10/22/2026, 12:00:00 AM ~ 10/23/2026, 12:00:00 AM (UTC)

Recent Announcements

Amazon OpenSearch Serverless now supports a 5-second default refresh interval

Starting today, Amazon OpenSearch Serverless reduces the default refresh interval to 5 seconds for both search and vector collections. The refresh interval controls how quickly newly ingested documents become available for search. By lowering the default from 10 seconds to 5 seconds, newly written data becomes searchable faster, giving applications quicker read-after-write visibility automatically, with no configuration changes and no action required on your part.\n This improvement applies to the next generation of Amazon OpenSearch Serverless and takes effect by default on both search and vector collections. Faster refresh is valuable for workloads that continuously ingest data and need near real-time retrieval, including full-text search over frequently updated content as well as vector use cases such as retrieval-augmented generation (RAG), semantic search, and recommendation and personalization systems. This means applications and AI agents can retrieve and act on the most recently ingested data sooner, improving the relevance and accuracy of search results and RAG responses for datasets that change frequently.

The 5-second default refresh interval is available in all 24 regions where Amazon OpenSearch Serverless is available today. To learn more, see the Amazon OpenSearch Serverless documentation.

AWS CloudFormation now simplifies stack update rollback operations

AWS CloudFormation now makes it simpler to complete an update rollback and get back to deploying. An update rollback returns your stack to a known good state. Starting today, if some resources cannot roll back automatically, force rollback enables you to complete the rollback it in a single operation. It works across every CloudFormation workflow, from the AWS CLI, and SDKs to AWS CDK and AI agents.\n With force rollback, CloudFormation completes the update rollback and returns your stack to a deployable state in a single operation, instead of requiring you to identify, list, and retry the operation for each affected resource. Any resource that still needs attention, such as one affected a change outside CloudFormation, is recorded for you to review and resolve it before your next deployment.

To get started, run continue update rollback with the force rollback option, then create a drift-aware change set to review any configuration differences before your next deployment. To learn more, see  Continue rolling back an update  in the AWS CloudFormation User Guide.

Force rollback is available in all Regions at no additional cost. Refer to the  AWS Region table  for service availability details.

Amazon SageMaker AI announces training session APIs for serverless model customization

Amazon SageMaker AI serverless model customization now supports training session APIs, giving customers full control over their post-training algorithm without provisioning or managing GPU infrastructure. Managed techniques, including SFT, DPO, and other reinforcement learning methods, let customers pick a technique and a model and have SageMaker AI run the training. With training session APIs, teams get to write the training loop, whether that’s reproducing SFT or DPO on their own terms or implementing a custom loss, a new RL reward signal, a data curriculum, or an algorithm straight from the latest research. Operations such as forward_backward(), optim_step(), and sample() become API calls against a live, stateful session, so customers compose whatever training procedure their technique requires while SageMaker AI handles GPU allocation, model and LoRA loading, distributed execution, fault recovery, checkpoint storage, and security.\n Customers can call these operations directly from their own Python code to drive reinforcement learning, supervised fine-tuning, or custom training objectives against a live session, with checkpointing and resume built in. Billing is per token processed, so local computation like reward scoring isn’t billed as GPU time. SageMaker AI is also releasing the SageMaker training sessions cookbook, a set of ready-to-run SFT and RL recipes customers can use as blueprints for their own training loops.

Training session APIs are generally available today in US East (N. Virginia), US West (Oregon), and Asia Pacific (Mumbai). To get started, install the SageMaker Python SDK and clone the cookbook, or launch a notebook in SageMaker Studio and connect a coding agent. To learn more, visit the documentation and the Amazon SageMaker AI webpage.

AWS announces AWS Interconnect - last mile preview with Megaport

AWS launches AWS Interconnect - last mile preview with Megaport, a fully managed connectivity offering that allows customers to connect their branch offices, data centers, and remote locations to AWS with just a few clicks, eliminating the friction and complexity of network setup. As a milestone collaboration between AWS and Megaport, AWS Interconnect - last mile combines AWS cloud innovation with Megaport operating one of the world’s largest Software Defined Networks (SDN), with 1,200+ enabled data centers across 31 countries.\n Through the AWS Management Console, customers can now instantly establish private, high-speed connections to AWS by simply choosing their preferred AWS Region, bandwidth speed, Direct Connect Gateway ID and partner subscriber ID. Once initiated, AWS generates an activation key to complete connectivity provisioning with Megaport. The launch simplifies the connectivity experience by pre-provisioning capacity and automating complex network configuration including BGP peering, VLAN configuration, and ASN assignment. At GA, customers will also be able to dynamically scale bandwidth from 1 Gbps to 100 Gbps through the AWS Console. The service is designed for high availability, with zero down-time maintenance, backed by a 99.99% SLA. 

