Kategorie: Amazon AWS

Amazon EC2 C8i-flex instances are now available in Europe (Ireland, London), and Asia Pacific (New Zealand) regions

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8i-flex instances are available in the Europe (Ireland, London), and Asia Pacific (New Zealand) regions. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. C8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver up to 20% higher performance than C7i-flex instances, with even higher gains for specific workloads. The C8i-flex are up to 60% faster for NGINX web applications, up to 40% faster for AI deep learning recommendation models, and 35% faster for Memcached stores compared to C7i-flex. C8i-flex are the easiest way to get price performance benefits for a majority of compute intensive workloads like web and application servers, databases, caches, Apache Kafka, Elasticsearch, and enterprise applications. They offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information about the new C8i-flex instances visit the AWS News blog.
Quelle: aws.amazon.com

Amazon SageMaker Unified Studio now supports multiple code spaces within projects for IAM domains

Amazon SageMaker Unified Studio now lets data workers create and manage multiple code spaces (individually configured development environments) within a single project for IAM domains. Previously, projects were limited to one JupyterLab space and one Code Editor space embedded in the project. With this launch, you can now parallelly work on different workstreams or experiments with different compute and storage configuration needs, giving developers the flexibility they need as their workloads scale. For instance, data scientists can now work in parallel on any long running data transformation and model training workloads within the same project using separate spaces.
With multiple spaces, each one maintains its own persistent Amazon EBS volume, ensuring that your files, data, and session state are preserved independently. You can scale compute and storage up or down per space, pause and resume them at any time, and customize the runtime environment for each specific task. Spaces can either be opened in dedicated browser tabs or connected to a local IDE if you prefer your own development environment, with full functionality including Amazon Q paid tier support. This is particularly beneficial for builders who need isolated environments for parallel workstreams while still working within a single collaborative project. 
This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more about code spaces in SageMaker Unified Studio projects, see Managing Code Spaces in the Amazon SageMaker User Guide.
Quelle: aws.amazon.com

Amazon EC2 for SQL Server HA now supports health notifications

Today, AWS announced that you can now receive notifications through the AWS Health Dashboard when Amazon EC2 for SQL Server cannot detect a valid SQL Server High Availability (HA) status.
This enhancement is particularly valuable for customers who register EC2 SQL HA clusters through AWS Console or CloudFormation automation to reduce license included costs. You can receive this notification through multiple channels, including AWS Health, Amazon EventBridge events, and email. These notifications will guide you to respond quickly, helping avoid unexpected billing costs or charges.
To learn more, access the High Availability for SQL Server on Amazon EC2 user guide for additional details.  This feature is accessible in all AWS Regions where Amazon EC2 SQL HA is available and the AWS GovCloud (US) Regions.
Quelle: aws.amazon.com

Amazon Bedrock AgentCore adds new features to help developers build agents faster

Today, Amazon Bedrock AgentCore introduces new features to help developers go from an idea to working agent prototype faster and manage the full agent lifecycle from a single platform: a managed harness (in preview), the AgentCore CLI, and AgentCore skills for coding assistants.
The managed harness (preview) lets developers define an agent by specifying a model, system prompt, and tools, then run it immediately with no orchestration code required. The harness manages the full agent loop: reasoning, tool selection, action execution, and response streaming. Each session gets its own microVM with filesystem and shell access. The harness is model agnostic with the ability to switch models mid-session. Any configuration set at create time can be overridden per invocation, so developers experiment without redeploying. When developers need full control, they can export the harness orchestration in Strands-based code. Filesystem persistence (preview) externalizes the local session state, allowing agents to suspend mid-task and resume exactly where they left off. As a prototype evolves, developers can easily add evaluations to measure quality, memory for personalization, or additional tools and skills. When it’s time to promote a validated concept, the AgentCore CLI deploys with the governance and audibility of infrastructure-as-code. AWS CDK is supported today as a resource manager, with Terraform coming soon. The AgentCore CLI has been optimized for coding assistant control, with pre-built skills that provide accurate, up-to-date AgentCore guidance. AgentCore skills are available today through Kiro Power, with support for Claude Code, Codex, and Cursor coming next week.
The managed harness (preview) in AgentCore is available in four AWS Regions: US West (Oregon), US East (N. Virginia), Europe (Frankfurt), and Asia Pacific (Sydney). The AgentCore CLI is available in 14 AWS Regions where AgentCore is available. There is no additional charge for the harness, CLI, or skills. Learn more through the blog, and visit the documentation to get started.
Quelle: aws.amazon.com

AWS Secrets Manager extends managed external secrets to MongoDB Atlas and Confluent Cloud

