Rethinking Background Workloads with Azure Functions on Azure Container Apps
Objective Azure Container Apps provides a flexible platform for running background workloads, supporting multiple execution models to address different workload needs. Two commonly used models are: Azure Functions on Azure Container Apps - overview of Azure functions Azure Container Apps Jobs – overview of Container App Jobs Both are first‑class capabilities on the same platform and are designed for different types of background processing. This blog explores Use Cases where Azure Functions on Azure Container Apps are best suited Use Cases where Container App Jobs provide advantages Use Cases where Azure Functions on Azure Container Apps Are suited Azure Functions on Azure Container Apps are particularly well suited for event‑driven and workflow‑oriented background workloads, where work is initiated by external signals and coordination is a core concern. The following use cases illustrate scenarios where the Functions programming model aligns naturally with the workload, allowing teams to focus on business logic while the platform handles triggering, scaling, and coordination. Event‑Driven Data Ingestion Pipelines For ingestion pipelines where data arrives asynchronously and unpredictably. Example: A retail company processes inventory updates from hundreds of suppliers. Files land in Blob Storage overnight, varying widely in size and arrival time. In this scenario: Each file is processed independently as it arrives Execution is driven by actual data arrival, not schedules Parallelism and retries are handled by the platform .blob_trigger(arg_name="blob", path="inventory-uploads/{name}", connection="StorageConnection") async def process_inventory(blob: func.InputStream): data = blob.read() # Transform and load to database await transform_and_load(data, blob.name) Multi‑Step, Event‑Driven Processing Workflows Functions works well for workloads that involve multiple dependent steps, where each step can fail independently and must be retried or resumed safely. Example: An order processing workflow that includes validation, inventory checks, payment capture, and fulfilment notifications. Using Durable Functions: Workflow state persisted automatically Each step can be retried independently Execution resumes from the point of failure rather than restarting Durable Functions on Container Apps solves this declaratively: .orchestration_trigger(context_name="context") def order_workflow(context: df.DurableOrchestrationContext): order = context.get_input() # Each step is independently retryable with built-in checkpointing validated = yield context.call_activity("validate_order", order) inventory = yield context.call_activity("check_inventory", validated) payment = yield context.call_activity("capture_payment", inventory) yield context.call_activity("notify_fulfillment", payment) return {"status": "completed", "order_id": order["id"]} Scheduled, Recurring Background Tasks For time‑based background work that runs on a predictable cadence and is closely tied to application logic. Example: Daily financial summaries, weekly aggregations, or month‑end reconciliation reports. Timer‑triggered Functions allow: Schedules to be defined in code Logic to be versioned alongside application code Execution to run in the same Container Apps environment as other services .timer_trigger(schedule="0 0 6 * * *", arg_name="timer") async def daily_financial_summary(timer: func.TimerRequest): if timer.past_due: logging.warning("Timer is running late!") await generate_summary(date.today() - timedelta(days=1)) await send_to_stakeholders() Long‑Running, Parallelizable Workloads Scenarios which require long‑running workloads to be decomposed into smaller units of work and coordinated as a workflow. Example: A large data migration processing millions of records. With Durable Functions: Work is split into independent batches Batches execute in parallel across multiple instances Progress is checkpointed automatically Failures are isolated to individual batches .orchestration_trigger(context_name="context") def migration_orchestrator(context: df.DurableOrchestrationContext): batches = yield context.call_activity("get_migration_batches") # Process all batches in parallel across multiple instances tasks = [context.call_activity("migrate_batch", b) for b in batches] results = yield context.task_all(tasks) yield context.call_activity("generate_report", results) Use Cases where Container App Jobs are a Best Fit Azure Container Apps Jobs are well suited for workloads that require explicit execution control or full ownership of the runtime and lifecycle. Common examples include: Batch Processing Using Existing Container Images Teams often have existing containerized batch workloads such as data processors, ETL tools, or analytics jobs that are already packaged and validated. When refactoring these workloads into a Functions programming model is not desirable, Container Apps Jobs allow them to run unchanged while integrating into the Container Apps environment. Large-Scale Data Migrations and One-Time Operations Jobs are a natural fit for one‑time or infrequently run migrations, such as schema upgrades, backfills, or bulk data transformations. These workloads are typically: Explicitly triggered Closely monitored Designed to run to completion under controlled conditions The ability to manage execution, retries, and shutdown behavior directly is often important in these scenarios. Custom Runtime or Specialized Dependency Workloads Some workloads rely on: Specialized runtimes Native system libraries Third‑party tools or binaries When these requirements fall outside the supported Functions runtimes, Container Apps Jobs provide the flexibility to define the runtime environment exactly as needed. Externally Orchestrated or Manually Triggered Workloads In some architectures, execution is coordinated by an external system such as: A CI/CD pipeline An operations workflow A custom scheduler or control plane Container Apps Jobs integrate well into these models, where execution is initiated explicitly rather than driven by platform‑managed triggers. Long-Running, Single-Instance Processing For workloads that are intentionally designed to run as a single execution unit without fan‑out, trigger‑based scaling, or workflow orchestration Jobs provide a straightforward execution model. This includes tasks where parallelism, retries, and state handling are implemented directly within the application. Making the Choice Consideration Azure Functions on Azure Container Apps Azure Container Apps Jobs Trigger model Event‑driven (files, messages, timers, HTTP, events) Explicit execution (manual, scheduled, or externally triggered) Scaling behavior Automatic scaling based on trigger volume / queue depth Fixed or explicitly defined parallelism Programming model Functions programming model with triggers, bindings, Durable Functions General container execution model State management Built‑in state, retries, and checkpointing via Durable Functions Custom state management required Workflow orchestration Native support using Durable Functions Must be implemented manually Boilerplate required Minimal (no polling, retry, or coordination code) Higher (polling, retries, lifecycle handling) Runtime flexibility Limited to supported Functions runtimes Full control over runtime and dependencies Getting Started on Functions on Azure Container Apps If you’re already running on Container Apps, adding Functions is straightforward: Your Functions run alongside your existing apps, sharing the same networking, observability, and scaling infrastructure. Check out the documentation for details - Getting Started on Functions on Azure Container Apps # Create a Functions app in your existing Container Apps environment az functionapp create \ --name my-batch-processor \ --storage-account mystorageaccount \ --environment my-container-apps-env \ --workload-profile-name "Consumption" \ --runtime python \ --functions-version 4 Getting Started on Container App Jobs on Azure Container Apps If you already have an Azure Container Apps environment, you can create a job using the Azure CLI. Checkout the documentation for details - Jobs in Azure Container Apps az containerapp job create \ --name my-job \ --resource-group my-resource-group \ --environment my-container-apps-env \ --trigger-type Manual \ --image mcr.microsoft.com/k8se/quickstart-jobs:latest \ --cpu 0.25 \ --memory 0.5Gi Quick Links Azure Functions on Azure Container Apps overview Create your Azure Functions app through custom containers on Azure Container Apps Run event-driven and batch workloads with Azure Functions on Azure Container AppsRethinking 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: 80Seamless Migrations From Self Hosted Nginx Ingress To The AKS App Routing Add-On
