Kategorie: Amazon AWS

AWS IAM identity federation to external services is now available in AWS European Sovereign Cloud Region

AWS Identity and Access Management (IAM) now enables AWS workloads in the AWS European Sovereign Cloud (Germany) Region to securely authenticate with external services using short-lived JSON Web Tokens (JWTs). The AWS European Sovereign Cloud is an independent cloud for Europe entirely located within the European Union (EU), designed to help customers meet their evolving sovereignty requirements.
With outbound identity federation, AWS workloads can securely authenticate with third-party cloud providers, SaaS providers, and self-hosted applications without using long-term credentials or implementing complex workarounds. Customers can exchange their AWS IAM credentials for cryptographically signed, short-lived JWTs, providing a simple and secure mechanism to access external services. These tokens contain rich context about the AWS workloads, enabling external services to implement fine-grained access control. Administrators can control access to token generation and enforce token properties (such as lifetime, audience, and signing algorithms) using IAM policies and audit token usage using CloudTrail logs, allowing them to meet their organization’s security and compliance requirements.
To learn more, visit the outbound identity federation product page, see the IAM user guide, or read the AWS News Blog Post.
Quelle: aws.amazon.com

Amazon Bedrock now supports OpenAI models in India

Amazon Bedrock now supports the OpenAI GPT-5.6 models (Terra and Luna) in India, with India Geo cross-Region inference. Customers with regulatory requirements of in-country inferencing can now use OpenAI models at scale ensuring that inferencing is processed within India.
Cross-Region inference automatically routes inference requests across multiple AWS Regions to give you higher throughput, without you needing to manage capacity across multiple Regions. The new India Geo inference profiles—in.openai.gpt-5.6-terra for Terra and in.openai.gpt-5.6-luna for Luna—route requests only within the India geography, across AWS Regions such as Asia Pacific (Mumbai) and Asia Pacific (Hyderabad), so you can scale to meet demand while keeping data processed within India to meet data residency requirements. The models run on the bedrock-runtime endpoint with support for the Responses, Chat Completions, and Converse APIs, and work with the same account-level controls you already use for other models on Bedrock, including model invocation logging (deliverable to Amazon S3 or Amazon CloudWatch Logs), Amazon CloudWatch metrics, and cost itemization in AWS Cost Explorer and the AWS Cost and Usage Report.
OpenAI models with India cross-Region inference are available in the Asia Pacific (Mumbai) and Asia Pacific (Hyderabad) Regions. To get started, review the model cards for GPT-5.6 (Terra and Luna) and the Cross-Region inference section in the Amazon Bedrock User Guide.
Quelle: aws.amazon.com

Amazon Bedrock now supports SpaceXAI Grok 4.6

Amazon Bedrock now supports SpaceXAI Grok 4.6, SpaceXAI’s latest flagship model built for long-running agents and ambitious interactive and visual work. Grok 4.6 offers a 500K context window and configurable reasoning  efforts (low, medium, high, xhigh).  Grok 4.6 builds on previous generations of Grok with a particular focus on staying with complex tasks across many steps, whether researching a topic, analyzing information, working across a codebase, or turning an idea into a polished application. According to SpaceXAI, it achieves frontier intelligence across several agentic coding and knowledge work benchmarks, matching other frontier models specialized for coding. With Bedrock, customers can access the model with enterprise-grade security and privacy,  comprehensive monitoring and logging, and the flexibility to scale across AWS Regions with cross-Region inference.  Grok 4.6 is available in all AWS Regions where Amazon Bedrock is offered. To get started, review the model card for Grok 4.6 in the Amazon Bedrock User Guide.
Quelle: aws.amazon.com

