Category: Advanced (300)

On Amazon EKS Auto Mode, node failures are detected, drained, and replaced automatically before anyone reaches for a laptop. This post shows how the Node Monitoring Agent and Karpenter form a detect-and-replace cycle that runs by default, why specific faults trigger node replacement, and how to collect node diagnostics without SSH.

Deploy a single CloudFormation stack that builds a GPU batch inference pipeline on Amazon ECS Managed Instances. It uses Amazon SQS for job buffering and Application Auto Scaling to scale to zero when idle, so you pay only for active inference time.

In this post, we explain how zone-aware routing works and walk you through setting up a multi-AZ ECS cluster to see it in action.

In this post, we walk you through three main steps: First, you create an AWS CodeConnections host in your VPC with connectivity to your private Git server. Second, you establish a connection that Argo CD can use. Finally, you deploy a sample application to verify the integration. By the end, you have a secure way to deploy applications from private repositories.

In this post, you walk through a practical migration scenario where a Node.js web application running on EC2 instances is migrated into a highly scalable, containerized service on EKS Auto Mode. You will learn how to configure and use the AWS and Amazon EKS MCP Servers with Kiro CLI to automate critical migration tasks from Dockerfile creation and image optimization to Kubernetes manifest generation and production deployment on EKS Auto Mode.

In this post, we demonstrate how to use Envoy’s External Processing filter (ext_proc) to solve this challenge on Amazon EKS. This solution captures complete request and response data without modifying application code, providing the compliance-grade audit trails that regulators require.

This post demonstrates how AWS DevOps Agent diagnoses Amazon Elastic Kubernetes Service (Amazon EKS) API server performance degradation, specifically 429 throttling and API Priority and Fairness (APF) seat exhaustion.

In this post, we walk through the performance and scalability improvements we shipped across the four pillars of EKS Auto Mode: runtime, compute, storage, and networking.

This post demonstrates an automated solution that combines AI-powered risk analysis with GitOps principles to streamline Amazon EKS AMI updates while maintaining appropriate human oversight through familiar GitHub workflows.

In this post, we explore how to maximize Auto Mode’s value through comprehensive cost visibility, proactive governance, and continuous optimization strategies. We cover essential cost management dimensions: establishing spending visibility, forecasting resource needs, implementing governance controls, and measuring efficiency improvements. For both new and experienced Amazon EKS Auto Mode users, this guide offers actionable insights to balance performance, reliability, and cost-efficiency in Kubernetes deployments.