8/11/2026, 12:00:00 AM ~ 8/12/2026, 12:00:00 AM (UTC)

Recent Announcements

Amazon EC2 R8a instances are now available in Canada (Central) region

Starting today, Amazon EC2 R8a instances are now available in Canada (Central) Region. These instances, feature 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to R7a instances.\n R8a instances deliver 45% more memory bandwidth compared to R7a instances, making these instances ideal for latency sensitive workloads. Compared to Amazon EC2 R7a instances, R8a instances provide up to 60% faster performance for GroovyJVM, allowing higher request throughput and better response times for business-critical applications.

Built on the AWS Nitro System using sixth generation Nitro Cards, R8a instances are ideal for high performance, memory-intensive workloads, such as SQL and NoSQL databases, distributed web scale in-memory caches, in-memory databases, real-time big data analytics, and Electronic Design Automation (EDA) applications. R8a instances offer 12 sizes including 2 bare metal sizes. Amazon EC2 R8a instances are SAP-certified, and providing 38% more SAPS compared to R7a instances.

To get started, sign in to the AWS Management Console. For more information about the new instances, visit the Amazon EC2 R8a instance page.

Amazon Bedrock expands IAM principal cost allocation to the bedrock-mantle endpoint

Amazon Bedrock is a fully managed service that provides secure, enterprise-grade access to high-performing foundation models from leading AI companies, enabling you to build and scale generative AI applications. Amazon Bedrock now supports cost allocation by AWS Identity and Access Management (IAM) principal, including IAM users and roles, for model inference requests made through the bedrock-mantle endpoint. This extends the capability previously available for the bedrock-runtime endpoint, helping customers attribute inference costs across users, teams, projects, and applications.\n Customers can tag IAM users and roles with attributes such as team, project, or cost center, activate them as cost allocation tags, and analyze bedrock-mantle inference costs by those tags in AWS Cost Explorer or at the line-item level in AWS Cost and Usage Report 2.0 (CUR 2.0). To get started, activate your IAM principal tags in the AWS Billing and Cost Management console. Then filter or group costs by those tags in Cost Explorer, or create a CUR 2.0 data export and select Include caller identity (IAM principal) allocation data.

This feature is available in all AWS Regions where the bedrock-mantle endpoint is available. To learn more, see Using IAM principal for cost allocation and IAM principal attribution in Amazon Bedrock.

LocateAnything-3B, Qwen-AgentWorld-35B-A3B, and Qwen3.5-122B-A10B models now available on Amazon SageMaker JumpStart

NVIDIA’s LocateAnything-3B, Qwen’s Qwen-AgentWorld-35B-A3B, and Qwen’s Qwen3.5-122B-A10B models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models bring specialized capabilities spanning visual grounding, agent environment simulation, and large-scale multimodal reasoning, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.\n These models address different enterprise AI challenges with specialized capabilities:

LocateAnything-3B is optimized for fast, high-quality visual grounding and object localization from natural language instructions. It uses a Parallel Box Decoding (PBD) framework that decodes bounding boxes and points as atomic units in a single step, preserving geometric coherence and unlocking substantial parallelism. It enables precise object localization, dense detection, and point-based localization across diverse domains in both Enterprise Intelligence and Physical AI applications.

Qwen-AgentWorld-35B-A3B excels in simulating agent environments across seven interaction domains: tool calling, search, terminal, software engineering, Android, web, and OS interaction. It is the first language world model to cover all seven domains within a single model, predicting next environment states given an agent’s action and interaction history via long chain-of-thought reasoning—trained on over 10 million real-world interaction trajectories.

Qwen3.5-122B-A10B provides high-performance multimodal reasoning with production-friendly efficiency. It features 122B total parameters with only 10B activated per token through a hybrid architecture integrating Gated Delta Networks with sparse Mixture-of-Experts (256 experts), delivering strong reasoning, coding, agents, and visual understanding performance with a native 262K context window and minimal latency overhead.

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 SageMaker JumpStart model catalog in the SageMaker console 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.

NVIDIA Nemotron 3.5 Lightning model is now available on Amazon SageMaker JumpStart

NVIDIA’s Nemotron 3.5 Lightning is now available on Amazon SageMaker JumpStart, giving AWS customers access to the fastest open model in its class for persistent agent workloads and rapid task execution.\n Nemotron 3.5 Lightning is engineered for persistent agents and high-throughput enterprise automation across domains including personal assistants, financial document processing, cybersecurity triage, and telecom operations. Built on a hybrid Mixture-of-Experts (MoE) architecture with 30B total parameters and just 3B active per forward pass, it achieves up to 4x the throughput (~410 tokens/sec) and 30% faster task completion over comparable models. Distilled from Nemotron 3 Ultra, it handles up to 1M tokens of context via DFlash speculative decoding and integrates directly with popular agent harnesses. The model is fully open-trained on open datasets thereby allowing enterprises to post-train for their own tools, workflows, and policies, and deploy with complete ownership across edge, on-premises, or cloud infrastructure.

With SageMaker JumpStart, customers can deploy this model in a few clicks to power their specific AI workloads.

