Azure Kubernetes Service Baseline - The Hard Way, Third time's a charm
1 Access management Azure Kubernetes Service (AKS) supports Microsoft Entra ID integration, which allows you to control access to your cluster resources using Azure role-based access control (RBAC). In this tutorial, you will learn how to integrate AKS with Microsoft Entra ID and assign different roles and permissions to three types of users: An admin user, who will have full access to the AKS cluster and its resources. A backend ops team, who will be responsible for managing the backend application deployed in the AKS cluster. They will only have access to the backend namespace and the resources within it. A frontend ops team, who will be responsible for managing the frontend application deployed in the AKS cluster. They will only have access to the frontend namespace and the resources within it. By following this tutorial, you will be able to implement the least privilege access model, which means that each user or group will only have the minimum permissions required to perform their tasks. 1.1 Introduction In this third part of the blog series, you will learn how to: Harden your AKS cluster. - Update an existing AKS cluster to support Microsoft Entra ID integration enabled. Create a Microsoft Entra ID admin group and assign it the Azure Kubernetes Service Cluster Admin Role. Create a Microsoft Entra ID backend ops group and assign it the Azure Kubernetes Service Cluster User Role. Create a Microsoft Entra ID frontend ops group and assign it the Azure Kubernetes Service Cluster User Role. Create Users in Microsoft Entra ID Create role bindings to grant access to the backend ops group and the frontend ops group to their respective namespaces. Test the access of each user type by logging in with different credentials and running kubectl commands. 1.2 Prequisities: This section outlines the recommended prerequisites for setting up Microsoft entra ID with AKS. Highly recommended to complete Azure Kubernetes Service Baseline - The Hard Way here! or follow the Microsoft official documentation for a quick start here! Note that you will need to create 2 namespaces in kubernetes one called frontend and the second one called backend. 1.3 Target Architecture Throughout this article, this is the target architecture we will aim to create: all procedures will be conducted by using Azure CLI. The current architecture can be visualized as followed: 1.4 Deployment 1.4.1 Prepare Environment Variables This code defines the environment variables for the resources that you will create later in the tutorial. Note: Ensure environment variable $STUDENT_NAME and placeholder <TENANT SUB DOMAIN NAME>is set before adding the code below. # Define the name of the admin group ADMIN_GROUP='ClusterAdminGroup-'${STUDENT_NAME} # Define the name of the frontend operations group OPS_FE_GROUP='Ops_Fronted_team-'${STUDENT_NAME} # Define the name of the backend operations group OPS_BE_GROUP='Ops_Backend_team-'${STUDENT_NAME} # Define the Azure AD UPN (User Principal Name) for the frontend operations user AAD_OPS_FE_UPN='opsfe-'${STUDENT_NAME}'@<SUB DOMAIN TENANT NAME HERE>.onmicrosoft.com' # Define the display name for the frontend operations user AAD_OPS_FE_DISPLAY_NAME='Frontend-'${STUDENT_NAME} # Placeholder for the frontend operations user password AAD_OPS_FE_PW=<ENTER USER PASSWORD> # Define the Azure AD UPN for the backend operations user AAD_OPS_BE_UPN='opsbe-'${STUDENT_NAME}'@<SUB DOMAIN TENANT NAME HERE>.onmicrosoft.com' # Define the display name for the backend operations user AAD_OPS_BE_DISPLAY_NAME='Backend-'${STUDENT_NAME} # Placeholder for the backend operations user password AAD_OPS_BE_PW=<ENTER USER PASSWORD> # Define the Azure AD UPN for the cluster admin user AAD_ADMIN_UPN='clusteradmin'${STUDENT_NAME}'@<SUB DOMAIN TENANT NAME HERE>.onmicrosoft.com' # Placeholder for the cluster admin user password AAD_ADMIN_PW=<ENTER USER PASSWORD> # Define the display name for the cluster admin user AAD_ADMIN_DISPLAY_NAME='Admin-'${STUDENT_NAME} 1.4.2 Create Microsoft Entra ID Security Groups We will now start by creating 3 security groups for respective team. Create the security group for Cluster Admins az ad group create --display-name $ADMIN_GROUP --mail-nickname $ADMIN_GROUP 2. Create the security group for Application Operations Frontend Team az ad group create --display-name $OPS_FE_GROUP --mail-nickname $OPS_FE_GROUP 3. Create the security group for Application Operations Backend Team az ad group create --display-name $OPS_FE_GROUP --mail-nickname $OPS_FE_GROUP Current architecture can now be illustrated as follows: 1.4.3 Integrate AKS with Microsoft Entra ID 1. Lets update our existing AKS cluster to support Microsoft Entra ID integration, and configure a cluster admin group, and disable local admin accounts in AKS, as this will prevent anyone from using the --admin switch to get full cluster credentials. az aks update -g $SPOKE_RG -n $AKS_CLUSTER_NAME-${STUDENT_NAME} --enable-azure-rbac --enable-aad --disable-local-accounts Current architecture can now be described as follows: 1.4.4 Scope and Role Assignment for Security Groups This chapter describes how to create the scope for the operation teams to perform their daily tasks. The scope is based on the AKS resource ID and a fixed path in AKS, which is /namespaces/. The scope will assign the Application Operations Frontend Team to the frontend namespace and the Application Operation Backend Team to the backend namespace. Lets start by constructing the scope for the operations team. AKS_BACKEND_NAMESPACE='/namespaces/backend' AKS_FRONTEND_NAMESPACE='/namespaces/frontend' AKS_RESOURCE_ID=$(az aks show -g $SPOKE_RG -n $AKS_CLUSTER_NAME-${STUDENT_NAME} --query 'id' --output tsv) 2. Lets fetch the Object ID of the operations teams and admin security groups. Application Operation Frontend Team. FE_GROUP_OBJECT_ID=$(az ad group show --group $OPS_FE_GROUP --query 'id' --output tsv) Application Operation Backend Team. BE_GROUP_OBJECT_ID=$(az ad group show --group $OPS_BE_GROUP --query 'id' --output tsv Admin. ADMIN_GROUP_OBJECT_ID=$(az ad group show --group $ADMIN_GROUP --query 'id' --output tsv) 3) This commands will grant the Application Operations Frontend Team group users the permissions to download the credential for AKS, and only operate within given namespace. az role assignment create --assignee $FE_GROUP_OBJECT_ID --role "Azure Kubernetes Service RBAC Writer" --scope ${AKS_RESOURCE_ID}${AKS_FRONTEND_NAMESPACE} az role assignment create --assignee $FE_GROUP_OBJECT_ID --role "Azure Kubernetes Service Cluster User Role" --scope ${AKS_RESOURCE_ID} 4) This commands will grant the Application Operations Backend Team group users the permissions to download the credential for AKS, and only operate within given namespace. az role assignment create --assignee $BE_GROUP_OBJECT_ID --role "Azure Kubernetes Service RBAC Writer" --scope ${AKS_RESOURCE_ID}${AKS_BACKEND_NAMESPACE} az role assignment create --assignee $BE_GROUP_OBJECT_ID --role "Azure Kubernetes Service Cluster User Role" --scope ${AKS_RESOURCE_ID} 5) This command will grant the Admin group users the permissions to connect to and manage all aspects of the AKS cluster. az role assignment create --assignee $ADMIN_GROUP_OBJECT_ID --role "Azure Kubernetes Service RBAC Cluster Admin" --scope ${AKS_RESOURCE_ID} Current architecture can now be described as follows: 1.4.5 Create Users and Assign them to Security Groups. This exercise will guide you through the steps of creating three users and adding them to their corresponding security groups. Create