The Kubernetes Steering Committee has announced that the Nginx Ingress controller will be retired in March 2026. That' not far away, and once this happens Nginx Ingress will not receive any further updates, including security patches. Continuing to run the standalone Nginx Ingress controller past the end of March could open you up to security risks. Azure Kubernetes Service (AKS) offers a managed routing add-on which also implements Nginx as the Ingress Controller. Microsoft has recently committed to supporting this version of Nginx Ingress until November 2026. There is also an updated version of the App Routing add-on in the works, that will be based on Istio to allow for transition off Nginx Ingress. This new App Routing add-on will support Gateway API based ingress only, so there will be some migration required if you are using the Ingress API. There is tooling availible to support migration from Ingress to Gateway API, such as the Ingress2Gateway tool. If you are already using the App Routing add-on then you are supported until November and have extra time to either move to the new Istio based solution when it is released or migrate to another solution such as App Gateway for Containers. However, if you are running the standalone version of Nginx Ingress, you may want to consider migrating to the App Routing add-on to give you some extra time. To be very clear, migrating to the App Routing add-on does not solve the problem; it buys you some more time until November and sets you up for a transition to the future Istio based App Routing add-on. Once you complete this migration you will need to plan to either move to the new version based on Istio, or migrate to another Ingress solution, before November. This rest of this article walks through migrating from BYO Nginx to the App Routing add-on without disrupting your existing traffic. How Parallel Running Works The key to a zero-downtime migration is that both controllers can run simultaneously. Each controller uses a different IngressClass, so Kubernetes routes traffic based on which class your Ingress resources reference. Your BYO Nginx uses the nginx IngressClass and runs in the ingress-nginx namespace. The App Routing add-on uses the webapprouting.kubernetes.azure.com IngressClass and runs in the app-routing-system namespace. They operate completely independently, each with its own load balancer IP. This means you can: Enable the add-on alongside your existing controller Create new Ingress resources targeting the add-on (or duplicate existing ones) Validate everything works via the add-on's IP Cut over DNS or backend pool configuration Remove the old Ingress resources once you're satisfied At no point does your production traffic go offline. Enabling the App Routing add-on Start by enabling the add-on on your existing cluster. This doesn't touch your BYO Nginx installation. bash az aks approuting enable \ --resource-group <resource-group> \ --name <cluster-name> </cluster-name></resource-group> Wait for the add-on to deploy. You can verify it's running by checking the app-routing-system namespace: kubectl get pods -n app-routing-system kubectl get svc -n app-routing-system You should see the Nginx controller pod running and a service called nginx with a load balancer IP. This IP is separate from your BYO controller's IP. bash # Get both IPs for comparison BYO_IP=$(kubectl get svc ingress-nginx-controller -n ingress-nginx \ -o jsonpath='{.status.loadBalancer.ingress[0].ip}') add-on_IP=$(kubectl get svc nginx -n app-routing-system \ -o jsonpath='{.status.loadBalancer.ingress[0].ip}') echo "BYO Nginx IP: $BYO_IP" echo "add-on IP: $add-on_IP" Both controllers are now running. Your existing applications continue to use the BYO controller because their Ingress resources still reference ingressClassName: nginx. Migrating Applications: The Parallel Ingress Approach For production workloads, create a second Ingress resource that targets the add-on. This lets you validate everything before cutting over traffic. Here's an example. Your existing Ingress might look like this: apiVersion: networking.k8s.io/v1 kind: Ingress metadata: name: myapp-ingress-byo namespace: myapp annotations: nginx.ingress.kubernetes.io/rewrite-target: / spec: ingressClassName: nginx # BYO controller rules: - host: myapp.example.com http: paths: - path: / pathType: Prefix backend: service: name: myapp port: number: 80 Create a new Ingress for the add-on: apiVersion: networking.k8s.io/v1 kind: Ingress metadata: name: myapp-ingress-add-on namespace: myapp annotations: nginx.ingress.kubernetes.io/rewrite-target: / spec: ingressClassName: webapprouting.kubernetes.azure.com # add-on controller rules: - host: myapp.example.com http: paths: - path: / pathType: Prefix backend: service: name: myapp port: number: 80 Apply this new Ingress resource. The add-on controller picks it up and configures routing, but your production traffic still flows through the BYO controller because DNS (or your backend pool) still points to the old IP. Validating Before Cutover Test the new route via the add-on IP before touching anything else: # For public ingress with DNS curl -H "Host: myapp.example.com" http://$add-on_IP # For private ingress, test from a VM in the VNet curl -H "Host: myapp.example.com" http://$add-on_IP Run your full validation suite against this IP. Check TLS certificates, path routing, authentication, rate limiting, custom headers, and anything else your application depends on. If you have monitoring or synthetic tests, point them at the add-on IP temporarily to gather confidence. If something doesn't work, you can troubleshoot without affecting production. The BYO controller is still handling all real traffic. Cutover To Routing Add-on If your ingress has a public IP and you're using DNS to route traffic, the cutover is straightforward. Lower your DNS TTL well in advance. Set it to 60 seconds at least an hour before you plan to cut over. This ensures changes propagate quickly and you can roll back fast if needed. When you're ready, update your DNS A record to point to the add-on IP If your ingress has a private IP and sits behind App Gateway, API Management, or Front Door, the cutover involves updating the backend pool instead of DNS. In-Place Patching: The Faster But Riskier Option If you're migrating a non-critical application or an internal service where some downtime is acceptable, you can patch the ingressClassName in place: kubectl patch ingress myapp-ingress-byo -n myapp \ --type='json' \ -p='[{"op":"replace","path":"/spec/ingressClassName","value":"webapprouting.kubernetes.azure.com"}]' This is atomic from Kubernetes' perspective. The BYO controller immediately drops the route, and the add-on controller immediately picks it up. In practice, there's usually a few seconds gap while the add-on configures Nginx and reloads. Once this change is made, the Ingress will not work until