GPT-5.6 Sol, Terra, and Luna now support 1 million token context windows on Amazon Bedrock, enabling you to process full codebases, lengthy documents, and multi-turn agent histories in a single request. Models reason over broader context and return more accurate, coherent responses without chunking or information loss.

With a context window size of 1 million tokens, you can analyze entire repositories in a single pass for code review and migration, process long-form legal or regulatory documents end-to-end, and maintain full conversation history in multi-step agentic workflows. Prompt caching with explicit cache breakpoints applies to long context requests, so repeated context is billed at a 90% discount. Pricing matches OpenAI first-party rates and usage counts toward your AWS commitments.

GPT-5.6 Sol is available in the following AWS Regions: US East (N. Virginia) and US East (Ohio). GPT-5.6 Terra and Luna are available in US East (N. Virginia), US East (Ohio), and US West (Oregon). Get started with Sol, Terra, and Luna using the Amazon Bedrock Console or the Responses API on the bedrock-mantle endpoint. To learn more, see the Amazon Bedrock documentation and read the launch blog post.

 

​GPT-5.6 Sol, Terra, and Luna now support 1 million token context windows on Amazon Bedrock, enabling you to process full codebases, lengthy documents, and multi-turn agent histories in a single request. Models reason over broader context and return more accurate, coherent responses without chunking or information loss.
With a context window size of 1 million tokens, you can analyze entire repositories in a single pass for code review and migration, process long-form legal or regulatory documents end-to-end, and maintain full conversation history in multi-step agentic workflows. Prompt caching with explicit cache breakpoints applies to long context requests, so repeated context is billed at a 90% discount. Pricing matches OpenAI first-party rates and usage counts toward your AWS commitments.
GPT-5.6 Sol is available in the following AWS Regions: US East (N. Virginia) and US East (Ohio). GPT-5.6 Terra and Luna are available in US East (N. Virginia), US East (Ohio), and US West (Oregon). Get started with Sol, Terra, and Luna using the Amazon Bedrock Console or the Responses API on the bedrock-mantle endpoint. To learn more, see the Amazon Bedrock documentation and read the launch blog post.  

AWS Transform continuous modernization is now generally available in all AWS Regions where AWS Transform is supported. This capability helps engineering teams analyze and remediate technical debt across source code repositories at scale. Teams can connect GitHub organizations, GitLab groups, and Bitbucket workspaces, run analyses on demand or on a recurring schedule, and prioritize findings across technical debt, security, agentic readiness, modernization readiness, and custom analysis criteria.

With today’s launch, you can connect source code providers, initiate and schedule analyses, review findings, and create remediations directly from the AWS Transform web application. For findings with an associated remediation, continuous modernization creates branches and opens pull requests or merge requests containing validated code changes for review. Analysis and remediation run in your AWS account using your credentials, while your source code remains under your control.

You can also use the AWS Transform Kiro Power, agent plugins, or AWS Transform CLI to work from your IDE or terminal, analyze local repositories, organize repositories using labels, and run analyses locally or remotely using Amazon EC2 or AWS Batch. To get started, open the AWS Transform web application or use the AWS Transform Kiro Power and agent plugins. To learn more, see AWS Transform continuous modernization in the AWS Transform User Guide.

 

​AWS Transform continuous modernization is now generally available in all AWS Regions where AWS Transform is supported. This capability helps engineering teams analyze and remediate technical debt across source code repositories at scale. Teams can connect GitHub organizations, GitLab groups, and Bitbucket workspaces, run analyses on demand or on a recurring schedule, and prioritize findings across technical debt, security, agentic readiness, modernization readiness, and custom analysis criteria. With today’s launch, you can connect source code providers, initiate and schedule analyses, review findings, and create remediations directly from the AWS Transform web application. For findings with an associated remediation, continuous modernization creates branches and opens pull requests or merge requests containing validated code changes for review. Analysis and remediation run in your AWS account using your credentials, while your source code remains under your control. You can also use the AWS Transform Kiro Power, agent plugins, or AWS Transform CLI to work from your IDE or terminal, analyze local repositories, organize repositories using labels, and run analyses locally or remotely using Amazon EC2 or AWS Batch. To get started, open the AWS Transform web application or use the AWS Transform Kiro Power and agent plugins. To learn more, see AWS Transform continuous modernization in the AWS Transform User Guide.  

