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Amazon Bedrock expands support for request-level usage attribution

Amazon Bedrock customers can now attribute model inference usage to specific teams, applications, environments, and experiments at the individual request level on the InvokeModel and InvokeModelWithResponseStream APIs. This gives customers fine-grained visibility into how their Amazon Bedrock usage is distributed across their organization, helping them understand consumption patterns, optimize spend, and report usage back to internal stakeholders without provisioning additional resources. This launch builds on Amazon Bedrock’s existing portfolio of usage attribution capabilities. Customers can already attribute model inference usage at the resource and identity level using application inference profiles, IAM principal-based attribution, project-level tracking on the OpenAI-compatible bedrock-mantle endpoint, and workspace-level tracking for Anthropic Claude models. For finer-grained, per-request attribution, the Converse and ConverseStream APIs have supported request-level metadata since launch. Today’s release brings the same capability to the InvokeModel and InvokeModelWithResponseStream APIs, giving customers a consistent way to tag inference calls across the entire bedrock-runtime endpoint. With this launch, customers can tag each Amazon Bedrock model inference call with attributes like team, project, or environment, and analyze usage by these tags in Amazon Bedrock model invocation logs. To get started, enable model invocation logging in the AWS Region where you call Amazon Bedrock, then add metadata to your inference requests. This feature is available in all AWS commercial Regions where Amazon Bedrock is available. To learn more, see Request metadata. 
Quelle: aws.amazon.com

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Amazon RDS Custom now supports the latest GDR updates for Microsoft SQL Server

Amazon Relational Database Service (Amazon RDS) Custom for SQL Server now supports the latest General Distribution Release (GDR) updates for Microsoft SQL Server. This release includes support for SQL Server 2019 CU32+GDR KB5084816 (RDS version 15.00.4465.1.v1) and SQL Server 2022 CU24+GDR KB5083252 (RDS version 16.00.4250.1.v1). The GDR updates address vulnerabilities described in CVE-2026-32167 and CVE-2026-32176. For additional information on the improvements and fixes included in these updates, see Microsoft documentation for KB5084816, KB5083252. You can upgrade your Amazon RDS Custom for SQL Server instances to apply these recommended updates using Amazon RDS Management Console, or by using the AWS SDK or CLI. To learn more about upgrading your database instances, see Amazon RDS Custom User Guide.
Quelle: aws.amazon.com

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Amazon Aurora MySQL 8.4 is now generally available

Amazon Aurora MySQL-Compatible Edition now supports MySQL 8.4, a community MySQL Long Term Support (LTS) major version. Aurora MySQL 8.4 launches with compatibility for community MySQL 8.4.7 and introduces aligned version numbering, so the version number you run on Aurora matches the community MySQL version it is compatible with. Aurora also manages the underlying patch on your behalf, simplifying day-to-day operations. Aurora MySQL now targets major versions within 12 months of community MySQL LTS releases, minor versions within 3 months of each community minor, and an Aurora LTS minor within 12 months of each major. For engine specific release objectives, see the Aurora and RDS open source release calendar announcement. Aurora MySQL 8.4 strengthens security defaults for new clusters. TLS is enforced by default with only TLS 1.2 and 1.3 supported, new accounts use the caching_sha2_password authentication plugin, and password validation policies are customizable through DB cluster parameter groups. Automated upgrade prechecks identify compatibility issues before your cluster goes offline, giving you confidence before you upgrade. To learn more about the Aurora MySQL 8.4 customer experience, refer to the Aurora MySQL 8.4 launch announcement blog. You can upgrade your database using Amazon RDS Blue/Green Deployments, in-place upgrade, or restore from a snapshot. Learn more about performing major version upgrades in the Amazon Aurora User Guide. You can also migrate to Aurora MySQL 8.4 from external MySQL sources using AWS Database Migration Service or Percona XtraBackup. Aurora MySQL 8.4 is available in all AWS Regions where Aurora MySQL is available. Amazon Aurora MySQL is designed for unparalleled high performance and availability at global scale with full MySQL compatibility. It provides scale-to-zero serverless compute, Aurora Global Database for Multi-Region resilience, Aurora I/O-Optimized for improved price performance on I/O-intensive workloads, and built-in security and continuous backups. To get started with Amazon Aurora, take a look at our getting started page.
Quelle: aws.amazon.com

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Amazon SageMaker AI now supports OpenAI-compatible APIs for inference endpoints

Amazon SageMaker Inference now supports OpenAI-compatible APIs, so you can use the tools and frameworks you already know, like the OpenAI SDK, LangChain, and Strands Agents, to connect directly to your SageMaker endpoints. Switching requires nothing more than changing an endpoint URL — no custom integration code, no SDK wrappers, no rewrites. With this launch, you no longer need to adopt a different API format or change your authentication approach. Simply change your endpoint URL, and your existing SDK calls, streaming logic, and framework integrations continue to work as-is. You immediately gain the ability to choose your own GPU instances, keep data in your own VPC, run any open source or fine-tuned model, and scale with auto-scaling policies tuned to your workload. Authentication uses existing AWS credentials with automatic token refresh, so there is nothing extra to manage in production. This capability is available today in US East (N. Virginia), US West (Oregon), US East (Ohio), Asia Pacific (Mumbai), Asia Pacific (Jakarta), Europe (Ireland), Europe (Frankfurt), South America (São Paulo), Asia Pacific (Tokyo), Asia Pacific (Seoul), Europe (London), Asia Pacific (Singapore), Asia Pacific (Sydney), and Canada (Central). To learn more and get started, read the launch blog or visit the SageMaker Inference documentation.
Quelle: aws.amazon.com