you update your DNS or backend pool details to point to the new IP. Decommissioning the BYO Nginx Controller Once all your applications are migrated and you're confident everything works, you can remove the BYO controller. First, verify nothing is still using it: kubectl get ingress --all-namespaces \ -o custom-columns='NAMESPACE:.metadata.namespace,NAME:.metadata.name,CLASS:.spec.ingressClassName' \ | grep -v "webapprouting" If that returns only the header row (or is empty), you're clear to proceed. If it shows any Ingress resources, you've still got work to do. Remove the BYO Nginx Helm release: helm uninstall ingress-nginx -n ingress-nginx kubectl delete namespace ingress-nginx The Azure Load Balancer provisioned for the BYO controller will be deprovisioned automatically. Verify only the add-on IngressClass remains: kubectl get ingressclass You should see only webapprouting.kubernetes.azure.com. Key Differences Between BYO Nginx and the App Routing add-on The add-on runs the same Nginx binary, so most of your configuration carries over. However, there are a few differences worth noting. TLS Certificates: The BYO setup typically uses cert-manager or manual Secrets for certificates. The add-on supports this, but it also integrates natively with Azure Key Vault. If you want to use Key Vault, you need to configure the add-on with the appropriate annotations. Otherwise, your existing cert-manager setup continues to work. DNS Management: If you're using external-dns with your BYO controller, it works with the add-on too. The add-on also has native integration with Azure DNS zones if you want to simplify your setup. This is optional. Custom Nginx Configuration: With BYO Nginx, you have full access to the ConfigMap and can customise the global Nginx configuration extensively. The add-on restricts this because it's managed by Azure. If you've done significant customisation (Lua scripts, custom modules, etc.), audit carefully to ensure the add-on supports what you need. Most standard configurations work fine. Annotations: The nginx.ingress.kubernetes.io/* annotations work the same way. The add-on adds some Azure-specific annotations for WAF integration and other features, but your existing annotations should carry over without changes. What Comes Next This migration gets you onto a supported platform, but it's temporary. November 2026 is not far away, and you'll need to plan your next move. Microsoft is building a new App Routing add-on based on Istio. This is expected later in 2026 and will likely become the long-term supported option. Keep an eye on Azure updates for announcements about preview availability and migration paths. If you need something production-ready sooner, App Gateway for Containers is worth evaluating. It's built on Envoy and supports the Kubernetes Gateway API, which is the future direction for ingress in Kubernetes. The Gateway API is more expressive than the Ingress API and is designed to be vendor-neutral. For now, getting off the unsupported BYO Nginx controller is the priority. The App Routing add-on gives you the breathing room to make an informed decision about your long-term strategy rather than rushing into something because you're running out of time.Beyond the Desktop: The Future of Development with Microsoft Dev Box and GitHub Codespaces
The modern developer platform has already moved past the desktop. We’re no longer defined by what’s installed on our laptops, instead we look at what tooling we can use to move from idea to production. An organisations developer platform strategy is no longer a nice to have, it sets the ceiling for what’s possible, an organisation can’t iterate it's way to developer nirvana if the foundation itself is brittle. A great developer platform shrinks TTFC (time to first commit), accelerates release velocity, and maybe most importantly, helps alleviate everyday frictions that lead to developer burnout. Very few platforms deliver everything an organization needs from a developer platform in one product. Modern development spans multiple dimensions, local tooling, cloud infrastructure, compliance, security, cross-platform builds, collaboration, and rapid onboarding. The options organizations face are then to either compromise on one or more of these areas or force developers into rigid environments that slow productivity and innovation. This is where Microsoft Dev Box and GitHub Codespaces come into play. On their own, each addresses critical parts of the modern developer platform: Microsoft Dev Box provides a full, managed cloud workstation. Dev Box gives developers a consistent, high-performance environment while letting central IT apply strict governance and control. Internally at Microsoft, we estimate that usage of Dev Box by our development teams delivers savings of 156 hours per year per developer purely on local environment setup and upkeep. We have also seen significant gains in other key SPACE metrics reducing context-switching friction and improving build/test cycles. Although the benefits of Dev Box are clear in the results demonstrated by our customers it is not without its challenges. The biggest challenge often faced by Dev Box customers is its lack of native Linux support. At the time of writing and for the foreseeable future Dev Box does not support native Linux developer workstations. While WSL2 provides partial parity, I know from my own engineering projects it still does not deliver the full experience. This is where GitHub Codespaces comes into this story. GitHub Codespaces delivers instant, Linux-native environments spun up directly from your repository. It’s lightweight, reproducible, and ephemeral ideal for rapid iteration, PR testing, and cross-platform development where you need Linux parity or containerized workflows. Unlike Dev Box, Codespaces can run fully in Linux, giving developers access to native tools, scripts, and runtimes without workarounds. It also removes much of the friction around onboarding: a new developer can open a repository and be coding in minutes, with the exact environment defined by the project’s devcontainer.json. That said, Codespaces isn’t a complete replacement for a full workstation. While it’s perfect for isolated project work or ephemeral testing, it doesn’t provide the persistent, policy-controlled environment that enterprise teams often require for heavier workloads or complex toolchains. Used together, they fill the gaps that neither can cover alone: Dev Box gives the enterprise-grade foundation, while Codespaces provides the agile, cross-platform sandbox. For organizations, this pairing sets a higher ceiling for developer productivity, delivering a truly hybrid, agile and well governed developer platform. Better Together: Dev Box and GitHub Codespaces in action Together, Microsoft Dev Box and GitHub Codespaces deliver a hybrid developer platform that combines consistency, speed, and flexibility. Teams can spin up full, policy-compliant Dev Box workstations preloaded with enterprise tooling, IDEs, and local testing infrastructure, while Codespaces provides ephemeral, Linux-native environments tailored to each project. One of my favourite use cases is having local testing setups like a Docker Swarm cluster, ready to go in either Dev Box or Codespaces. New developers can jump in and start running services or testing microservices immediately, without spending hours on environment setup. Anecdotally, my time to first commit and time to delivering “impact” has been significantly faster on projects where one or both technologies provide local development services out of the box. Switching between Dev Boxes and Codespaces is seamless every environment keeps its own libraries, extensions, and settings intact, so developers can jump between projects without reconfiguring or breaking dependencies. The result is a turnkey, ready-to-code experience that maximizes productivity, reduces friction, and lets teams focus entirely on building, testing, and shipping software. To showcase this value, I thought I would walk through an example scenario. In this scenario I want to simulate a typical modern developer workflow. Let's look at a day in the life of a developer on this hybrid platform building an IOT project using Python and React. Spin up a ready-to-go workstation (Dev Box) for Windows development and heavy builds. Launch a Linux-native Codespace for cross-platform services, ephemeral testing, and PR work. Run "local" testing like a Docker Swarm cluster, database, and message queue ready to go out-of-the-box. Switch seamlessly between environments without losing project-specific configurations, libraries, or extensions. 