IAM Policy Autopilot now supports Terraform plan files

IAM Policy Autopilot can now generate baseline IAM policies directly from a Terraform plan file. IAM Policy Autopilot is an open source tool, launched at re:Invent 2025, that analyzes your code to deterministically create scoped-down IAM policies you can refine as your application evolves, reducing the time you spend writing IAM policies and troubleshooting access issues. Until now the tool analyzed application source code, but it was not possible to generate policies for deploying AWS infrastructure defined via Infrastructure as Code.
Now you can pass a Terraform plan file as input, and IAM Policy Autopilot applies a deterministic analysis to produce a policy scoped to the CRUD functions of the resources in that plan. The generated policies reference specific resource ARNs rather than wildcards, when possible. Supporting policy generation for deploying AWS infrastructure defined via Terraform has been the most requested capability since IAM Policy Autopilot launched, and it complements the existing Terraform-aware analysis, which cross-references Terraform resource definitions with SDK calls in your application code to resolve ARNs.
IAM Policy Autopilot is available at no additional cost and runs on your own machine. To get started, visit the IAM Policy Autopilot GitHub repository.
Quelle: aws.amazon.com

Amazon MWAA Serverless now supports PythonOperator and BashOperator

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Serverless now supports running custom Python functions and shell scripts directly in the serverless runtime using PythonOperator and BashOperator. With this launch, data engineering teams can execute the code patterns they rely on daily, including data transformations, format conversions, and data quality checks, without provisioning additional infrastructure.
Package your Python modules or shell scripts as code packages, upload them to Amazon S3, and reference them when creating or updating a workflow. The service snapshots your code at workflow creation time and uses that snapshot for all subsequent runs, ensuring consistency across executions.
This feature is available in all AWS Regions where Amazon MWAA Serverless is available. To learn more, visit Using Python and Bash operators.
Quelle: aws.amazon.com

AgentCore payments is now generally available in Amazon Bedrock AgentCore

Today, AWS announces the general availability of AgentCore payments, a capability within Amazon Bedrock AgentCore that enables AI agents to autonomously discover, access, and pay for paid APIs, MCPs, and content with just a few lines of code. AgentCore payments provides the security, guardrails, and observability enterprises need to deploy transacting agents in production at scale.
AgentCore payments integrates with Coinbase and Stripe Privy wallets for microtransactions, provides payment orchestration across protocols, enforces configurable payment limits at the infrastructure layer, and delivers end-to-end observability through AgentCore Observability. At general availability, AgentCore payments includes Quick Create for Coinbase credential provisioning directly within the AgentCore console, a curated Coinbase Bazar MCP server of pay-per-use x402 endpoints via AgentCore gateway, support for the Machine Payment Protocol (MPP), and the “upto” scheme in the x402 protocol for pay-per-inference and dynamic pricing use cases.
Developers can get started using coding assistant skills such as Claude Code, Kiro, and Codex, AgentCore CLI, or AWS Management Console – follow this link.
AgentCore payments is available in the regions listed here. To learn more, visit the AgentCore payments documentation or the AWS News Blog.
To learn about pricing, visit AgentCore pricing.
Quelle: aws.amazon.com

Amazon MSK now supports configuring custom domain names for MSK Provisioned clusters

You can now easily configure custom domain names on Amazon MSK Provisioned clusters, on either ZooKeeper or KRaft mode for metadata management. This capability helps client applications maintain the same connection endpoints, simplifying cluster migrations, disaster recovery failovers, and scaling operations without reconfiguration.
Previously, customers configured custom domain names manually on each broker. Additionally, on KRaft-based clusters, customers could not configure custom domain names. With this launch, you can now easily define a custom domain once at the cluster level, and Amazon MSK automatically applies it to every broker in the cluster, eliminating the need to configure each broker individually. The configuration persists through scaling operations and works identically on both ZooKeeper and KRaft-based clusters. This is particularly useful for customers who route traffic through Network Load Balancers, require persistent endpoints across cluster operations, or must adhere to organizational naming conventions for security and compliance.
You can configure custom domain names on all new and existing MSK Provisioned clusters, in all AWS Regions where Amazon MSK Provisioned is available, at no additional cost. To learn more, see the Amazon MSK Developer Guide.
Quelle: aws.amazon.com