AWS Organizations customers can now view their maximum number of accounts quota and its utilization directly through AWS Service Quotas instead of relying on AWS Support or AWS account teams to determine their current account limit.

Customers can now proactively plan account growth with ease by monitoring their current account quota utilization and requesting increases before reaching their limit. They can view this quota by logging into management account and accessing the Service Quotas console or calling the Service Quotas GetServiceQuota API.

This quota visibility is available now available in US East (N. Virginia). To learn more, see viewing service quotas in the AWS Service Quotas documentation. For more information about AWS Organizations quotas and service limits, see the AWS Organizations documentation.

 

​AWS Organizations customers can now view their maximum number of accounts quota and its utilization directly through AWS Service Quotas instead of relying on AWS Support or AWS account teams to determine their current account limit. Customers can now proactively plan account growth with ease by monitoring their current account quota utilization and requesting increases before reaching their limit. They can view this quota by logging into management account and accessing the Service Quotas console or calling the Service Quotas GetServiceQuota API. This quota visibility is available now available in US East (N. Virginia). To learn more, see viewing service quotas in the AWS Service Quotas documentation. For more information about AWS Organizations quotas and service limits, see the AWS Organizations documentation.  

Amazon GameLift Streams now offers stream URLs, which give end users temporary, unauthenticated access to a playable stream session in a supported web browser. Recipients need no AWS account, no credentials, and no software install.

To share a playable stream, create a stream URL for a stream group and one of its applications, set how long the stream URL stays valid and how many sessions it can start, and send the link. Each person who opens the link starts an independent stream session, and Amazon GameLift Streams routes them to a nearby streaming location from the locations you selected. No client integration or backend service is required. You can create, monitor, and revoke stream URLs in the Amazon GameLift Streams console or with the new CreateStreamUrl, GetStreamUrl, ListStreamUrls, and RevokeStreamUrl APIs.

There is no additional charge for stream URLs. You are charged for the stream capacity that sessions started from a stream URL consume, as described on the Amazon GameLift Streams pricing page. For a full list of supported Regions, see the AWS Region table.

To get started, see Share stream sessions with stream URLs in the Amazon GameLift Streams Developer Guide and the CreateStreamUrl API Reference. To learn more about the service, see the Amazon GameLift Streams product page. 

 

​Amazon GameLift Streams now offers stream URLs, which give end users temporary, unauthenticated access to a playable stream session in a supported web browser. Recipients need no AWS account, no credentials, and no software install.
To share a playable stream, create a stream URL for a stream group and one of its applications, set how long the stream URL stays valid and how many sessions it can start, and send the link. Each person who opens the link starts an independent stream session, and Amazon GameLift Streams routes them to a nearby streaming location from the locations you selected. No client integration or backend service is required. You can create, monitor, and revoke stream URLs in the Amazon GameLift Streams console or with the new CreateStreamUrl, GetStreamUrl, ListStreamUrls, and RevokeStreamUrl APIs.
There is no additional charge for stream URLs. You are charged for the stream capacity that sessions started from a stream URL consume, as described on the Amazon GameLift Streams pricing page. For a full list of supported Regions, see the AWS Region table.
To get started, see Share stream sessions with stream URLs in the Amazon GameLift Streams Developer Guide and the CreateStreamUrl API Reference. To learn more about the service, see the Amazon GameLift Streams product page.   

Amazon Web Services (AWS) announces the availability of high performance Storage Optimized Amazon EC2 I7i instances in the Asia Pacific (Thailand) and Israel (Tel Aviv) Regions. Powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, these new instances deliver up to 23% better compute performance and more than 10% better price performance over previous generation I4i instances. Powered by 3rd generation AWS Nitro SSDs, I7i instances offer up to 45TB of NVMe storage with up to 50% better real-time storage performance, up to 50% lower storage I/O latency, and up to 60% lower storage I/O latency variability compared to I4i instances.