9:00 AM – Morning Kickoff on Dev Box I start my day on my Microsoft Dev Box, which gives me a fully-configured Windows environment with VS Code, design tools, and Azure integrations. I select my teams project, and the environment is pre-configured for me through the Dev Box catalogue. Fortunately for me, its already provisioned. I could always self service another one using the "New Dev Box" button if I wanted too. I'll connect through the browser but I could use the desktop app too if I wanted to. My Tasks are: Prototype a new dashboard widget for monitoring IoT device temperature. Use GUI-based tools to tweak the UI and preview changes live. Review my Visio Architecture. Join my morning stand up. Write documentation notes and plan API interactions for the backend. In a flash, I have access to my modern work tooling like Teams, I have this projects files already preloaded and all my peripherals are working without additional setup. Only down side was that I did seem to be the only person on my stand up this morning? Why Dev Box first: GUI-heavy tasks are fast and responsive. Dev Box’s environment allows me to use a full desktop. Great for early-stage design, planning, and visual work. Enterprise Apps are ready for me to use out of the box (P.S. It also supports my multi-monitor setup). I use my Dev Box to make a very complicated change to my IoT dashboard. Changing the title from "IoT Dashboard" to "Owain's IoT Dashboard". I preview this change in a browser live. (Time for a coffee after this hardwork). The rest of the dashboard isnt loading as my backend isnt running... yet. 10:30 AM – Switching to Linux Codespaces Once the UI is ready, I push the code to GitHub and spin up a Linux-native GitHub Codespace for backend development. Tasks: Implement FastAPI endpoints to support the new IoT feature. Run the service on my Codespace and debug any errors. Why Codespaces now: Linux-native tools ensure compatibility with the production server. Docker and containerized testing run natively, avoiding WSL translation overhead. The environment is fully reproducible across any device I log in from. 12:30 PM – Midday Testing & Sync I toggle between Dev Box and Codespaces to test and validate the integration. I do this in my Dev Box Edge browser viewing my codespace (I use my Codespace in a browser through this demo to highlight the difference in environments. In reality I would leverage the VSCode "Remote Explorer" extension and its GitHub Codespace integration to use my Codespace from within my own desktop VSCode but that is personal preference) and I use the same browser to view my frontend preview. I update the environment variable for my frontend that is running locally in my Dev Box and point it at the port running my API locally on my Codespace. In this case it was a web socket connection and HTTPS calls to port 8000. I can make this public by changing the port visibility in my Codespace. https://fluffy-invention-5x5wp656g4xcp6x9-8000.app.github.dev/api/devices wss://fluffy-invention-5x5wp656g4xcp6x9-8000.app.github.dev/ws This allows me to: Preview the frontend widget on Dev Box, connecting to the backend running in Codespaces. Make small frontend adjustments in Dev Box while monitoring backend logs in Codespaces. Commit changes to GitHub, keeping both environments in sync and leveraging my CI/CD for deployment to the next environment. We can see the Dev Box running local frontend and the Codespace running the API connected to each other, making requests and displaying the data in the frontend! Hybrid advantage: Dev Box handles GUI previews comfortably and allows me to live test frontend changes. Codespaces handles production-aligned backend testing and Linux-native tools. Dev Box allows me to view all of my files in one screen with potentially multiple Codespaces running in browser of VS Code Desktop. Due to all of those platform efficiencies I have completed my days goals within an hour or two and now I can spend the rest of my day learning about how to enable my developers to inner source using GitHub CoPilot and MCP (Shameless plug). The bottom line There are some additional considerations when architecting a developer platform for an enterprise such as private networking and security not covered in this post but these are implementation details to deliver the described developer experience. Architecting such a platform is a valuable investment to deliver the developer platform foundations we discussed at the top of the article. While in this demo I have quickly built I was working in a mono repository in real engineering teams it is likely (I hope) that an application is built of many different repositories. The great thing about Dev Box and Codespaces is that this wouldn’t slow down the rapid development I can achieve when using both. My Dev Box would be specific for the project or development team, pre loaded with all the tools I need and potentially some repos too! When I need too I can quickly switch over to Codespaces and work in a clean isolated environment and push my changes. In both cases any changes I want to deliver locally are pushed into GitHub (Or ADO), merged and my CI/CD ensures that my next step, potentially a staging environment or who knows perhaps *Whispering* straight into production is taken care of. Once I’m finished I delete my Codespace and potentially my Dev Box if I am done with the project, knowing I can self service either one of these anytime and be up and running again! Now is there overlap in terms of what can be developed in a Codespace vs what can be developed in Azure Dev Box? Of course, but as organisations prioritise developer experience to ensure release velocity while maintaining organisational standards and governance then providing developers a windows native and Linux native service both of which are primarily charged on the consumption of the compute* is a no brainer. There are also gaps that neither fill at the moment for example Microsoft Dev Box only provides windows compute while GitHub Codespaces only supports VS Code as your chosen IDE. It's not a question of which service do I choose for my developers, these two services are better together! *Changes have been announced to Dev Box pricing. A W365 license is already required today and dev boxes will continue to be managed through Azure. For more information please see: Microsoft Dev Box capabilities are coming to Windows 365 - Microsoft Dev Box | Microsoft LearnExploring Traffic Manager Integration for External DNS
