10/16/2026, 12:00:00 AM ~ 10/19/2026, 12:00:00 AM (UTC)

Recent Announcements

AWS Lambda durable functions are now available in AWS GovCloud (US) Regions

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.\nLambda 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.

Nemotron 3.5 Super VL model now available on Amazon SageMaker JumpStart

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.\nNemotron 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 model by Reflection is now available on Amazon SageMaker JumpStart and AWS Trainium

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.\nEngineering 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.

AWS Transform announces Azure-to-AWS cloud-native code conversion

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.\nAWS 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.

Second-generation AWS Outposts racks now supported in the AWS Mexico (Central) Region

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. \n 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 End User Messaging adds registration improvements for faster, higher-quality submissions

AWS End User Messaging now makes it easier to complete messaging registrations correctly the first time, with clearer guidance, explicit field requirements, and help avoiding the most common rejection reasons. Registrations ask for compliance evidence such as opt-in proof and sample messages, and submissions are often delayed or denied when that evidence is incomplete or a required field is missed. These improvements give you worked examples, surface exactly which fields you must provide, and walk you through the content that most often causes denials.\n User Guide.

AWS End User Messaging now supports Brand Profiles

Today, AWS End User Messaging announces Brand Profiles, which let you store your brand and company information once and reuse it across your messaging registrations. To send messages on SMS, RCS, and WhatsApp, you register your brand with carriers and messaging providers, and today you fill in the same company details, address, contact information, and use case by hand on every form, for each channel and country. That repeated entry slows every new registration, and a typo or other error can get one rejected and delay when you can start sending. With Brand Profiles, you enter this information once and apply it to each new registration, so you register faster and keep your details consistent across channels and countries. You can use Brand Profiles whether you send for your own brand or manage several brands in a single AWS account.\n User Guide.

AWS End User Messaging now offers managed OTP verification end-to-end

Today, AWS End User Messaging announces end-to-end managed one-time passcode (OTP) verification in Notify. You get a working OTP solution in under five minutes, including AWS phone numbers and code verification, without setting up a phone number, registering with carriers, or managing how messages are delivered. Before, you generated the passcode yourself and passed it to Notify to send, and you built and maintained the logic to create, store, and expire codes. Now Notify generates the code, delivers it, and verifies the code the recipient enters, so you ship a complete verification flow and skip that work entirely. You can deliver codes over text, voice, and now WhatsApp.\n User Guide.

AWS End User Messaging adds a guided wizard for RCS registration videos

Today, AWS End User Messaging adds a wizard that orchestrates the entire video capture for your RCS agent registration. RCS registrations in multiple countries, including the United States, require a screen recording that demonstrates your messaging experience. To record it, you have to send a specific sequence of messages to a test device, and until now you scripted those messages yourself, which easily introduced errors and made a mismatched or incomplete video one of the hardest parts of registering for RCS.\n User Guide.

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