Amazon Cloud Map now supports cross-account service discovery through integration with Amazon Resource Access Manager (Amazon RAM). This enhancement lets you seamlessly manage and discover cloud resources—such as Amazon ECS tasks, Amazon EC2 instances, and Amazon DynamoDB tables—across Amazon accounts. By sharing your Amazon Cloud Map namespace via Amazon RAM, workloads in other accounts can discover and manage resources registered in that namespace. This enhancement simplifies resource sharing, reduces duplication, and promotes consistent service discovery across environments for organizations with multi-account architectures.

You can now share your Amazon Cloud Map namespaces using Amazon RAM with individual Amazon accounts, specific Organizational Units (OUs), or your entire Amazon Organization. To get started, create a resource share in Amazon RAM, add the namespaces you want to share, and specify the principals (accounts, OUs, or the organization) that should have access. This enables platform engineers to maintain a centralized service registry—or a small set of registries—and share them across multiple accounts, simplifying service discovery. Application developers can then build services that rely on a consistent, shared registry without worrying about availability or synchronization across accounts. Amazon Cloud Map’s cross-account service discovery support improves operational efficiency and makes it easier to scale service discovery as your organization grows by reducing duplication and streamlining access to namespaces.

This feature is available now in all commercial AWS Regions via the Amazon Management Console, API, SDK, CLI, and CloudFormation. To learn more, please refer to the Amazon Cloud Map documentation.

 

​Amazon Cloud Map now supports cross-account service discovery through integration with Amazon Resource Access Manager (Amazon RAM). This enhancement lets you seamlessly manage and discover cloud resources—such as Amazon ECS tasks, Amazon EC2 instances, and Amazon DynamoDB tables—across Amazon accounts. By sharing your Amazon Cloud Map namespace via Amazon RAM, workloads in other accounts can discover and manage resources registered in that namespace. This enhancement simplifies resource sharing, reduces duplication, and promotes consistent service discovery across environments for organizations with multi-account architectures. You can now share your Amazon Cloud Map namespaces using Amazon RAM with individual Amazon accounts, specific Organizational Units (OUs), or your entire Amazon Organization. To get started, create a resource share in Amazon RAM, add the namespaces you want to share, and specify the principals (accounts, OUs, or the organization) that should have access. This enables platform engineers to maintain a centralized service registry—or a small set of registries—and share them across multiple accounts, simplifying service discovery. Application developers can then build services that rely on a consistent, shared registry without worrying about availability or synchronization across accounts. Amazon Cloud Map’s cross-account service discovery support improves operational efficiency and makes it easier to scale service discovery as your organization grows by reducing duplication and streamlining access to namespaces. This feature is available now in all commercial AWS Regions via the Amazon Management Console, API, SDK, CLI, and CloudFormation. To learn more, please refer to the Amazon Cloud Map documentation.  

SageMaker HyperPod task governance now supports fine-grained compute quota allocation of GPU, Trainium accelerator, vCPU, and vCPU memory within an instance. Administrators can allocate fine-grained compute quota across teams, optimizing compute resource distribution and staying within budget.

Data scientists often execute LLM tasks, like training or inference, that do not require entire HyperPod instances, leading to underutilization of accelerated compute resources. HyperPod task governance enables administrators to manage compute quota allocation across teams. With this capability, administrators can now strategically allocate compute resources, ensuring fair access, preventing resource monopolization, and maximizing cluster utilization. This capability enables fine-grained compute quota allocation in addition to instance-level allocation, aligning with organizational workload demands.

SageMaker HyperPod task governance is available in all AWS Regions where HyperPod is available: US East (N. Virginia), US West (N. California), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Stockholm), and South America (São Paulo).

To learn more, visit SageMaker HyperPod webpage, and HyperPod task governance documentation.

