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Amazon Web Services Updates(221)

Machine-translated and quality-filtered from the Amazon Web Services official feed.

Last updated: October 12, 2026 at 11:10 PM
General Availability

Amazon CloudFront now supports AWS Lambda Web Functions as an origin

CloudFront now supports AWS Lambda Web Functions as an origin, providing TLS termination at the edge, caching, and custom domains with managed TLS certificates. No changes to application code are needed, and CloudFront Origin Access Control restricts access to the Lambda Web Function.

대상Backend developers building serverless applications on AWS Lambda
기능TLS termination at the edge, caching, custom domains with managed TLS certificates
리전All regions, except CloudFront China region

Amazon SageMaker Unified Studio now supports custom Tooling blueprints

<p>Amazon SageMaker Unified Studio now supports custom Tooling blueprints, giving domain administrators the ability to define the foundation of every project by using their own AWS CloudFormation templates. Administrators can tailor project environments to meet organization-specific requirements, such as IAM role names that comply with company naming standards or custom permissions boundaries instead of AWS managed policies.</p> <p>Amazon SageMaker Unified Studio administrators start by authoring a CloudFormation template that describes the project environment they need, then register it as a custom Tooling blueprint. The service validates the template at registration and again after each project deployment, confirming that the required resources exist before any team member uses the project. Templates can include any CloudFormation-supported resource, such as AWS Lake Formation grants, Amazon Athena workgroups, or VPC security groups, and work across multiple AWS accounts and Regions without per-project edits. At deployment time, the service automatically populates reserved template parameters, such as the project ID and domain ID, so administrators define the template once and apply it everywhere.</p> <p>This feature is available in all <a href="https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/supported-regions.html">AWS Regions</a> where Amazon SageMaker Unified Studio is available. To learn more, visit the <a href="https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/tooling-custom-blueprints.html">Custom blueprints as Tooling documentation</a>.</p>

AWS Cost Explorer, Budgets, and Dashboards now support Amazon Bedrock product attributes

<p>AWS Cost Explorer, AWS Budgets, and AWS Cost Management Dashboards now let you analyze Amazon Bedrock costs by product attributes, a new dimension that breaks down Bedrock cost by model, model provider, inference type, and feature. In Cost Explorer and Dashboards, you can group and filter costs by these attributes. In Budgets, you can filter budgets by the same attributes.</p> <p>With this launch, finance and platform teams get a direct view of which models and workloads drive their Bedrock spend, and can act on it in the billing tools they already use. The attributes include model (such as Claude Sonnet 5 or Claude Haiku 4.5), model provider (such as Anthropic or Cohere), inference type (such as input tokens or output tokens), and feature (such as on-demand inference or reranker). You can combine product attributes with cost allocation tags on application inference profiles, projects, or workspaces to see which applications drive spend on each model, and with IAM principal tags to see which users or roles drive it. Group a month of Bedrock spend by model in Cost Explorer to find your most expensive models, build a dashboard widget that tracks cost per provider, or set a budget that alerts on spend for a single model.</p> <p>The Amazon Bedrock product attribute dimension is available today at no additional charge in all AWS Regions, excluding the AWS GovCloud (US) Regions and the China (Beijing) and China (Ningxia) Regions. To learn more, see the user guides for <a href="https://docs.aws.amazon.com/cost-management/latest/userguide/ce-what-is.html">Cost Explorer</a>, <a href="https://docs.aws.amazon.com/cost-management/latest/userguide/budgets-managing-costs.html">Budgets</a>, and <a href="https://docs.aws.amazon.com/cost-management/latest/userguide/dashboards.html">Dashboards</a>.</p>

AWS Control Tower AFT now supports plan-only customization runs

<p>AWS Control Tower Account Factory for Terraform (AFT) now supports plan-only customization runs, giving administrators the ability to preview Terraform changes before applying them across their managed accounts. <br> AFT is an open-source Terraform module that automates account provisioning and customization in AWS Control Tower environments. Previously, invoking customizations always triggered a full Terraform apply with no way to preview changes, making it difficult to validate expected outcomes or catch unintended configuration drift before a broad rollout. With plan-only runs, administrators can now trigger a Terraform plan for global and account customizations without applying any changes. This capability supports safe pre-rollout validation, drift detection, and integration into CI/CD review workflows. Plan-only runs are compatible across all AFT-supported Terraform distributions, including open source, Terraform Cloud, and Terraform Enterprise.</p> <p>This feature is available in all AWS Regions where AWS Control Tower Account Factory for Terraform is supported. To get started, see the <a href="https://docs.aws.amazon.com/controltower/latest/userguide/aft-overview.html">AFT documentation</a> and the <a href="https://github.com/aws-ia/terraform-aws-control_tower_account_factory/releases/tag/1.22.0">AFT 1.22.0 release notes</a> for configuration details. For more information, see the <a href="https://docs.aws.amazon.com/controltower/latest/userguide/what-is-control-tower.html">AWS Control Tower User Guide</a>.</p>

