EC2 Image Builder now supports using existing instances from an Auto Scaling Group (ASG) for building images, enabling faster builds by eliminating the time spent in launching and bootstrapping new instances and reusing already running EC2 instances.

With this feature, you can provide an ASG in your Image Builder infrastructure configuration. Instead of launching a new instance at the build time, Image Builder selects an already-running instance from this ASG, applies your build components, and creates the image. The ASG continuously replenishes the pool as instances are used for builds, ensuring instances are always available for subsequent pipeline runs.

This feature is fully opt-in and does not affect existing pipelines. Pipelines without an ASG configured continue to use the default launch path with no behavior change.

This feature is available in all AWS Regions at no additional cost. To configure ASG integration in your Image Builder pipelines, see the EC2 Image Builder documentation.

 

​EC2 Image Builder now supports using existing instances from an Auto Scaling Group (ASG) for building images, enabling faster builds by eliminating the time spent in launching and bootstrapping new instances and reusing already running EC2 instances.
With this feature, you can provide an ASG in your Image Builder infrastructure configuration. Instead of launching a new instance at the build time, Image Builder selects an already-running instance from this ASG, applies your build components, and creates the image. The ASG continuously replenishes the pool as instances are used for builds, ensuring instances are always available for subsequent pipeline runs.
This feature is fully opt-in and does not affect existing pipelines. Pipelines without an ASG configured continue to use the default launch path with no behavior change.
This feature is available in all AWS Regions at no additional cost. To configure ASG integration in your Image Builder pipelines, see the EC2 Image Builder documentation.  

AWS Partner Central now gives AWS Partners more visibility into how the Solution Matching Engine (SME) contributes to their co-sell pipeline. The Solution Matching Engine performance section in Partner Analytics helps partners understand how SME performs for their company and AWS Marketplace listings.

Partners can view recommendations, SME-attributed opportunities, their share of AWS-originated opportunities, stall rate, and pipeline attributed to SME. They can filter by recommendation type or AWS Marketplace listing and explore top customer profiles by Region, industry, and segment. These insights help partners assess their visibility and decide where to improve their listings and opportunity information.

All partners eligible for APN Customer Engagements (ACE) can find the section on the Opportunity pipeline analysis page under Partner Analytics in AWS Partner Central. To learn more about SME and how to strengthen your visibility with AWS sellers, read Solution Matching Engine: Your Gateway to AWS Sellers.

 

​AWS Partner Central now gives AWS Partners more visibility into how the Solution Matching Engine (SME) contributes to their co-sell pipeline. The Solution Matching Engine performance section in Partner Analytics helps partners understand how SME performs for their company and AWS Marketplace listings.
Partners can view recommendations, SME-attributed opportunities, their share of AWS-originated opportunities, stall rate, and pipeline attributed to SME. They can filter by recommendation type or AWS Marketplace listing and explore top customer profiles by Region, industry, and segment. These insights help partners assess their visibility and decide where to improve their listings and opportunity information.
All partners eligible for APN Customer Engagements (ACE) can find the section on the Opportunity pipeline analysis page under Partner Analytics in AWS Partner Central. To learn more about SME and how to strengthen your visibility with AWS sellers, read Solution Matching Engine: Your Gateway to AWS Sellers.  

Amazon GuardDuty now supports optional detection rules for Runtime Monitoring. You can enable more than 50 Runtime Monitoring rules that help detect process, file, and network activity that might be routine in one environment but a sign of compromise in another, such as modifying sudoers files or disabling audit logging.

The new rules provide detections for Amazon EC2 instances, Amazon EKS, and Amazon ECS on both EC2 and AWS Fargate, and span 13 finding types mapped to MITRE ATT&CK® tactics including persistence, privilege escalation, execution, defense evasion, discovery, exfiltration, and command and control. This release introduces four new finding types: CommandAndControl:Runtime/SuspiciousCommand, CredentialAccess:Runtime/SensitiveFileRead, Discovery:Runtime/SensitiveFileRead, and Exfiltration:Runtime/SuspiciousCommand.

