale-out: Integration with KEDA for fully parallelized, high-throughput ingestion pipeline Evaluation Workflow with RAG Metrics: Integrated workflow with built-in or customer-provided sample dataset Read more about Edge RAG in this blog: https://aka.ms/AzureEdgeAISearchenabledbyArc. AI Workloads for Disconnected Operations In fully disconnected (air-gapped or non-internet) environments, such as those often found in government and defense sectors, technologies like RAG, can be deployed on-premises or in secure private clouds. This is currently available with limited access. Use Cases: Video analysis: Automatically analyzes video and audio content to extract metadata such as objects and scenes. Use cases include live video and analysis, mission debriefing and training, and modern safety. Models consumption: A central repository for securely managing, sharing, and deploying AI/ML models. Use cases: model governance, rapid deployment of mission-specific models, and inter-agency collaboration. Retrieval-Augmented Generation (RAG): Combines LLMs with a document retrieval system to generate accurate, context-aware responses based on internal knowledge bases. Use cases include field briefings, legal and policy compliance, and cybersecurity incident response. Transforming Industries with AI: Real-World Stories from the Edge Across industries, organizations are embracing AI to solve complex challenges, enhance operations, and deliver better outcomes. From healthcare to manufacturing, retail to energy, and even national security, Azure AI solutions are powering innovation at scale. In the manufacturing sector, a global company sought to optimize production and reduce costly downtime. Azure AI Video Indexer monitored video feeds from production lines to catch defects early, while custom predictive maintenance models from the Model Catalog helped prevent equipment failures. RAG provided real-time insights into operations, empowering managers to make smarter decisions by asking questions. These tools collectively boosted efficiency, minimized downtime, and improved product quality. At Airports, Azure AI helped enhance passenger experience and safety. From monitoring queue lengths and tracking vehicles to detecting falls and identifying restricted area breaches, the combination of Azure Local, Video Indexer, Azure IoT for Operations, and custom AI created a smarter, safer airport environment. Retailers, too, are reaping the benefits. A major retail chain used Azure AI to understand in-store customer behavior through video analytics, optimize inventory with demand forecasting models, and personalize shopping experiences using RAG. These innovations led to better customer engagement, streamlined inventory management, and increased sales. In Healthcare, a leading provider operating multiple hospitals and clinics nationwide faced the daunting task of analyzing massive volumes of patient data—from medical records and imaging to real-time feeds from wearable devices. With strict privacy regulations in play, they turned to Azure AI. Using Azure AI Video Indexer, they analyzed imaging data like X-rays and MRIs to detect anomalies. The Model Catalog enabled predictive analytics to identify high-risk patients and forecast readmissions. Meanwhile, Retrieval-Augmented Generation (RAG) gave doctors instant access to patient histories and relevant medical literature. The result? More accurate diagnoses, better patient care, and full regulatory compliance. These stories highlight how Azure Arc enabled AI workloads are not just a set of tools—they are a catalyst for transformation. Whether it’s saving lives, improving safety, or driving business growth, the impact is real, measurable, and growing every day. Learn More Whether you are tuning in online or joining us in person, we wish you a fun and exciting Build 2025! The advancements in AI at the edge are set to revolutionize how we build, deploy, and manage applications, providing greater speed, agility, and security for businesses around the world. Recommended Build Sessions: Breakout session (BRK188): Power your AI apps across cloud and edge with Azure Arc Breakout session (BRK183): Improving App Health with Health Modeling and Chaos Engineering Breakout session (BRK 195): Inside Azure innovations with Mark Russinovich Breakout session (BRK 168): AI and Agent Observability in Azure AI Foundry and Azure MonitorWelcoming the Next Wave at Build: New Partners Join the Azure Arc ISV Program
We are thrilled to announce the second round of partners joining the Azure Arc ISV Partner Program for Microsoft Build. Following its successful launch at Ignite last fall, this innovative program continues to grow, enabling partners to publish their offers on the Azure Marketplace for deployment to Arc-enabled Kubernetes clusters. With this new wave, we’re also expanding the solution landscape by introducing four new categories—Security, Networking & Service Mesh, API Infrastructure & Management, and Monitoring & Observability. These additions reflect the evolving needs of hybrid and multi-cloud environments and highlight the breadth of innovation our partners bring to the Azure Arc ecosystem. This new wave of collaborations marks a significant milestone in our journey to foster a vibrant ecosystem of