Migrating to the next generation of Virtual Nodes on Azure Container Instances (ACI)
What is ACI/Virtual Nodes? Azure Container Instances (ACI) is a fully-managed serverless container platform which gives you the ability to run containers on-demand without provisioning infrastructure. Virtual Nodes on ACI allows you to run Kubernetes pods managed by an AKS cluster in a serverless way on ACI instead of traditional VM‑backed node pools. From a developer’s perspective, Virtual Nodes look just like regular Kubernetes nodes, but under the hood the pods are executed on ACI’s serverless infrastructure, enabling fast scale‑out without waiting for new VMs to be provisioned. This makes Virtual Nodes ideal for bursty, unpredictable, or short‑lived workloads where speed and cost efficiency matter more than long‑running capacity planning. Introducing the next generation of Virtual Nodes on ACI The newer Virtual Nodes v2 implementation modernises this capability by removing many of the limitations of the original AKS managed add‑on and delivering a more Kubernetes‑native, flexible, and scalable experience when bursting workloads from AKS to ACI. In this article I will demonstrate how you can migrate an existing AKS cluster using the Virtual Nodes managed add-on (legacy), to the new generation of Virtual Nodes on ACI, which is deployed and managed via Helm. More information about Virtual Nodes on Azure Container Instances can be found here, and the GitHub repo is available here. Advanced documentation for Virtual Nodes on ACI is also available here, and includes topics such as node customisation, release notes and a troubleshooting guide. Please note that all code samples within this guide are examples only, and are provided without warranty/support. Background Virtual Nodes on ACI is rebuilt from the ground-up, and includes several fixes and enhancements, for instance: Added support/features VNet peering, outbound traffic to the internet with network security groups Init containers Host aliases Arguments for exec in ACI Persistent Volumes and Persistent Volume Claims Container hooks Confidential containers (see supported regions list here) ACI standby pools Support for image pulling via Private Link and Managed Identity (MSI) Planned future enhancements Kubernetes network policies Support for IPv6 Windows containers Port Forwarding Note: The new generation of the add-on is managed via Helm rather than as an AKS managed add-on. Requirements & limitations Each Virtual Nodes on ACI deployment requires 3 vCPUs and 12 GiB memory on one of the AKS cluster’s VMs Each Virtual Nodes node supports up to 200 pods DaemonSets are not supported Virtual Nodes on ACI requires AKS clusters with Azure CNI networking (Kubenet is not supported, nor is overlay networking) Virtual Nodes on ACI is incompatible with API server authorized IP ranges for AKS (because of the subnet delegation to ACI) Migrating to the next generation of Virtual Nodes on Azure Container Instances via Helm chart For this walkthrough, I'm using Bash via Windows Subsystem for Linux (WSL), along with the Azure CLI. Direct migration is not supported, and therefore the steps below show an example of removing Virtual Nodes managed add-on and its resources and then installing the Virtual Nodes on ACI Helm chart. In this walkthrough I will explain how to delete and re-create the Virtual Nodes subnet, however if you need to preserve the VNet and/or use a custom subnet name, refer to the Helm customisation steps here. Prerequisites A recent version of the Azure CLI An Azure subscription with sufficient ACI quota for your selected region Helm Deployment steps Initialise environment variables location=northeurope rg=rg-virtualnode-demo vnetName=vnet-virtualnode-demo clusterName=aks-virtualnode-demo aksSubnetName=subnet-aks vnSubnetName=subnet-vn Scale-down any running Virtual Nodes workloads (example below): kubectl delete deploy <deploymentName> -n <namespace> Drain and cordon the legacy Virtual Nodes node: kubectl drain virtual-node-aci-linux Disable the Virtual Nodes managed add-on (legacy): az aks disable-addons --resource-group $rg --name $clusterName --addons virtual-node Export a backup of the original subnet configuration: az network vnet subnet show --resource-group $rg --vnet-name $vnetName --name $vnSubnetName > subnetConfigOriginal.json Delete the original subnet (subnets cannot be renamed and therefore must be re-created): az network vnet subnet delete -g $rg -n $vnSubnetName --vnet-name $vnetName Create the new Virtual Nodes on ACI subnet (replicate the configuration of the original subnet but with the specific name value of cg): vnSubnetId=$(az network vnet subnet create \ --resource-group $rg \ --vnet-name $vnetName \ --name cg \ --address-prefixes 10.241.0.0/16 \ --delegations Microsoft.ContainerInstance/containerGroups --query id -o tsv) Assign the cluster's -kubelet identity Contributor access to the infrastructure resource group, and Network Contributor access to the ACI subnet: nodeRg=$(az aks show --resource-group $rg --name $clusterName --query nodeResourceGroup -o tsv) nodeRgId=$(az group show -n $nodeRg --query id -o tsv) agentPoolIdentityId=$(az aks show --resource-group $rg --name $clusterName --query "identityProfile.kubeletidentity.resourceId" -o tsv) agentPoolIdentityObjectId=$(az identity show --ids $agentPoolIdentityId --query principalId -o tsv) az role assignment create \ --assignee-object-id "$agentPoolIdentityObjectId" \ --assignee-principal-type ServicePrincipal \ --role "Contributor" \ --scope "$nodeRgId" az role assignment create \ --assignee-object-id "$agentPoolIdentityObjectId" \ --assignee-principal-type ServicePrincipal \ --role "Network Contributor" \ --scope "$vnSubnetId" Download the cluster's kubeconfig file: az aks get-credentials -n $clusterName -g $rg Clone the virtualnodesOnAzureContainerInstances GitHub repo: git clone https://github.com/microsoft/virtualnodesOnAzureContainerInstances.git Install the Virtual Nodes on ACI Helm chart: helm install <yourReleaseName> <GitRepoRoot>/Helm/virtualnode Confirm the Virtual Nodes node shows within the cluster and is in a Ready state (virtualnode-n): $ kubectl get node NAME STATUS ROLES AGE VERSION aks-nodepool1-35702456-vmss000000 Ready <none> 4h13m v1.33.6 aks-nodepool1-35702456-vmss000001 Ready <none> 4h13m v1.33.6 virtualnode-0 Ready <none> 162m v1.33.7 Delete the previous Virtual Nodes node from the cluster: kubectl delete node virtual-node-aci-linux Test and confirm pod scheduling on Virtual Node: apiVersion: v1 kind: Pod metadata: annotations: name: demo-pod spec: containers: - command: - /bin/bash - -c - 'counter=1; while true; do echo "Hello, World! Counter: $counter"; counter=$((counter+1)); sleep 1; done' image: mcr.microsoft.com/azure-cli name: hello-world-counter resources: limits: cpu: 2250m memory: 2256Mi requests: cpu: 100m memory: 128Mi nodeSelector: virtualization: virtualnode2 tolerations: - effect: NoSchedule key: virtual-kubelet.io/provider operator: Exists If the pod successfully starts on the Virtual Node, you should see similar to the below: $ kubectl get pod -o wide demo-pod NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES demo-pod 1/1 Running 0 95s 10.241.0.4 vnode2-virtualnode-0 <none> <none> Modify your deployments to run on Virtual Nodes on ACI For Virtual Nodes managed add-on (legacy), the following nodeSelector and tolerations are used to run pods on Virtual Nodes: nodeSelector: kubernetes.io/role: agent kubernetes.io/os: linux type: virtual-kubelet tolerations: - key: virtual-kubelet.io/provider operator: Exists - key: azure.com/aci effect: NoSchedule For Virtual Nodes on ACI, the nodeSelector/tolerations are slightly different: nodeSelector: virtualization: virtualnode2 tolerations: - effect: NoSchedule key: virtual-kubelet.io/provider operator: Exists Troubleshooting Check the virtual-node-admission-controller and virtualnode-n pods are running within the vn2 namespace: $ kubectl get pod -n vn2 NAME READY STATUS RESTARTS AGE virtual-node-admission-controller-54cb7568f5-b7hnr 1/1 Running 1 (5h21m ago) 5h21m virtualnode-0 6/6 Running 6 (4h48m ago) 4h51m If these pods are in a Pending state, your node pool(s) may not have enough resources available to schedule them (use kubectl describe pod to validate). If the virtualnode-n pod is crashing, check the logs of the proxycri container to see whether there are any Managed Identity permissions issues (the cluster's -agentpool MSI needs to have Contributor access on the infrastructure resource group): kubectl logs -n vn2 virtualnode-0 -c proxycri Further troubleshooting guidance is available within the official documentation. Support If you have issues deploying or using Virtual Nodes on ACI, add a GitHub issue hereEven simpler to Safely Execute AI-generated Code with Azure Container Apps Dynamic Sessions