I7i instances offer compute and storage performance for x86-based storage optimized instances in Amazon EC2 ideal for I/O intensive and latency-sensitive workloads that demand very high random IOPS performance with real-time latency to access the small to medium size datasets. Additionally, torn write prevention feature support up to 16KB block sizes, enabling customers to eliminate database performance bottlenecks.

I7i instances are available in eleven sizes – nine virtual sizes up to 48xlarge and two bare metal sizes – delivering up to 100Gbps of network bandwidth and 60Gbps of Amazon Elastic Block Store (EBS) bandwidth. To learn more, visit the I7i instances page.

 

​Amazon Web Services (AWS) announces the availability of high performance Storage Optimized Amazon EC2 I7i instances in the Asia Pacific (Thailand) and Israel (Tel Aviv) Regions. Powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, these new instances deliver up to 23% better compute performance and more than 10% better price performance over previous generation I4i instances. Powered by 3rd generation AWS Nitro SSDs, I7i instances offer up to 45TB of NVMe storage with up to 50% better real-time storage performance, up to 50% lower storage I/O latency, and up to 60% lower storage I/O latency variability compared to I4i instances. I7i instances offer compute and storage performance for x86-based storage optimized instances in Amazon EC2 ideal for I/O intensive and latency-sensitive workloads that demand very high random IOPS performance with real-time latency to access the small to medium size datasets. Additionally, torn write prevention feature support up to 16KB block sizes, enabling customers to eliminate database performance bottlenecks. I7i instances are available in eleven sizes – nine virtual sizes up to 48xlarge and two bare metal sizes – delivering up to 100Gbps of network bandwidth and 60Gbps of Amazon Elastic Block Store (EBS) bandwidth. To learn more, visit the I7i instances page.  

AWS HealthOmics now supports task-level timeout for Workflow Description Language (WDL) workflows, enabling you to set maximum execution duration for individual tasks. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows. 

With task-level timeout, you can define time bounds on individual WDL tasks to control costs and enable automated error recovery. HealthOmics provides the omicsTimeout runtime attribute that you can add to any task’s runtime section to specify the maximum duration a task is allowed to run. When a task exceeds the specified duration, HealthOmics stops the task and sets the task and run statuses to failed. The omicsTimeout attribute accepts duration values with standard time units (such as 90s, 2h, 1d). This prevents tasks from consuming resources and helps you set cost guardrails during workflow development. 

Task-level timeout for WDL workflows is available in all supported AWS HealthOmics Regions: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Seoul, Singapore, Tokyo). To learn more, visit the WDL workflow definition specifics documentation. 

 

​AWS HealthOmics now supports task-level timeout for Workflow Description Language (WDL) workflows, enabling you to set maximum execution duration for individual tasks. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows. 
With task-level timeout, you can define time bounds on individual WDL tasks to control costs and enable automated error recovery. HealthOmics provides the omicsTimeout runtime attribute that you can add to any task’s runtime section to specify the maximum duration a task is allowed to run. When a task exceeds the specified duration, HealthOmics stops the task and sets the task and run statuses to failed. The omicsTimeout attribute accepts duration values with standard time units (such as 90s, 2h, 1d). This prevents tasks from consuming resources and helps you set cost guardrails during workflow development. 
Task-level timeout for WDL workflows is available in all supported AWS HealthOmics Regions: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Seoul, Singapore, Tokyo). To learn more, visit the WDL workflow definition specifics documentation.   

AWS Resilience Hub now offers recommended resilience tests that help platform engineering and site reliability teams validate how their services respond to and recover from known failure scenarios. 

Resilience Hub provides pre-configured tests based on your service’s architecture, configuration, and resilience policy. It uses AWS Fault Injection Service (FIS) to inject controlled faults and then evaluates whether your service recovers within your defined recovery objectives. With the AWS-recommended resilience tests, teams can validate readiness for scenarios such as Availability Zone impairment, Regional impairment, and dependency failure. Each test automatically targets resources in the service, injects the required faults, produces a pass or fail outcome based on alarm evaluation and recovery objectives, then generates a detailed test report.