innovation and excellence. This expansion marks a significant step forward in building a dynamic and innovative ecosystem that drives success for both customers and partners alike. What is Azure Arc? Azure Arc is the bridge that extends Azure to on-premises, edge, or even multi-cloud environments. It simplifies governance and management by delivering the consistency of the Azure platform. The ability to create offerings for Azure Arc in the marketplace is a significant benefit to our partners, allowing them to integrate with Azure services and tools and access a large and diverse customer base. Azure Arc enables partners to validate their applications and offer them to customers so they can manage their Kubernetes cluster on Azure. Edge developers can leverage these building blocks to develop their enterprise applications, and we aim to provide them with a one-stop shop in Azure Marketplace. Meet our partners The Azure Arc ISV Partner Program is focusing on expanding categories such as security, networking & service mesh, API infrastructure & management, monitoring & observability. We are excited to introduce our esteemed partners, HashiCorp, Traefik Labs, Solo.io, and Dynatrace, who have Arc-enabled their applications and will now be available on the Azure Marketplace. Here’s a closer look at their offerings: HashiCorp HashiCorp is a leading provider of infrastructure automation and security solutions for modern, dynamic IT environments. HashiCorp Vault Enterprise for Azure Arc enables organizations to manage access to secrets and protect sensitive data using identity-based security principles. As enterprises shift to hybrid and multi-cloud architectures, traditional perimeter-based security models fall short. Vault helps to address this challenge by authenticating every user and application, authorizing access based on identity and policy, encrypting secrets, and injecting just-in-time credentials. It also helps to automate the rotation of secrets, certificates, and encryption keys—reducing operational risk and improving compliance. By integrating with Azure Arc, Vault Enterprise can be deployed and managed alongside other Azure Arc-enabled services. This allows organizations to consistently enforce zero trust security practices—whether workloads run on-premises, in Azure, or in other cloud environments—while benefiting from centralized governance and compliance visibility through the Azure control plane. To deploy HashiCorp Vault Enterprise for Azure Arc, visit aka.ms/HashiCorpForAzureArc. To learn more about HashiCorp Vault Enterprise on Azure Arc, visit HashiCorp Vault Traefik Labs Traefik for Azure Arc empowers organizations to modernize and scale their AI and API runtime infrastructure across any Kubernetes in hybrid and multi-cloud environments. With over 3.3 billion downloads and 250,000+ production nodes globally, Traefik can be deployed in three modular and progressive phases—Application Proxy, API & AI Gateway, and API Management—meeting users where they are on their journey and enabling seamless transitions without vendor lock-in or disruptive migrations. Traefik helps deliver zero-config service discovery across Kubernetes and other orchestrators, efficiently replacing legacy tools with simplified traffic routing and management. As needs grow, they more easily transition to comprehensive AI and API Gateway capabilities with centralized authentication and authorization, semantic caching for AI workloads, and data governance for responsible AI deployments. The final evolution helps introduce complete API governance, observability, self-service developer portals, and instant mock APIs—enabling unified management across both traditional and AI-enabled services without disruptive architectural changes. By combining Azure Arc with Traefik, organizations gain more unified control over API and AI workloads, enhanced by features like semantic caching and content guard. This integration helps bridge fragmented environments, accelerates deployment, and enable clearer versioning boundaries—fundamental for scaling AI and API services across distributed systems. To deploy Traefik for Azure Arc, visit aka.ms/TraefikForAzureArc. To learn more about Traefik for Azure Arc and get started, visit aka.ms/TraefikForArcJumpstart. Solo.io Solo.io is a leading provider of service mesh and API infrastructure solutions for cloud-native applications. Istio for Azure Arc, powered by Solo.io, helps deliver an enterprise-grade service mesh experience through Istio in Ambient Mode—specifically optimized for Azure Arc-enabled Kubernetes clusters. This modern, sidecar-less architecture helps to simplify deployment, reduces operational overhead, and improves resource efficiency while maintaining Istio’s advanced capabilities. The solution provides robust Layer 7 traffic management, zero-trust security with mutual TLS and fine-grained authorization, and deep observability through distributed tracing and logging. It’s ideal for IT operations, DevOps, and security teams managing workloads in regulated industries like finance, healthcare, retail, and technology—where resilience, security, and visibility are important. By using Istio for Azure Arc, organizations can deploy and manage service mesh consistently across hybrid and multi-cloud