Amazon DynamoDB global tables with multi-Region strong consistency now supports additional AWS Regions and cross-continent configurations

<p>Starting today, Amazon DynamoDB global tables with multi-Region strong consistency (MRSC) is available in five additional AWS Regions: Canada (Central), Europe (Stockholm), Europe (Spain), Asia Pacific (Mumbai), and Asia Pacific (Singapore), bringing the total number of supported Regions to 15. You can now create an MRSC global table in any three-Region configuration across the 15 supported AWS Regions, configured with either three replicas or two replicas and one witness Region. This includes configurations that span North America, Europe, and Asia Pacific.</p> <p>MRSC lets you build highly available multi-Region applications with a recovery point objective (RPO) of zero. By choosing replica Regions closer to your users in more geographies, you can route applications to nearby DynamoDB endpoints for strongly consistent reads while keeping your application available during Regional impairments. MRSC is ideal for global applications with strict consistency requirements, such as user profile management, inventory tracking, order state, and entitlement management.</p> <p>To get started and view the complete list of supported Regions and configuration options, see the <a href="https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/V2globaltables_HowItWorks.html#V2globaltables_HowItWorks.consistency-modes.mrsc">DynamoDB Developer Guide</a>. To learn more about building resilient multi-Region applications, visit the <a href="https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/V2globaltables.tutorial.html#create-gt-mrsc">DynamoDB global tables page</a>.</p>

AWS Marketplace launches an AI agent skill for usage-based metering integration

<p>AWS announces the general availability of the AWS Marketplace metering agent skill, an AI-guided experience that helps sellers build, deploy, and validate a usage-based (pay-as-you-go) SaaS metering integration from within their AI coding assistant. The skill guides sellers through the complete integration journey — gathering their product type, pricing model, and usage dimensions; recommending the correct metering API; generating integration code and a customized AWS CloudFormation stack tailored to their configuration; and running an end-to-end test against AWS Marketplace before any production code ships.</p> <p>Previously, sellers integrating metering navigated documentation, workshops, and trial-and-error API calls, where common mistakes such as mismatched dimension names or invalid timestamps create billing gaps discovered days later. The metering agent skill cross-validates dimensions against the seller's actual product configuration, applies built-in guardrails, deploys a serverless metering pipeline (using ResolveCustomer API, BatchMeterUsage API, and Amazon EventBridge for subscription events), and verifies the integration with a live test. It also supports <a href="https://aws.amazon.com/blogs/awsmarketplace/complete-guide-to-upgrading-your-saas-product-to-aws-marketplace-concurrent-agreements/">Concurrent Agreements</a> and helps existing sellers inspect, debug, and analyze their metering records.</p> <p>The skill is available through the <a href="https://docs.aws.amazon.com/agent-toolkit/latest/userguide/getting-started-aws-mcp-server.html"><u>AWS MCP Server</u></a> in any AI coding assistant that supports MCP, including Amazon Q Developer, Kiro, and other MCP-compatible clients, with no plugin installation required.</p> <p>To get started, visit <a href="https://docs.aws.amazon.com/marketplace/latest/userguide/metering-for-usage.html#configure-applicati

Claude Opus 5.5 is now available on AWS GovCloud (US)

<p>AWS GovCloud (US) now offers Claude Opus 5.5, Anthropic’s most capable Opus model yet and, the first of the Claude 5.5 model family, a better collaborator that handles long-running coding and knowledge work, reporting back clearly on what it did, what it found, and what it needs next.</p> <p>According to Anthropic, Claude Opus 5.5 completes tasks using fewer tokens than Claude Opus 5, at a lower price per token, with cheaper cache reads stacking on top of the efficiency gain. Claude Opus 5.5 is the enterprise workhorse, a clear step up from Opus 5 on the work teams count on Opus to do. It handles long-running coding and knowledge work, and reports back like a good teammate, surfacing what it did, what it found, and what it needs from you. It thinks adaptively on every request, deciding how much effort each task needs.  </p> <p>Amazon Bedrock gives you Opus 5.5’s advanced capabilities with zero data retention (ZDR) support by default. It keeps your data within AWS infrastructure with regional data residency and provides access through a unified service with AWS-managed features like Guardrails and Knowledge Bases. To learn more, see the Amazon Bedrock <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-anthropic-claude-opus-5-5.html">documentation</a> and <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/models-region-compatibility.html?refid=7ecf60df-6136-414c-a7c3-6aa4d2d6019f#model-regions-anthropic">regional availability.</a></p>