When you deploy externally accessible applications into Kubernetes, there is usually a requirement for creating some DNS records pointing to these applications, to allow your users to resolve them. Rather than manually creating these DNS records, there are tools that will do this work for you, one of which is External DNS. External DNS can watch your Kubernetes resource configuration for specific annotations and then use these to create DNS records in your DNS zone. It has integrations with many DNS providers, including Azure DNS. This solution works well and is in use by many customers using AKS and Azure DNS. Where we hit a limitation with External DNS in Azure is in scenarios where we are need to distribute traffic across multiple clusters for load balancing and global distribution. There are a few ways to achieve this global distribution in Azure, one way is to use Azure Traffic Manger. Unlike something like Azure Front Door, Azure Traffic Manager is a DNS based global load balancer. Traffic is directed to your different AKS clusters based on DNS resolution using Traffic Manager. When a user queries your Traffic Manager CNAME, they hit the Traffic Manager DNS servers, which then return a DNS record for a specific cluster based on your load balancing configuration. Traffic Manager can then introduce advanced load balancing scenarios such as: Geographic routing - direct users to the nearest endpoint based on their location Weighted distribution - split traffic percentages (e.g., 80% to one region, 20% to another) Priority-based failover - automatic disaster recovery with primary/backup regions Performance-based routing - direct users to the endpoint with lowest latency So, given Azure Traffic Manager is a essentially a DNS service, it would be good if we could manage it using External DNS. We already use External DNS to create DNS records for each of our individual clusters, so why not use it to also create the Traffic Manager DNS configuration to load balance across them. This would also provides the added benefit of allowing us to change our load balancing strategy or configuration by making changes to our External DNS annotations. Unfortunately, an External DNS integration for Traffic Manager doesn't currently exist. In this post, I'll walk through a proof-of-concept for a provider I built to explore whether this integration is viable, and share what I learned along the way. External DNS Webhook Provider External DNS has two types of integrations. The first, and most common for "official" providers are "In-tree" providers, which are the integrations that have been created by External DNS contributors and sit within the central External DNS repository. This includes the Azure DNS provider. The second type of provider is the WebHook provider, which allows for external contributors to easily create their own providers without the need to submit them to the core External DNS repo and go through that release process. We are going to use the WebHook provider External DNS has begun the process of phasing out "In-tree" providers and replacing with WebHook ones. No new "In-tree" providers will be accepted. By using the WebHook provider mechanism, I was able to create a proof of concept Azure Traffic Manager provider that does the following: Watches Kubernetes Services for Traffic Manager annotations Automatically creates and manages Traffic Manager profiles and endpoints Syncs state between your Kubernetes clusters and Azure Enables annotation-driven configuration - no manual Traffic Manager management needed Handles duplication so that when your second cluster attempts to create a Traffic Manager record it adds an endpoint to the existing instance Works alongside the standard External DNS Azure provider for complete DNS automation Here's what the architecture looks like: Example Configuration: Multi-Region Deployment Let me walk you through a practical example using this provider to see how it works. We will deploy an application across two Azure regions with weighted traffic distribution and automatic failover. This example assumes you have built and deployed the PoC provider as discussed below. Step 1: Deploy Your Application You deploy your application to both East US and West US AKS clusters. Each deployment is a standard Kubernetes Service with LoadBalancer type. We then apply annotations that tell our External DNS provider how to create and configure the Traffic Manger resource. These annotations are defined as part of our plugin. apiVersion: v1 kind: Service metadata: name: my-app-east namespace: production annotations: # Standard External DNS annotation external-dns.alpha.kubernetes.io/hostname: my-app-east.example.com # Enable Traffic Manager integration external-dns.alpha.kubernetes.io/webhook-traffic-manager-enabled: "true" external-dns.alpha.kubernetes.io/webhook-traffic-manager-resource-group: "my-tm-rg" external-dns.alpha.kubernetes.io/webhook-traffic-manager-profile-name: "my-app-global" # Weighted routing configuration external-dns.alpha.kubernetes.io/webhook-traffic-manager-weight: "70" external-dns.alpha.kubernetes.io/webhook-traffic-manager-endpoint-name: "east-us" external-dns.alpha.kubernetes.io/webhook-traffic-manager-endpoint-location: "eastus" # Health check configuration external-dns.alpha.kubernetes.io/webhook-traffic-manager-monitor-path: "/health" external-dns.alpha.kubernetes.io/webhook-traffic-manager-monitor-protocol: "HTTPS" spec: type: LoadBalancer ports: - port: 443 targetPort: 8080 selector: app: my-app The West US deployment has identical annotations, except: Weight: "30" (sending 30% of traffic here initially) Endpoint name: "west-us" Location: "westus" Step 2: Automatic Resource Creation When you deploy these Services, here's what happens automatically: Azure Load Balancer provisions and assigns public IPs to each Service External DNS (Azure provider) creates A records: my-app-east.example.com → East US LB IP my-app-west.example.com → West US LB IP External DNS (Webhook provider) creates a Traffic Manager profile named my-app-global Webhook adds endpoints to the profile: East endpoint (weight: 70, target: my-app-east.example.com) West endpoint (weight: 30, target: my-app-west.example.com) External DNS (Azure provider) creates a CNAME: my-app.example.com → Traffic Manager FQDN Now when users access my-app.example.com, Traffic Manager routes 70% of traffic to East US and 30% to West US, with automatic health checking on both endpoints. Step 3: Gradual Traffic Migration Want to shift more traffic to West US? Just update the annotations: kubectl annotate service my-app-east \ external-dns.alpha.kubernetes.io/webhook-traffic-manager-weight="50" \ --overwrite kubectl annotate service my-app-west \ external-dns.alpha.kubernetes.io/webhook-traffic-manager-weight="50" \ --overwrite Within minutes, traffic distribution automatically adjusts to 50/50. This enables: Blue-green deployments - test new versions with small traffic percentages Canary releases - gradually increase traffic to new deployments Geographic optimisation - adjust weights based on user distribution Step 4: Automatic Failover If the East US cluster becomes unhealthy, Traffic Manager's health checks detect this and automatically fail over 100% of traffic to West US, no manual intervention required. How It Works The webhook implements External DNS's webhook provider protocol: 1. Negotiate Capabilities External DNS queries the webhook to determine supported features and versions. 