environments, accelerating application delivery while maintaining control and compliance. To deploy Istio for Azure Arc, visit aka.ms/IstioForAzureArc. To learn more about Istio for Azure Arc, visit Istio by Solo.io. Dynatrace Dynatrace is a leading provider of AI-driven monitoring and performance analytics solutions. Dynatrace Operator helps streamlines your processes, gains insights, and accelerates innovation with its powerful AI-driven platform. Now available through the Microsoft Azure Marketplace, this solution more easily integrates with your Microsoft ecosystem—from Azure to Arc-enabled Kubernetes Service and beyond. With Dynatrace Operator, you can build custom apps and automations tailored to your unique business needs, empowering you to work smarter, not harder. Visualize and fully understand your entire Hybrid cloud ecosystem in real time, plus benefit from automated identification and illustration of application dependencies and their underlying infrastructure, delivering enriched, contextualized data for more informed decisions. Designed to help enterprises automate, analyze, and innovate faster, Dynatrace Operator is your key to unlocking efficiency and growth. By combining Azure Arc with Dynatrace Operator, organizations can deploy and manage monitoring and performance analytics consistently across hybrid and multi-cloud environments, accelerating application delivery while maintaining control and compliance. To deploy Dynatrace Operator for Azure Arc, visit aka.ms/DynatraceOperatorForArc. To learn more about Dynatrace Operator for Azure Arc, visit Dynatrace | Kubernetes monitoring. Become an Arc-enabled Partner These partners have collaborated with Microsoft to join our ISV ecosystem, helping provide resilient and scalable applications more readily accessible for our Azure Arc customers via the Azure Marketplace. Joining forces with Microsoft enables partners to stay ahead of the technological curve, strengthen customer relationships, and contribute to transformative digital changes across industries. We look forward to expanding this program to include more ISVs, enhancing the experience for customers using Arc enabled Kubernetes clusters. As we continue to expand our Azure Arc ISV Partner Program, stay tuned for more blogs on the new partners being published to the Azure Marketplace. To reach out and learn more about the Azure Arc ISV Partner Program visit: What is the Azure Arc ISV Partner program? or reach out to us at https://aka.ms/AzureArcISV.Announcing Jumpstart ArcBox 25Q1 general availability
We are thrilled to announce the first major update to ArcBox following our release of ArcBox 3.0 in August 2024. ArcBox has been an invaluable resource for IT professionals, DataOps teams, and DevOps practitioners, providing comprehensive solutions to evaluate how to deploy, manage, and operate Arc-enabled environments. With this release, we have introduced Windows Server 2025 on both the ArcBox-Client as well as in a nested VM, making it possible for you to evaluate a range of new features and enhancements that elevate the functionality, performance, and user experience. WinGet and Windows Terminal Integration One of the standout enhancements in Windows Server 2025 is the inclusion of WinGet and Windows Terminal. These tools are now built-in components of Windows Server 2025 and no longer require bootstrapping in our automation processes. Advanced Management Capabilities for Arc-enabled servers Windows Server 2025 introduces new management capabilities specifically designed for Arc-enabled servers. These capabilities enhance the control and oversight of server environments, providing more robust tools for monitoring, configuration, and maintenance. The enhancements are now available in ArcBox to be evaluated. SSH Included and Enabled Another significant update in Windows Server 2025 is the inclusion of SSH as a native component. This addition is a major step forward, as it eliminates the need for external SSH installations. However, it is important to note that while SSH is included, it needs to be enabled manually. This feature enhances secure access to servers, facilitating more efficient remote management and operations. In ArcBox, SSH is enabled by the automated setup and ready to start evaluating. SSH for Arc-enabled servers enables SSH based connections to Arc-enabled servers without requiring a public IP address or additional open ports. This functionality can be used interactively, automated, or with existing SSH based tooling, allowing existing management tools to have a greater impact on Azure Arc-enabled servers. You can use Azure CLI or Azure PowerShell to connect to one of the Azure Arc-enabled servers using SSH. In addition to SSH, you can also connect to the Azure Arc-enabled servers, Windows Server virtual machines using Remote Desktop tunneled via SSH. Also, Remote PowerShell over SSH is available for Windows and Linux machines. SSH for Arc-enabled servers also enables SSH-based PowerShell Remoting connections to Arc-enabled servers without requiring a public IP address or additional open ports. After setting up the configuration, we can use native PowerShell Remoting commands. Configurable SQL Server Edition to support Performance Dashboards ArcBox now provides the flexibility