Kimi K3 by Moonshot AI is now generally available on Amazon Bedrock

<p>Amazon Bedrock continues to expand its open weight model portfolio with the same security and governance that customers rely on. Today, Kimi K3 from Moonshot AI is generally available on Amazon Bedrock, giving you a powerful new option for coding and knowledge work.</p> <p>According to Moonshot AI, Kimi K3 is its most capable model and the first open model to reach 2.8 trillion parameters. It combines native vision capabilities with a 1-million-token context window, making it well suited to long-running coding sessions across large repositories, multi-document analysis including scanned pages and screenshots, and extended agent workflows. <a href="https://www.kimi.com/blog/kimi-k3">Moonshot AI reports</a> an approximate 2.5x improvement in scaling efficiency over Kimi K2. On Amazon Bedrock, Kimi K3 runs within the same security boundary as proprietary models, and the same controls for access, encryption, and auditing across your model portfolio. Kimi K3 is the first open weight model on Amazon Bedrock to support <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html">explicit prompt caching</a>, helping reduce latency and input costs when reusing context across model calls.</p> <p>Kimi K3 is available in all <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/models-region-compatibility.html">AWS Regions</a> where Amazon Bedrock is available through cross-Region inferencing. To get started, visit the <a href="https://us-east-1.console.aws.amazon.com/bedrock-mantle/home?region=us-east-1#">Amazon Bedrock Console</a>. To learn more, see the <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-cards-moonshot-ai.html">Amazon Bedrock documentation</a> and read the <a href="https://aws.amazon.com/blogs/machine-learning/introducing-kimi-k3-on-amazon-bedrock/

AWS PrivateLink announces Tunnel Endpoints to access network segments

<p>AWS PrivateLink customers can now use VPC endpoint to privately and securely access network segments in another VPC/account. They can use a ‘tunnel’ endpoint, a new type of VPC endpoint, to tunnel into the network segment and access resources located within it.</p> <p>AWS PrivateLink is a highly available and scalable technology that enables private access across VPC and account boundaries to load balanced services, appliances, and resources such as databases and domains. Prior to this launch, customers who wanted to share their resources with another party such as an external vendor had to do it one at a time by creating a Resource Configuration for every resource. Now, customers can create a Resource Configuration to represent a CIDR range in their network, and share it with a vendor via AWS Resource Access Manager (RAM). The vendor can then create a tunnel endpoint and use GENEVE encapsulation to tunnel through it into the customer’s VPC to access resources located in CIDR range specified by the customer. There is an hourly charge for the tunnel endpoint and a per-GB charge for data processed through it. Please refer to the pricing page for <a href="https://aws.amazon.com/privatelink/pricing/">AWS PrivateLink</a>.  <br> <br> The capability is available in the following <a href="https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/">AWS Regions</a>: US East (N. Virginia), US East (Ohio), US West (N. California), US West (Oregon), Africa (Cape Town), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Asia Pacific (Jakarta), Asia Pacific (Malaysia), Asia Pacific (Melbourne), Asia Pacific (Mumbai), Asia Pacific (Osaka), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Canada West (Calgary), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Milan), Europe (Paris), Europe (Spain), Europe (Stockholm), E

Amazon SES now supports tenant-level deliverability insights

<p><a href="https://aws.amazon.com/ses/">Amazon Simple Email Service (SES)</a> now supports tenant-level deliverability insights in Virtual Deliverability Manager (VDM). <a href="https://docs.aws.amazon.com/ses/latest/dg/tenants.html">Tenant management</a> allows email senders to isolate email sending across customers, business units, or applications. Previously, VDM provided deliverability metrics at the account, ISP, sending identity, and configuration set levels. Now, customers who use tenants and have enabled VDM can also monitor the deliverability of each tenant.</p> <p>The VDM dashboard adds a Tenants view with per-tenant metrics (send volume, deliveries, bounces, complaints, opens, and clicks) and a detail page that breaks those metrics down by mailbox provider and by associated identities and configuration sets. Customers can also search and export a tenant's sent messages, or query tenant metrics programmatically using the BatchGetMetricData API operation's new TENANT_NAME dimension. This helps senders identify the tenant causing a deliverability issue and correct it without impact to their other tenants.</p> <p>Virtual Deliverability Manager is available in all AWS Regions where Amazon SES is available.</p> <p>To learn more, visit the <a href="https://console.aws.amazon.com/ses">Amazon SES console</a> or refer to the Virtual Deliverability Manager dashboard documentation in the <a href="https://docs.aws.amazon.com/ses/latest/dg/vdm-dashboard.html">Amazon SES Developer Guide</a>.</p>