To get started, visit the GuardDuty console. You can enable rules in dry-run mode to evaluate detection efficacy before enabling live detection.

Custom Detection Rules for Runtime Monitoring is available in all AWS commercial Regions and the AWS GovCloud (US) Regions. To learn more, see Amazon GuardDuty Custom Detection Rules. To receive programmatic updates on new Amazon GuardDuty features and threat detections, subscribe to the Amazon GuardDuty SNS topic.

 

​Amazon GuardDuty now supports optional detection rules for Runtime Monitoring. You can enable more than 50 Runtime Monitoring rules that help detect process, file, and network activity that might be routine in one environment but a sign of compromise in another, such as modifying sudoers files or disabling audit logging.
The new rules provide detections for Amazon EC2 instances, Amazon EKS, and Amazon ECS on both EC2 and AWS Fargate, and span 13 finding types mapped to MITRE ATT&CK® tactics including persistence, privilege escalation, execution, defense evasion, discovery, exfiltration, and command and control. This release introduces four new finding types: CommandAndControl:Runtime/SuspiciousCommand, CredentialAccess:Runtime/SensitiveFileRead, Discovery:Runtime/SensitiveFileRead, and Exfiltration:Runtime/SuspiciousCommand.
To get started, visit the GuardDuty console. You can enable rules in dry-run mode to evaluate detection efficacy before enabling live detection.
Custom Detection Rules for Runtime Monitoring is available in all AWS commercial Regions and the AWS GovCloud (US) Regions. To learn more, see Amazon GuardDuty Custom Detection Rules. To receive programmatic updates on new Amazon GuardDuty features and threat detections, subscribe to the Amazon GuardDuty SNS topic.  

Amazon CloudWatch log centralization is now available in AWS GovCloud (US) and 13 additional commercial Regions. CloudWatch log centralization copies log data from multiple AWS accounts and Regions into a single destination account, integrating with AWS Organizations for efficient aggregation without the need for custom solutions.

With this launch, organizations in the AWS GovCloud (US) partition and commercial partition can each centralize logs across their accounts and Regions for centralized monitoring. You can scope centralization rules to your entire organization, specific organizational units, or selected accounts and copy logs into a single account while preserving source context through enriched system fields.

CloudWatch log centralization is now additionally available in Africa (Cape Town), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Asia Pacific (Jakarta), Asia Pacific (Malaysia), Asia Pacific (Melbourne), Canada West (Calgary), Europe (Milan), Europe (Spain), Europe (Zurich), Israel (Tel Aviv), Middle East (Bahrain), Middle East (UAE), AWS GovCloud (US-East), and AWS GovCloud (US-West). For a complete list of supported Regions, see the AWS Region table.

To get started, create a centralization rule in the Amazon CloudWatch console, or by using the AWS CLI or AWS SDKs. To learn more, see Log centralization in the Amazon CloudWatch Logs User Guide. Customers can centralize one copy of logs for free; additional copies are charged at $0.05/GB. For details, see Amazon CloudWatch pricing.

 

​Amazon CloudWatch log centralization is now available in AWS GovCloud (US) and 13 additional commercial Regions. CloudWatch log centralization copies log data from multiple AWS accounts and Regions into a single destination account, integrating with AWS Organizations for efficient aggregation without the need for custom solutions.
With this launch, organizations in the AWS GovCloud (US) partition and commercial partition can each centralize logs across their accounts and Regions for centralized monitoring. You can scope centralization rules to your entire organization, specific organizational units, or selected accounts and copy logs into a single account while preserving source context through enriched system fields.
CloudWatch log centralization is now additionally available in Africa (Cape Town), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Asia Pacific (Jakarta), Asia Pacific (Malaysia), Asia Pacific (Melbourne), Canada West (Calgary), Europe (Milan), Europe (Spain), Europe (Zurich), Israel (Tel Aviv), Middle East (Bahrain), Middle East (UAE), AWS GovCloud (US-East), and AWS GovCloud (US-West). For a complete list of supported Regions, see the AWS Region table.
To get started, create a centralization rule in the Amazon CloudWatch console, or by using the AWS CLI or AWS SDKs. To learn more, see Log centralization in the Amazon CloudWatch Logs User Guide. Customers can centralize one copy of logs for free; additional copies are charged at $0.05/GB. For details, see Amazon CloudWatch pricing.  