to deploy SQL Server Standard or Enterprise editions on the ArcBox-SQL guest VM, replacing the previously default Developer edition. This enhancement empowers users to experience advanced Arc-enabled SQL Server monitoring through Performance Dashboard reports. Available in both the ITPro and DataOps configurations, this feature ensures tailored performance monitoring capabilities for diverse use cases. To configure the SQL Server edition during deployment: Portal Deployment: Specify the desired SQL Server edition during setup. Bicep Deployment: Use the sqlServerEdition parameter to define the edition. ARM Template Deployment: Set the edition via the sqlServerEdition parameter. Below is an example Performance Dashboard report from an Arc-enabled SQL Server using the Standard or Enterprise editions, highlighting comprehensive insights and monitoring capabilities. Cost Optimizations We optimized the storage costs significantly by changing the ArcBox Client VM data disk from Premium SSD to Premium SSD v2. This change allows for better performance at a lower cost, making ArcBox even more economical for various use cases. With this optimization, users can enjoy faster data access speeds and increased storage efficiency. We also introduced support for enabling Azure VM Spot pricing for the ArcBox Client VM, allowing users to take advantage of cost savings on unused Azure capacity. This feature is ideal for workloads that can tolerate interruptions, providing an economical option for testing and development environments. By leveraging Spot pricing, users can significantly reduce their operational costs while maintaining the flexibility and scalability offered by Azure. You may leverage the advisor on the Azure Spot Virtual Machine pricing page to estimate costs for your selected region. Here is an example for running the ArcBox Client Virtual Machine in the East US region: Visit the ArcBox FAQ to see the updated price estimates for running ArcBox in your environment. The new deployment parameter enableAzureSpotPricing is disabled by default, so users who wants to take advantage of this capability will need to opt-in. Along with the option to opt-in for Azure Spot pricing, we also added new parameters for enabling Auto Shutdown: Auto Shutdown is enabled by default, and will configure the built-on Auto-shutdown feature for Azure VMs: Summary The latest update to ArcBox not only focuses on new features but also on enhancing overall cost and performance. The integration of new operating system versions and management capabilities ensures a smoother, more efficient workflow for IT professionals, DataOps teams, and DevOps practitioners to evaluate Azure Arc services. We invite our community to explore these new features and take full advantage of the enhanced capabilities of ArcBox with Windows Server 2025 support. Your feedback is invaluable to us, and we look forward to hearing about your experiences and insights as you navigate these new enhancements. Watch our release announcement episode of Jumpstart Lightning and get started today by visiting aka.ms/JumpstartArcBox!Announcing Preview of Azure AI Video Indexer Cloud-to-Edge Version
Introducing the Public Preview of Azure Video Indexer enabled by Arc: Harness the full capabilities of Video and Audio Content Anywhere. We are excited to unveil that Azure Video Indexer cloud offering is expanding, as part of Microsoft’s adaptive cloud approach. The newly released Video Indexer extension can run within Azure Arc-enabled Kubernetes cluster. This tool allows you to harness the full capabilities of video and audio content from any location. Register for the public preview by submitting this form.Fault Tolerant, Durable, Edge Kubernetes Storage with Azure Container Storage enabled by Azure Arc
Azure Container Storage enabled by Azure Arc [Previously named “Edge Storage Accelerator”] – a groundbreaking addition to our Azure storage solutions, designed to revolutionize data handling at the edge. We invite you to explore the capabilities of Edge Volumes and experience the benefits of advanced edge storage solutions firsthand. At the edge, customers have many struggles with data: sharing, resiliency, storage capacity, space management, and cloud connection, among others. We are proud to announce Azure Container Storage enabled by Azure Arc, a first-party Arc Extension designed to solve these customer Kubernetes storage challenges. ACSA offers a fault tolerant, highly available, persistent ReadWriteMany file system using Kubernetes native PVCs. Simply write to an ACSA PVC as if it were your local file system. ACSA offers two main storage configuration options. The Local Shared Edge Volume allows for shared ReadWriteMany storage that remains local to your Kubernetes cluster. This configuration is ideal for persistent application storage, such as databases, data historians, and other data processing scenarios. The Ingest Cloud Edge Volume, ACSA’s second configuration option, uploads data written by applications to Azure storage destinations, such as Blob, ADLSgen2, and OneLake Fabric. Ingest volumes also have user configurable policies for flexibility of data upload throughput and ordering. ACSA follows security best practices by leveraging Managed Identity capabilities and always implementing the latest version of the Blob SDK. Cloud Edge Volumes are disconnection tolerant to losses in network connectivity: ACSA will continue to accept application writes and will automatically upload that data once the connection is reestablished. ACSA accepts all file data you create at the edge, whether it be parquet files, time series data, photos, video, etc. With the option to keep it local to your Kubernetes cluster or send it to a cloud destination, ACSA can handle it for you. ACSA is available as a standard component of the Azure IoT Operations GA release and is suitable for production workloads. Try Out Edge Volumes Today! 