The recommended testing on the next generation of the AWS Resilience Hub is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), Europe (Ireland), Europe (London), Europe (Frankfurt), Europe (Paris), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), and South America (São Paulo).

To get started, visit the AWS console. To learn more about recommended resilience testing see the product page for the next generation of AWS Resilience Hub. 

 

​AWS Resilience Hub now offers recommended resilience tests that help platform engineering and site reliability teams validate how their services respond to and recover from known failure scenarios. 
Resilience Hub provides pre-configured tests based on your service’s architecture, configuration, and resilience policy. It uses AWS Fault Injection Service (FIS) to inject controlled faults and then evaluates whether your service recovers within your defined recovery objectives. With the AWS-recommended resilience tests, teams can validate readiness for scenarios such as Availability Zone impairment, Regional impairment, and dependency failure. Each test automatically targets resources in the service, injects the required faults, produces a pass or fail outcome based on alarm evaluation and recovery objectives, then generates a detailed test report.
The recommended testing on the next generation of the AWS Resilience Hub is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), Europe (Ireland), Europe (London), Europe (Frankfurt), Europe (Paris), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), and South America (São Paulo).
To get started, visit the AWS console. To learn more about recommended resilience testing see the product page for the next generation of AWS Resilience Hub.   

Amazon SageMaker AI serverless model customization now supports full fine-tuning for over 25 open-source models. These include popular models from gpt-oss, Gemma, Llama, Nemotron, and Qwen model families. In addition to parameter-efficient methods such as LoRA, which update a small subset of model weights, you can now update all parameters in the model for deeper adaptation when your use case requires it.

Full fine-tuning allows the model to more thoroughly learn your domain-specific patterns, terminology, and task structure. This is particularly valuable when you need the model to acquire capabilities beyond surface-level style adjustments, such as learning specialized reasoning patterns, adopting complex output formats, or internalizing domain knowledge from large proprietary datasets. With serverless model customization, SageMaker manages all infrastructure provisioning and training orchestration, so you can run full fine-tuning jobs without provisioning or managing any infrastructure and you pay only for what you use.

Serverless full fine-tuning on SageMaker is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To get started, navigate to the JumpStart and Models page in Amazon SageMaker Studio to launch a customization job, or use the SageMaker Python SDK. To learn more, and see the supported list of models, see the Amazon SageMaker AI model customization documentation.

 

​Amazon SageMaker AI serverless model customization now supports full fine-tuning for over 25 open-source models. These include popular models from gpt-oss, Gemma, Llama, Nemotron, and Qwen model families. In addition to parameter-efficient methods such as LoRA, which update a small subset of model weights, you can now update all parameters in the model for deeper adaptation when your use case requires it. Full fine-tuning allows the model to more thoroughly learn your domain-specific patterns, terminology, and task structure. This is particularly valuable when you need the model to acquire capabilities beyond surface-level style adjustments, such as learning specialized reasoning patterns, adopting complex output formats, or internalizing domain knowledge from large proprietary datasets. With serverless model customization, SageMaker manages all infrastructure provisioning and training orchestration, so you can run full fine-tuning jobs without provisioning or managing any infrastructure and you pay only for what you use. Serverless full fine-tuning on SageMaker is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To get started, navigate to the JumpStart and Models page in Amazon SageMaker Studio to launch a customization job, or use the SageMaker Python SDK. To learn more, and see the supported list of models, see the Amazon SageMaker AI model customization documentation.  