To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console 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.

AWS Glue adds one-click access to SageMaker Unified Studio from the AWS console

AWS Glue now provides direct access to Amazon SageMaker Unified Studio, helping data engineers and analysts move from viewing the catalog in the Glue console to querying their data, running data quality checks, and building data pipelines in SageMaker Unified Studio with a single click. This new integration helps customers who already work in the Glue console access data and AI capabilities in SageMaker Unified Studio. With this launch, SageMaker Unified Studio can now be accessed by a single click from S3 Tables, Athena, EMR, Redshift and Glue consoles.\n When working in the AWS Glue console to browse catalog tables or build ETL jobs, you now have one-click access to open SageMaker Unified Studio, and can immediately begin working with your data in the catalog or query your data using SageMaker Notebooks using the same IAM role. For Glue console customers who have not yet set up SageMaker Unified Studio, a new inline permissions panel helps you create and configure the required IAM policies directly within the setup workflow, without navigating to the IAM console and switching browser tabs. You can use your existing IAM role and customize the permissions in-context, reducing the steps required to get started.

This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is supported. To get started, navigate to the AWS Glue console.

AWS Secrets Manager adds managed external secrets support for Jenkins and SonarQube

AWS Secrets Manager now extends its managed external secrets capability to include Jenkins API Tokens and SonarQube Tokens, enabling you to automatically rotate these third-party credentials directly from the AWS console without writing any custom rotation code.\n For Jenkins, Secrets Manager mints a new token and revokes the old one only after the replacement is verified active, so your continuous integration and continuous delivery (CI/CD) jobs transition without interruption. Rotation supports both self-rotation, where the token being rotated authenticates its own replacement, and admin-assisted rotation, where a separate admin token performs the generate and revoke operations. For SonarQube, you can rotate three types of tokens — User Tokens, Global Analysis Tokens, and Project Analysis Tokens — via SonarQube’s Web API. User Tokens support self-rotation, while analysis tokens are rotated using an admin token.

These integrations join existing managed external secrets support for BigID, Confluent Cloud, Datadog, GitLab, MongoDB Atlas, Okta, Paddle, Salesforce, and Snowflake.

Jenkins and SonarQube managed external secrets are available in all AWS Regions where AWS Secrets Manager managed external secrets is supported. To learn more, visit the  AWS Secrets Manager managed external secrets documentation .

Amazon Connect Customer launches performance dashboard for Cases

Amazon Connect Customer now provides a performance dashboard for cases that helps managers monitor case volume, resolution trends, and performance against service level agreement (SLA) targets. Managers can compare current and prior-period performance across metrics such as cases created, average resolution time, first-contact resolution percentage, and SLA achievement rate. They can also analyze trends across dimensions such as case template, assigned user, or assigned queue. For example, a manager can identify that the billing team missed more SLA targets for refund cases than in the prior period, investigate the causes, and prioritize process improvements.\n Cases is available in the following AWS regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Africa (Cape Town). To learn more and get started, visit the Cases webpage and documentation.

AWS Clean Rooms supports exporting privacy-enhanced analysis logs for SQL

AWS Clean Rooms now supports exporting privacy-enhanced analysis logs for SQL analyses, offering customers greater optimization and troubleshooting capabilities. With this launch, you can export privacy-enhanced analysis logs to an S3 bucket for SQL queries that ran in an AWS Clean Rooms collaboration, providing insight into Spark execution details that can help you optimize and troubleshoot your queries. Collaboration owners grant a member the ability to export analysis logs when they create a collaboration or submit a change request to grant the ability to a member of an existing collaboration. After a query runs, you can export the privacy-enhanced analysis logs to your desired S3 path. For example, a third-party measurement provider collaborating with a publisher can identify an anomalous data skew that is causing a query to run slower than usual, accelerating time-to-resolution and optimizing costs. \n AWS Clean Rooms helps companies and their partners easily analyze and collaborate on their collective datasets without revealing or copying one another’s underlying data. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms.

Amazon RDS for MariaDB now supports MariaDB 12.3

Starting today, Amazon RDS for MariaDB supports MariaDB major version 12.3, the latest Long-Term Support release from the MariaDB community. This release supports MariaDB 12.3.2 minor version.  \n MariaDB 12.3 includes Oracle TO_DATE() function compatibility, reducing the code changes needed when migrating applications from Oracle to MariaDB. It adds an IS JSON predicate, so you can validate JSON documents natively in the database rather than in application code. The query optimizer now handles reorderable LEFT JOIN statements and ordered scans over RANGE partitions more efficiently, improving performance for these queries without application changes. For more details, refer to the MariaDB 12.3 release notes and RDS MariaDB release notes. 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 RDS User Guide. You can also migrate to RDS for MariaDB 12.3 from external MariaDB sources using AWS Database Migration Service.   Amazon RDS for MariaDB makes it simple to set up, operate, and scale MariaDB deployments in the cloud. Learn more about pricing details and regional availability at Amazon RDS for MariaDB. Create or update a fully managed Amazon RDS for MariaDB database in the Amazon RDS Management Console.

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