Second-generation AWS Outposts racks are now supported in the AWS Mexico (Central) Region. Outposts racks extend AWS infrastructure, AWS services, APIs, and tools to virtually any on-premises data center or colocation space for a truly consistent hybrid experience. 

 

Organizations of every size, from startups to enterprises to public sector agencies, can now order Outposts racks connected to this newly supported Region to optimize for latency and data residency needs. This regional expansion provides additional flexibility in the number of AWS Regions that customers’ Outposts can connect to.

To learn more about second-generation Outposts racks, read the blog post and the user guide. For the latest list of countries and territories and the AWS Regions where second-generation Outposts racks are supported, see the Outposts rack FAQs page.

 

​Second-generation AWS Outposts racks are now supported in the AWS Mexico (Central) Region. Outposts racks extend AWS infrastructure, AWS services, APIs, and tools to virtually any on-premises data center or colocation space for a truly consistent hybrid experience. 

 

Organizations of every size, from startups to enterprises to public sector agencies, can now order Outposts racks connected to this newly supported Region to optimize for latency and data residency needs. This regional expansion provides additional flexibility in the number of AWS Regions that customers’ Outposts can connect to.
To learn more about second-generation Outposts racks, read the blog post and the user guide. For the latest list of countries and territories and the AWS Regions where second-generation Outposts racks are supported, see the Outposts rack FAQs page.  

AWS Lambda durable functions are now available in the AWS GovCloud (US-West) and AWS GovCloud (US-East) Regions. Lambda durable functions enable developers to build reliable multi-step applications and AI workflows within the Lambda developer experience.

Lambda durable functions extend the Lambda programming model with new primitives in your event handler, such as «step» and «wait», allowing you to checkpoint progress, automatically recover from failures, and pause executions without incurring compute charges for on-demand functions. With availability in the AWS GovCloud (US) Regions, government customers and regulated industries can orchestrate complex processes such as order workflows, user onboarding, and AI-assisted tasks while meeting stringent compliance and security requirements.

You can activate durable functions for new Python (versions 3.13 and 3.14), Node.js (versions 22 and 24), Go (versions 1.24 or later), .Net (version 2.2), or Java (17+) based Lambda functions using the AWS Lambda API, AWS Management Console, or AWS SDK. You can also use infrastructure as code tools such as AWS CloudFormation, AWS Serverless Application Model (AWS SAM), and the AWS Cloud Development Kit (AWS CDK).

For more information on durable functions, visit the AWS Lambda durable functions product page or the AWS Lambda Developer Guide. To learn about pricing, visit AWS Lambda pricing. For region availability, visit the AWS Capabilities by Region page. 

 