 

​SageMaker HyperPod task governance now supports fine-grained compute quota allocation of GPU, Trainium accelerator, vCPU, and vCPU memory within an instance. Administrators can allocate fine-grained compute quota across teams, optimizing compute resource distribution and staying within budget. Data scientists often execute LLM tasks, like training or inference, that do not require entire HyperPod instances, leading to underutilization of accelerated compute resources. HyperPod task governance enables administrators to manage compute quota allocation across teams. With this capability, administrators can now strategically allocate compute resources, ensuring fair access, preventing resource monopolization, and maximizing cluster utilization. This capability enables fine-grained compute quota allocation in addition to instance-level allocation, aligning with organizational workload demands. SageMaker HyperPod task governance is available in all AWS Regions where HyperPod is available: US East (N. Virginia), US West (N. California), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Stockholm), and South America (São Paulo). To learn more, visit SageMaker HyperPod webpage, and HyperPod task governance documentation.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8g instances are available in AWS Asia Pacific (Jakarta)region. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 R8g instances are ideal for memory-intensive workloads such as databases, in-memory caches, and real-time big data analytics. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads.

AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. AWS Graviton4-based R8g instances offer larger instance sizes with up to 3x more vCPU (up to 48xlarge) and memory (up to 1.5TB) than Graviton3-based R7g instances. These instances are up to 30% faster for web applications, 40% faster for databases, and 45% faster for large Java applications compared to AWS Graviton3-based R7g instances. R8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS).

To learn more, see Amazon EC2 R8g Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8g instances are available in AWS Asia Pacific (Jakarta)region. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 R8g instances are ideal for memory-intensive workloads such as databases, in-memory caches, and real-time big data analytics. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads. AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. AWS Graviton4-based R8g instances offer larger instance sizes with up to 3x more vCPU (up to 48xlarge) and memory (up to 1.5TB) than Graviton3-based R7g instances. These instances are up to 30% faster for web applications, 40% faster for databases, and 45% faster for large Java applications compared to AWS Graviton3-based R7g instances. R8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). To learn more, see Amazon EC2 R8g Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.  

Amazon FSx for NetApp ONTAP, a fully managed shared storage service built on NetApp’s popular ONTAP file system, now allows you to decrease your file system’s solid-state drive (SSD) storage capacity, enabling you to more efficiently run project-based workloads with varying active working sets. You can provision SSD capacity upfront to meet peak usage needs—for periodic reporting, analytics, or large-scale data ingestion and processing—and then easily decrease SSD capacity to reduce storage costs.

An FSx for ONTAP file system offers two storage tiers: a provisioned high-performance SSD tier for your workload’s active working set and a fully elastic capacity pool cost-optimized for infrequently accessed data. Until now, you could only increase your file system’s SSD capacity as your workload’s active working set grew. Starting today, you can decrease your file system’s SSD capacity in-place with just a few clicks in the Amazon FSx console, allowing you to deliver optimal performance during peak usage for workloads such as Electronic Design Automation and media processing, then scale down SSD capacity once data processing is complete. You can also accelerate data migrations by temporarily increasing SSD capacity to enable faster data ingestion, then right-sizing SSD capacity after data has been tiered to the capacity pool.

You can decrease SSD storage capacity on all FSx for ONTAP second-generation file systems in all AWS Regions where FSx for ONTAP second-generation file systems are available. For more information, see the FSx for ONTAP user guide.

 

​Amazon FSx for NetApp ONTAP, a fully managed shared storage service built on NetApp’s popular ONTAP file system, now allows you to decrease your file system’s solid-state drive (SSD) storage capacity, enabling you to more efficiently run project-based workloads with varying active working sets. You can provision SSD capacity upfront to meet peak usage needs—for periodic reporting, analytics, or large-scale data ingestion and processing—and then easily decrease SSD capacity to reduce storage costs. An FSx for ONTAP file system offers two storage tiers: a provisioned high-performance SSD tier for your workload’s active working set and a fully elastic capacity pool cost-optimized for infrequently accessed data. Until now, you could only increase your file system’s SSD capacity as your workload’s active working set grew. Starting today, you can decrease your file system’s SSD capacity in-place with just a few clicks in the Amazon FSx console, allowing you to deliver optimal performance during peak usage for workloads such as Electronic Design Automation and media processing, then scale down SSD capacity once data processing is complete. You can also accelerate data migrations by temporarily increasing SSD capacity to enable faster data ingestion, then right-sizing SSD capacity after data has been tiered to the capacity pool. You can decrease SSD storage capacity on all FSx for ONTAP second-generation file systems in all AWS Regions where FSx for ONTAP second-generation file systems are available. For more information, see the FSx for ONTAP user guide.  