the Admin user. az ad user create --display-name $AAD_ADMIN_DISPLAY_NAME --user-principal-name $AAD_ADMIN_UPN --password $AAD_ADMIN_PW 2. Assign the admin user to admin group for the AKS cluster. First identify the object id of the user as we will need this number to assign the user to the admin group. ADMIN_USER_OBJECT_ID=$(az ad user show --id $AAD_ADMIN_UPN --query 'id' --output tsv) 3. Assign the user to the admin security group. az ad group member add --group $ADMIN_GROUP --member-id $ADMIN_USER_OBJECT_ID 4. Create the frontend operations user. az ad user create --display-name $AAD_OPS_FE_DISPLAY_NAME --user-principal-name $AAD_OPS_FE_UPN --password $AAD_OPS_FE_PW 5. Assign the frontend operations user to frontend security group for the AKS cluster. First identify the object id of the user as we will need this number to assign the user to the frontend security group. FE_USER_OBJECT_ID=$(az ad user show --id $AAD_OPS_FE_UPN --query 'id' --output tsv) 6. Assign the user to the frontend security group. az ad group member add --group $OPS_FE_GROUP --member-id $FE_USER_OBJECT_ID 7. Create the backend operations user. az ad user create --display-name $AAD_OPS_BE_DISPLAY_NAME --user-principal-name $AAD_OPS_BE_UPN --password $AAD_OPS_BE_PW 8. Assign the backend operations user to backend security group for the AKS cluster. First identify the object id of the user as we will need this number to assign the user to the backend security group. BE_USER_OBJECT_ID=$(az ad user show --id $AAD_OPS_BE_UPN --query 'id' --output tsv) 9. Assign the user to the backend security group. az ad group member add --group $OPS_BE_GROUP --member-id $BE_USER_OBJECT_ID Current architecture can now be described as follows: 1.4.6 Validate your deployment in the Azure portal. Navigate to the Azure portal at https://portal.azure.com and enter your login credentials. Once logged in, on your top left hand side, click on the portal menu (three strips). From the menu list click on Microsoft Entra ID. On your left hand side menu under Manage click on Users. Validate that your users are created, there shall be three users, each user name shall end with your student name. On the top menu bar click on the Users link. On your left hand side menu under Manage click on Groups. Ensure you have three groups as depicted in the picture, the group names should end with your student name. Click on security group called Ops_Backend_team-YOUR STUDENT NAME. On your left hand side menu click on Members, verify that your user Backend-YOUR STUDENT NAME is assigned. On your left hand side menu click on Azure role Assignments, from the drop down menu select your subscription. Ensure the following roles are assigned to the group: Azure Kubernetes service Cluster User Role assigned on the Cluster level and Azure Kubernetes Service RBAC Writer assigned on the namespace level called backend. 11.On the top menu bar click on Groups link. Repeat step 7 - 11 for Ops_Frontend_team-YOUR STUDENT NAME and ClusterAdminGroup-YOUR STUDENT NAME 1.4.7 Validate the Access for the Different Users. This section will demonstrate how to connect to the AKS cluster from the jumpbox using the user account defined in Microsoft Entra ID. Note: If you deployed your AKS cluster using the quick start method We will check two things: first, that we can successfully connect to the cluster; and second, that the Operations teams have access only to their own namespaces, while the Admin has full access to the cluster. Navigate to the Azure portal at https://portal.azure.com and enter your login credentials. Once logged in, locate and select your rg-hub where the Jumpbox has been deployed. Within your resource group, find and click on the Jumpbox VM. In the left-hand side menu, under the Operations section, select Bastion. Enter the credentials for the Jumpbox VM and verify that you can log in successfully. First remove the existing stored configuration that you have previously downloaded with Azure CLI and kubectl. From the Jumpbox VM execute the following commands: rm -R .azure/ rm -R .kube/ Note: The .azure and .kube directories store configuration files for Azure and Kubernetes, respectively, for your user account. Removing these files triggers a login prompt, allowing you to re-authenticate with different credentials. 7. Retrieve the username and password for Frontend user. Important: Retrieve the username and password from your local shell, and not the shell from Jumpbox VM. echo $AAD_OPS_FE_UPN echo $AAD_OPS_FE_PW 8. From the Jumpbox VM initiate the authentication process. az login Example output: bash azureuser@Jumpbox-VM:~$ az login To sign in, use a web browser to open the page https://microsoft.com/devicelogin and enter the code XXXXXXX to authenticate. 9. Open a new tab in your web browser and access https://microsoft.com/devicelogin. Enter the generated code, and press Next 10. You will be prompted with an authentication window asking which user you want to login with select Use another account and supply the username in the AAD_OPS_FE_UPN variable and password from variable AAD_OPS_FE_PW and then press Next. Note: When you authenticate with a user for the first time, you will be prompted by Microsoft Authenticator to set up Multi-Factor Authentication (MFA). Choose "I want to setup a different method" option from the drop-down menu, and select Phone, supply your phone number, and receive a one-time passcode to authenticate to Azure with your user account. 11. From the Jumpbox VM download AKS cluster credential. SPOKE_RG=rg-spoke STUDENT_NAME= AKS_CLUSTER_NAME=private-aks az aks get-credentials --resource-group $SPOKE_RG --name $AKS_CLUSTER_NAME-${STUDENT_NAME} You should see a similar output as illustrated below: bash azureuser@Jumpbox-VM:~$ az aks get-credentials --resource-group $SPOKE_RG --name $AKS_CLUSTER_NAME-${STUDENT_NAME} Merged "private-aks" as current context in /home/azureuser/.kube/config azureuser@Jumpbox-VM:~$ 12. You should be able to list all pods in namespace frontend. You will now be prompted to authenticate your user again, as this time it will validate your newly created user permissions within the AKS cluster. Ensure you login with the user you created i.e $AAD_OPS_FE_UPN, and not your company email address. kubectl get po -n frontend Example output: azureuser@Jumpbox-VM:~$ kubectl get po -n frontend To sign in, use a web browser to open the page https://microsoft.com/devicelogin and enter the code XXXXXXX to authenticate. NAME READY STATUS RESTARTS AGE nginx 1/1 Running 0 89m 13. Try to list pods in default namespace bash kubectl get pods Example output: bash azureuser@Jumpbox-VM:~$ kubectl get po Error from server (Forbidden): pods is forbidden: User "opsfe-test@xxxxxxxxxx.onmicrosoft.com" cannot list resource "pods" in API group "" in the namespace "default": User does not have access t o the resource in Azure. Update role assignment to allow access. 14. Repeat step 6 and 13 for the remaining users, and see how their permissions differs. # Username and password for Admin user execute the command from your local shell and not from Jumpbox VM echo $AAD_ADMIN_UPN echo $AAD_ADMIN_PW # Username and password for Backend user execute the command from your local shell and not from Jumpbox VM echo $AAD_OPS_BE_UPN echo $AAD_OPS_BE_PW 🎉 Congratulations, you made it to the end! You’ve just navigated the wild waters of Microsoft Entra ID and AKS — and lived to tell the tale. Whether you’re now a cluster conqueror or an identity integration ninja, give yourself a high five (or a kubectl get pods if that’s more your style). Now go forth and secure those clusters like the cloud hero you are. 🚀 And remember: with great identity comes great responsibility.Building the Agentic Future