📄 Get started by visiting this documentation. Jumpstart Drops make installation a breeze. Try out both Local Shared and Cloud Ingest.Outperform the competition with a unified approach to data and AI
Organizations around the world are excited by the opportunity to leverage data to improve productivity and efficiency while also creating new revenue streams and competitive differentiation. With the increasing availability of AI tools to reason over the massive quantity of data produced each day, these goals have never been closer to reality. However, the ability to effectively harness AI at scale across large, distributed data estate requires a new approach. At Ignite 2024, Microsoft is making a variety of exciting announcements regarding how customers can collect data from any source, process and analyze that data locally and globally for AI-enabled insights anywhere, and securely share those insights with employees and the ecosystem. These announcements include the General Availability of Azure IoT Operations, the General Availability of Fabric Real-Time Intelligence, new capabilities for Azure Video Indexer, enabled by Arc, and a new Arc-enabled service focused on enabling generative AI applications at the edge. These announcements build upon Azure's adaptive cloud approach, a new paradigm to accelerate adoption of cloud innovations in scenarios extending beyond our hyperscale datacenters. The adaptive cloud approach unifies hybrid, multi-cloud, and edge infrastructure with a common operations, innovation, and insights platform, enabled by Azure Arc. Organizations that choose to embrace this methodology can drive consistency in how they manage and secure infrastructure, applications and data, as well as in how they collect and contextualize data. The ability to reduce operational complexity and improve visibility can serve as a powerful combination to help organizations accelerate digital transformation outcomes. Distributed organizations have distributed data Today, it is common for organizations to have a data estate that is distributed across a variety of physical locations. According to global research firm Gartner: "Gartner® predicts that, by 2025, more than 50% of enterprise managed-data will be created and processed outside the data center or cloud.”* There are a variety of reasons why distributed data is the norm versus an exception. Many organizations have a global footprint or have operations that span multiple physical locations. Additionally, organizations in any industry may go through acquisitions, introducing new physical and digital technology infrastructure. They may do business in countries with specific data privacy or security requirements. They may also be leveraging data for real-time or near real-time business processes that require very low latency. While there are valid reasons data lives in multiple locations and systems, this reality can also impede the ability to derive value from that data. Imagine a manufacturer with a goal to improve its yield across sites, an energy company aiming to reduce its global carbon footprint - or a retailer that would like to improve the in-store experience by leveraging consumer data across brands. None of these scenarios can be accomplished with a fragmented enterprise data management strategy. In addition, many organizations are operating within complex ecosystems where the ability to share data and insights is paramount. Data sitting in multiple places and under the governance of multiple systems can also act as a barrier to valuable ecosystem participation. Leveraging an adaptive cloud approach to unify data and AI across a distributed estate As we spend time with customers around the globe with distributed data estates, it is clear a more holistic approach to data is required to achieve their business goals. There are several aspects of this holistic approach – the ability to collect data from any data source, the ability to process and analyze that data locally and globally for AI-enabled actionable insights anywhere, and the ability to securely share those insights with the people that need them to do their jobs, as well as key ecosystem partners. Microsoft’s adaptive cloud approach provides capabilities across each of these domains: Azure IoT Operations Today we’re announcing the General Availability of Azure IoT Operations, enabling IoT edge data collection, processing, and delivery of edge data into global systems. Azure IoT Operations provides the ability to connect and communicate with OPC UA servers, edge data normalization and contextualization, scalable, bidirectional edge to cloud communication