AI agents are writing code. The question is: where does that code run? If it runs in your process, a single hallucinated import os; os.remove('/') can ruin your day. Azure Container Apps dynamic sessions solve this with on-demand sandboxed environments — Hyper-V isolated, fully managed, and ready in milliseconds. Thanks to your feedback, Dynamic Sessions are now easier to use with AI via MCP. Agents can quickly start a session interpreter and safely run code – all using a built-in MCP endpoint. Additionally - new starter samples show how to invoke dynamic sessions from Microsoft Agent Framework with code interpreter and with a custom container for even more versatility. What Are Dynamic Sessions? A session pool maintains a reservoir of pre-warmed, isolated sandboxes. When your app needs one, it’s allocated instantly via REST API. When idle, it’s destroyed automatically after provided session cool down period. What you get: Strong isolation - Each session runs in its own Hyper-V sandbox — enterprise-grade security Millisecond startup -Pre-warmed pool eliminates cold starts Fully managed - No infra to maintain — automatic lifecycle, cleanup, scaling Simple access - Single HTTP endpoint, session identified by a unique ID Scalable - Hundreds to thousands of concurrent sessions Two Session Types 1. Code Interpreter — Run Untrusted Code Safely Code interpreter sessions accept inline code, run it in a Hyper-V sandbox, and return the output. Sessions support network egress and persistent file systems within the session lifetime. Three runtimes are available: Python — Ships with popular libraries pre-installed (NumPy, pandas, matplotlib, etc.). Ideal for AI-generated data analysis, math computation, and chart generation. Node.js — Comes with common npm packages. Great for server-side JavaScript execution, data transformation, and scripting. Shell — A full Linux shell environment where agents can run arbitrary commands, install packages, start processes, manage files, and chain multi-step workflows. Unlike Python/Node.js interpreters, shell sessions expose a complete OS — ideal for agent-driven DevOps, build/test environments, CLI tool execution, and multi-process pipelines. 2. Custom Containers — Bring Your Own Runtime Custom container sessions let you run your own container image in the same isolated, on-demand model. Define your image, and Container Apps handles the pooling, scaling, and lifecycle. Typical use cases are hosting proprietary runtimes, custom code interpreters, and specialized tool chains. This sample (Azure Samples) dives deeper into Customer Containers with Microsoft agent Framework orchestration. MCP Support for Dynamic Sessions Dynamic sessions also support Model Context Protocol (MCP) on both shell and Python session types. This turns a session pool into a remote MCP server that AI agents can connect to — enabling tool execution, file system access, and shell commands in a secure, ephemeral environment. With an MCP-enabled shell session, an Azure Foundry agent can spin up a Flask app, run system commands, or install packages — all in an isolated container that vanishes when done. The MCP server is enabled with a single property on the session pool (isMCPServerEnabled: true), and the resulting endpoint + API key can be plugged directly into Azure Foundry as a connected tool. For a step-by-step walkthrough, see How to add an MCP tool to your Azure Foundry agent using dynamic sessions. Deep Dive: Building an AI Travel Agent with Code Interpreter Sessions Let’s walk through a sample implementation — a travel planning agent that uses dynamic sessions for both static code execution (weather research) and LLM-generated code execution (charting). Full source: github.com/jkalis-MS/AIAgent-ACA-DynamicSession Architecture Travel Agent Architecture Component Purpose Microsoft Agent Framework Agent runtime with middleware, telemetry, and DevUI Azure OpenAI (GPT-4o) LLM for conversation and code generation ACA Session Pools Sandboxed Python code interpreter Azure Container Apps Hosts the agent in a container Application Insights Observability for agent spans The agent implements with two variants switchable in the Agent Framework DevUI — tools in ACA Dynamic Session (sandbox) and tools running locally (no isolation) — making the security value immediately visible. Scenario A: Static Code in a Sandbox — Weather Research The agent sends pre-written Python code to the session pool to fetch live weather data. The code runs with network egress enabled, calls the Open-Meteo API, and returns formatted results — all without touching the host process. import requests from azure.identity import DefaultAzureCredential credential = DefaultAzureCredential() token = credential.get_token("https://dynamicsessions.io/.default") response = requests.post( f"{pool_endpoint}/code/execute?api-version=2024-02-02-preview&identifier=weather-session-1", headers={"Authorization": f"Bearer {token.token}"}, json={"properties": { "codeInputType": "inline", "executionType": "synchronous", "code": weather_code, # Python that calls Open-Meteo API }}, ) result = response.json()["properties"]["stdout"] Scenario B: LLM-Generated Code in a Sandbox — Dynamic Charting This is where it gets interesting. The user asks “plot a chart comparing Miami and Tokyo weather.” The agent: Fetches weather data Asks Azure OpenAI to generate matplotlib code using a tightly-scoped system prompt Safety-checks the generated code for forbidden imports (subprocess, os.system, etc.) Wraps the code with data injection and sends it to the sandbox Downloads the resulting PNG from the sandbox’s /mnt/data/ directory from openai import AzureOpenAI # 1. LLM generates chart code client = AzureOpenAI(azure_endpoint=endpoint, api_key=key, api_version="2024-12-01-preview") generated_code = client.chat.completions.create( model="gpt-4o", messages=[{"role": "system", "content": CODE_GEN_PROMPT}, {"role": "user", "content": f"Weather data: {weather_json}"}], temperature=0.2, ).choices[0].message.content # 2. Execute in sandbox requests.post( f"{pool_endpoint}/code/execute?api-version=2024-02-02-preview&identifier=chart-session-1", headers={"Authorization": f"Bearer {token.token}"}, json={"properties": { "codeInputType": "inline", "executionType": "synchronous", "code": f"import json, matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nweather_data = json.loads('{weather_json}')\n{generated_code}", }}, ) # 3. Download the chart img = requests.get( f"{pool_endpoint}/files/content/chart.png?api-version=2024-02-02-preview&identifier=chart-session-1", headers={"Authorization": f"Bearer {token.token}"}, ).content The result — a dark-themed dual-subplot chart comparing maximal and minimal temperature forecast chart example rendered by the Chart Weather tool in Dynamic Session: Authentication The agent uses DefaultAzureCredential locally and ManagedIdentityCredential when deployed. Tokens are cached and refreshed automatically: from azure.identity import DefaultAzureCredential token = DefaultAzureCredential().get_token("https://dynamicsessions.io/.default") auth_header = f"Bearer {token.token}" # Uses ManagedIdentityCredential automatically when deployed to Container Apps Observability The agent uses Application Insights for end-to-end tracing. The Microsoft Agent Framework exposes OpenTelemetry spans for invoke_agent, chat, and execute tool — wired to Azure Monitor with custom exporters: from azure.monitor.opentelemetry import configure_azure_monitor from agent_framework.observability import create_resource, enable_instrumentation # Configure Azure Monitor first configure_azure_monitor( connection_string="InstrumentationKey=...", resource=create_resource(), # Uses OTEL_SERVICE_NAME, etc. enable_live_metrics=True, ) # Then activate Agent Framework's telemetry code paths, optional if ENABLE_INSTRUMENTATION and/or ENABLE_SENSITIVE_DATA are set in env vars enable_instrumentation(enable_sensitive_data=False) This gives you traces for every agent invocation, tool execution (including sandbox timing), and LLM call — visible in the Application Insights transaction search and end-to-end transaction view in the new Agents blade in Application Insights. You can also open a detailed dashboard by clicking Explore in Grafana. Session pools emit their own metrics and logs for monitoring sandbox utilization and performance. Combined with the agent-level Application Insights traces, you can get full visibility from the user prompt → agent → LLM → sandbox execution → response — across both your application and the infrastructure running untrusted code. Deploy with One Command The project includes full Bicep infrastructure-as-code. A single azd up provisions Azure OpenAI, Container Apps, Session Pool (with egress enabled), Container Registry, Application Insights, and all role assignments. azd auth login azd up Next Steps Dynamic sessions documentation – Microsoft Learn MCP + Shell sessions tutorial - How to add an MCP tool to your Foundry agent Custom container sessions sample - github.com/Azure-Samples/dynamic-sessions-custom-container AI Agent + Dynamic Sessions - github.com/jkalis-MS/AIAgent-ACA-DynamicSessionRethinking Ingress on Azure: Application Gateway for Containers Explained
Introduction Azure Application Gateway for Containers is a managed Azure service designed to handle incoming traffic for container-based applications. It brings Layer-7 load balancing, routing, TLS termination, and web application protection outside of the Kubernetes cluster and into an Azure-managed data plane. By separating traffic management from the cluster itself, the service reduces operational complexity while providing a more consistent, secure, and scalable way to expose container workloads on Azure. Service Overview What Application Gateway for Containers does Azure Application Gateway for Containers is a managed Layer-7 load balancing and ingress service built specifically for containerized workloads. Its main job is to receive incoming application traffic (HTTP/HTTPS), apply routing and security rules, and forward that traffic to the right backend containers running in your Kubernetes cluster. Instead of deploying and operating an ingress controller inside the cluster, Application Gateway for Containers runs outside the cluster, as an Azure-managed data plane. It integrates natively with Kubernetes through the Gateway API (and Ingress API), translating Kubernetes configuration into fully managed Azure networking behavior. In practical terms, it handles: HTTP/HTTPS routing based on hostnames, paths, headers, and methods TLS termination and certificate management Web Application Firewall (WAF) protection Scaling and high availability of the ingress layer All of this is provided as a managed Azure service, without running ingress pods in your cluster. What problems it solves Application Gateway for Containers addresses several common challenges teams face with traditional Kubernetes ingress setups: Operational overhead Running ingress controllers inside the cluster means managing upgrades, scaling, certificates, and availability yourself. Moving ingress to a managed Azure service significantly reduces this burden. Security boundaries By keeping traffic management and WAF outside the cluster, you reduce the attack surface of the Kubernetes environment and keep security controls aligned with Azure-native services. Consistency across environments Platform teams can offer a standard, Azure-managed ingress layer that behaves the same way across clusters and environments, instead of relying on different in-cluster ingress configurations. Separation of responsibilities Infrastructure teams manage the gateway and security policies, while application teams focus on Kubernetes resources like routes and services. How it differs from classic Application Gateway While both services share the “Application Gateway” name, they target different use cases and operating models. In the traditional model of using Azure Application Gateway is a general-purpose Layer-7 load balancer primarily designed for VM-based or service-based backends. It relies on centralized configuration through Azure resources and is not Kubernetes-native by design. Application Gateway for Containers, on the other hand: Is designed specifically for container platforms Uses Kubernetes APIs (Gateway API / Ingress) instead of manual listener and rule configuration Separates control plane and data plane more cleanly Enables faster, near real-time updates driven by Kubernetes changes Avoids running ingress components inside the cluster In short, classic Application Gateway is infrastructure-first, while Application Gateway for Containers is platform- and Kubernetes-first. Architecture at a Glance At a high level, Azure Application Gateway for Containers is built around a clear separation between control plane and data plane. This separation is one of the key architectural ideas behind the service and explains many of its benefits. Control plane and data plane The control plane is responsible for configuration and orchestration. It listens to Kubernetes resources—such as Gateway API or Ingress objects—and translates them into a running gateway configuration. When you create or update routing rules, TLS settings, or security policies in Kubernetes, the control plane picks up those changes and applies them automatically. The data plane is where traffic actually flows. It handles incoming HTTP and HTTPS requests, applies routing rules, performs TLS termination, and forwards traffic to the correct backend services inside your cluster. This data plane is fully managed by Azure and runs outside of the Kubernetes cluster, providing isolation and high availability by design. Because the data plane is not deployed as pods inside the cluster, it does not consume cluster resources and does not need to be scaled or upgraded by the customer. Managed components vs customer responsibilities One of the goals of Application Gateway for Containers is to reduce what customers need to operate, while still giving them control where it matters. Managed by Azure Application Gateway for Containers data plane Scaling, availability, and patching of the gateway Integration with Azure networking Web Application Firewall engine and updates Translation of Kubernetes configuration into gateway rules Customer-managed Kubernetes resources (Gateway API or Ingress) Backend services and workloads TLS certificates and references Routing and security intent (hosts, paths, policies) Network design and connectivity to the cluster This split allows platform teams to keep ownership of the underlying Azure infrastructure, while application teams interact with the gateway using familiar Kubernetes APIs. The result is a cleaner operating model with fewer moving parts inside the cluster. In short, Application Gateway for Containers acts as an Azure-managed ingress layer, driven by Kubernetes configuration but operated outside the cluster. This architecture keeps traffic management simple, scalable, and aligned with Azure-native networking and security services. Traffic Handling and Routing This section explains what happens to a request from the moment it reaches Azure until it is forwarded to a container running in your cluster. Traffic Flow: From Internet to Pod Azure Application Gateway for Containers (AGC) acts as the specialized "front door" for your Kubernetes workloads. By sitting outside the cluster, it manages high-volume traffic ingestion so your environment remains focused on application logic rather than networking overhead. The Request Journey Once a request is initiated by a client—such as a browser or an API—it follows a streamlined path to your container: 1. Entry via Public Frontend: The request reaches AGC’s public frontend endpoint. Note: While private frontends are currently the most requested feature and are under high-priority development, the service currently supports public-facing endpoints. 