As a business built by developers, for developers, Microsoft has spent decades making it faster, easier and more exciting to create great software. And developers everywhere have turned everything from BASIC and the .NET Framework, to Azure, VS Code, GitHub and more into the digital world we all live in today. But nothing compares to what’s on the horizon as agentic AI redefines both how we build and the apps we’re building. In fact, the promise of agentic AI is so strong that market forecasts predict we’re on track to reach 1.3 billion AI Agents by 2028. Our own data, from 1,500 organizations around the world, shows agent capabilities have jumped as a driver for AI applications from near last to a top three priority when comparing deployments earlier this year to applications being defined today. Of those organizations building AI agents, 41% chose Microsoft to build and run their solutions, significantly more than any other vendor. But within software development the opportunity is even greater, with approximately 50% of businesses intending to incorporate agentic AI into software engineering this year alone. Developers face a fascinating yet challenging world of complex agent workflows, a constant pipeline of new models, new security and governance requirements, and the continued pressure to deliver value from AI, fast, all while contending with decades of legacy applications and technical debt. This week at Microsoft Build, you can see how we’re making this future a reality with new AI-native developer practices and experiences, by extending the value of AI across the entire software lifecycle, and by bringing critical AI, data, and toolchain services directly to the hands of developers, in the most popular developer tools in the world. Agentic DevOps AI has already transformed the way we code, with 15 million developers using GitHub Copilot today to build faster. But coding is only a fraction of the developer’s time. Extending agents across the entire software lifecycle, means developers can move faster from idea to production, boost code quality, and strengthen security, while removing the burden of low value, routine, time consuming tasks. We can even address decades of technical debt and keep apps running smoothly in production. This is the foundation of agentic DevOps—the next evolution of DevOps, reimagined for a world where intelligent agents collaborate with developer teams and with each other. Agents introduced today across GitHub Copilot and Azure operate like a member of your development team, automating and optimizing every stage of the software lifecycle, from performing code reviews, and writing tests to fixing defects and building entire specs. Copilot can even collaborate with other agents to complete complex tasks like resolving production issues. Developers stay at the center of innovation, orchestrating agents for the mundane while focusing their energy on the work that matters most. Customers like EY are already seeing the impact: “The coding agent in GitHub Copilot is opening up doors for each developer to have their own team, all working in parallel to amplify their work. Now we're able to assign tasks that would typically detract from deeper, more complex work, freeing up several hours for focus time." - James Zabinski, DevEx Lead at EY You can learn more about agentic DevOps and the new capabilities announced today from Amanda Silver, Corporate Vice President of Product, Microsoft Developer Division, and Mario Rodriguez, Chief Product Office at GitHub. And be sure to read more from GitHub CEO Thomas Dohmke about the latest with GitHub Copilot. At Microsoft Build, see agentic DevOps in action in the following sessions, available both in-person May 19 - 22 in Seattle and on-demand: BRK100: Reimagining Software Development and DevOps with Agentic AI BRK 113: The Agent Awakens: Collaborative Development with GitHub Copilot BRK118: Accelerate Azure Development with GitHub Copilot, VS Code & AI BRK131: Java App Modernization Simplified with AI BRK102: Agent Mode in Action: AI Coding with Vibe and Spec-Driven Flows BRK101: The Future of .NET App Modernization Streamlined with AI New AI Toolchain Integrations Beyond these new agentic capabilities, we’re also releasing new integrations that bring key services directly to the tools developers are already using. From the 150 million GitHub users to the 50 million monthly users of the VS Code family, we’re making it easier for developers everywhere to build AI apps. If GitHub Copilot changed how we write code, Azure AI Foundry is changing what we can build. And the combination of the two is incredibly powerful. Now we’re bringing leading models from Azure AI Foundry directly into your GitHub experience and workflow, with a new native integration. GitHub models lets you experiment with leading models from OpenAI, Meta, Cohere, Microsoft, Mistral and more. Test and compare performance while building models directly into your codebase all within in GitHub. You can easily select the best model performance and price side by side and swap models with a simple, unified API. And keeping with our enterprise commitment, teams can set guardrails so model selection is secure, responsible, and in line with your team’s policies. Meanwhile, new Azure Native Integrations gives developers seamless access to a curated set of 20 software services from DataDog, New Relic, Pinecone, Pure Storage Cloud and more, directly through Azure portal, SDK, and CLI. With Azure Native Integrations, developers get the flexibility to work with their preferred vendors across the AI toolchain with simplified single sign-on and management, while staying in Azure. Today, we are pleased to announce the addition of even more developer services: Arize AI: Arize’s platform provides essential tooling for AI and agent evaluation, experimentation, and observability at scale. With Arize, developers can easily optimize AI applications through tools for tracing, prompt engineering, dataset curation, and automated evaluations. Learn more. LambdaTest HyperExecute: LambdaTest HyperExecute is an AI-native test execution platform designed to accelerate software testing. It enables developers and testers to run tests up to 70% faster than traditional cloud grids by optimizing test orchestration, observability and streamlining TestOps to expedite release cycles. Learn more. Mistral: Mistral and Microsoft announced a partnership today, which includes integrating Mistral La Plateforme as part of Azure Native Integrations. Mistral La Plateforme provides pay-as-you-go API access to Mistral AI's latest large language models for text generation, embeddings, and function calling. Developers can use this AI platform to build AI-powered applications with retrieval-augmented generation (RAG), fine-tune models for domain-specific tasks, and integrate AI agents into enterprise workflows. MongoDB (Public Preview): MongoDB Atlas is a fully managed cloud database that provides scalability, security, and multi-cloud support for modern applications. Developers can use it to store and search vector embeddings, implement retrieval-augmented generation (RAG), and build AI-powered search and recommendation systems. Learn more. Neon: Neon Serverless Postgres is a fully managed, autoscaling PostgreSQL database designed for instant provisioning, cost efficiency, and AI-native workloads. Developers can use it to rapidly spin up databases for AI agents, store