Amazon ECS deployment observability for Amazon ECS Managed Daemons in AWS Management Console

<p><a href="https://aws.amazon.com/ecs/">Amazon Elastic Container Service</a> (Amazon ECS) now provides a consolidated deployment view for Amazon ECS Managed Daemons, giving you a single place to see how far a deployment has progressed, what is failing, and what could stop it. This helps you monitor Managed Daemon deployments at a glance and resolve issues faster, whether a rollout is in progress or you are reviewing what happened after it completed, eliminating the need to piece together deployment status from multiple sources.<br> <br> The deployment view presents a lifecycle timeline with timestamps at each step and the total deployment duration, including the rollback path if a deployment is interrupted. Progress bars for each capacity provider track instances completed, in progress, and remaining, as well as instances draining and being replaced when a capacity provider is removed. A monitoring panel shows deployment circuit breaker, deployment alarm, and container health check state, each with live Amazon CloudWatch alarm detail and whether it can stop the deployment. If a daemon task fails, the affected capacity provider shows the stop reason with links to the task, its logs, and the matching troubleshooting guide, including the failure that caused a rollback. The view also displays target and source revisions with instance counts, drain percentage, and bake time.<br> <br> This enhancement is available as a console-only view in all AWS Commercial Regions at no additional cost. To get started with Amazon ECS Managed Daemons, refer to our <a href="https://docs.aws.amazon.com/AmazonECS/latest/developerguide/managed-daemons.html">documentation</a>.</p>

Amazon Connect Customer can now import evaluation form PDFs using AI

<p>Amazon Connect Customer now lets managers import a PDF of an evaluation form from a third-party quality management application, and automatically recreates it in Connect using AI, making it easier to migrate an existing quality program into Connect. Instead of rebuilding an existing form question by question, you upload the PDF and choose the preferred scoring method, either percentage or points-based, and Connect Customer automatically extracts the sections, questions, answer options, and scoring into a draft evaluation form. You can also provide natural language instructions to import the form more accurately or to adjust it as it's created — for example, splitting a broad question into simpler ones — minimizing the manual edits needed before you activate the form.</p> <p>This feature is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Canada (Central), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Europe (Frankfurt). To learn more, please visit our <a href="https://docs.aws.amazon.com/connect/latest/adminguide/create-evaluation-forms.html#import-form-pdf">documentation</a> and our <a href="https://aws.amazon.com/products/connect/customer/conversational-analytics/">webpage</a>. For information about Amazon Connect Customer pricing, please visit our <a href="https://aws.amazon.com/products/connect/customer/pricing/?trk=758059ab-77e5-44fc-8a4a-1be845ee42b9&sc_channel=ps">pricing</a> page.</p>

Amazon SageMaker AI now supports instance preference lists for training and processing jobs

<p>Today, Amazon SageMaker AI announces instance preference lists for training and processing jobs, making it easier and faster to find compute capacity for your workloads. Many AI training, fine-tuning, and data processing workloads run comparably well on any of several instance types or sizes. However, before now, you had to name only one instance type at the time of job submission and wait for SageMaker to find that specific instance for your job. For high-demand GPUs during peak periods, where wait times can be unpredictable, customers sometimes had to build complex retry logic or concurrently submit multiple jobs with different instance types to find the first available option. Now you can simply provide a prioritized list of the instance types your workload accepts, and SageMaker automatically runs your job on the first available configuration from your preferences. With this solution, your training or processing job will likely start sooner.</p> <p>To use this feature, you specify your instance type and count preferences in priority order when submitting the training or processing job. For example, your list might contain a preference of two instances of ml.g6.48xlarge or four instances of ml.g5.48xlarge. SageMaker works through the list and launches your job on the first configuration where capacity is available. You can also configure the capacity sourcing from on-demand sources or from your reserved SageMaker Flexible Training Plans within the same job submission. This feature simplifies the process of getting compute for your jobs during high-demand periods and reduces the undifferentiated manual retrying you would otherwise do, all within the SageMaker training and processing job APIs you already use.</p> <p>Instance preference lists for SageMaker training and processing jobs is available today in all AWS Regions where SageMaker is available through the SageMaker CLIs, APIs, SDKs and Console UI. To learn more, see our <a