2. Rule Evaluation: AGC evaluates the incoming request against the routing rules you’ve defined using standard Kubernetes resources (Gateway API or Ingress). 3. Direct Pod Proxying: Once a rule is matched, AGC forwards the traffic directly to the backend pods within your cluster. 4. Azure Native Delivery: Because AGC operates as a managed data plane outside the cluster, traffic reaches your workloads via Azure networking. This removes the need for managing scaling or resource contention for in-cluster ingress pods. Flexibility in Security and Routing The architecture is designed to be as "hands-off" or as "hands-on" as your security policy requires: Optional TLS Offloading: You have full control over the encryption lifecycle. Depending on your specific use case, you can choose to perform TLS termination at the gateway to offload the compute-intensive decryption, or maintain encryption all the way to the container for end-to-end security. Simplified Infrastructure: By using AGC, you eliminate the "hop" typically required by in-cluster controllers, allowing the gateway to communicate with pods with minimal latency and high predictability. Kubernetes Integration Application Gateway for Containers is designed to integrate natively with Kubernetes, allowing teams to manage ingress behavior using familiar Kubernetes resources instead of Azure-specific configuration. This makes the service feel like a natural extension of the Kubernetes platform rather than an external load balancer. Gateway API as the primary integration model The Gateway API is the preferred and recommended way to integrate Application Gateway for Containers with Kubernetes. With the Gateway API: Platform teams define the Gateway and control how traffic enters the cluster. Application teams define routes (such as HTTPRoute) to expose their services. Responsibilities are clearly separated, supporting multi-team and multi-namespace environments. Application Gateway for Containers supports core Gateway API resources such as: GatewayClass Gateway HTTPRoute When these resources are created or updated, Application Gateway for Containers automatically translates them into gateway configuration and applies the changes in near real time. Ingress API support For teams that already use the traditional Kubernetes Ingress API, Application Gateway for Containers also provides Ingress support. This allows: Reuse of existing Ingress manifests A smoother migration path from older ingress controllers Gradual adoption of Gateway API over time Ingress resources are associated with Application Gateway for Containers using a specific ingress class. While fully functional, the Ingress API offers fewer capabilities and less flexibility compared to the Gateway API. How teams interact with the service A key benefit of this integration model is the clean separation of responsibilities: Platform teams Provision and manage Application Gateway for Containers Define gateways, listeners, and security boundaries Own network and security policies Application teams Define routes using Kubernetes APIs Control how their applications are exposed Do not need direct access to Azure networking resources This approach enables self-service for application teams while keeping governance and security centralized. Why this matters By integrating deeply with Kubernetes APIs, Application Gateway for Containers avoids custom controllers, sidecars, or ingress pods inside the cluster. Configuration stays declarative, changes are automated, and the operational model stays consistent with Kubernetes best practices. Security Capabilities Security is a core part of Azure Application Gateway for Containers and one of the main reasons teams choose it over in-cluster ingress controllers. The service brings Azure-native security controls directly in front of your container workloads, without adding complexity inside the cluster. Web Application Firewall (WAF) Application Gateway for Containers integrates with Azure Web Application Firewall (WAF) to protect applications against common web attacks such as SQL injection, cross-site scripting, and other OWASP Top 10 threats. A key differentiator of this service is that it leverages Microsoft's global threat intelligence. This provides an enterprise-grade layer of security that constantly evolves to block emerging threats, a significant advantage over many open-source or standard competitor WAF solutions. Because the WAF operates within the managed data plane, it offers several operational benefits: Zero Cluster Footprint: No WAF-specific pods or components are required to run inside your Kubernetes cluster, saving resources for your actual applications. Edge Protection: Security rules and policies are applied at the Azure network edge, ensuring malicious traffic is blocked before it ever reaches your workloads. Automated Maintenance: All rule updates, patching, and engine maintenance are handled entirely by Azure. Centralized Governance: WAF policies can be managed centrally, ensuring consistent security enforcement across multiple teams and namespaces—a critical requirement for regulated environments. TLS and certificate handling TLS termination happens directly at the gateway. HTTPS traffic is decrypted at the edge, inspected, and then forwarded to backend services. Key points: Certificates are referenced from Kubernetes configuration TLS policies are enforced by the Azure-managed gateway Applications receive plain HTTP traffic, keeping workloads simpler This approach allows teams to standardize TLS behavior across clusters and environments, while avoiding certificate logic inside application pods. Network isolation and exposure control Because Application Gateway for Containers runs outside the cluster, it provides a clear security boundary between external traffic and Kubernetes workloads. Common patterns include: Internet-facing gateways with WAF protection Private gateways for internal or zero-trust access Controlled exposure of only selected services By keeping traffic management and security at the gateway layer, clusters remain more isolated and easier to protect. Security by design Overall, the security model follows a simple principle: inspect, protect, and control traffic before it enters the cluster. This reduces the attack surface of Kubernetes, centralizes security controls, and aligns container ingress with Azure’s broader security ecosystem. Scale, Performance, and Limits Azure Application Gateway for Containers is built to handle production-scale traffic without requiring customers to manage capacity, scaling rules, or availability of the ingress layer. Scalability and performance are handled as part of the managed service. Interoperability: The Best of Both Worlds A common hesitation when adopting cloud-native networking is the fear of vendor lock-in. Many organizations worry that using a provider-specific ingress service will tie their application logic too closely to a single cloud’s proprietary configuration. Azure Application Gateway for Containers (AGC) addresses this directly by utilizing the Kubernetes Gateway API as its primary integration model. This creates a powerful decoupling between how you define your traffic and how that traffic is actually delivered. Standardized API, Managed Execution By adopting this model, you gain two critical advantages simultaneously: Zero Vendor Lock-In (Standardized API): Your routing logic is defined using the open-source Kubernetes Gateway API standard. Because HTTPRoute and Gateway resources are community-driven standards, your configuration remains portable and familiar to any Kubernetes professional, regardless of the underlying infrastructure. Zero Operational Overhead (Managed Implementation): While the interface is a standard Kubernetes API, the implementation is a high-performance Azure-managed service. You gain the benefits of an enterprise-grade load balancer—automatic scaling, high availability, and integrated security—without the burden of managing, patching, or