AWS

Amazon EKS enables cluster administrators to tune parameters for core control plane components like the API server, scheduler, and controller manager. This update allows for customized pod placement strategies, such as shifting from LeastAllocated to MostAllocated to pack workloads more densely. Operators can also adjust horizontal pod autoscaling responsiveness and configure resource lifecycle settings like event retention duration.

• Tune scheduler, controller manager, and API server parameters beyond default EKS settings.
• Switch pod placement to MostAllocated to pack workloads and reduce node count.
• Adjust HPA responsiveness to better match demand changes and optimize resource usage.
• Configure resource lifecycle parameters, including event retention duration, for better control.

Amazon Bedrock now supports cost allocation by IAM principal for model inference requests routed through the bedrock-mantle endpoint. This feature extends existing cost attribution capabilities from the bedrock-runtime endpoint to the mantle interface. Users can tag IAM identities with attributes like team or project to track spending accurately in AWS Cost Explorer.

• Enable IAM-based cost tracking for inference requests via the bedrock-mantle endpoint.
• Tag IAM users and roles with team or project attributes for granular cost visibility.
• Analyze mantle endpoint spending alongside runtime endpoint costs in Cost Explorer.
• Attribute AI model inference expenses directly to specific users, teams, or applications.

Amazon Quick’s agentic AI capabilities are now available in AWS GovCloud (US-West), providing government and regulated teams with an isolated, FedRAMP Class D authorized environment. The service transforms analytics into actionable insights, allowing users to build custom chat agents for workflows like procurement and ATO compliance. Data remains hosted and processed entirely within the US-West region, with Spaces enforcing least-privilege access controls.

• Agentic AI is now accessible in GovCloud US-West for FedRAMP Class D workloads.
• Custom chat agents support specific workflows like procurement and grants management.
• Data stays within US-West, ensuring strict geographic and compliance boundaries.
• Spaces enforce least-privilege access to secure agent interactions and data.

Amazon EC2 R8a instances are now available in the Canada (Central) region, built on 5th Gen AMD EPYC processors and sixth-generation Nitro Cards. These instances deliver up to 30% higher performance and 45% more memory bandwidth compared to R7a instances. They are optimized for latency-sensitive, memory-intensive workloads and show up to 60% faster performance for GroovyJVM.

• R8a instances launched in Canada Central using AWS Nitro System.
• Up to 30% better performance and 19% better price-performance than R7a.
• 45% increase in memory bandwidth for latency-sensitive workloads.
• Up to 60% faster GroovyJVM performance for business-critical apps.
• Ideal for high-performance, memory-intensive database workloads.

AWS Clean Rooms now allows collaboration members to export privacy-enhanced analysis logs for SQL queries to S3. These logs contain Spark execution details, enabling better optimization and troubleshooting of queries within secure collaborations. Access is controlled by collaboration owners, who can grant export permissions during setup or via change requests.

• Export privacy-enhanced Spark logs for SQL queries to S3 for deeper debugging.
• Collaboration owners control export permissions via initial setup or change requests.
• Logs provide execution details to help optimize query performance in Clean Rooms.
• Supports third-party measurement providers collaborating with publishers.

AWS introduces a revert-export command within the DynamoDB Bulk Executor tool to undo accidental data changes without requiring a full table restore. By leveraging incremental exports to Amazon S3, engineers can target specific subsets of changes or fix individual items using transforms. This approach provides a more granular and efficient recovery mechanism compared to restoring entire tables.

• Use revert-export to undo unwanted writes without full table restores
• Target specific subsets of changes via incremental S3 exports and transforms
• Fix individual items during recovery for precise data correction
• Avoids downtime and performance impact associated with full table restores