AWS Interconnect - last mile is available in the San Jose (us-west-1) and N. Virginia (us-east-1) metros through Megaport. You can create a preview Interconnect using the AWS Management Console, Command Line Interface (CLI), or API. Partners can also easily adopt via a published open API package on GitHub. For more information, see the AWS Interconnect - last mile documentation and pricing pages.

Amazon SageMaker AI now supports serverless model customization for Muse Glimmer, Qwen 3.8 and Ministral 3 models

Amazon SageMaker AI now supports serverless model customization for Muse Glimmer, Qwen3.8 27B, and Ministral 3 3B, 8B, and 14B models. In addition to deploying these models on SageMaker AI, you can now adapt them to your specific domains and workflows, using supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement fine-tuning (RFT) techniques. You can also adapt these models with either low-rank adaptation (LoRA) or full fine-tuning techniques. This launch adds these models to a wide variety of models available for serverless customization on SageMaker AI, including models from the Nova, Gemma 4, Nemotron 3.5, Qwen, Llama, gpt-oss, and DeepSeek families.\nModel customization lets you tailor foundation models with your proprietary data so a smaller, right-sized model can match frontier-model quality on your tasks, while also reducing cost and latency. You can use labeled data with SFT to improve accuracy on domain-specific tasks, preference data with DPO to align outputs with your organization’s tone, or reward signals with RFT to improve performance on new tasks. With serverless customization, SageMaker AI handles all infrastructure provisioning and training orchestration, so you can focus on your data and evaluation rather than cluster management, and only pay for what you use. Serverless model customization on SageMaker AI is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To get started, navigate to the Models page in Amazon SageMaker Studio to launch a customization job, use the SageMaker Python SDK or use our Agent skills with your choice of coding assistant. To learn more and see the full supported list of models, see the Amazon SageMaker AI model customization documentation.

AWS Lambda MicroVMs is now available in 4 additional AWS regions

AWS Lambda MicroVMs is now available in 4 additional AWS Regions: Asia Pacific (Hong Kong), Asia Pacific (Malaysia), Asia Pacific (Thailand), and Europe (Spain). With this launch, you can use Lambda MicroVMs in a total of 14 AWS Regions.\n Lambda MicroVMs is a serverless compute primitive that provides VM-level isolation, near-instant launch and resume speeds, and state preservation for executing user or AI generated code.  Developers can use Lambda MicroVMs to give each user or job their own isolated compute environment without managing virtualization infrastructure or choosing between isolation, speed, and state retention. MicroVMs enable secure, stateful, and responsive code execution for applications including AI coding assistants, interactive development environments, and vulnerability scanners.

With today’s launch, developers can run Lambda MicroVMs closer to their end-users in 4 additional Regions, helping meet latency and data residency requirements. To get started, create a MicroVM image from your Dockerfile, then launch MicroVMs from that image. Give each user or job their own MicroVM with a dedicated HTTPS URL that supports popular connectivity protocols such as HTTP/2, gRPC, and WebSockets. You can use MicroVMs through the AWS Lambda console, AWS CloudFormation, AWS Cloud Development Kit, or with your preferred Agentic development tools using the Agent Toolkit for AWS.

To learn more, visit the AWS Lambda MicroVMs developer guide. For information on pricing, see Lambda MicroVMs pricing. Visit the AWS Capabilities by Region page for the latest region availability.

Amazon Quick Now Available in the AWS Singapore Region

Amazon Quick is now available in the AWS Asia Pacific (Singapore) region (ap-southeast-1). This launch allows customers in Singapore to access the full power of Amazon Quick while meeting local requirements for data sovereignty.\n Amazon Quick provides business users an agentic teammate that quickly answers questions at work and turns those answers into actions. With Amazon Quick, every user is empowered to make better decisions faster and take actions without switching applications using AI they can trust. Today’s launch allows customers to take advantage of Amazon Quick’s capabilities including AI-powered chat, Research, Spaces, and QuickSight dashboards — with their data stored and processed locally within the AWS Singapore region. 

For a full list of AWS regions where Amazon Quick is available, visit the Quick regional availability page. To learn more, visit the Amazon Quick documentation or product detail page.

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