Autoscaling Kubernetes with Custom Prometheus Metrics

Standard CPU and memory metrics often fall short when scaling modern applications. To handle real-world traffic patterns effectively, your Kubernetes clusters need to scale based on application-specific data like request rates or queue depths.\n\nThis text-based course guides you through the entire process of setting up a Horizontal Pod Autoscaler (HPA) driven by custom metrics. You will learn how to instrument your application, expose metrics to Prometheus, and configure the custom metrics API to make your workloads truly responsive to user demand.\n\nWhat you'll learn:\n- Understand core Kubernetes autoscaling concepts and how the HPA interacts with metric APIs\n- Instrument application code to expose custom Prometheus metrics\n- Configure the Prometheus Adapter to translate custom metrics for the Kubernetes API\n- Define and deploy an HPA resource that targets your application-specific metrics\n- Apply troubleshooting techniques to diagnose common autoscaling delays and metric mismatches\n- Practice securing your metrics pipeline using Kubernetes role-based access control (RBAC)\n\nYou will start with the fundamental concepts of Kubernetes metrics architecture before moving on to hands-on configuration, adapter setup, and final HPA deployment. This course is designed for software developers, DevOps beginners, and system administrators who want to move beyond basic CPU/memory scaling. No advanced scaling experience is required. Start building more resilient, self-scaling Kubernetes deployments today.