Containers

In this post, we guide you through the process of migrating from AWS App Mesh to Amazon VPC Lattice, highlighting key considerations and benefits that this transition offers for your cloud infrastructure. We demonstrate how to migrate an IT Inventory Management System application from AWS App Mesh to VPC Lattice using Amazon ECS, with detailed steps for creating VPC Lattice resources, updating task definitions, and implementing blue/green deployment strategies.

In this post, Amazon ECS announces support for IPv6-only workloads, allowing users to run containerized applications in IPv6-only environments without IPv4 dependencies while maintaining compatibility with existing applications and AWS services. The new capability helps organizations address IPv4 address exhaustion challenges, streamline network architecture, improve security posture, and meet compliance requirements for IPv6 adoption.

In this post, we demonstrate how to configure Amazon Route 53 to enable unique failover behavior for each application within multi-tenant Amazon EKS environments across AWS Regions. This solution allows organizations to maintain the cost benefits of shared infrastructure while meeting diverse availability requirements by implementing application-specific health checks that provide granular control over failover scenarios.

In this post, we demonstrate how to use a Raspberry Pi 5 as an Amazon EKS hybrid node to process edge workloads while maintaining cloud connectivity. We show how to set up an EKS cluster that connects cloud and edge infrastructure, secure connectivity using WireGuard VPN, enable container networking with Cilium, and implement a real-world IoT application using an ultrasonic sensor that demonstrates edge-cloud integration.

In this post, we explore the migration path from AWS CodeDeploy to Amazon ECS for blue/green deployments, discussing key architectural differences and implementation considerations. We examine three different migration approaches – in-place update, new service with existing load balancer, and new service with new load balancer – along with their respective trade-offs in terms of complexity, risk, downtime, and cost.

In this post, we explore how Amazon ECS’s native support for blue/green deployments can be extended using lifecycle hooks to integrate test suites, manual approvals, and metrics into deployment pipelines.

In this post, we introduce an automated, GitOps-driven approach to resource optimization in Amazon EKS using AWS services such as Amazon Managed Service for Prometheus and Amazon Bedrock. The solution helps optimize Kubernetes resource allocation through metrics-driven analysis, pattern-aware optimization strategies, and automated pull request generation while maintaining GitOps principles of collaboration, version control, and auditability.

In this post, we explore how to build highly available Kubernetes applications using Amazon EKS Auto Mode by implementing critical features like Pod Disruption Budgets, Pod Readiness Gates, and Topology Spread Constraints. Through various test scenarios including pod failures, node failures, AZ failures, and cluster upgrades, we demonstrate how these implementations maintain service continuity and maximize uptime in EKS Auto Mode environments.

In this post, we show you how to swiftly deploy inference workloads on EKS Auto Mode and demonstrate key features that streamline GPU management. We walk through a practical example by deploying open weight models from OpenAI using vLLM, while showing best practices for model deployment and maintaining operational efficiency.

In this post, we demonstrate how to utilize the Kubecost Amazon EKS add-on to reduce infrastructure costs and enhance Kubernetes efficiency through Container Request Right Sizing, which helps identify and fix inefficient container resource configurations. We explore how to review Kubecost’s right sizing recommendations and implement them through either one-time updates or scheduled automated resizing within Amazon EKS environments for continuous resource optimization.