vector embeddings with pgvector, and scale AI applications seamlessly. Learn more. Java and .Net App Modernization Shipping to production isn’t the finish line—and maintaining legacy code shouldn’t slow you down. Today we’re announcing comprehensive resources to help you successfully plan and execute app modernization initiatives, along with new agents in GitHub Copilot to help you modernize at scale, in a fraction of the time. In fact, customers like Ford China are seeing breakthrough results, reducing up to 70% of their Java migration efforts by using GitHub Copilot to automate middleware code migration tasks. Microsoft’s App Modernization Guidance applies decades of enterprise apps experience to help you analyze production apps and prioritize modernization efforts, while applying best practices and technical patterns to ensure success. And now GitHub Copilot transforms the modernization process, handling code assessments, dependency updates, and remediation across your production Java and .NET apps (support for mainframe environments is coming soon!). It generates and executes update plans automatically, while giving you full visibility, control, and a clear summary of changes. You can even raise modernization tasks in GitHub Issues from our proven service Azure Migrate to assign to developer teams. Your apps are more secure, maintainable, and cost-efficient, faster than ever. Learn how we’re reimagining app modernization for the era of AI with the new App Modernization Guidance and the modernization agent in GitHub Copilot to help you modernize your complete app estate. Scaling AI Apps and Agents Sophisticated apps and agents need an equally powerful runtime. And today we’re advancing our complete portfolio, from serverless with Azure Functions and Azure Container Apps, to the control and scale of Azure Kubernetes Service. At Build we’re simplifying how you deploy, test, and operate open-source and custom models on Kubernetes through Kubernetes AI Toolchain Operator (KAITO), making it easy to inference AI models with the flexibility, auto-scaling, pay-per-second pricing, and governance of Azure Container Apps serverless GPU, helping you create real-time, event-driven workflows for AI agents by integrating Azure Functions with Azure AI Foundry Agent Service, and much, much more. The platform you choose to scale your apps has never been more important. With new integrations with Azure AI Foundry, advanced automation that reduces developer overhead, and simplified operations, security and governance, Azure’s app platform can help you deliver the sophisticated, secure AI apps your business demands. To see the full slate of innovations across the app platform, check out: Powering the Next Generation of AI Apps and Agents on the Azure Application Platform Tools that keep pace with how you need to build This week we’re also introducing new enhancements to our tooling to help you build as fast as possible and explore what’s next with AI, all directly from your editor. GitHub Copilot for Azure brings Azure-specific tools into agent mode in VS Code, keeping you in the flow as you create, manage, and troubleshoot cloud apps. Meanwhile the Azure Tools for VS Code extension pack brings everything you need to build apps on Azure using GitHub Copilot to VS Code, making it easy to discover and interact with cloud services that power your applications. Microsoft’s gallery of AI App Templates continues to expand, helping you rapidly move from concept to production app, deployed on Azure. Each template includes fully working applications, complete with app code, AI features, infrastructure as code (IaC), configurable CI/CD pipelines with GitHub Actions, along with an application architecture, ready to deploy to Azure. These templates reflect the most common patterns and use cases we see across our AI customers, from getting started with AI agents to building GenAI chat experiences with your enterprise data and helping you learn how to use best practices such as keyless authentication. Learn more by reading the latest on Build Apps and Agents with Visual Studio Code and Azure Building the agentic future The emergence of agentic DevOps, the new wave of development powered by GitHub Copilot and new services launching across Microsoft Build will be transformative. But just as we’ve seen over the first 50 years of Microsoft’s history, the real impact will come from the global community of developers. You all have the power to turn these tools and platforms into advanced AI apps and agents that make every business move faster, operate more intelligently and innovate in ways that were previously impossible. Learn more and get started with GitHub CopilotEnhance Your Linux Workloads with Azure Files NFS v4.1: Secure, Scalable, and Flexible
Enhance your Linux workloads with Azure Files NFS v4.1, enterprise-grade solution. With new support for in-transit encryption and RESTful access, it delivers robust security and flexible data access for mission-critical and data-intensive applications.Powering the Next Generation of AI Apps and Agents on the Azure Application Platform
Generative AI is already transforming how businesses operate, with organizations seeing an average return of 3.7x for every $1 of investment [The Business Opportunity of AI, IDC study commissioned by Microsoft]. Developers sit at the center of this transformation, and their need for speed, flexibility, and familiarity with existing tools is driving the demand for application platforms that integrate AI seamlessly into their current development workflows. To fully realize the potential of generative AI in applications, organizations must provide developers with frictionless access to AI models, frameworks, and environments that enable them to scale AI applications. We see this in action at organizations like Accenture, Assembly Software, Carvana, Coldplay (Pixel Lab), Global Travel Collection, Fujitsu, healow, Heineken, Indiana Pacers, NFL Combine, Office Depot, Terra Mater Studios (Red Bull), and Writesonic. Today, we’re excited to announce new innovations across the Azure Application Platform to meet developers where they are and help enterprises accelerate their AI transformation. The Azure App Platform offers managed Kubernetes (Azure Kubernetes Service), serverless (Azure Container Apps and Azure Functions), PaaS (Azure App Service) and integration (Azure Logic Apps and API Management). Whether you’re modernizing existing applications or creating new AI apps and agents, Azure provides a developer‑centric App Platform—seamlessly integrated with Visual Studio, GitHub, and Azure AI Foundry—and backed by a broad portfolio of fully managed databases, from Azure Cosmos DB to Azure Database for PostgreSQL and Azure SQL Database. Innovate faster with AI apps and agents In today’s fast-evolving AI landscape, the key to staying competitive is being able to move from AI experimentation to production quickly and easily. Whether you’re deploying open-source AI models or integrating with any of the 1900+ models in Azure AI Foundry, the Azure App Platform provides a streamlined path for building and scaling AI apps and agents. Kubernetes AI Toolchain Operator (KAITO) for AKS add-on (GA) and Azure Arc extension (preview) simplifies deploying, testing, and operating open-source and custom models on Kubernetes. Automated GPU provisioning, pre-configured settings, workspace customization, real-time deployment tracking, and built-in testing interfaces significantly reduce infrastructure overhead and accelerate AI development. Visual Studio Code integration