2. Adjust Endpoints External DNS sends all discovered endpoints to the webhook. The webhook: Filters for Services with Traffic Manager annotations Validates configuration Enriches endpoints with metadata Returns only Traffic Manager-enabled endpoints 3. Record State External DNS queries the webhook for current Traffic Manager state. The webhook: Syncs profiles from Azure Converts to External DNS endpoint format Returns CNAME records pointing to Traffic Manager FQDNs 4. Apply Changes External DNS sends CREATE/UPDATE/DELETE operations. The webhook: Creates Traffic Manager profiles as needed Adds/updates/removes endpoints Configures health monitoring Updates in-memory state cache The webhook uses Azure SDK for Go to interact with the Traffic Manager API and maintains an in-memory cache of profile state to optimise performance and reduce API calls. Proof of Concept 🛑 Important: This is a Proof of Concept This project is provided as example code to demonstrate the integration pattern between External DNS and Traffic Manager. It is not a supported product and comes with no SLAs, warranties, or commitments. The code is published to help you understand how to build this type of integration. If you decide to implement something similar for your production environment, you should treat this as inspiration and build your own solution that you can properly test, secure, and maintain. Think of this as a blueprint, not a finished product. With that caveat out of the way, if you want to experiment with this approach, the PoC is available on GitHub: github.com/sam-cogan/external-dns-traffic-manager. The readme file containers detailed instructions on how to deploy the PoC into a single and multi-cluster environment, along with demo applications to try it out. Use Cases This integration unlocks several powerful scenarios: Multi-Region High Availability - Deploy your application across multiple Azure regions with automatic DNS-based load balancing and health-based failover. No additional load balancers or gateways required. Blue-Green Deployments - Deploy a new version alongside your current version, send 5% of traffic to test, gradually increase, and roll back instantly if issues arise by changing annotations. Geographic Distribution - Route European users to your Europe region and US users to your US region automatically using Traffic Manager's geographic routing with the same annotation-based approach. Disaster Recovery - Configure priority-based routing with your primary region at priority 1 and DR region at priority 2. Traffic automatically fails over when health checks fail. Cost Optimisation - Use weighted routing to balance traffic across regions based on capacity and costs. Send more traffic to regions where you have reserved capacity or lower egress costs. Considerations and Future Work This is a proof of concept and should be thoroughly tested before production use. Some areas for improvement: Current Limitations In-memory state only - no persistent storage (restarts require resync) Basic error handling - needs more robust retry logic Limited observability - could use more metrics and events Manual CRD cleanup - DNSEndpoint CRDs need manual cleanup when switching providers Potential Enhancements Support for more endpoint types - currently focuses on ExternalEndpoints Advanced health check configuration - custom intervals, timeouts, and thresholds Metric-based routing decisions - integrate with Azure Monitor for intelligent routing GitOps integration - Flux/ArgoCD examples and best practices Helm chart - simplified deployment If you try this out or have ideas for improvements, please open an issue or PR on GitHub. Wrapping Up This proof of concept shows that External DNS and Traffic Manager can work together nicely. Since Traffic Manager is really just an advanced DNS service, bringing it into External DNS's annotation-driven workflow makes a lot of sense. You get the same declarative approach for both basic DNS records and sophisticated traffic routing. While this isn't production-ready code (and you shouldn't use it as-is), it demonstrates a viable pattern. If you're dealing with multi-region Kubernetes deployments and need intelligent DNS-based routing, this might give you some ideas for building your own solution. The code is out there for you to learn from, break, and hopefully improve upon. If you build something based on this or have feedback on the approach, I'd be interested to hear about it. Resources GitHub Repository: github.com/sam-cogan/external-dns-traffic-manager External DNS Documentation: kubernetes-sigs.github.io/external-dns Azure Traffic Manager: learn.microsoft.com/azure/traffic-manager Webhook Provider Guide: External DNS Webhook TutorialSimplifying Image Signing with Notary Project and Artifact Signing (GA)
Securing container images is a foundational part of protecting modern cloud‑native applications. Teams need a reliable way to ensure that the images moving through their pipelines are authentic, untampered, and produced by trusted publishers. We’re excited to share an updated approach that combines the Notary Project, the CNCF standard for signing and verifying OCI artifacts, with Artifact Signing—formerly Trusted Signing—which is now generally available as a managed signing service. The Notary Project provides an open, interoperable framework for signing and verification across container images and other OCI artifacts, while Notary Project tools like Notation and Ratify enable enforcement in CI/CD pipelines and Kubernetes environments. Artifact Signing complements this by removing the operational complexity of certificate management through short‑lived certificates, verified Azure identities, and role‑based access control, without changing the underlying standards. If you previously explored container image signing using Trusted Signing, the core workflows remain unchanged. As Artifact Signing reaches GA, customers will see updated terminology across documentation and tooling, while existing Notary Project–based integrations continue to work without disruption. Together, Notary Project and Artifact Signing make it easier for teams to adopt image signing as a scalable platform capability—helping ensure that only trusted artifacts move from build to deployment with confidence. Get started Sign container images using Notation CLI Sign container images in CI/CD pipelines Verify container images in CI/CD pipelines Verify container images in AKS Extend signing and verification to all OCI artifacts in registries Related content Simplifying Code Signing for Windows Apps: Artifact Signing (GA) Simplify Image Signing and Verification with Notary Project (preview article)Deploy Dynatrace OneAgent on your Container Apps
TOC Introduction Setup References 1. Introduction Dynatrace OneAgent is an advanced monitoring tool that automatically collects performance data across your entire IT environment. It provides deep visibility into applications, infrastructure, and cloud services, enabling real-time observability. OneAgent supports multiple platforms, including containers, VMs, and serverless architectures, ensuring seamless monitoring with minimal configuration. It captures detailed metrics, traces, and logs, helping teams diagnose performance issues, optimize resources, and enhance user experiences. With AI-driven insights, OneAgent proactively detects anomalies and automates root cause analysis, making it an essential component for modern DevOps, SRE, and cloud-native monitoring strategies. 2. Setup 1. After registering your account, go to the control panel and search for Deploy OneAgent. 2. Obtain your Environment ID and create a PaaS token. Be sure to save them for later use. 3. In your local environment's console, log in to the Dynatrace registry. docker login -u XXX XXX.live.dynatrace.com # XXX is your Environment ID # Input PaaS Token when password prompt 4. Create a Dockerfile and an sshd_config file. FROM mcr.microsoft.com/devcontainers/javascript-node:20 # Change XXX into your Environment ID COPY --from=XXX.live.dynatrace.com/linux/oneagent-codemodules:all / / ENV LD_PRELOAD /opt/dynatrace/oneagent/agent/lib64/liboneagentproc.so # SSH RUN apt-get update \ && apt-get install -y --no-install-recommends dialog openssh-server tzdata screen lrzsz htop cron \ && echo "root:Docker!" | chpasswd \ && mkdir -p /run/sshd \ && chmod 700 /root/.ssh/ \ && chmod 600 /root/.ssh/id_rsa COPY ./sshd_config /etc/ssh/ # OTHER EXPOSE 2222 CMD ["/usr/sbin/sshd", "-D", "-o", "ListenAddress=0.0.0.0"] Port 2222 ListenAddress 0.0.0.0 LoginGraceTime 180 X11Forwarding yes Ciphers aes128-cbc,3des-cbc,aes256-cbc,aes128-ctr,aes192-ctr,aes256-ctr MACs hmac-sha2-256,hmac-sha2-512,hmac-sha1,hmac-sha1-96 StrictModes yes SyslogFacility DAEMON PasswordAuthentication yes PermitEmptyPasswords no PermitRootLogin yes Subsystem sftp internal-sftp AllowTcpForwarding yes 5. Build the container and push it to Azure Container Registry (ACR). # YYY is your ACR name docker build -t oneagent:202503201710 . --no-cache # you could setup your own image name docker tag oneagent:202503201710 YYY.azurecr.io/oneagent:202503201710 docker push YYY.azurecr.io/oneagent:202503201710 6. Create an Azure Container App (ACA), set Ingress to port 3000, allow all inbound traffic, and specify the ACR image you just created. 