Category: Amazon Elastic Kubernetes Service

Amazon EKS introduces Provisioned Control Plane, a new capability that allows you to pre-allocate control plane capacity for predictable, high-performance Kubernetes operations at scale. In this post, we explore how this enhanced option complements the Standard Control Plane by offering multiple scaling tiers (XL, 2XL, 4XL) with well-defined performance characteristics for API request concurrency, pod scheduling rates, and cluster database size—enabling you to handle demanding workloads like ultra-scale AI training, high-performance computing, and mission-critical applications with confidence.

Amazon EKS Blueprints for CDK now supports EKS Auto Mode, enabling developers to deploy fully managed Kubernetes clusters with minimal configuration while AWS automatically handles infrastructure provisioning, compute scaling, and core add-on management. In this post, we explore how this integration combines EKS Blueprints’ declarative infrastructure-as-code approach with EKS Auto Mode’s hands-off cluster operations, providing three practical deployment patterns—from basic clusters to specialized ARM-based and AI/ML workloads—that let teams focus on application development rather than infrastructure management .

In this post, we explore how to enhance and monitor network performance for ML inference workloads running on Amazon EKS using the newly launched Container Network Observability feature. We demonstrate practical use cases through a sample Stable Diffusion image generation workload, showing how platform teams can visualize service communication, analyze traffic patterns, investigate latency issues, and identify network bottlenecks—ultimately improving metrics like inference latency and time to first token.

In this post, we introduce key considerations for optimizing Amazon EKS costs in production environments through detailed workload analysis and comprehensive monitoring. We demonstrate proven best practices to maximize cost savings while maintaining performance and resilience, supported by real-world examples showing how to eliminate resource waste from overprovisioned pods, excessive replica counts, and fragmented node pools.

Learn how to manage your Amazon Elastic Kubernetes Service (Amazon EKS) clusters through simple conversations instead of complex kubectl commands or deep Kubernetes expertise. This post shows you how to use the new fully managed EKS Model Context Protocol (MCP) Server in Preview to deploy applications, troubleshoot issues, and upgrade clusters using natural language with no deep Kubernetes expertise required. We’ll walk through real scenarios showing how conversational AI turns multi-step manual tasks into simple natural language requests.

In this post, we provide a comprehensive guide to the 48 Amazon EKS and Kubernetes sessions at AWS re:Invent 2025, covering everything from simplified cluster management with Amazon EKS Auto Mode to advanced AI/ML workload orchestration, platform engineering, and production-grade security strategies. Whether you’re new to Kubernetes or architecting multi-region platforms, you’ll find sessions tailored to your needs across workshops, chalk talks, builders sessions, and hands-on experiences at the Containers kiosk in the AWS Village.

In this post, we demonstrate how to monitor network performance for Amazon EKS workloads using new advanced network observability features powered by Network Flow Monitor. We explore how to capture Kubernetes-enriched network metrics, export them to AWS Managed Open-Source services like Amazon Managed Service for Prometheus and Amazon Managed Grafana, and visualize critical performance indicators including throughput, packet drops, latency, and connection states across your containerized services.

In this post, we explore how AWS and NVIDIA Run:ai are extending GPU fractionalization and orchestration capabilities beyond traditional cloud regions to edge environments, including AWS Local Zones, Outposts, and EKS Hybrid Nodes. The collaboration addresses the growing demand for distributed AI/ML workloads that require efficient GPU resource management across geographically separated locations while maintaining consistent performance, compliance, and cost optimization .

In this post, we explore advanced traffic routing patterns with the Kubernetes Gateway API through a practical Calendar web application example, demonstrating how it streamlines and standardizes application connectivity and service mesh integration in Kubernetes. The post covers three key use cases: exposing applications to external clients through hostname-based routing, implementing canary deployments between microservices using gRPC traffic splitting, and controlling egress traffic to external services with security policies.

In this post, we explore how KubeArmor, an open source container-aware security enforcement system, enhances the security posture of containerized workloads running on EKS Auto Mode clusters. Although EKS Auto Mode significantly streamlines cluster management by automating control plane and node operations, securing the workloads running within the cluster remains a critical user responsibility.