enables developers to quickly prototype, deploy, and manage models. Learn more. Serverless GPU integration with AI Foundry Models (preview) offers a new deployment target for easy AI model inferencing. Azure Container Apps serverless GPU offers unparalleled flexibility to run any supported model. It features automatic scaling, pay-per-second pricing, robust data governance, and built-in enterprise networking and security support, making it an ideal solution for scalable and secure AI deployments. Learn more. Azure Functions integration with AI Foundry Agent Service (GA) enables you to create real-time, event-driven workflows for AI agents without managing infrastructure. This integration enables agents to securely invoke Azure Functions to execute business logic, access systems, or process data on demand. It unlocks scalable, cost-efficient automation for intelligent applications that respond dynamically to user input or events. Learn more. Azure Functions enriches Azure OpenAI extension (preview) to automate embeddings for real-time RAG, semantic search, and function calling with built-in support for AI Search, Azure Cosmos DB for MongoDB and Azure Data Explorer vector stores. Learn more. Azure Functions MCP extension adds support for instructions and monitoring (preview) making it easier to build and operate remote MCP servers at cloud scale. With this update, developers can deliver richer AI interactions by providing capabilities and context to large language models directly from Azure Functions. This enables AI agents to both call functions and respond intelligently with no separate orchestration layer required. Learn more. Harnessing AI to drive intelligent business processes As AI continues to grow in adoption, its ability to automate complex business process workflows becomes increasingly valuable. Azure Logic Apps empowers organizations to build, orchestrate, and monitor intelligent, agent-driven workflows. Logic Apps agent loop orchestrates agentic business processes (preview) with goal-based automation using AI-powered reasoning engines such as OpenAI’s GPT-4o or GPT-4.1. Instead of building fixed flows, users can define the desired outcomes, and Agent loop action in Logic Apps figures out the steps dynamically. With 1400+ out-of-the-box connectors to various enterprise systems and SaaS applications, and full observability, Logic Apps enables you to rapidly deliver on all business process needs with agentic automation. Learn more. Enable intelligent data pipelines for RAG using Logic Apps (preview) with new native integrations with Azure Cosmos DB and Azure AI Search. Teams can ingest content into vector stores and databases through low-code templates. No custom code required. This enables AI agents to ground responses in proprietary data, improving relevance and accuracy for real business outcomes. Learn more. Empower AI agents to act with Logic Apps in AI Foundry (preview) across enterprise systems using low-code automation. Prebuilt connectors and templates simplify integration with Microsoft and third-party services from databases to SaaS apps. This gives developers and business users a faster way to orchestrate intelligent actions, automate complex workflows, and operationalize AI across the organization. Learn more. Scale AI innovation across your enterprise As AI adoption grows, so does the need for visibility and control over how models are accessed and utilized. Azure API Management helps you achieve this with advanced tools that ensure governance, security, and efficient management of your AI APIs. Expanded AI Gateway capabilities in Azure API Management (GA) give organizations deeper control, observability, and governance for generative AI workloads. Key additions include LLM Logging for prompts, completions, and token usage insights; session-aware load balancing to maintain context in multi-turn chats; robust guardrails through integration with Azure AI Content Safety service, and direct onboarding of models from Azure AI Foundry. Customers can also now apply GenAI-specific policies to AWS Bedrock model endpoints, enabling unified governance across multi-cloud environments. Learn more. Azure API Management support for Model Context Protocol (preview) makes it easy to expose existing APIs as secure, agent-compatible endpoints. You can apply gateway policies such as authentication, rate limiting, caching, and authorization to protect MCP servers. This ensures consistent, centralized policy enforcement across all your MCP-enabled APIs. With minimal effort, you can transform APIs into AI-ready services that integrate seamlessly with autonomous agents. Learn more. Azure API Center introduces private MCP registry and streamlined discovery (preview) giving organizations full control over which services are discoverable. Role-Based Access Control (RBAC) allows teams to manage who can find, use, and update MCP servers based on organizational roles. Developers can now discover and consume MCP-enabled APIs directly through the API Center portal. These updates improve governance and simplify developer experience for AI agent development. Learn more. Simplify operations for AI apps and agents in production Moving AI applications from proof-of-concept to production requires an environment that scales securely, cost-effectively, and reliably. The Azure App Platform continues to evolve with enhancements that remove operational friction, so you can deploy your AI apps, agents and scale with confidence. App Service Premium v4 Plan (preview) delivers up to 25% better performance and up to 24% cost savings over the previous generation—ideal for scalable, secure web apps. App Service Premium v4 helps modernize both Windows and Linux applications with better performance, security, and DevOps integration. It now offers a more cost-effective solution for customers seeking a fully managed PaaS, reducing infrastructure overhead while supporting today’s demanding AI applications. Learn more. AKS security dashboard (GA) provides unified visibility and automated remediation powered by Microsoft Defender for Containers—helping operations stay ahead of threats and compliance needs without leaving the Azure portal. Learn more. AKS Long-Term Support (GA) introduces 2-year support for all versions of Kubernetes after 1.27, in addition to the standard community-supported versions. This extended support model enables teams to reduce upgrade frequency and complexity, ensure platform stability, and provide greater operational flexibility. Learn more. Dynamic service recommendations for AKS (preview) streamlines the process of selecting and connecting services to your Azure Kubernetes Service cluster by offering tailored Azure service recommendations directly in the Azure portal. It uses in-portal intelligence to suggest the right services based on your usage patterns, making it easier to choose what’s best for your workloads. Learn more. Azure Functions Flex Consumption adds support for availability zones and smaller instance sizes (preview) to improve reliability and resiliency for critical workloads. The new 512 MB memory option helps customers fine-tune resource usage and reduce costs for lightweight functions. These updates are available in Australia East, East Asia, Sweden Central, and UK South, and can be enabled on both new and existing Flex Consumption apps. Learn more. Join us at Microsoft Build, May 19-22 The future of AI applications is here, and it’s powered by Azure. From APIs to automation, from web apps to Kubernetes, and from cloud to edge, we’re building the foundation for the next era of intelligent software. Whether you're modernizing existing systems or pioneering the next big thing in AI, Azure gives you the tools, performance, and governance to build boldly. Our platform innovations are designed to simplify your path, remove operational friction, and help you scale with confidence. Explore the various breakout, demo and lab sessions at Microsoft Build, May 19-22, to dive deeper into these Azure App Platform innovations. We can’t wait to see what you will build next!Reimagining App Modernization for the Era of AI