7. Once the container starts, open a console and run the following command to create a temporary HTTP server simulating a Node.js app. mkdir app && cd app echo 'console.log("Node.js app started...")' > index.js npm init -y npm install express cat <<EOF > server.js const express = require('express'); const app = express(); app.get('/', (req, res) => res.send('hello')); app.listen(3000, () => console.log('Server running on port 3000')); EOF # Please Press Ctrl + C to terminate the next command and run again for 3 times node server.js 8. You should now see the results on the ACA homepage. 9. Go back to the Dynatrace control panel, search for Host Classic, and you should see the collected data. 3. References Integrate OneAgent on Azure App Service for Linux and containers — Dynatrace Docs
Proactive Cloud Ops with SRE Agent: Scheduled Checks for Cloud Optimization
The Cloud Optimization Challenge Your cloud environment is always changing: New features ship weekly Traffic patterns shift seasonally Costs creep up quietly Security best practices evolve Teams spin up resources and forget them It's Monday morning. You open the Azure portal. Everything looks... fine. But "fine" isn't great. That VM has been at 8% CPU for weeks. A Key Vault secret expires in 12 days. Nothing's broken. But security is drifting, costs are creeping, and capacity gaps are growing silently. The question isn't "is something broken?" it's "could this be better?" Four Pillars of Cloud Optimization Pillar What Teams Want The Challenge Security Stay compliant, reduce risk Config drift, legacy settings, expiring creds Cost Spend efficiently, justify budget Hard to spot waste across 100s of resources Performance Meet SLOs, handle growth Know when to scale before demand hits Availability Maximize uptime, build resilience Hidden dependencies, single points of failure Most teams check these sometimes. SRE Agent checks them continuously. Enter SRE Agent + Scheduled tasks SRE Agent can pull data from Azure Monitor, resource configurations, metrics, logs, traces, errors, cost data and analyze it on a schedule. If you use tools outside Azure (Datadog, PagerDuty, Splunk), you can connect those via MCP servers so the agent sees your full observability stack. My setup uses Azure-native sources. Here's how I wired it up. How I Set It Up: Step by Step Step 1: Create SRE Agent with Subscription Access I created an SRE Agent without attaching it to any specific resource group. Instead, I gave it Reader access at the subscription level. This lets the agent scan across all my resource groups for optimization opportunities. No resource group configuration needed. The agent builds a knowledge graph of everything VMs, storage accounts, Key Vaults, NSGs, web apps across the subscription. Step 2: Create and Upload My Organization Practices I created an org-practices.md file that defines what "good" looks like for my team: I uploaded this to SRE Agent's knowledge base. Now the agent knows our bar, not just Azure defaults. 👉 See my full org-practices.md Source repos for this demo: security-demoapp - App with intentional security misconfigurations costoptimizationapp - App with cost optimization opportunities Step 3: Connect to Teams Channel I connected SRE Agent to my team's Teams channel so findings land where we already work. Critical findings get immediate notifications. Warnings go into a daily digest. No more logging into separate dashboards. The insights come to us. Step 4: Connect Resource Groups to GitHub Repos Add the two resource groups to the SRE Agent and link the apps to their corresponding GitHub repos: Resource Group GitHub Repository rg-security-opt-demo security-demoapp rg-cost-opt-sreademo costoptimizationapp This enables the agent to create GitHub issues for findings linking violations directly to the repo responsible for that infrastructure. Step 5: Test with Prompts Before setting up automation, I tested the agent with manual prompts to make sure it was finding the right issues. The agent ran the checks, compared against my org-practices.md, and identified the issues. Security Check: Scan resource group "rg-security-opt-demo" for any violations of our security practices defined in org-practices.md in your knowledge base. list violations with severity and remediation steps. Make sure to check against all critical requirements and send message in teams channel with your findings and create an issue in the github repo https://github.com/dm-chelupati/security-demoapp.git Cost Check: Scan resource group "rg-cost-opt-sreademo" for any violations of our costpractices defined in org-practices.md in your knowledge base. list violations with severity and remediation steps. Make sure to check against all critical requirements and send message in teams channel with your findings and create an issue in the github repo https://github.com/dm-chelupati/costoptimizationapp.git Step 6: Check Output via GitHub Issues After running prompts, I checked GitHub. The agent had created issues. Each issue has the root cause, impact, and fix ready for the team to action or for Coding Agent to pick up and create a PR. 👉 See the actual issues created: Security findings issue Cost findings issue Step 7: Set Up Scheduled Triggers This is where it gets powerful. I configured recurring schedules: Weekly Security Check (Wednesdays 8 AM): Create a scheduled trigger that performs security practices checks against the org practices in knowledge base org-practices.md, creates github issue and send teams message on a weekly basis Wednesdays at 8 am UTC Weekly Cost Review (Mondays 8 AM): Create a scheduled trigger that performs cost practices checks against the org practices in knowledge base org-practices.md, creates github issue and send teams message on a weekly basis on Mondays at 8 am UTC Now optimization runs automatically. Every week, fresh findings land in GitHub Issues and Teams. Why Context Makes the SRE Agent Powerful Think about hiring a new SRE. They're excellent at their craft—they know Kubernetes, networking, Azure inside out. But on day one, they can't solve problems in your environment yet. Why? They don't have context: What are your SLOs? What's "acceptable" latency for your app? When do you rotate secrets? Monthly? Quarterly? Before each release? Which resources are production-critical vs. dev experiments? What's your tagging policy? Who owns what? How do you deploy? GitOps? Pipelines? Manual approvals? A great engineer becomes your great engineer once they learn how your team operates. SRE Agent works the same way. Out of the box, it knows Azure resource types, networking, best practices. But it doesn't know your bar. Is 20% CPU utilization acceptable or wasteful? Should secrets expire in 30 days or 90? Are public endpoints ever okay, or never? The more context you give the agent, your SLOs, your runbooks, your policies, the more it reasons like a team member who understands your environment, not just Azure in general. That's why Step 2 matters so much. When I uploaded our standards, the agent stopped checking generic Azure best practices and started checking our best practices. Bring your existing knowledge: You don't have to start from scratch. If your team's documentation already lives in Atlassian Confluence, SharePoint, or other tools, you can connect those via MCP servers. The agent pulls context from where your team already works, no need to duplicate content. Why This Matters Before this setup, optimization was a quarterly thing. Now it happens automatically: Before After Check security when audit requests it Daily automated posture check Find waste when finance complains Weekly savings report in Teams Discover capacity issues during incidents Scheduled headroom analysis Expire credentials and debug at 2 AM 30-day warning with exact secret names Optimization isn't a project anymore. It's a practice. Try It Yourself Create an SRE Agent with access to your subscription Upload your team's standards (security policies, cost thresholds, tagging rules) Set up a scheduled trigger, start with a daily security check Watch the first report land in Teams See what you've been missing while everything looked "fine." Learn More Azure SRE Agent documentation Azure SRE Agent blogs Azure SRE Agent community Azure SRE Agent home page Azure SRE Agent pricing Azure SRE Agent is currently in preview. Get StartedFind the Alerts You Didn't Know You Were Missing with Azure SRE Agent