through native integration with Microsoft Fabric, tooling to power event driven applications at the edge, and an intuitive user interface to manage assets and configure data pipelines. Microsoft Fabric Microsoft Fabric is a comprehensive, end-to-end analytics and data platform built to meet the needs of organizations seeking a unified solution. It brings together all aspects of data management- spanning data integration, movement, processing, ingestion, transformation, real-time event routing, and reporting - into a single platform. Since the launch of Fabric Real-Time Intelligence at Microsoft Build, there's been continued customer excitement about the ability to build real-time solutions quickly and easily to solve key business problems. And now, Fabric Real-Time Intelligence is Generally Available. Microsoft Fabric offers a complete set of capabilities for event and streaming data with Real-Time Intelligence, including ingestion, transformation, storage, analytics, visualization, tracking, AI, and real-time actions. These capabilities are critical to help derive insights and meaning from the vast amount of data available through Azure IoT Operations. Real-Time Intelligence complements Fabric’s extensive suite of capabilities, including Data Engineering, Data Factory, Data Science, Data Warehousing, Power BI, and Database management, all integrated as part of a cohesive data experience. Instead of relying on different databases or data warehouses, organizations can centralize data storage with OneLake. Analytical models can be informed by bringing together data from multiple sources in one place. AI capabilities are seamlessly embedded within Fabric, eliminating the need for manual integration. With Fabric, customers can also easily transition their raw data into actionable insights for business users. From a data governance standpoint, Microsoft Fabric provides a set of capabilities that help customers manage, protect, monitor, and improve the discoverability of an organization's sensitive information. Azure IoT Operations is Azure Arc-enabled so when deploying applications to the edge, common standards, access controls, and policies are applied to data across distributed environments as well. This consistent approach to data governance across the data estate ensures the right people and partners can access the data and insights they need to perform their business function. Scaling AI initiatives on a unified data foundation Once organizations have a consistent edge to cloud data foundation that includes proper governance and security mechanisms, they are well positioned to scale their AI initiatives across the enterprise. As organizations invest in solutions build on the Azure AI platform, they want to be able to deploy them to any location where insights are needed and manage them in a consistent way. The adaptive cloud approach extends Kubernetes to the edge with Arc-enabled Kubernetes, providing the ability to create and deploy container-based AI applications to the edge and manage those over their lifecycle. As mentioned earlier, Azure-Arc managed applications also inherit policies from the Azure Resource Manager, providing access control for AI generated insights as well. An example of such an application is Azure AI Video Indexer. Azure AI Video Indexer enabled by Arc is an Azure Arc extension enabled service that runs video and audio analysis, and generative AI on edge devices. The solution is designed to run on Azure Arc enabled Kubernetes and supports many audio and video formats, including MP4 and other common formats. At Ignite, a new multi-modal video description feature is being launched in preview. This functionality leverages the generative AI Phi3.5v to produce video summaries by examining text, audio, and visual components without the need to view the entire video. At Ignite, we are also announcing the private preview of a new Arc-enabled service focused on enabling generative AI applications at the edge. This service is deployable as a turnkey Arc for Kubernetes extension and allows customers to use industry-leading small and large language models to search their on-premises data using a technique called Retrieval Augmented Generation, or “RAG”. With it, organizations can use Azure-consistent tools and APIs to create Copilot-like experiences, while meeting data locality and compliance requirements. As the technology to derive value from data continues to become more intelligent and more accessible, the need for a modern data foundation to support those tools increases as well. This modern data foundation must support the ability to uncover and act on insights anywhere and do so in a secure manner. At Ignite this year, we’re looking forward to meeting with you to discuss how Azure’s adaptive cloud approach can help unify your distributed data estate and scale your business outcomes. Learn more: You can find a full listing of opportunities to learn more about our Adaptive cloud approach at Ignite here: Adaptive Cloud at Ignite – GitHub. Source: * Gartner, Hype Cycle™ for Edge Computing, 2024, Thomas Bittman, 15 July 2024 (Report accessible to Gartner subscribers only). GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and HYPE CYCLE is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.