This blog highlights the key announcements and innovations from Microsoft Build 2025. It focuses on how AI is transforming the software development lifecycle, particularly in app modernization. Key topics include the use of GitHub Copilot for accelerating development and modernization, the introduction of Azure SRE agent for managing production systems, and the launch of the App Modernization Guidance to help organizations modernize their applications with AI-first design. The blog emphasizes the strategic approach to modernization, aiming to reduce complexity, improve agility, and deliver measurable business outcomesWhat’s new in Observability at Build 2025
At Build 2025, we are excited to announce new features in Azure Monitor designed to enhance observability for developers and SREs, making it easier for you to streamline troubleshooting, improve monitoring efficiency, and gain deeper insights into application performance. With our new AI-powered tools, customizable alerts, and advanced visualization capabilities, we’re empowering developers to deliver high-quality, resilient applications with greater operational efficiency. AI-Powered Troubleshooting Capabilities We are excited to disclose two new AI-powered features, as well as share an update to a GA feature, which enhance troubleshooting and monitoring: AI-powered investigations (Public Preview): Identifies possible explanations for service degradations via automated analyses, consolidating all observability-related data for faster problem mitigation. Attend our live demo at Build and learn more here. Health models (Public Preview – coming in June 2025): Significantly improves the efficiency of detecting business-impacting issues in workloads, empowering organizations to deliver applications with operational efficiency and resilience through a full-stack view of workload health. Attend our live demo at Build to get a preview of the experience and learn more here. AI-powered Application Insights Code Optimizations (GA): Provides code-level suggestions for running .NET apps on Azure. Now, it’s easier to get code-level suggestions with GitHub Copilot coding agent (preview) and GitHub Copilot for Azure in VS Code. Learn more here. Enhanced AI and agent observability Azure Monitor and Azure AI Foundry now jointly offer real-time monitoring and continuous evaluation of AI apps and agentic systems in production. These capabilities are deeply integrated with the Foundry Observability experience and allow you to track key metrics such as performance, quality, safety, and resource usage. Features include: Unified observability dashboard for generative AI apps and agents (Public Preview): Provides full-stack visibility of AI apps and infrastructure with AI app metrics surfaced in both Azure Monitor and Foundry Observability. Alerts: Data is published to Azure Monitor Application Insights, allowing users to set alerts and analyze them for troubleshooting. Debug with tracing capabilities: Enables detailed root-cause analysis of issues like groundedness regressions. Learn more in our breakout session at Build! Improved Visualization We have expanded our visualization capabilities, particularly for Kubernetes services: Azure Monitor dashboards with Grafana (Public Preview): Create and edit Grafana dashboards directly in the Azure Portal with no additional cost. This includes dashboards for Azure Kubernetes Services (AKS) and other Azure resources. Learn more. Managed Prometheus Visualizations: Supports managed Prometheus visualizations for both AKS clusters (GA) and Arc-enabled Kubernetes clusters (Public Preview), offering a more cost-efficient and performant solution. Learn more. Customized and Simplified Monitoring Through enhancements to alert customization, we’re making it easier for you to get started with monitoring: Prometheus community recommended alerts: Offers one-click enablement of Prometheus recommended alerts for AKS clusters (GA) and Arc-enabled Kubernetes clusters (Public Preview), providing comprehensive alerting coverage across cluster, node, and pod levels. Simple log alerts (Public Preview): Designed to provide a simplified and more intuitive experience for monitoring and alerting, Simple log alerts evaluate each row individually, providing faster alerting compared to traditional log alerts. Simple log alerts support multiple log tiers, including Analytics and Basic Logs, which previously did not have any alerting solution. Learn more. Customizable email subjects for log search alerts (Public Preview): Allows customers to personalize the subject lines of alert emails including dynamic values, making it easier to quickly identify and respond to alerts. Send a custom event from the Azure Monitor OpenTelemetry Distro (GA): Offers developers a way to track user or system actions that matter the most to their business objectives, now available in the Azure Monitor OpenTelemetry Distro. Learn more. Application Insights auto-instrumentation for Java and Node Microservices on AKS (Public Preview): Easily monitor your Java and Node deployments without changing your code by leveraging auto-instrumentation that is integrated into the AKS cluster. These capabilities will help you easily assess the performance of your application and identify the cause of incidents efficiently. Learn more. Enhancements for Large Enterprises and Government Entities Azure Monitor Logs is introducing several new features aimed at supporting highly sensitive and high-volume logs, empowering large enterprises and government entities. With better data control and access, developers at these organizations can work better with IT Professionals to improve the reliability of their applications. Workspace replication (GA): Enhances resilience to regional incidents by enabling cross-regional workspace replication. Logs are ingested in both regions, ensuring continued observability through dashboards, alerts, and advanced solutions like Microsoft Sentinel. Granular RBAC (Public Preview): Supports granular role-based access control (RBAC) using Azure Attribute-Based Access Control (ABAC). This allows organizations to have row-level control on which data is visible to specific users. Data deletion capability (GA): Allows customers to quickly mark unwanted log entries, such as sensitive or corrupt data, as deleted without physically removing them from storage. It’s useful for unplanned deletions using filters to target specific records, ensuring data integrity for analysis. Process more log records in the Azure Portal (GA): Supports up to 100,000 records per query in the Azure Portal, enabling deeper investigations and broader data analysis directly within the portal without need for additional tools. We’re proud to further Azure Monitor's commitment to providing comprehensive and efficient observability solutions for developers, SREs, and IT Professionals alike. For more information, chat with Observability experts through the following sessions at Build 2025: BRK168: AI and Agent Observability with Azure AI Foundry and Azure Monitor BRK188: Power your AI Apps Across Cloud and Edge with Azure Arc DEM547: Enable application monitoring and troubleshooting faster with Azure Monitor DEM537: Mastering Azure Monitor: Essential Tips in 15 Minutes Expo Hall (Meet the Experts): Azure Arc and Azure Monitor boothBuild secure, flexible, AI-enabled applications with Azure Kubernetes Service