I had 6 alert rules. CPU. Memory. Pod restarts. Container errors. OOMKilled. Job failures. I thought I was covered. Then my app went down. I kept refreshing the Azure portal, waiting for an alert. Nothing. That's when it hit me: my alerts were working perfectly. They just weren't designed for this failure mode. Sound familiar? The Problem Every Developer Knows If you're a developer or DevOps engineer, you've been here: a customer reports an issue, you scramble to check your monitoring, and then you realize you don't have the right alerts set up. By the time you find out, it's already too late. You set up what seems like reasonable alerting and assume you're covered. But real-world failures are sneaky. They slip through the cracks of your carefully planned thresholds. My Setup: AKS with Redis I love to vibe code apps using GitHub Copilot Agent mode with Claude Opus 4.5. It's fast, it understands context, and it lets me focus on building rather than boilerplate. For this project, I built a simple journal entry app: AKS cluster hosting the web API Azure Cache for Redis storing journal data Azure Monitor alerts for CPU, memory, pod restarts, container errors, OOMKilled, and job failures Seemed solid. What could go wrong? The Scenario: Redis Password Rotation Here's something that happens constantly in enterprise environments: the security team rotates passwords. It's best practice. It's in the compliance checklist. And it breaks things when apps don't pick up the new credentials. I simulated exactly this. The pods came back up. But they couldn't connect to Redis (as expected). The readiness probes started failing. The LoadBalancer had no healthy backends. The endpoint timed out. And not a single alert fired. Using SRE Agent to Find the Alert Gaps Instead of manually auditing every alert rule and trying to figure out what I missed, I turned to Azure SRE Agent. I asked it a simple question: "My endpoint is timing out. What alerts do I have, and why didn't any of them fire?" Within minutes, it had diagnosed the problem. Here's what it found: My Existing Alerts Why They Didn't Fire High CPU/Memory No resource pressure,just auth failures Pod Restarts Pods weren't restarting, just unhealthy Container Errors App logs weren't being written OOMKilled No memory issues Job Failures No K8s jobs involved The gaps SRE Agent identified: ❌ No synthetic URL availability test ❌ No readiness/liveness probe failure alerts ❌ No "pods not ready" alerts scoped to my namespace ❌ No Redis connection error detection ❌ No ingress 5xx/timeout spike alerts ❌ No per-pod resource alerts (only node-level) SRE Agent didn't just tell me what was wrong, it created a GitHub issue with : KQL queries to detect each failure type Bicep code snippets for new alert rules Remediation suggestions for the app code Exact file paths in my repo to update Check it out: GitHub Issue How I Built It: Step by Step Let me walk you through exactly how I set this up inside SRE Agent. Step 1: Create an SRE Agent I created a new SRE Agent in the Azure portal. Since this workflow analyzes alerts across my subscription (not just one resource group), I didn't configure any specific resource groups. Instead, I gave the agent's managed identity Reader permissions on my entire subscription. This lets it discover resources, list alert rules, and query Log Analytics across all my resource groups. Step 2: Connect GitHub to SRE Agent via MCP I added a GitHub MCP server to give the agent access to my source code repository.MCP (Model Context Protocol) lets you bring any API into the agent. If your tool has an API, you can connect it. I use GitHub for both source code and tracking dev tickets, but you can connect to wherever your code lives (GitLab, Azure DevOps) or your ticketing system (Jira, ServiceNow, PagerDuty). Step 3: Create a Subagent inside SRE Agent for managing Azure Monitor Alerts I created a focused subagent with a specific job and only the tools it needs: Azure Monitor Alerts Expert Prompt: " You are expert in managing operations related to azure monitor alerts on azure resources including discovering alert rules configured on azure resources, creating new alert rules (with user approval and authorization only), processing the alerts fired on azure resources and identifying gaps in the alert rules. You can get the resource details from azure monitor alert if triggered via alert. If not, you need to ask user for the specific resource to perform analysis on. You can use az cli tool to diagnose logs, check the app health metrics. You must use the app code and infra code (bicep files) files you have access to in the github repo <insert your repo> to further understand the possible diagnoses and suggest remediations. Once analysis is done, you must create a github issue with details of analysis and suggested remediation to the source code files in the same repo." Tools enabled: az cli – List resources, alert rules, action groups Log Analytics workspace querying – Run KQL queries for diagnostics GitHub MCP – Search repositories, read file contents, create issues Step 4: Ask the Subagent About Alert Gaps I gave the agent context and asked a simple question: "@AzureAlertExpert: My API endpoint http://132.196.167.102/api/journals/john is timing out. What alerts do I have configured in rg-aks-journal, and why didn't any of them fire? The agent did the analysis autonomously and summarized findings with suggestions to add new alert rules in a GitHub issue. Here's the agentic workflow to perform azure monitor alert operations Why This Matters Faster response times. Issues get diagnosed in minutes, not hours of manual investigation. Consistent analysis. No more "I thought we had an alert for that" moments. The agent systematically checks what's covered and what's not. Proactive coverage. You don't have to wait for an incident to find gaps. Ask the agent to review your alerts before something breaks. The Bottom Line Your alerts have gaps. You just don't know it until something slips through. I had 6 alert rules and still missed a basic failure. My pods weren't restarting, they were just unhealthy. My CPU wasn't spiking, the app was just returning errors. None of my alerts were designed for this. You don't need to audit every alert rule manually. Give SRE Agent your environment, describe the failure, and let it tell you what's missing. Stop discovering alert gaps from customer complaints. Start finding them before they matter. A Few Tips Give the agent Reader access at subscription level so it can discover all resources Use a focused subagent prompt, don't try to do everything in one agent Test your MCP connections before running workflows What Alert Gaps Have Burned You? What's the alert you wish you had set up before an incident? Credential rotation? Certificate expiry? DNS failures? Let us know in the comments.