Announcing the Azure Arc ISV Partner Program at Ignite
Empowering Partners and Enhancing Customer Experience We are thrilled to introduce the newly launched Azure Arc ISV Partner Program at Ignite! This innovative and growing ecosystem partner program allows them to publish offers on the Azure Marketplace that can be deployed to Arc-enabled Kubernetes clusters. Customers can now access validated, enterprise-grade applications and tools to enhance their Azure Arc development, while ISVs benefit from a deeper integration with Azure Arc services and access to the Arc enabled customer base. All marketplace images have been validated across the Azure Arc platform with the support of both Microsoft and partner teams. With the solutions each partner has made available on the Azure marketplace, the integration with Azure Arc offers central governance to build robust applications with consistent security and reliability for any hybrid deployments. What is Azure Arc? Azure Arc is a platform that extends Azure to datacenters, on-premises, edge, or even multi-cloud environments. It simplifies governance and management by delivering the consistency of the Azure platform. The ability to create offerings for Azure Arc in the marketplace is a significant benefit to our partners, allowing them to integrate with Azure services and tools and access a large and diverse customer base. Azure Arc also provides validated applications for customers to manage their Kubernetes clusters on our platform. Edge developers leverage the open-source community to build their enterprise applications, and we aim to provide them with a one-stop shop in Azure Marketplace, offering a choice of Kubernetes-based building blocks needed to develop their applications. Meet our partners With our Ignite launch, we have built the foundation of an ecosystem that is designed to bring the best capabilities and innovations to our marketplace, focused on leading building block categories: databases, big data/analytics, and messaging. We are excited to introduce our esteemed partners, (CloudCasa, MongoDB, Redis, MinIO, DataStax) who have Arc enabled their application and will now be available on the Azure Marketplace. Here’s a closer look at their offerings: CloudCasa CloudCasa is a leading provider of Kubernetes backup and recovery solutions. By Arc-enabling their application, CloudCasa offers robust, secure, and easy-to-use backup services for Kubernetes, ensuring the protection and availability of critical data. With CloudCasa, your Arc enabled Kubernetes deployments across hybrid environments are fully protected, ensure that your data is safe and recoverable, no matter the scenario. CloudCasa’s integration with Azure Arc offers three key components: handling persistent volume with or without CSI snapshots, unified management and monitoring across environments, and disaster recovery and migration for AKS hybrid. One-way CloudCasa manages persistent storage is that it natively integrates with Container Storage Interface snapshots, ensuring that all your persistent volumes can be captured and protected without interrupting your workloads. CloudCasa also provides a powerful disaster recovery and migration solution. For AKS on Azure Stack HCI, this means you can confidently deploy hybrid and edge clusters, knowing that you have a trusted solution to recover from any disaster, or even perform seamless migrations from edge to cloud or vice versa. To explore CloudCasa’s full capabilities for Azure Arc-enabled Kubernetes clusters, visit the CloudCasa Marketplace listing for Azure Arc or find out more at cloudcasa.io. For personalized assistance, feel free to contact casa@cloudcasa.io. DataStax DataStax is a leading provider of Gen AI solutions for AI developers. With DataStax HCD (Hyper-Converged Database), businesses can harness the power of Apache Cassandra, the highly scalable and resilient NoSQL database, to manage large volumes of structured and vector data with ease. By Arc-enabling their applications, DataStax HCD offers users a “single pane of glass” for streamlined deployment, monitoring, and lifecycle management of their entire infrastructure. Ensuring consistent operations across on-premises, Azure, and multi-cloud environments makes Azure with HCD an ideal choice for mission-critical applications. The combination of Azure Arc central governance and Mission Control, DataStax’s operations platform, on HCD will allow for provisioning of resources for on-premises and on the cloud. HCD brings to Azure Arc database management and the ability to support workloads and AI systems at scale with no single point of failure. DataStax HCD brings three key benefits to Azure Arc: data replication and distribution, node repair, and vector