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M8g instances are available in AWS Asia Pacific (Seoul) region. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 M8g instances are built for general-purpose workloads, such as application servers, microservices, gaming servers, midsize data stores, and caching fleets. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads.

AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. These instances offer larger instance sizes with up to 3x more vCPUs and memory compared to Graviton3-based Amazon M7g instances. AWS Graviton4 processors are up to 40% faster for databases, 30% faster for web applications, and 45% faster for large Java applications than AWS Graviton3 processors. M8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS).

To learn more, see Amazon EC2 M8g Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) M8g instances are available in AWS Asia Pacific (Seoul) region. These instances are powered by AWS Graviton4 processors and deliver up to 30% better performance compared to AWS Graviton3-based instances. Amazon EC2 M8g instances are built for general-purpose workloads, such as application servers, microservices, gaming servers, midsize data stores, and caching fleets. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software to enhance the performance and security of your workloads. AWS Graviton4-based Amazon EC2 instances deliver the best performance and energy efficiency for a broad range of workloads running on Amazon EC2. These instances offer larger instance sizes with up to 3x more vCPUs and memory compared to Graviton3-based Amazon M7g instances. AWS Graviton4 processors are up to 40% faster for databases, 30% faster for web applications, and 45% faster for large Java applications than AWS Graviton3 processors. M8g instances are available in 12 different instance sizes, including two bare metal sizes. They offer up to 50 Gbps enhanced networking bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). To learn more, see Amazon EC2 M8g Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.  

Starting today, Amazon EC2 High Memory U7i instances with 12TB of memory (u7i-12tb.224xlarge) are now available in the US East (Ohio) region. U7i-12tb instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-12tb instances offer 12TiB of DDR5 memory enabling customers to scale transaction processing throughput in a fast-growing data environment.

U7i-12tb instances offer 896 vCPUs, support up to 100Gbps Elastic Block Storage (EBS) for faster data loading and backups, deliver up to 100Gbps of network bandwidth, and support ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.

To learn more about U7i instances, visit the High Memory instances page.

 

​Starting today, Amazon EC2 High Memory U7i instances with 12TB of memory (u7i-12tb.224xlarge) are now available in the US East (Ohio) region. U7i-12tb instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-12tb instances offer 12TiB of DDR5 memory enabling customers to scale transaction processing throughput in a fast-growing data environment. U7i-12tb instances offer 896 vCPUs, support up to 100Gbps Elastic Block Storage (EBS) for faster data loading and backups, deliver up to 100Gbps of network bandwidth, and support ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server. To learn more about U7i instances, visit the High Memory instances page.  

Amazon OpenSearch Service expands its modernized operational analytics experience to seven new regions, including Asia Pacific (Hyderabad), Asia Pacific (Osaka), Asia Pacific (Seoul), Europe (Milan), Europe (Zurich), Europe (Spain), and US-West (N. California) enabling users to gain insights across data spanning managed domains and serverless collections from a single endpoint. The expansion includes Workspaces to enhance collaboration and productivity, allowing teams to create dedicated spaces. Discover is revamped to provide a unified log exploration experience supporting languages such as Piped-Processing-Language (PPL) and SQL, in addition to DQL and Lucene. Discover now features a data selector to support multiple sources, new visual design and query autocomplete for improved usability. This experience ensures users can access the latest UI enhancements, regardless of version of underlying managed cluster or collection.

The expanded OpenSearch analytics helps users gain insights from their operational data by providing purpose-built features for observability, security analytics, and search use cases. With the enhanced Discover interface, users can now analyze data from multiple sources without switching tools, improving efficiency. Workspaces enable better collaboration by creating dedicated environments for teams to work on dashboards, saved queries, and other relevant content. Availability of the latest UI updates across all versions ensures uninterrupted access to the newest features and tools.

OpenSearch UI can connect to OpenSearch domains (above version 1.3) and OpenSearch serverless collections. It is now available in 22 AWS commercial regions. To get started, create an OpenSearch application in AWS Management Console. Learn more at Amazon OpenSearch Service Developer Guide.

