9/15/2026, 12:00:00 AM ~ 9/16/2026, 12:00:00 AM (UTC)

Recent Announcements

AWS Billing Conductor now supports custom rates and usage tier pricing configurations

AWS Billing Conductor now lets you define custom rate pricing for AWS services, including defining custom usage tiers to configure rates by the desired usage volume. \n Customers and Partners using AWS Billing Conductor to model commercial agreements with subsidiaries, affiliates, or end customers can now more easily reflect their negotiated pricing on pro forma billing data. Using SKU-scoped pricing rules, you can enter a custom rate and configure usage tier thresholds — instead of marking up or down from the public on-demand rates tied to pre-defined AWS usage tiers. 

By configuring exact rates and tier breaks directly in a pricing rule, you no longer need to calculate percentage-based markups or markdowns against public on-demand pricing to model your commercial agreements. This gives you precise control over your pro forma billing configuration.

Custom rates and custom usage tiers configuration via SKU-scoped pricing rules is available in all commercial AWS Regions, excluding the Amazon Web Services China (Beijing) Region, operated by Sinnet, and the Amazon Web Services China (Ningxia) Region, operated by NWCD. To learn more, visit the AWS Billing Conductor product page, or review the User Guide.

Amazon SageMaker AI now supports instance preference lists for training and processing jobs

Today, Amazon SageMaker AI announces instance preference lists for training and processing jobs, making it easier and faster to find compute capacity for your workloads. Many AI training, fine-tuning, and data processing workloads run comparably well on any of several instance types or sizes. However, before now, you had to name only one instance type at the time of job submission and wait for SageMaker to find that specific instance for your job. For high-demand GPUs during peak periods, where wait times can be unpredictable, customers sometimes had to build complex retry logic or concurrently submit multiple jobs with different instance types to find the first available option. Now you can simply provide a prioritized list of the instance types your workload accepts, and SageMaker automatically runs your job on the first available configuration from your preferences. With this solution, your training or processing job will likely start sooner.\n To use this feature, you specify your instance type and count preferences in priority order when submitting the training or processing job. For example, your list might contain a preference of two instances of ml.g6.48xlarge or four instances of ml.g5.48xlarge. SageMaker works through the list and launches your job on the first configuration where capacity is available. You can also configure the capacity sourcing from on-demand sources or from your reserved SageMaker Flexible Training Plans within the same job submission. This feature simplifies the process of getting compute for your jobs during high-demand periods and reduces the undifferentiated manual retrying you would otherwise do, all within the SageMaker training and processing job APIs you already use.

Instance preference lists for SageMaker training and processing jobs is available today in all AWS Regions where SageMaker is available through the SageMaker CLIs, APIs, SDKs and Console UI. To learn more, see our documentation or our launch blog.

Analyze your CloudTrail events using natural language in Amazon Q Console

AWS CloudTrail, a service that records API activity across your AWS account for security auditing, compliance, and operational troubleshooting, now integrates with Amazon Q Console to help you investigate your AWS account activity using natural language. You can ask Amazon Q Console questions about your CloudTrail configuration, query your logged events for security investigations, and troubleshoot operational issues without writing queries or manually parsing log files.\n With this integration, you can ask Amazon Q Console to check whether your CloudTrail trails are properly configured, identify gaps in your logging coverage, and confirm which data event sources you are tracking. You can investigate security concerns by asking who accessed a specific IAM role, what changes were made to your VPC configuration, or whether there were unauthorized access attempts in the past week. For operational troubleshooting, you can ask Amazon Q Console to find who created or deleted specific resources, identify which API calls are generating errors, trace activity from a specific IP address, or determine why your bill spiked. Amazon Q Console can query your CloudTrail trails, associated CloudWatch log groups, and event data stores on your behalf, providing answers grounded in your actual account activity rather than generic documentation. 

This integration is available in all AWS commercial regions where Amazon Q Console is supported. To get started, open Amazon Q in AWS Management Console and ask questions about your CloudTrail configuration or account activity. For more information, visit the AWS CloudTrail documentation.

Amazon Connect Customer now enables agents to bid on preferred shifts

Amazon Connect Customer now enables agents to bid on preferred shifts, giving them more control over their work schedules. Schedulers first establish agent ranking either by uploading a CSV file or generating a randomized ranking. Connect Customer then uses forecasted demand and shift profiles to generate available shifts and presents them to agents to rank. For example, in a Monday–Friday 6AM–10PM shift profile with 9-hour shifts, required shifts are 6AM–3PM (500 agents), 9AM–6PM (800 agents), and 1PM–10PM (600 agents). Once the bidding window closes, Connect Customer automatically assigns each agent to their highest-ranked available shift while using agent ranking as a tiebreaker. Shift bidding gives agents a structured way to influence their own schedules, while reducing the time schedulers spend on manual shift assignments, improving both agent satisfaction and scheduling efficiency.\n This feature is available in all AWS Regions where Amazon Connect Customer agent scheduling is available. To learn more about Amazon Connect Customer agent scheduling, click here.

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