troubleshooting proxy pods inside your cluster. The "Pragmatic" Advantage As highlighted in recent architectural discussions, moving from traditional Ingress to the Gateway API is about more than just new features; it’s about interoperability. It allows platform teams to offer a consistent, self-service experience to developers while retaining the ability to leverage the best-in-class performance and security that only a native cloud provider can offer. The result is a future-proof architecture: your teams use the industry-standard language of Kubernetes to describe what they need, and Azure provides the managed muscle to make it happen. Scaling model Application Gateway for Containers uses an automatic scaling model. The gateway data plane scales up or down based on incoming traffic patterns, without manual intervention. From an operator’s perspective: There are no ingress pods to scale No node capacity planning for ingress No separate autoscaler to configure Scaling is handled entirely by Azure, allowing teams to focus on application behavior rather than ingress infrastructure. Performance characteristics Because the data plane runs outside the Kubernetes cluster, ingress traffic does not compete with application workloads for CPU or memory. This often results in: More predictable latency Better isolation between traffic management and application execution Consistent performance under load The service supports common production requirements such as: High concurrent connections Low-latency HTTP and HTTPS traffic Near real-time configuration updates driven by Kubernetes changes Service limits and considerations Like any managed service, Application Gateway for Containers has defined limits that architects should be aware of when designing solutions. These include limits around: Number of listeners and routes Backend service associations Certificates and TLS configurations Throughput and connection scaling thresholds These limits are documented and enforced by the platform to ensure stability and predictable behavior. For most application platforms, these limits are well above typical usage. However, they should be reviewed early when designing large multi-tenant or high-traffic environments. Designing with scale in mind The key takeaway is that Application Gateway for Containers removes ingress scaling from the cluster and turns it into an Azure-managed concern. This simplifies operations and provides a stable, high-performance entry point for container workloads. When to Use (and When Not to Use) Scenario Use it? Why Kubernetes workloads on Azure ✅ Yes The service is designed specifically for container platforms and integrates natively with Kubernetes APIs. Need for managed Layer-7 ingress ✅ Yes Routing, TLS, and scaling are handled by Azure without in-cluster components. Enterprise security requirements (WAF, TLS policies) ✅ Yes Built-in Azure WAF and centralized TLS enforcement simplify security. Platform team managing ingress for multiple apps ✅ Yes Clear separation between platform and application responsibilities. Multi-tenant Kubernetes clusters ✅ Yes Gateway API model supports clean ownership boundaries and isolation. Desire to avoid running ingress controllers in the cluster ✅ Yes No ingress pods, no cluster resource consumption. VM-based or non-container backends ❌ No Classic Application Gateway is a better fit for non-container workloads. Simple, low-traffic test or dev environments ❌ Maybe not A lightweight in-cluster ingress may be simpler and more cost-effective. Need for custom or unsupported L7 features ❌ Maybe not Some advanced or niche ingress features may not yet be available. Non-Kubernetes platforms ❌ No The service is tightly integrated with Kubernetes APIs. When to Choose a Different Path: Azure Container Apps While Application Gateway for Containers provides the ultimate control for Kubernetes environments, not every project requires that level of infrastructure management. For teams that don't need the full flexibility of Kubernetes and are looking for the fastest path to running containers on Azure without managing clusters or ingress infrastructure at all, Azure Container Apps offers a specialized alternative. It provides a fully managed, serverless container platform that handles scaling, ingress, and networking automatically "out of the box". Key Differences at a Glance Feature AGC + Kubernetes Azure Container Apps Control Granular control over cluster and ingress. Fully managed, serverless experience. Management You manage the cluster; Azure manages the gateway. Azure manages both the platform and ingress. Best For Complex, multi-team, or highly regulated environments. Rapid development and simplified operations. Appendix - Routing configuration examples The following examples show how Application Gateway for Containers can be configured using both Gateway API and Ingress API for common routing and TLS scenarios. More examples can be found here, in the detailed documentation. HTTP listener apiVersion: gateway.networking.k8s.io/v1 kind: HTTPRoute metadata: name: app-route spec: parentRefs: - name: agc-gateway rules: - backendRefs: - name: app-service port: 80 Path routing logic apiVersion: gateway.networking.k8s.io/v1 kind: HTTPRoute metadata: name: path-routing spec: parentRefs: - name: agc-gateway rules: - matches: - path: type: PathPrefix value: /api backendRefs: - name: api-service port: 80 - backendRefs: - name: web-service port: 80 Weighted canary / rollout apiVersion: gateway.networking.k8s.io/v1 kind: HTTPRoute metadata: name: canary-route spec: parentRefs: - name: agc-gateway rules: - backendRefs: - name: app-v1 port: 80 weight: 80 - name: app-v2 port: 80 weight: 20 TLS Termination apiVersion: networking.k8s.io/v1 kind: Ingress metadata: name: app-ingress spec: ingressClassName: azure-alb-external tls: - hosts: - app.contoso.com secretName: tls-cert rules: - host: app.contoso.com http: paths: - path: / pathType: Prefix backend: service: name: app-service port: number: 80
Stop Running Runbooks at 3 am: Let Azure SRE Agent Do Your On-Call Grunt Work
Your pager goes off. It's 2:47am. Production is throwing 500 errors. You know the drill - SSH into this, query that, check these metrics, correlate those logs. Twenty minutes later, you're still piecing together what went wrong. Sound familiar? The On-Call Reality Nobody Talks About Every SRE, DevOps engineer, and developer who's carried a pager knows this pain. When incidents hit, you're not solving problems - you're executing runbooks. Copy-paste this query. Check that dashboard. Run these az commands. Connect the dots between five different tools. It's tedious. It's error-prone at 3am. And honestly? It's work that doesn't require human creativity but requires human time. What if an AI agent could do this for you? Enter Azure SRE Agent + Runbook Automation Here's what I built: I gave SRE Agent a simple markdown runbook containing the same diagnostic steps I'd run manually during an incident. The agent executes those steps, collects evidence, and sends me an email with everything I need to take action. No more bouncing between terminals. No more forgetting a step because it's 3am and your brain is foggy. What My Runbook Contains Just the basics any on-call would run: az monitor metrics – CPU, memory, request rates Log Analytics queries – Error patterns, exception details, dependency failures App Insights data – Failed requests, stack traces, correlation IDs az containerapp logs – Revision logs, app configuration That's it. Plain markdown with KQL queries and CLI commands. Nothing fancy. What the Agent Does Reads the runbook from its knowledge base Executes each diagnostic step Collects results and evidence Sends me an email with analysis and findings I wake up to an email that says: "CPU spiked to 92% at 2:45am, triggering connection pool exhaustion. Top exception: SqlException (1,832 occurrences). Errors correlate with traffic spike. Recommend scaling to 5 replicas." All the evidence. All the queries used. All the timestamps. Ready for me to act. How to Set This Up (6 Steps) Here's how you can build this yourself: Step 1: Create SRE Agent Create a new SRE Agent in the Azure portal. No Azure resource