 

​Amazon OpenSearch Service expands its modernized operational analytics experience to seven new regions, including Asia Pacific (Hyderabad), Asia Pacific (Osaka), Asia Pacific (Seoul), Europe (Milan), Europe (Zurich), Europe (Spain), and US-West (N. California) enabling users to gain insights across data spanning managed domains and serverless collections from a single endpoint. The expansion includes Workspaces to enhance collaboration and productivity, allowing teams to create dedicated spaces. Discover is revamped to provide a unified log exploration experience supporting languages such as Piped-Processing-Language (PPL) and SQL, in addition to DQL and Lucene. Discover now features a data selector to support multiple sources, new visual design and query autocomplete for improved usability. This experience ensures users can access the latest UI enhancements, regardless of version of underlying managed cluster or collection. The expanded OpenSearch analytics helps users gain insights from their operational data by providing purpose-built features for observability, security analytics, and search use cases. With the enhanced Discover interface, users can now analyze data from multiple sources without switching tools, improving efficiency. Workspaces enable better collaboration by creating dedicated environments for teams to work on dashboards, saved queries, and other relevant content. Availability of the latest UI updates across all versions ensures uninterrupted access to the newest features and tools. OpenSearch UI can connect to OpenSearch domains (above version 1.3) and OpenSearch serverless collections. It is now available in 22 AWS commercial regions. To get started, create an OpenSearch application in AWS Management Console. Learn more at Amazon OpenSearch Service Developer Guide.  

AWS Batch for ECS Fargate now supports AWS Graviton-based compute with AWS Fargate Spot. This capability helps you run fault-tolerant Arm-based applications with up to 70% discount compared to Fargate prices. AWS Graviton processors are custom-built by AWS to deliver the best price-performance for cloud workloads.

AWS Batch for ECS Fargate enables customers to deploy and build workloads at scale in a serverless manner. Starting today, customers can further optimize for costs by running fault-tolerant Arm-based workloads on AWS Fargate Spot. To get started, create a new Fargate configured Compute Environment (CE), select ARM64 as the cpuArchitecture, and choose FARGATE_SPOT as the type. You can then connect it to existing job queues or create a new one for your workload. AWS Batch will leverage spare AWS Graviton-based compute capacity available in the AWS cloud for running your service or task. You can now get the simplicity of serverless compute with familiar cost optimization levers of Spot capacity with Graviton-based compute.

This capability is now available for AWS Batch in all commercial and the AWS GovCloud (US) Regions. To learn more, see Batch’s updated RuntimePlatform API and AWS Batch for ECS Fargate documentation. 

 

​AWS Batch for ECS Fargate now supports AWS Graviton-based compute with AWS Fargate Spot. This capability helps you run fault-tolerant Arm-based applications with up to 70% discount compared to Fargate prices. AWS Graviton processors are custom-built by AWS to deliver the best price-performance for cloud workloads. AWS Batch for ECS Fargate enables customers to deploy and build workloads at scale in a serverless manner. Starting today, customers can further optimize for costs by running fault-tolerant Arm-based workloads on AWS Fargate Spot. To get started, create a new Fargate configured Compute Environment (CE), select ARM64 as the cpuArchitecture, and choose FARGATE_SPOT as the type. You can then connect it to existing job queues or create a new one for your workload. AWS Batch will leverage spare AWS Graviton-based compute capacity available in the AWS cloud for running your service or task. You can now get the simplicity of serverless compute with familiar cost optimization levers of Spot capacity with Graviton-based compute. This capability is now available for AWS Batch in all commercial and the AWS GovCloud (US) Regions. To learn more, see Batch’s updated RuntimePlatform API and AWS Batch for ECS Fargate documentation.   

AWS Config now supports 10 additional AWS resource types. This expansion provides greater coverage over your AWS environment, enabling you to more effectively discover, assess, audit, and remediate an even broader range of resources.

With this launch, if you have enabled recording for all resource types, then AWS Config will automatically track these new additions. The newly supported resource types are also available in Config rules and Config aggregators.