Building AI applications has never been more accessible. With advancements in tools and platforms, developers can now create sophisticated AI solutions that drive innovation and efficiency across various industries. For many, Kubernetes stands out as natural choice for running AI applications and agents due to its robust orchestration capabilities, scalability, and flexibility. In this blog, we will explore the latest advancements in Azure Kubernetes Service (AKS) we are announcing at Microsoft Build 2025, designed to enhance flexibility, bolster security, and seamlessly integrate AI capabilities into your Kubernetes environments. These updates will empower developers to create sophisticated AI solutions, improve operational efficiency, and drive innovation across various industries. Let's dive into the key highlights: Simplify building AI apps Enhancing the intelligence and automation of your Kubernetes environments can greatly improve your operations and development workflows. New AKS features make it easier to integrate AI, simplify processes, streamline deployments, and get smart recommendations for optimizing workloads. This means you can deploy AI-powered apps more efficiently, save time with automated deployments, and receive tailored service recommendations to get you started faster. Deploy open-source and custom models from cloud to edge with the Kubernetes AI toolchain operator (KAITO) add-on for AKS and Arc extension. KAITO streamlines AI model deployment, fine-tuning, inferencing, and development on Kubernetes by providing dynamic scaling, version control, and resource optimization. Easily select the right Azure services for your applications with customized Azure service recommendations in Azure Portal. Once you have deployed your recommended services, you can use the service connector to easily connect the service to your AKS cluster. Streamline the path to cloud-native development with Automated Deployments in AKS. New support for Azure DevOps, AKS-ready templates, and service connectors make it easier than ever to generate Dockerfiles and Kubernetes manifests and connect your applications to popular Azure services. Simplify multi-cluster management and streamline GitOps workflows. Automated Deployments in Azure Kubernetes Fleet Manager (public preview) let you connect GitHub repositories to a hub cluster, enabling continuous deployment by building, containerizing, and staging applications with GitHub Actions triggered on code updates. Operate with flexibility In the ever-evolving landscape of app development, flexibility is often key to maintaining operational efficiency and adaptability while meeting the dynamic demands of your business. The latest updates in AKS aim to provide greater flexibility by simplifying management, improving resource utilization, and providing more control over your deployments. Whether you're looking to streamline namespace management, ensure concurrency control, or optimize VM selection, these new capabilities will help you achieve greater operational efficiency and adaptability in your AKS clusters. Gain more flexibility and control over your Kubernetes upgrade timelines with long term support (LTS), now for all Kubernetes versions after 1.27. LTS extends support by an extra year beyond the community end-of-life, giving you more time to plan and execute upgrades on your schedule. All AKS supported Kubernetes version release updates are available in AKS release tracker. Improve reliability and safeguard your AKS configurations during concurrent operations with eTags concurrency control, now generally available. This built-in mechanism detects and prevents conflicting changes, ensuring only the most recent and valid updates are applied to your cluster. Enhance performance and reliability while optimizing resource utilization. Smart VM Defaults (generally available) automatically select the optimal default VM SKU for you based on available capacity and quota. Boost MySQL and PostgreSQL throughput by up to 5x with performance enhancements on ephemeral disks with Azure Container Storage v1.3.0 (generally available). Use cost-effective alerting strategies for AKS to reduce alerting costs while maintaining proactive visibility into container health and performance with Azure Monitor. Detect and resolve placement drift with new conflict-handling strategies in Azure Kubernetes Fleet Manager, giving you more control over multi-cluster workload consistency. Strengthen your security posture As organizations scale their cloud-native applications, securing every layer of the Kubernetes stack becomes mission-critical. AKS continues to meet this challenge with a wave of new security capabilities designed to protect your workloads, streamline compliance, and reduce operational risk. From runtime threat detection and image signature enforcement to a unified security dashboard, AKS now offers a more comprehensive, integrated approach to cluster protection—backed by Microsoft Defender for Cloud and Azure Policy. Whether you're managing a single cluster or operating at fleet scale, these innovations help you stay ahead of threats while maintaining agility. Secure your Kubernetes environment more effectively with the AKS Security Dashboard. Available through the Azure portal, it offers comprehensive visibility and automated remediation for security issues—helping you detect, prioritize, and resolve risks with greater confidence. Proactively block risky workloads by gating vulnerable deployments in AKS (public preview), which uses Microsoft Defender for Cloud to evaluate container images against your org’s security policies and vulnerability assessments—ensuring only compliant deployments reach your clusters. Gain deeper visibility into runtime risks with Agentless runtime vulnerability assessment for AKS-owned images (public preview), helping you identify CVEs and recommended fixes tied to specific AKS versions. Additionally, registry-agnostic agentless runtime container vulnerability assessment (public preview) provides comprehensive vulnerability assessment and remediation for container images, regardless of their registry source. Detect threats in real time with DNS Lookup Threat Detection and malware detection for AKS nodes, both in public preview via Microsoft Defender for Cloud. These features monitor suspicious DNS activity and scan nodes for vulnerabilities and malware—boosting your runtime protection. Onboard clusters with flexibility using resource-level onboarding for individual AKS clusters in Defender for Cloud, now in public preview. This enables agentless, sensor-based alerts directly in the AKS dashboard. Establish trusted connections with custom certificate authority support in AKS (generally available), allowing secure communication between your cluster and private registries, proxies, and firewalls. Keep your Kubernetes traffic private and protected with API Server VNet Integration in AKS (generally available). By routing communication between the API server and your cluster nodes entirely through a private network, you avoid public exposure and complex tunneling—making your setup both simpler and more secure. AKS at Microsoft Build 2025 These new features and updates for AKS are set to provide