Amazon ECR now supports 25 replication rules per registry

Amazon Elastic Container Registry (Amazon ECR) has increased the maximum number of replication rules per registry from 10 to 25.
Previously, customers with complex multi-region or multi-account architectures were constrained to 10 replication rules per registry, requiring them to consolidate replication configurations to work within that constraint. With this update, customers can define up to 25 replication rules per registry, enabling more precise replication strategies for use cases like distributing images across many regions for low-latency pulls, or replicating to multiple production and staging accounts.
This service limit increase is available in all AWS Regions where Amazon ECR is supported. To learn more, visit the Amazon ECR product page and refer to the Amazon ECR User Guide. 
Quelle: aws.amazon.com

Amazon Bedrock expands API support and introduces Cross Region Inferencing for OpenAI models

Amazon Bedrock now supports the OpenAI GPT-5.6 models (Sol, Terra, and Luna) on the bedrock-runtime endpoint, with support for the Responses, Converse, and Chat Completions APIs. It also adds support for cross-Region inference, allowing customers to use Global and Geo cross-Region inference to access higher throughput and lower inference costs.  
Cross-Region inference automatically routes inference requests across multiple AWS Regions to give you higher throughput, without you needing to manage capacity across multiple Regions. Geo cross region inference routes requests within a predefined geography—including new US Geo (US CRIS) support with this launch—so you can scale while keeping data processed within that geography, while Global cross region inference serve requests from any commercial AWS Region where the model is available, giving you the broadest access to Bedrock capacity and the highest throughput during demand spikes. With Global cross-Region inference you also get lower costs as Global inferencing is priced lower per token for OpenAI models than in-Region and Geo inferencing. This launch also expands API support—you can also use OpenAI GPT models with the Responses API, Chat Completions API, and the Converse API on the bedrock-runtime endpoint. Because these native OpenAI APIs now run on bedrock-runtime, the models work with the same account-level controls you already use for other models on Bedrock: usage appears in Bedrock model invocation logging (deliverable to Amazon S3 or Amazon CloudWatch Logs) and in Amazon CloudWatch metrics covering invocation counts, token counts, latency, throttles, and errors, and it is itemized in AWS Cost Explorer and the AWS Cost and Usage Report so you can attribute spend by model.
Cross-Region inference for OpenAI models is available in all AWS Regions where OpenAI models on Amazon Bedrock are offered. To get started, review the model cards for GPT 5.6 (Sol, Tera and Luna) in the Amazon Bedrock User Guide.
Quelle: aws.amazon.com

AWS CloudShell now includes a built-in visual file editor

AWS CloudShell now includes a built-in visual file editor that you can launch directly from your shell session using a single ‘edit’ command, no setup required. CloudShell provides a browser-based shell environment that uses your existing AWS Management Console credentials, making it a popular choice for developers, DevOps engineers, and cloud administrators managing scripts, infrastructure-as-code, agentic workflows, and AWS Lambda workflows. This new capability extends CloudShell’s utility by bringing a familiar, GUI-based editing experience directly into your console session.
Previously, editing files in CloudShell required using terminal-based editors such as Vim or Emacs, or downloading files locally and re-uploading them after making changes. This added friction to common workflows. The built-in visual editor eliminates this by supporting syntax highlighting, find-and-replace, multi-line selection, copy-paste, and undo-redo in a single browser session. Whether you are updating a deployment script, modifying an agent steering file, editing an AWS CloudFormation template, or fixing an AWS Lambda function, the editor enables a seamless edit-and-run experience.
This feature is available in all AWS Regions where AWS CloudShell is available.
To learn more about the built-in visual file editor and how to get started, visit the AWS CloudShell documentation.
Quelle: aws.amazon.com