groups to configure. If your apps run on Azure, the agent pulls context from the incident itself. If your apps run elsewhere, you don't need Azure resource configuration at all. Step 2: Grant Reader Permission (Optional) If your runbooks execute against Azure resources, assign Reader role to the SRE Agent's managed identity on your subscription. This allows the agent to run az commands and query metrics. Skip this if your runbooks target non-Azure apps. Step 3: Add Your Runbook to SRE Agent's Knowledge base You already have runbooks, they're in your wiki, Confluence, or team docs. Just add them as .md files to the agent's knowledge base. To learn about other ways to link your runbooks to the agent, read this Step 4: Connect Outlook Connect the agent to your Outlook so it can send you the analysis email with findings. Step 5: Create a Subagent Create a subagent with simple instructions like: "You are an expert in triaging and diagnosing incidents. When triggered, search the knowledge base for the relevant runbook, execute the diagnostic steps, collect evidence, and send an email summary with your findings." Assign the tools the agent needs: RunAzCliReadCommands – for az monitor, az containerapp commands QueryLogAnalyticsByWorkspaceId – for KQL queries against Log Analytics QueryAppInsightsByResourceId – for App Insights data SearchMemory – to find the right runbook SendOutlookEmail – to deliver the analysis Step 6: Set Up Incident Trigger Connect your incident management tool - PagerDuty, ServiceNow, or Azure Monitor alerts and setup the incident trigger to the subagent. When an incident fires, the agent kicks off automatically. That's it. Your agentic workflow now looks like this: This Works for Any App, Not Just Azure Here's the thing: SRE Agent is platform agnostic. It's executing your runbooks, whatever they contain. On-prem databases? Add your diagnostic SQL. Custom monitoring stack? Add those API calls. The agent doesn't care where your app runs. It cares about following your runbook and getting you answers. Why This Matters Lower MTTR. By the time you're awake and coherent, the analysis is done. Consistent execution. No missed steps. No "I forgot to check the dependencies" at 4am. Evidence for postmortems. Every query, every result, timestamped and documented. Focus on what matters. Your brain should be deciding what to do not gathering data. The Bottom Line On-call runbook execution is the most common, most tedious, and most automatable part of incident response. It's grunt work that pulls engineers away from the creative problem-solving they were hired for. SRE Agent offloads that work from your plate. You write the runbook once, and the agent executes it every time, faster and more consistently than any human at 3am. Stop running runbooks. Start reviewing results. Try it yourself: Create a markdown runbook with your diagnostic queries and commands, add it to your SRE Agent's knowledge base, and let the agent handle your next incident. Your 3am self will thank you.Announcing General Availability of Larger Container Sizes on Azure Container Instances
ACI provides a fast and simple way to run containers in the cloud. As a serverless solution, ACI eliminates the need to manage underlying infrastructure, automatically scaling to meet application demands. Customers benefit from using ACI because it offers flexible resource allocation, pay-per-use pricing, and rapid deployment, making it easier to focus on development and innovation without worrying about infrastructure management. Today, we are excited to announce the general availability of larger container sizes on Azure Container Instances (ACI). Customers can now deploy workloads with higher vCPU and memory for standard containers, confidential containers, containers with virtual networks, and containers utilizing virtual nodes to connect to Azure Kubernetes Service (AKS). After improving this feature based on customer feedback, ACI now supports vCPU counts greater than 4 and memory capacities of 16 GB, with a maximum of 31 vCPU and 240 GB for standard containers and a maximum of 31 vCPU and 180 GB for confidential containers. Benefits of Larger Container Sizes on ACI Enhanced Performance More vCPUs mean more processing power, allowing for more efficient handling of complex tasks and applications. The enhanced performance from more vCPUs and larger GB capacity offers faster processing times and reduced latency, which can translate to cost savings in terms of time and productivity. Larger container groups with more GB can handle bigger datasets and more extensive workloads, making them ideal for data-intensive applications. Simplified Scalability Larger container groups provide the flexibility to scale up resources even higher as needed, accommodating growing business demands without compromising performance. Larger container SKUs can simplify the scaling process. Instead of managing many smaller containers, you can scale your applications with fewer, larger ones, potentially reducing the need for frequent scaling adjustments. Scenarios for Larger Container Sizes Data Inferencing Larger container SKUs are ideal for data inferencing tasks that require robust computational power. Examples include real-time fraud detection in financial transactions, predictive maintenance in manufacturing, and personalized recommendation engines in e-commerce. These containers ensure efficient and secure processing of large datasets for accurate predictions and insights. Collaborative Analytics When multiple parties need to share and analyze data, larger container SKUs provide a secure and efficient solution. For instance, companies in healthcare can collaborate on patient data analytics while maintaining confidentiality. Similarly, research institutions can share large datasets for scientific studies without compromising data privacy. Big Data Processing Organizations dealing with large-scale data processing can benefit from the enhanced capacity of larger container SKUs. Examples include processing customer data for targeted marketing campaigns, analyzing social media trends for sentiment analysis, and conducting large-scale financial modeling for risk assessment. These containers ensure efficient handling of extensive workloads. High-Performance Computing High-performance computing applications, such as climate modeling, genomic research, and computational fluid dynamics, demand substantial computational power. Larger container SKUs provide the necessary resources to support these intensive tasks, enabling precise simulations and faster results. How to start using Larger Container Sizes To begin using Larger Container Sizes, follow these steps. If you plan to run containers larger than 4 vCPU and 16 GB, you must request quota. Once your quota has been allocated, you can deploy your container groups through Azure portal, Azure CLI, PowerShell, ARM template, or any other method that allows you to connect to your container groups in Azure. Here are some tutorials for how to deploy containers using different methods. Quickstart - Deploy Docker container to container instance - Azure CLI - Azure Container Instances | Microsoft Learn Quickstart - Deploy Docker container to container instance - Portal - Azure Container Instances | Microsoft Learn Quickstart - Deploy Docker container to container instance - PowerShell - Azure Container Instances | Microsoft Learn Quickstart - create a container instance - Bicep - Azure Container Instances | Microsoft Learn Quickstart - Create a container instance - Azure Resource Manager template - Azure Container Instances | Microsoft Learn To learn more about Azure Container Instances, see Serverless containers in Azure - Azure Container Instances | Microsoft Learn.Azure Container Registry Repository Permissions with Attribute-based Access Control (ABAC)