​AWS Lambda durable functions are now available in the AWS GovCloud (US-West) and AWS GovCloud (US-East) Regions. Lambda durable functions enable developers to build reliable multi-step applications and AI workflows within the Lambda developer experience.
Lambda durable functions extend the Lambda programming model with new primitives in your event handler, such as «step» and «wait», allowing you to checkpoint progress, automatically recover from failures, and pause executions without incurring compute charges for on-demand functions. With availability in the AWS GovCloud (US) Regions, government customers and regulated industries can orchestrate complex processes such as order workflows, user onboarding, and AI-assisted tasks while meeting stringent compliance and security requirements.
You can activate durable functions for new Python (versions 3.13 and 3.14), Node.js (versions 22 and 24), Go (versions 1.24 or later), .Net (version 2.2), or Java (17+) based Lambda functions using the AWS Lambda API, AWS Management Console, or AWS SDK. You can also use infrastructure as code tools such as AWS CloudFormation, AWS Serverless Application Model (AWS SAM), and the AWS Cloud Development Kit (AWS CDK).
For more information on durable functions, visit the AWS Lambda durable functions product page or the AWS Lambda Developer Guide. To learn about pricing, visit AWS Lambda pricing. For region availability, visit the AWS Capabilities by Region page.   

NVIDIA’s Nemotron 3.5 Super VL is now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This vision-language model from the Nemotron 3.5 family accepts images, video, and text as input and generates text, enabling customers to deploy multimodal AI solutions on AWS infrastructure.

Nemotron 3.5 Super VL is designed for document intelligence, GUI understanding, video question answering, and multimodal agentic applications. It pairs a 120B-parameter hybrid Mixture-of-Experts (MoE) language backbone with 12B active parameters and a vision encoder, supporting context lengths of up to 1 million tokens. The model includes a built-in Multi-Token Prediction (MTP) head for speculative decoding to accelerate text generation, with reasoning configurable per request. It supports ten languages including English, French, Spanish, German, Japanese, Hindi, Korean, and Chinese. Post-trained with multimodal supervised fine-tuning on image, video, document, chart, and GUI data followed by GRPO reinforcement learning, it delivers structured JSON output, tool calling, and configurable reasoning for enterprise agent workflows. SageMaker JumpStart offers two variants: NVFP4 for optimized inference on G7 instances with NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, and BF16 reference weights on ml.p5en.48xlarge for customization including SFT, RL, distillation, and domain adaptation.

With SageMaker JumpStart, customers can deploy Nemotron 3.5 Super VL 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’s Nemotron 3.5 Super VL is now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This vision-language model from the Nemotron 3.5 family accepts images, video, and text as input and generates text, enabling customers to deploy multimodal AI solutions on AWS infrastructure.
Nemotron 3.5 Super VL is designed for document intelligence, GUI understanding, video question answering, and multimodal agentic applications. It pairs a 120B-parameter hybrid Mixture-of-Experts (MoE) language backbone with 12B active parameters and a vision encoder, supporting context lengths of up to 1 million tokens. The model includes a built-in Multi-Token Prediction (MTP) head for speculative decoding to accelerate text generation, with reasoning configurable per request. It supports ten languages including English, French, Spanish, German, Japanese, Hindi, Korean, and Chinese. Post-trained with multimodal supervised fine-tuning on image, video, document, chart, and GUI data followed by GRPO reinforcement learning, it delivers structured JSON output, tool calling, and configurable reasoning for enterprise agent workflows. SageMaker JumpStart offers two variants: NVFP4 for optimized inference on G7 instances with NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, and BF16 reference weights on ml.p5en.48xlarge for customization including SFT, RL, distillation, and domain adaptation.
With SageMaker JumpStart, customers can deploy Nemotron 3.5 Super VL 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.  

Beam, the first open weight model offered by US-based Reflection, is now available in Amazon SageMaker JumpStart, the model hub in Amazon SageMaker AI. According to Beam, they operate at the frontier of open weight models in its class, with strong performance across reasoning, coding, and agentic benchmarks. With it, customers can now build advanced coding agents and automated workflows on a frontier open weight model while keeping control of their data and infrastructure and adapting the model to their business. Beam runs alongside other models that customers already use in SageMaker AI and scales to meet production demand with the same controls, audit trails, and monitoring built into every SageMaker AI deployment. Additionally, Beam was ported and optimized for AWS Trainium3, giving customers a high-performance, cost-efficient option for deploying and scaling the model from day one. “Reflection is delivering frontier AI that developers can actually control, not just rent through an API. Bringing Beam to SageMaker AI puts that capability in front of millions of developers on infrastructure they already trust, running in their own AWS account, on their own data, fully in their control,» said Misha Laskin, co-founder and CEO, Reflection.

