Cloud Computing Köln

Introducing the Amazon EKS Hybrid Nodes gateway for hybrid Kubernetes networking

Amazon Elastic Kubernetes Service (EKS) now offers the Amazon EKS Hybrid Nodes gateway, a feature that automates networking between your Amazon EKS cluster VPC and Kubernetes Pods running on Amazon EKS Hybrid Nodes. The Amazon EKS Hybrid Nodes gateway eliminates the need to make on-premises pod networks routable or coordinate network infrastructure changes when running in hybrid Kubernetes environments. Networking in hybrid Kubernetes environments can be complex, often requiring changes to on-premises routing configurations, coordination with network teams, and ongoing maintenance as workloads scale. The Amazon EKS Hybrid Nodes gateway addresses these challenges by automatically enabling Kubernetes control plane-to-webhook communication, pod-to-pod traffic across cloud and on-premises environments, and connectivity for AWS services such as Application Load Balancers, Network Load Balancers, and Amazon Managed Service for Prometheus. Customers deploy the Amazon EKS Hybrid Nodes gateway to Amazon EC2 instances using Helm, and the gateway automatically maintains VPC route tables as workloads scale. The Amazon EKS Hybrid Nodes gateway codebase is open source. The Amazon EKS Hybrid Nodes gateway is available in all AWS Regions where Amazon EKS Hybrid Nodes is available, except the China Regions. The Amazon EKS Hybrid Nodes gateway is offered at no additional charge. You pay for the underlying AWS infrastructure used to run the gateway, including Amazon EC2 instance charges and any associated data transfer fees. To get started, visit the Amazon EKS Hybrid Nodes gateway documentation.
Quelle: aws.amazon.com

Five new Qwen models for coding agents and efficient reasoning are now available in Amazon SageMaker JumpStart

Today, AWS announced the availability of Qwen3-Coder-Next, Qwen3-30B-A3B, Qwen3-30B-A3B-Thinking-2507, Qwen3-Coder-30B-A3B-Instruct, and Qwen3.5-4B in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These five models from Qwen bring specialized capabilities spanning agentic coding, efficient reasoning, extended thinking, and multimodal understanding, enabling customers to build sophisticated AI applications across diverse use cases on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Qwen3-Coder-Next excels at long-horizon reasoning, complex tool use, and recovery from execution failures, making it ideal for powering coding agents in CLI/IDE platforms.
Qwen3-30B-A3B uniquely supports seamless switching between thinking and non-thinking modes, making it well suited for general-purpose assistant tasks like multilingual dialogue, math reasoning, and tool calling.
Qwen3-30B-A3B-Thinking-2507 delivers significantly improved performance on complex reasoning tasks in math, science, and coding, with enhanced long-context understanding.
Qwen3-Coder-30B-A3B-Instruct is designed for agentic coding workflows with a custom function call format and repo-scale context understanding.
Qwen3.5-4B supports unified vision-language training and  201 languages, making it ideal for lightweight multimodal deployments.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

Amazon SageMaker now supports multi-region replication from IAM Identity Center

Amazon SageMaker now supports multi-region replication from IAM Identity Center (IdC), enabling you to deploy SageMaker Unified Studio domains in different regions from your IdC instance. This new capability empowers enterprise customers, particularly those in regulated industries like financial services and healthcare, to maintain compliance while leveraging centralized workforce identity management. As an Amazon SageMaker Unified Studio administrator, you can deploy SageMaker domains closer to your workforce based on data residency needs while maintaining seamless single sign-on (SSO) access. Organizations can address use cases such as maintaining IdC in one region while processing sensitive data in compliance-required regions, supporting global operations with centralized identity management, and meeting data sovereignty requirements without compromising SSO capabilities.
To get started see the SageMaker Unified Studio documentation and to learn about setting up IAM Identity Center multi-Region support see the IAM Identity Center User Guide.
Quelle: aws.amazon.com

AWS Backup adds Amazon Redshift Serverless and Aurora DSQL support for AWS Organizations backup policies

AWS Backup now supports Amazon Redshift Serverless namespaces and Amazon Aurora DSQL clusters as resource types in AWS Organizations backup policies. Organization administrators can now define backup policy rules that directly target these resource types across member accounts.
Previously, backing up Redshift Serverless namespaces and Aurora DSQL clusters through organization backup policies required using tag-based selections or backing up all resources in a member account. With this launch, administrators can specify these resource types directly in their backup policy selections, providing more precise control over which resources are included in or excluded from Organization-wide backup plans.
This capability is available in all AWS Commercial and GovCloud Regions where AWS Backup and the respective services are available. To get started, visit the AWS Organizations backup policies documentation or the AWS Backup console.
Quelle: aws.amazon.com

Amazon Aurora serverless: Up to 30% better performance, smarter scaling, and still scales to zero

Amazon Aurora serverless — the autoscaling database that scales up to support your most demanding workloads and down to zero when you don’t need it — just got faster and smarter, with up to 30% better performance than the previous version and enhanced scaling that understands your workload. It’s especially well-suited for agentic AI applications, which typically have bursts of activity, long idle windows, and unpredictable patterns. Aurora serverless handles all of it automatically, scaling capacity with your agents rather than against them, and you only pay for what you actually use. When not in use, the database automatically scales down to zero to save cost. With improved performance and scaling, you can now use serverless for even more demanding workloads. The enhanced scaling algorithm enables you to efficiently run workloads where multiple tasks compete for resources, such as busy web applications and API services. These improvements are available in platform version 4 at no additional cost. All new clusters, database restores, and new clones will automatically launch on platform version 4. Existing clusters on platform version 1, 2, or 3 can upgrade directly to platform version 4 by using pending maintenance action, stopping and restarting the cluster, or using blue/green deployments. You can verify your cluster’s platform version in the AWS Console under instance configuration section or via the RDS API’s ServerlessV2PlatformVersion parameter. To learn more, read the blog. Aurora serverless is an on-demand, automatic scaling configuration for Amazon Aurora. For pricing details and Region availability, visit Amazon Aurora Pricing. To learn more, read the documentation, and get started by creating an Aurora serverless database using only a few steps in the AWS Management Console.
Quelle: aws.amazon.com