You can now use AWS Config to monitor the following newly supported resource types in all AWS Regions where the supported resources are available:

Resource Types:

AWS::Backup::RestoreTestingPlan

AWS::CloudFront::RealtimeLogConfig

AWS::EC2::SecurityGroupVpcAssociation

AWS::EC2::VerifiedAccessInstance

AWS::KafkaConnect::CustomPlugin

AWS::OpenSearchServerless::SecurityConfig

AWS::Redshift::Integration

AWS::Route53Profiles::ProfileAssociation

AWS::SSMIncidents::ResponsePlan

AWS::Transfer::Server

 

​AWS Config now supports 10 additional AWS resource types. This expansion provides greater coverage over your AWS environment, enabling you to more effectively discover, assess, audit, and remediate an even broader range of resources. With this launch, if you have enabled recording for all resource types, then AWS Config will automatically track these new additions. The newly supported resource types are also available in Config rules and Config aggregators. You can now use AWS Config to monitor the following newly supported resource types in all AWS Regions where the supported resources are available:
Resource Types:
AWS::Backup::RestoreTestingPlan
AWS::CloudFront::RealtimeLogConfig
AWS::EC2::SecurityGroupVpcAssociation
AWS::EC2::VerifiedAccessInstance
AWS::KafkaConnect::CustomPlugin
AWS::OpenSearchServerless::SecurityConfig
AWS::Redshift::Integration
AWS::Route53Profiles::ProfileAssociation
AWS::SSMIncidents::ResponsePlan
AWS::Transfer::Server  

Amazon Braket now supports program sets, enabling quantum researchers to run complex workloads requiring hundreds of quantum circuit executions up to 24X faster. This new feature allows customers to submit up to 100 quantum programs or a single parametric circuit with up to 100 parameter values within a single quantum task. Program sets help minimize the time between subsequent circuit executions reducing quantum task processing overhead for complex algorithms.

Program sets are particularly valuable for researchers working with variational quantum algorithms (VQA), quantum machine learning models, and error mitigation techniques. Customers can create program sets using two approaches: submitting multiple independent circuits together, or submitting a single parametric circuit with parameter sets. Amazon Braket handles compilation and execution orchestration, returning results that include the status and outcomes for each quantum program. If individual programs within a program set fail during execution, customers receive partial results for successfully completed programs and error information for failed executions. When submitting a program set, you pay a single per-task fee accompanied by a per-shot fee based on the total number of successful shots across your workload in a program set.

Program sets are initially available on all superconducting quantum processing units (QPUs) from Rigetti in the US West (N. California) Region and IQM in the Europe (Stockholm) Region. Customers are able to submit program sets to Braket directly via the Amazon Braket SDK, from Qiskit via the Qiskit-Braket provider, or from PennyLane via the Amazon Braket PennyLane Plugin.

To learn more about program sets, visit the Amazon Braket developer guide, explore our new example notebooks, and visit our updated Amazon Braket management console.

 

​Amazon Braket now supports program sets, enabling quantum researchers to run complex workloads requiring hundreds of quantum circuit executions up to 24X faster. This new feature allows customers to submit up to 100 quantum programs or a single parametric circuit with up to 100 parameter values within a single quantum task. Program sets help minimize the time between subsequent circuit executions reducing quantum task processing overhead for complex algorithms. Program sets are particularly valuable for researchers working with variational quantum algorithms (VQA), quantum machine learning models, and error mitigation techniques. Customers can create program sets using two approaches: submitting multiple independent circuits together, or submitting a single parametric circuit with parameter sets. Amazon Braket handles compilation and execution orchestration, returning results that include the status and outcomes for each quantum program. If individual programs within a program set fail during execution, customers receive partial results for successfully completed programs and error information for failed executions. When submitting a program set, you pay a single per-task fee accompanied by a per-shot fee based on the total number of successful shots across your workload in a program set. Program sets are initially available on all superconducting quantum processing units (QPUs) from Rigetti in the US West (N. California) Region and IQM in the Europe (Stockholm) Region. Customers are able to submit program sets to Braket directly via the Amazon Braket SDK, from Qiskit via the Qiskit-Braket provider, or from PennyLane via the Amazon Braket PennyLane Plugin. To learn more about program sets, visit the Amazon Braket developer guide, explore our new example notebooks, and visit our updated Amazon Braket management console.