search capabilities to enhance your enterprise data workloads. To learn more about the full capabilities of DataStax HCD, please visit the DataStax HCD for Azure Arc or find out more on the HCD product page. MongoDB MongoDB Enterprise Advanced (EA) empowers customers to securely self-manage their MongoDB deployments on-premises or in hybrid environments, driving operational efficiency, performance, and control to meet specific infrastructure needs. Now with Arc-enablement, MongoDB EA allows developers to build, scale, and innovate faster by providing a robust and dynamic database solution across a multitude of environments. MongoDB’s document data model is intuitive and powerful, and it can easily handle a variety of data types and use cases efficiently. MongoDB EA includes advanced automation, reliable backups, monitoring capabilities, updating deployments, and integrating with various Kubernetes services. The MongoDB integration with Azure Arc provides three key benefits: support for multi-Kubernetes cluster deployments, centralized provisioning through the Azure portal, and leveraging the resilience of Kubernetes deployments. As Azure Arc provides centralized management of Kubernetes environments across a multitude of environments, MongoDB EA adds value with databases that can run across multiple Kubernetes clusters. To explore MongoDB EA on Azure Marketplace for Azure Arc - and to learn more about the full potential of this offering - please visit MongoDB Enterprise Advanced for Azure Arc. For licensing inquiries and to learn more about MongoDB Enterprise Advanced, please visit MongoDB's website. Redis Redis Software, an enterprise-grade, real-time data platform, offers an in-memory data structure store used as a cache, vector database, document database, streaming engine, and message broker. With its Arc-enabled application, Redis Software provides ultra-fast data access, real-time analytics, and seamless scalability. This makes Redis Software ideal for applications requiring high performance and low latency. Integrating with Azure Arc allows users to deploy Redis workloads across on-premises, Cloud and hybrid infrastructure. The benefits Redis Software brings to Azure Arc are support multi-core deployments, Active-Active geo-distribution, data tiering, high-availability with seamless failover and multiple level of on-disk persistence. As it is integrated with Arc, these Redis instances are located on-premises or on the cloud and can be managed centrally on the Azure portal. To explore Redis Software on the Azure Marketplace for Azure Arc, please visit Redis Software for Kubernetes for Azure Arc. You can learn more about licensing inquiries at Redis Software. MinIO MinIO AIStor is the standard for building large scale AI data infrastructure. It is a software-defined, S3 compatible object store that is optimized for the private cloud but will run anywhere - from the public cloud to the edge. Enterprises use AIStor to deliver against artificial intelligence, machine learning, analytics, application, backup and archival workloads - all from a single platform. It was built for the cloud operating model, so it is native to the technologies and architectures that define the cloud, such as: containerization, orchestration with Kubernetes, microservices and multi-tenancy. By Arc-enabling their application, MinIO ensures that Azure users can experience the unmatched scalability, robust security, and lightning-fast storage performance that has made MinIO the most widely integrated object store in the market today. Users can now run these hybrid or multi-cloud deployments on Azure Arc and manage them in a single pane of glass on the Azure portal. To deploy and learn more about MinIO AIStor on Azure Arc, please visit MinIO AIStor for Azure Arc here. For further information on MinIO AIStor for Azure Arc, please visit MinIO | AI Storage is Object Storage. Become an Arc Enabled Partner These partners have collaborated with Microsoft to join our ISV ecosystem, providing resilient and scalable applications readily accessible for our Azure Arc customers via the Azure Marketplace. Joining forces with Microsoft enables partners to stay ahead of the technological curve, strengthen customer relationships, and contribute to transformative digital changes across industries. We look forward to expanding this program to include more ISVs, enhancing the experience for customers using Arc enabled Kubernetes clusters. As we continue to expand our Azure Arc ISV Partner Program, stay tuned for more blogs on the new partners being published to the Azure Marketplace. To reach out and learn more about the Azure Arc ISV Partner Program, please feel free to reach out to us at https://aka.ms/AzureArcISV.