Engineering teams can use Beam to resolve software issues, generate and review code, and automate routine operations work. Businesses can also use it for complex workflows that span many steps and tools, and for advanced math and science reasoning. Because Beam is released under the Apache 2.0 license, customers can inspect and customize the model, while also running it in their own AWS accounts, so data stays private and under their control, protected by the same AWS security and compliance capabilities they already rely on.  “Advanced AI models like Beam, now available on Amazon SageMaker, let us run open weight frontier AI entirely inside our own secure environments, with full visibility into the weights, the data, and the pipeline. In the age of multi-model AI, the pairing of openness and managed infrastructure is an essential addition to the AI ecosystem. The freedom to deploy and adapt advanced AI, tailored to mission problems with client-specific security and controls, gives more agencies greater flexibility to deploy advanced AI across their enterprise.» said Garrett Berntsen, Chief AI Officer, Accenture Federal Services.

Beam is initially available in Amazon SageMaker JumpStart in US East (N. Virginia) and US East (Ohio). To start, search for «Beam» in SageMaker JumpStart within Amazon SageMaker Studio, or deploy it directly on SageMaker AI.  To learn more, read the AWS/Beam launch blog, SageMaker AI documentation and SageMaker JumpStart documentation.

 

​Beam, the first open weight model offered by US-based Reflection, is now available in Amazon SageMaker JumpStart, the model hub in Amazon SageMaker AI. According to Beam, they operate at the frontier of open weight models in its class, with strong performance across reasoning, coding, and agentic benchmarks. With it, customers can now build advanced coding agents and automated workflows on a frontier open weight model while keeping control of their data and infrastructure and adapting the model to their business. Beam runs alongside other models that customers already use in SageMaker AI and scales to meet production demand with the same controls, audit trails, and monitoring built into every SageMaker AI deployment. Additionally, Beam was ported and optimized for AWS Trainium3, giving customers a high-performance, cost-efficient option for deploying and scaling the model from day one. “Reflection is delivering frontier AI that developers can actually control, not just rent through an API. Bringing Beam to SageMaker AI puts that capability in front of millions of developers on infrastructure they already trust, running in their own AWS account, on their own data, fully in their control,» said Misha Laskin, co-founder and CEO, Reflection.
Engineering teams can use Beam to resolve software issues, generate and review code, and automate routine operations work. Businesses can also use it for complex workflows that span many steps and tools, and for advanced math and science reasoning. Because Beam is released under the Apache 2.0 license, customers can inspect and customize the model, while also running it in their own AWS accounts, so data stays private and under their control, protected by the same AWS security and compliance capabilities they already rely on.  “Advanced AI models like Beam, now available on Amazon SageMaker, let us run open weight frontier AI entirely inside our own secure environments, with full visibility into the weights, the data, and the pipeline. In the age of multi-model AI, the pairing of openness and managed infrastructure is an essential addition to the AI ecosystem. The freedom to deploy and adapt advanced AI, tailored to mission problems with client-specific security and controls, gives more agencies greater flexibility to deploy advanced AI across their enterprise.» said Garrett Berntsen, Chief AI Officer, Accenture Federal Services.
Beam is initially available in Amazon SageMaker JumpStart in US East (N. Virginia) and US East (Ohio). To start, search for «Beam» in SageMaker JumpStart within Amazon SageMaker Studio, or deploy it directly on SageMaker AI.  To learn more, read the AWS/Beam launch blog, SageMaker AI documentation and SageMaker JumpStart documentation.  