AWS Config now supports 15 additional AWS resource types across key services including Amazon Bedrock,  Amazon OpenSearch Serverless, and Amazon SageMaker. 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 resources are available:

Resource Types:

AWS::AppSync::DomainName	AWS::OpenSearchServerless::AccessPolicy
AWS::Bedrock::AutomatedReasoningPolicy	AWS::OpenSearchServerless::LifecyclePolicy
AWS::Bedrock::AutomatedReasoningPolicyVersion	AWS::SageMaker::ImageVersion
AWS::Bedrock::Blueprint	AWS::SageMaker::InferenceComponent
AWS::Bedrock::DataAutomationProject	AWS::SageMaker::PartnerApp
AWS::BedrockAgentCore::ApiKeyCredentialProvider	AWS::SageMaker::Project
AWS::Connect::UserHierarchyGroup	AWS::SageMaker::Space
AWS::Glue::Trigger

 

​AWS Config now supports 15 additional AWS resource types across key services including Amazon Bedrock,  Amazon OpenSearch Serverless, and Amazon SageMaker. 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 resources are available: Resource Types:

AWS::AppSync::DomainName
AWS::OpenSearchServerless::AccessPolicy

AWS::Bedrock::AutomatedReasoningPolicy
AWS::OpenSearchServerless::LifecyclePolicy

AWS::Bedrock::AutomatedReasoningPolicyVersion
AWS::SageMaker::ImageVersion

AWS::Bedrock::Blueprint
AWS::SageMaker::InferenceComponent

AWS::Bedrock::DataAutomationProject
AWS::SageMaker::PartnerApp

AWS::BedrockAgentCore::ApiKeyCredentialProvider
AWS::SageMaker::Project

AWS::Connect::UserHierarchyGroup
AWS::SageMaker::Space

AWS::Glue::Trigger  

Today, AWS Transform for full-stack Windows modernization announced general availability of offline source transformation, enabling customers to modernize Microsoft SQL Server databases to Amazon Aurora PostgreSQL without requiring a live database connection. AWS Transform now converts SQL Server storage objects, powered by AWS DMS, and code objects (stored procedures) using an agentic, interactive experience. Enterprises modernizing legacy .NET applications and their dependent SQL Server databases can now start their modernization by directly uploading the Data Design Language (DDL) source files from their databases.

With offline source transformation, customers upload SQL Server data design language (DDL) files, assess database and stored procedure complexity, and generate a customizable transformation plan. AWS Transform converts tables, schemas and converts code objects such as stored procedures and functions, validates functional equivalence, and deploys the converted schema to Aurora PostgreSQL. The same workflow transforms database dependent .NET applications to be PostgreSQL compatible .NET applications with updated connection strings, ADO.NET and Entity Framework data-access calls. To address remaining conversion issues, customers can iterate directly in the web console or hand off to their preferred IDE using the AWS Transform MCP server. A separate synthetic data workflow populates Aurora PostgreSQL with test data for end-to-end application validation.

AWS Transform for full-stack Windows modernization and offline source transformation is available in US East (N. Virginia). To get started, you can go to AWS Transform product page or see AWS Transform for full-stack Windows documentation.

 

​Today, AWS Transform for full-stack Windows modernization announced general availability of offline source transformation, enabling customers to modernize Microsoft SQL Server databases to Amazon Aurora PostgreSQL without requiring a live database connection. AWS Transform now converts SQL Server storage objects, powered by AWS DMS, and code objects (stored procedures) using an agentic, interactive experience. Enterprises modernizing legacy .NET applications and their dependent SQL Server databases can now start their modernization by directly uploading the Data Design Language (DDL) source files from their databases.
With offline source transformation, customers upload SQL Server data design language (DDL) files, assess database and stored procedure complexity, and generate a customizable transformation plan. AWS Transform converts tables, schemas and converts code objects such as stored procedures and functions, validates functional equivalence, and deploys the converted schema to Aurora PostgreSQL. The same workflow transforms database dependent .NET applications to be PostgreSQL compatible .NET applications with updated connection strings, ADO.NET and Entity Framework data-access calls. To address remaining conversion issues, customers can iterate directly in the web console or hand off to their preferred IDE using the AWS Transform MCP server. A separate synthetic data workflow populates Aurora PostgreSQL with test data for end-to-end application validation.
AWS Transform for full-stack Windows modernization and offline source transformation is available in US East (N. Virginia). To get started, you can go to AWS Transform product page or see AWS Transform for full-stack Windows documentation.