General Availability announcement Today marks the general availability of Azure Container Registry (ACR) repository permissions with Microsoft Entra attribute-based access control (ABAC). ABAC augments the familiar Azure RBAC model with namespace and repository-level conditions so platform teams can express least-privilege access at the granularity of specific repositories or entire logical namespaces. This capability is designed for modern multi-tenant platform engineering patterns where a central registry serves many business domains. With ABAC, CI/CD systems and runtime consumers like Azure Kubernetes Service (AKS) clusters have least-privilege access to ACR registries. Why this matters Enterprises are converging on a central container registry pattern that hosts artifacts and container images for multiple business units and application domains. In this model: CI/CD pipelines from different parts of the business push container images and artifacts only to approved namespaces and repositories within a central registry. AKS clusters, Azure Container Apps (ACA), Azure Container Instances (ACI), and consumers pull only from authorized repositories within a central registry. With ABAC, these repository and namespace permission boundaries become explicit and enforceable using standard Microsoft Entra identities and role assignments. This aligns with cloud-native zero trust, supply chain hardening, and least-privilege permissions. What ABAC in ACR means ACR registries now support a registry permissions mode called “RBAC Registry + ABAC Repository Permissions.” Configuring a registry to this mode makes it ABAC-enabled. When a registry is configured to be ABAC-enabled, registry administrators can optionally add ABAC conditions during standard Azure RBAC role assignments. This optional ABAC conditions scope the role assignment’s effect to specific repositories or namespace prefixes. ABAC can be enabled on all new and existing ACR registries across all SKUs, either during registry creation or configured on existing registries. ABAC-enabled built-in roles Once a registry is ABAC-enabled (configured to “RBAC Registry + ABAC Repository Permissions), registry admins can use these ABAC-enabled built-in roles to grant repository-scoped permissions: Container Registry Repository Reader: grants image pull and metadata read permissions, including tag resolution and referrer discoverability. Container Registry Repository Writer: grants Repository Reader permissions, as well as image and tag push permissions. Container Registry Repository Contributor: grants Repository Reader and Writer permissions, as well as image and tag delete permissions. Note that these roles do not grant repository list permissions. The separate Container Registry Repository Catalog Lister must be assigned to grant repository list permissions. The Container Registry Repository Catalog Lister role does not support ABAC conditions; assigning it grants permissions to list all repositories in a registry. Important role behavior changes in ABAC mode When a registry is ABAC-enabled by configuring its permissions mode to “RBAC Registry + ABAC Repository Permissions”: Legacy data-plane roles such as AcrPull, AcrPush, AcrDelete are not honored in ABAC-enabled registries. For ABAC-enabled registries, use the ABAC-enabled built-in roles listed above. Broad roles like Owner, Contributor, and Reader previously granted full control plane and data plane permissions, which is typically an overprivileged role assignment. In ABAC-enabled registries, these broad roles will only grant control plane permissions to the registry. They will no longer grant data plane permissions, such as image push, pull, delete or repository list permissions. ACR Tasks, Quick Tasks, Quick Builds, and Quick Runs no longer inherit default data-plane access to source registries; assign the ABAC-enabled roles above to the calling identity as needed. Identities you can assign ACR ABAC uses standard Microsoft Entra role assignments. Assign RBAC roles with optional ABAC conditions to users, groups, service principals, and managed identities, including AKS kubelet and workload identities, ACA and ACI identities, and more. Next steps Start using ABAC repository permissions in ACR to enforce least-privilege artifact push, pull, and delete boundaries across your CI/CD systems and container image workloads. This model is now the recommended approach for multi-tenant platform engineering patterns and central registry deployments. To get started, follow the step-by-step guides in the official ACR ABAC documentation: https://aka.ms/acr/auth/abacBuilding 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 CopilotReimagining 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 outcomesAnnouncing Public Preview of Larger Container Sizes on Azure Container Instances
ACI provides a fast and simple way to run containers in the cloud. As a serverless solution, ACI eliminates the need to manage underlying infrastructure, automatically scaling to meet application demands. Customers benefit from using ACI because it offers flexible resource allocation, pay-per-use pricing, and rapid deployment, making it easier to focus on development and innovation without worrying about infrastructure management. Today, we are excited to announce the public preview of larger container sizes on Azure Container Instances (ACI). Customers can now deploy workloads with higher vCPU and memory for standard containers, confidential containers, containers with virtual networks, and containers utilizing virtual nodes to connect to Azure Kubernetes Service (AKS). ACI now supports vCPU counts greater than 4 and memory capacities of 16 GB, with a maximum of 32 vCPU and 256 GB for standard containers and a maximum of 32 vCPU and 192 GB for confidential containers. Benefits of Larger Container Sizes on ACI Enhanced Performance More vCPUs mean more processing power, allowing for more efficient handling of complex tasks and applications. The enhanced performance from more vCPUs and larger GB capacity offers faster processing times and reduced latency, which can translate to cost savings in terms of time and productivity. Larger container groups with more GB can handle bigger datasets and more extensive workloads, making them ideal for data-intensive applications. Simplified Scalability Larger container groups provide the flexibility to scale up resources even higher as needed, accommodating growing business demands without compromising performance. Larger container SKUs can simplify the scaling process. Instead of managing many smaller containers, you can scale your applications with fewer, larger ones, potentially reducing the need for frequent scaling adjustments. Scenarios for Larger Container Sizes Data Inferencing Larger container SKUs are ideal for data inferencing tasks that require robust computational power. Examples include real-time fraud detection in financial transactions, predictive maintenance in manufacturing, and personalized recommendation engines in e-commerce. These containers ensure efficient and secure processing of large datasets for accurate predictions and insights. Collaborative Analytics When multiple parties need to share and analyze data, larger container SKUs provide a secure and efficient solution. For instance, companies in healthcare can collaborate on patient data analytics while maintaining confidentiality. Similarly, research institutions can share large datasets for scientific studies without compromising data privacy. Big Data Processing Organizations dealing with large-scale data processing can benefit from the enhanced capacity of larger container SKUs. Examples include processing customer data for targeted marketing campaigns, analyzing social media trends for sentiment analysis, and conducting large-scale financial modeling for risk assessment. These containers ensure efficient handling of extensive workloads. High-Performance Computing High-performance computing applications, such as climate modeling, genomic research, and computational fluid dynamics, demand substantial computational power. Larger container SKUs provide the necessary resources to support these intensive tasks, enabling precise simulations and faster results. How to start using Larger Container Sizes To begin using Larger Container Sizes, follow these steps. If you plan to run containers larger than 4 vCPU and 16 GB, you must request quota. Once your quota has been allocated, you can deploy your container groups through Azure portal, Azure CLI, PowerShell, ARM template, or any other method that allows you to connect to your container groups in Azure. Here are some tutorials for how to deploy containers using different methods. Quickstart - Deploy Docker container to container instance - Azure CLI - Azure Container Instances | Microsoft Learn Quickstart - Deploy Docker container to container instance - Portal - Azure Container Instances | Microsoft Learn Quickstart - Deploy Docker container to container instance - PowerShell - Azure Container Instances | Microsoft Learn Quickstart - create a container instance - Bicep - Azure Container Instances | Microsoft Learn Quickstart - Create a container instance - Azure Resource Manager template - Azure Container Instances | Microsoft Learn To learn more about Azure Container Instances, see Serverless containers in Azure - Azure Container Instances | Microsoft Learn.Using NVIDIA Triton Inference Server on Azure Container Apps