AWS Secrets Manager now supports managed external secrets for MongoDB Atlas and Confluent Cloud.
AWS Secrets Manager now supports managed external secrets for MongoDB Atlas and Confluent Cloud, enabling you to centrally manage and automatically rotate secrets for these third-party services directly from AWS Secrets Manager — without building or maintaining custom Lambda rotation functions.
The MongoDB Atlas integration supports two secret types: database user secrets (username-password authentication via SCRAM) and service account secrets (OAuth client ID and secret). The Confluent Cloud integration supports API key rotation for service accounts, with support for both cluster-scoped and cloud resource management keys. All integrations include automatic rotation enabled by default, eliminating hardcoded secrets and reducing the operational overhead of managing secrets across multiple platforms.
With managed external secrets, secret rotation is fully managed by AWS Secrets Manager using partner-provided rotation logic — no Lambda functions are deployed in your account. For example, a data pipeline using MongoDB Atlas and Confluent Kafka can now centralize secret management in AWS Secrets Manager, automatically rotating database and streaming platform secrets without modifying application code or managing separate rotation logic for each service.
MongoDB Atlas and Confluent Cloud integrations for managed external secrets are available in all AWS Regions where managed external secrets is supported, joining existing integrations with Salesforce, Snowflake, and BigID. To learn more, visit the AWS Secrets Manager managed external secrets documentation.
Quelle: aws.amazon.com

Amazon SageMaker AI now supports serverless model customization for Qwen3.5 models

Amazon SageMaker AI now supports serverless model customization for Qwen3.5, enabling you to fine-tune Qwen3.5 4B, 9B, and 27B parameter models using supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). Qwen3.5 is a popular open-weight model family from Alibaba Cloud. Before this launch, you could deploy these base models on SageMaker AI and now, you can also adapt them to your specific domains and workflows. 
Model customization enables you to tailor foundation models with your proprietary data so they more accurately reflect your domain knowledge, terminology, and quality standards. Rather than building models from scratch, fine-tuning lets you start from a capable base model and specialize it for your use cases, whether that’s improving accuracy on domain-specific tasks, aligning outputs with your organization’s tone, or improving performance on new tasks using your labeled data. With serverless customization, SageMaker AI handles all infrastructure provisioning and training orchestration, so you can focus on your data and evaluation rather than cluster management, and only pay for what you use.
Serverless model customization for Qwen3.5 on SageMaker AI is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and EU (Ireland). To get started, navigate to the Models page in Amazon SageMaker Studio to launch a customization job, or use the SageMaker Python SDK for programmatic access. To learn more, see the Amazon SageMaker AI model customization documentation.
Quelle: aws.amazon.com

Enhancements to AWS Network Firewall Managed Rules from AWS Marketplace Partners

AWS Network Firewall now supports expanded Managed Rules from AWS Marketplace partners, with new rule group optimizations through partners to include up to 10 million domain name indicators and up to 1 million IP addresses in their managed rule groups. Infoblox is expanding domain name indicators to protect your workloads from critical and high-risk domains. Lumen is introducing new rule groups to stop command and control attacks. ThreatSTOP is adding managed rules for Office of Foreign Assets Control (OFAC) sanctions and expanding global compliance protections with new European Union, Japan, and United Nations sanction coverage. These enhancements give you access to richer, more comprehensive threat intelligence directly within AWS Network Firewall, reducing the operational burden of managing threat feeds and enabling faster, more accurate protection against emerging threats. Whether you need to block malicious domains at scale, defend against command and control infrastructure, or enforce sanctions-based compliance policies, managed rules from AWS Marketplace partners provide ready-to-deploy, continuously updated protections for your cloud workloads. Managed rules for AWS Network Firewall are available from AWS Marketplace sellers of Check Point, Fortinet, Infoblox, Lumen, Rapid7, ThreatSTOP, and Trend Micro. AWS Marketplace rule groups are now available in 9 additional AWS Regions: Asia Pacific (Jakarta), Asia Pacific (Hyderabad), Asia Pacific (Melbourne), Asia Pacific (Malaysia), Canada West (Calgary), Europe (Zurich), Europe (Spain), Israel (Tel Aviv), and Mexico (Central). For a full list of supported regions, visit the AWS Regional Services page. To get started, visit the AWS Network Firewall console or browse available managed rules in AWS Marketplace. For more information, see the AWS Network Firewall product page and the service documentation.
Quelle: aws.amazon.com

Amazon Athena Spark adds support for AWS PrivateLink

Amazon Athena Spark now supports AWS PrivateLink so that you can access APIs and endpoints from your Amazon Virtual Private Cloud (VPC) without traversing the public internet. This feature can help you meet compliance requirements by allowing you to access and use Athena Spark APIs and endpoints entirely within the AWS network. You can now create AWS PrivateLink interface endpoints to connect from clients in your VPC. The Athena VPC endpoint supports all Athena Spark APIs and endpoints, including the Spark Connect, Spark Live UI and Spark History Server endpoints. Communication between your VPC and Athena Spark APIs and endpoints is then conducted entirely within the AWS network, providing a secure pathway for your data. To get started, you can create an interface VPC endpoint to connect to Amazon Athena Spark using the AWS Management Console or AWS Command Line Interface (AWS CLI) commands or AWS CloudFormation. This new feature is available in all AWS Regions where Amazon Athena Spark and AWS PrivateLink are available. For more information, refer to the AWS PrivateLink documentation and Athena Spark documentation.
 
Quelle: aws.amazon.com

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