greater flexibility, enhanced security, and advanced AI capabilities, empowering users to scale, secure, and optimize their Kubernetes environments like never before. To see these innovations in action and learn more about how they can benefit your organization, be sure to join us virtually or in person at Microsoft Build this week. Our experts will be showcasing these features in detail, providing live demonstrations, and answering any questions you may have. We hope to see you in Seattle or online! Session Code Session Title Date and time Streamed and recorded BRK188 Build and scale your AI apps with Kubernetes and Azure Arc Mon, May 19 | 3:00 PM - 4:00 PM PST Yes COMM416 Conversations: Let's talk container security and network monitoring Mon, May 19 | 4:00 PM - 4:45 PM PST No LAB346 Ethical Hacking with AKS: Hands-On Attack and Defense Strategies Tues, May 20 | 11:45 AM - 1:00 PM PST No LAB348 Integrate Azure Kubernetes Service apps with Active Directory Tues, May 20 | 1:45 PM - 3:00 PM PST No BRK181 Streamlining AKS Debugging: Techniques to solve common & complex problems Tues, May 20 | 3:00 PM - 4:00 PM PST Yes LAB342 Streamlining Kubernetes for developers with AKS Automatic Tues, May 20 | 3:30 PM - 4:45 AM PST No BRK185 Maximizing efficiency in cloud-native app design Wed, May 21 | 10:30 AM - 11:30 AM PST Yes COMM456 Table Talks: Stateful Containers on AKS Wed, May 21 | 11:00 AM - 12:00 PM PST No COMM451 Table Talks: AKS Ops, Well-Architected Cloud & AI Copilot Wed, May 21 | 1:00 PM – 2:00 PM PST No LAB348-R1 Integrate Azure Kubernetes Service apps with Active Directory Wed, May 21 | 1:00 PM - 2:15 PM PST No BRK191 Running Stateful Workloads on AKS Wed, May 21 | 2:00 PM - 3:00 PM PST Yes LAB345-R1 Deploying and Inferencing AI Applications on Kubernetes Wed, May 21 | 2:45 PM - 4:00 PM PST No COMM452 Table Talks: Troubleshooting AKS, Cost Optimization & AI in K8s Wed, May 21 | 3:00 PM - 4:00 PM PST No BRK193 Skip the YAML! Easily deploy apps to AKS with Automated Deployments Wed, May 21 | 3:30 PM - 4:30 PM PST Yes BRK194 Adventures in AI: Deploying and inferencing open source and custom models on K8s Wed, May 21 | 5:00 PM – 6:00 PM PST Yes LAB342-R1 Streamlining Kubernetes for developers with AKS Automatic Thurs, May 22 | 8:30 AM – 9:45 AM PST No LAB346-R1 Ethical Hacking with AKS: Hands-On Attack and Defense Strategies Thurs, May 22 | 10:15 AM – 11:30 AM PST No LAB345 Deploying and Inferencing AI Applications on Kubernetes Thurs, May 22 | 10:15 AM – 11:30 AM PST No ODLAB346 On-Demand: Ethical Hacking with AKS: Hands-On Attack and Defense Strategies On Demand No ODLAB348 On-Demand: Integrate Azure Kubernetes Service apps with Active Directory On Demand No
GA: Managed Prometheus visualizations in Azure Monitor for AKS — unified insights at your fingertips
We’re thrilled to announce the general availability (GA) of Managed Prometheus visualizations in Azure Monitor for AKS, along with an enhanced, unified AKS Monitoring experience. Troubleshooting Kubernetes clusters is often time-consuming and complex whether you're diagnosing failures, scaling issues, or performance bottlenecks. This redesign of the existing Insights experience brings all your key monitoring data into a single, streamlined view reducing the time and effort it takes to diagnose, triage, and resolve problems so you can keep your applications running smoothly with less manual work. By using Managed Prometheus, customers can also realize up to 80% savings on metrics costs and benefit from up to 90% faster blade load performance delivering both a powerful and cost-efficient way to monitor and optimize your AKS environment. What’s New in GA Since the preview release, we’ve added several capabilities: Control plane metrics: Gain visibility into critical components like the API server and ETCD database, essential for diagnosing cluster-level performance bottlenecks. Load balancer chart deep links: Jump directly into the networking drilldown view to troubleshoot failed connections and SNAT port issues more efficiently. Improved at-scale cluster view: Get a faster, more comprehensive overview across all your AKS clusters, making multi-cluster monitoring easier. Simplified Troubleshooting, End to End The enhanced AKS Monitoring experience provides both a basic (free) tier and an upgraded experience with Prometheus metrics and logging — all within a unified, single-pane-of-glass dashboard. Here’s how it helps you troubleshoot faster: Identify failing components immediately With new KPI Cards for Pod and Node Status, you can quickly spot pending or failed pods, high CPU/memory usage, or saturation issues, decreasing diagnosis time. Monitor and manage cluster scaling smoothly The Events Summary Card surfaces Kubernetes warnings and pending pod states, helping you respond to scale-related disruptions before they impact production. Pinpoint root causes of latency and connectivity problems Detailed node saturation metrics, plus control plane and load balancer insights, make it easier to isolate where slowdowns or failures are occurring — whether at the node, cluster, or network layer. Free vs. Upgraded Metrics Overview Here’s a quick comparison of what’s included by default versus what you get with the enhanced experience: Basic tier metrics Additional metrics in upgraded experience Alert summary card Historical Kubernetes events (30 days) Events summary card Warning events by reason Pod status KPI card Namespace CPU and memory % Node status KPI card Container logs by volume Node CPU and memory % Top five controllers by logs volume VMSS OS disk bandwidth consumed % (max) Packets dropped I/O VMSS OS disk IOPS consumed % (max) Load balancer SNAT port usage API server CPU % (max) (preview) API server memory % (max) (preview) ETCD database usage % (max) (preview) See What Customers Are Saying Early adopters have already seen meaningful improvements: "Azure Monitor managed Prometheus visualizations for Container Insights has been a game-changer for our team. Offloading the burden of self-hosting and maintaining our own Prometheus infrastructure has significantly reduced our operational overhead. With the managed add-on, we get the powerful insights and metrics we need without worrying about scalability, upgrades, or reliability. It seamlessly integrates into our existing Azure environment, giving us out-of-the-box visibility into our container workloads. This solution allows our engineers to focus more on building and delivering features, rather than managing monitoring infrastructure." – S500 customer in health care industry Get Started Today We’re committed to helping you optimize and manage your AKS clusters with confidence. Visit the Azure portal and explore the new AKS Monitoring experience today! Learn more: https://aka.ms/azmon-prometheus-visualizationsDiagnose Web App Issues Instantly—Just Drop a Screenshot into Conversational Diagnostics
It’s that time of year again—Microsoft Build 2025 is here! And in the spirit of pushing boundaries with AI, we’re thrilled to introduce a powerful new preview feature in Conversational Diagnostics. 📸 Diagnose with a Screenshot No more struggling to describe a tricky issue or typing out long explanations. With this new capability, you can simply paste, upload, or drag a screenshot into the chat. Conversational Diagnostics will analyze the image, identify the context, and surface relevant diagnostics for your selected Azure Resource—all in seconds. Whether you're debugging a web app or triaging a customer issue, this feature helps you move from problem to insight faster than ever. Thank you!