TOC Introduction to Triton System Architecture Architecture Focus of This Tutorial Setup Azure Resources File and Directory Structure ARM Template ARM Template From Azure Portal Testing Azure Container Apps Conclusion References 1. Introduction to Triton Triton Inference Server is an open-source, high-performance inferencing platform developed by NVIDIA to simplify and optimize AI model deployment. Designed for both cloud and edge environments, Triton enables developers to serve models from multiple deep learning frameworks, including TensorFlow, PyTorch, ONNX Runtime, TensorRT, and OpenVINO, using a single standardized interface. Its goal is to streamline AI inferencing while maximizing hardware utilization and scalability. A key feature of Triton is its support for multiple model execution modes, including dynamic batching, concurrent model execution, and multi-GPU inferencing. These capabilities allow organizations to efficiently serve AI models at scale, reducing latency and optimizing throughput. Triton also offers built-in support for HTTP/REST and gRPC endpoints, making it easy to integrate with various applications and workflows. Additionally, it provides model monitoring, logging, and GPU-accelerated inference optimization, enhancing performance across different hardware architectures. Triton is widely used in AI-powered applications such as autonomous vehicles, healthcare imaging, natural language processing, and recommendation systems. It integrates seamlessly with NVIDIA AI tools, including TensorRT for high-performance inference and DeepStream for video analytics. By providing a flexible and scalable deployment solution, Triton enables businesses and researchers to bring AI models into production with ease, ensuring efficient and reliable inferencing in real-world applications. 2. System Architecture Architecture Development Environment OS: Ubuntu Version: Ubuntu 18.04 Bionic Beaver Docker version: 26.1.3 Azure Resources Storage Account: SKU - General Purpose V2 Container Apps Environments: SKU - Consumption Container Apps: N/A Focus of This Tutorial This tutorial walks you through the following stages: Setting up Azure resources Publishing the project to Azure Testing the application Each of the mentioned aspects has numerous corresponding tools and solutions. The relevant information for this session is listed in the table below. Local OS Windows Linux Mac V How to setup Azure resources and deploy Portal (i.e., REST api) ARM Bicep Terraform V 3. Setup Azure Resources File and Directory Structure Please open a terminal and enter the following commands: git clone https://github.com/theringe/azure-appservice-ai.git cd azure-appservice-ai After completing the execution, you should see the following directory structure: File and Path Purpose triton/tools/arm-template.json The ARM template to setup all the Azure resources related to this tutorial, including a Container Apps Environments, a Container Apps, and a Storage Account with the sample dataset. ARM Template We need to create the following resources or services: Manual Creation Required Resource/Service Container Apps Environments Yes Resource Container Apps Yes Resource Storage Account Yes Resource Blob Yes Service Deployment Script Yes Resource Let’s take a look at the triton/tools/arm-template.json file. Refer to the configuration section for all the resources. Since most of the configuration values don’t require changes, I’ve placed them in the variables section of the ARM template rather than the parameters section. This helps keep the configuration simpler. However, I’d still like to briefly explain some of the more critical settings. As you can see, I’ve adopted a camelCase naming convention, which combines the [Resource Type] with [Setting Name and Hierarchy]. This makes it easier to understand where each setting will be used. The configurations in the diagram are sorted by resource name, but the following list is categorized by functionality for better clarity. Configuration Name Value Purpose storageAccountContainerName data-and-model [Purpose 1: Blob Container for Model Storage] Use this fixed name for the Blob Container. scriptPropertiesRetentionInterval P1D [Purpose 2: Script for Uploading Models to Blob Storage] No adjustments are needed. This script is designed to launch a one-time instance immediately after the Blob Container is created. It downloads sample model files and uploads them to the Blob Container. The Deployment Script resource will automatically be deleted after one day. caeNamePropertiesPublicNetworkAccess Enabled [Purpose 3: For Testing] ACA requires your local machine to perform tests; therefore, external access must be enabled. appPropertiesConfigurationIngressExternal true [Purpose 3: For Testing] Same as above. appPropertiesConfigurationIngressAllowInsecure true [Purpose 3: For Testing] Same as above. appPropertiesConfigurationIngressTargetPort 8000 [Purpose 3: For Testing] The Triton service container uses port 8000. appPropertiesTemplateContainers0Image nvcr.io/nvidia/tritonserver:22.04-py3 [Purpose 3: For Testing] The Triton service container utilizes this online resource. ARM Template From Azure Portal In addition to using az cli to invoke ARM Templates, if the JSON file is hosted on a public network URL, you can also load its configuration directly into the Azure Portal by following the method described in the article [Deploy to Azure button - Azure Resource Manager]. This is my example. Click Me After filling in all the required information, click Create. And we could have a test once the creation process is complete. 4. Testing Azure Container App In our local environment, use the following command to start a one-time Docker container. We will use NVIDIA's official test image and send a sample image from within it to the Triton service that was just deployed to Container Apps. # Replace XXX.YYY.ZZZ.azurecontainerapps.io with the actual FQDN of your app. There is no need to add https:// docker run --rm nvcr.io/nvidia/tritonserver:22.04-py3-sdk /workspace/install/bin/image_client -u XXX.YYY.ZZZ.azurecontainerapps.io -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg After sending the request, you should see the prediction results, indicating that the deployed Triton server service is functioning correctly. 5. Conclusion Beyond basic model hosting, Triton Inference Server's greatest strength lies in its ability to efficiently serve AI models at scale. It supports multiple deep learning frameworks, allowing seamless deployment of diverse models within a single infrastructure. With features like dynamic batching, multi-GPU execution, and optimized inference pipelines, Triton ensures high performance while reducing latency. While it may not replace custom-built inference solutions for highly specialized workloads, it excels as a standardized and scalable platform for deploying AI across cloud and edge environments. Its flexibility makes it ideal for applications such as real-time recommendation systems, autonomous systems, and large-scale AI-powered analytics. 6. References Quickstart — NVIDIA Triton Inference Server Deploying an ONNX Model — NVIDIA Triton Inference Server Model Repository — NVIDIA Triton Inference Server Triton Tutorials — NVIDIA Triton Inference Server