Today, AWS announced the general availability of application code conversion from Azure SDK to AWS SDK in AWS Transform, giving you an accelerated path to migrate Microsoft Azure cloud-native .NET applications to AWS. The AWS Transform .NET modernization agent identifies Azure service code and converts it to equivalent AWS service code. For example, an Azure Functions serverless function that uses Azure Cosmos DB can be converted to an AWS Lambda function that uses Amazon DynamoDB.

AWS Transform discovers the Azure SDK in your submitted source code and presents the conversion options in the assessment report and modernization plan. You can accept the options provided or adjust them based on your preferences before running the modernization. AWS Transform then converts the identified Azure service code to the equivalent AWS SDK calls, and validates that the changes are incorporated into the transformed code. The feature supports conversion of Azure Functions to AWS Lambda functions, Azure Blob Storage to Amazon S3, and Azure Queue Storage to Amazon SQS, with Azure Cosmos DB to Amazon DynamoDB conversion available in preview.

AWS Transform is available in eight AWS Regions: US East (N. Virginia), Asia Pacific (Mumbai, Tokyo, Seoul, Sydney), Canada (Central), and Europe (Frankfurt, London).

To learn more, see Convert cloud-native code to AWS in the AWS Transform User Guide.

 

​Today, AWS announced the general availability of application code conversion from Azure SDK to AWS SDK in AWS Transform, giving you an accelerated path to migrate Microsoft Azure cloud-native .NET applications to AWS. The AWS Transform .NET modernization agent identifies Azure service code and converts it to equivalent AWS service code. For example, an Azure Functions serverless function that uses Azure Cosmos DB can be converted to an AWS Lambda function that uses Amazon DynamoDB.
AWS Transform discovers the Azure SDK in your submitted source code and presents the conversion options in the assessment report and modernization plan. You can accept the options provided or adjust them based on your preferences before running the modernization. AWS Transform then converts the identified Azure service code to the equivalent AWS SDK calls, and validates that the changes are incorporated into the transformed code. The feature supports conversion of Azure Functions to AWS Lambda functions, Azure Blob Storage to Amazon S3, and Azure Queue Storage to Amazon SQS, with Azure Cosmos DB to Amazon DynamoDB conversion available in preview.
AWS Transform is available in eight AWS Regions: US East (N. Virginia), Asia Pacific (Mumbai, Tokyo, Seoul, Sydney), Canada (Central), and Europe (Frankfurt, London).
To learn more, see Convert cloud-native code to AWS in the AWS Transform User Guide.  

Web Search on Amazon Bedrock AgentCore is now available in four additional AWS Regions: US East (Ohio), US West (Oregon), Asia Pacific (Sydney), and AWS GovCloud (US-West). Web Search is a fully managed tool that provides AI agents with current grounding from the web, enabling more accurate and up-to-date responses for tasks such as research, summarization, and question answering.

With this expansion, customers can build agents that retrieve web information closer to their applications and end users with lower latency. Customers with government workloads can now use Web Search in the AWS GovCloud (US-West) Region to meet compliance and data residency requirements. 

To learn more about Web Search on Amazon Bedrock AgentCore, see the Web Search tool documentation. To learn about pricing, visit Amazon Bedrock AgentCore pricing. For Region availability, visit Supported AWS Regions.

 

​Web Search on Amazon Bedrock AgentCore is now available in four additional AWS Regions: US East (Ohio), US West (Oregon), Asia Pacific (Sydney), and AWS GovCloud (US-West). Web Search is a fully managed tool that provides AI agents with current grounding from the web, enabling more accurate and up-to-date responses for tasks such as research, summarization, and question answering.
With this expansion, customers can build agents that retrieve web information closer to their applications and end users with lower latency. Customers with government workloads can now use Web Search in the AWS GovCloud (US-West) Region to meet compliance and data residency requirements. 
To learn more about Web Search on Amazon Bedrock AgentCore, see the Web Search tool documentation. To learn about pricing, visit Amazon Bedrock AgentCore pricing. For Region availability, visit Supported AWS Regions.