Auto Scaling with AKS Tutorial


Auto scaling is a critical feature of Azure Kubernetes Service (AKS) that allows you to dynamically adjust the number of pods in your cluster based on resource utilization. This ensures that your applications can handle increased traffic and demand without manual intervention. In this tutorial, we will explore how to set up auto scaling in AKS using the Horizontal Pod Autoscaler (HPA) and demonstrate the process with step-by-step instructions.

Step 1: Configure Metrics Server

Before enabling auto scaling, you need to ensure that the Metrics Server is running in your AKS cluster. The Metrics Server collects resource utilization data required for scaling decisions. To install the Metrics Server, run the following command:

kubectl apply -f

Step 2: Deploy and Scale Your Application

Once the Metrics Server is installed, you can deploy and scale your application. Start by creating a deployment for your application using a YAML manifest file. Here's an example of a deployment YAML:

apiVersion: apps/v1 kind: Deployment metadata: name: my-app spec: replicas: 3 selector: matchLabels: app: my-app template: metadata: labels: app: my-app spec: containers: - name: my-app image: my-app:latest ports: - containerPort: 80

Replace "my-app" with the name of your application and "my-app:latest" with the image name and version. Save this YAML manifest as "my-app-deployment.yaml" and deploy it using the following command:

kubectl apply -f my-app-deployment.yaml

Next, create a Horizontal Pod Autoscaler (HPA) to automatically scale the number of pods based on CPU utilization. Here's an example of an HPA YAML:

apiVersion: autoscaling/v2beta2 kind: HorizontalPodAutoscaler metadata: name: my-app-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: my-app minReplicas: 1 maxReplicas: 10 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 50

This HPA configuration specifies that the number of pods should be scaled between 1 and 10, targeting an average CPU utilization of 50%. Save this YAML manifest as "my-app-hpa.yaml" and apply it using the following command:

kubectl apply -f my-app-hpa.yaml

Common Mistakes to Avoid

  • Not installing the Metrics Server: Auto scaling relies on the Metrics Server to collect resource utilization data. Forgetting to install it can prevent successful auto scaling.
  • Incorrect configuration of the HPA: Setting incorrect values for minimum and maximum replicas, target resource utilization, or missing references to the deployment can lead to undesired scaling behavior or errors.
  • Insufficient cluster resources: Auto scaling requires enough resources in the cluster to accommodate additional pods. Failing to allocate sufficient resources can limit the effectiveness of auto scaling.

Frequently Asked Questions (FAQs)

  1. Can I use custom metrics for auto scaling?

    Yes, AKS supports the use of custom metrics for auto scaling. You can configure custom metrics using Azure Monitor and use them in your HPA definition.

  2. Does AKS support both horizontal and vertical auto scaling?

    AKS supports horizontal auto scaling, where the number of pods is adjusted based on resource utilization. Vertical auto scaling, which adjusts the resources allocated to individual pods, is not natively supported by AKS.

  3. Can I define multiple HPAs for the same deployment?

    Yes, you can define multiple HPAs for the same deployment, each targeting different metrics. AKS evaluates all the HPAs and scales the deployment accordingly.

  4. How often does auto scaling occur?

    Auto scaling in AKS is triggered based on the metrics specified in the HPA configuration. The Metrics Server collects metrics at regular intervals, and auto scaling decisions are made based on those metrics.

  5. Can I disable auto scaling for a specific deployment?

    Yes, you can disable auto scaling for a specific deployment by deleting the corresponding HPA or setting the minimum and maximum replicas to the same value.


Auto scaling in Azure Kubernetes Service (AKS) enables your applications to dynamically adjust the number of pods based on resource utilization. By configuring the Metrics Server, deploying your application, and setting up the Horizontal Pod Autoscaler (HPA), you can automate the scaling process and ensure your application can handle varying workloads efficiently. Regular monitoring and fine-tuning of the auto scaling configuration will help you optimize resource utilization and application performance in your AKS cluster.