AWS Weekly Intelligence #7

19 September 2026 · Everything AWS shipped, 14 – 18 September

On this page

The week in one paragraph
Covered in depth
The one that makes a security control stop working without failing
A limit that became measurable, and a field that became wrong
Two services moved the unit of sharing
And one that tells you what was previously possible
Quieter, and worth knowing
What I would act on
Complete inventory: all 47 announcements
Official AWS references

The week in one paragraph

Forty-seven announcements in the What's New feed for the week, and the five worth your time share a shape: each moves a boundary that something else was already built against. Root user sign-in became resilient across three Regions — and took its audit event with it, away from the single-Region alarm most security baselines still use. STS collapsed several opaque session limits into one measurable 4,096-byte ceiling, and deprecated the element your tooling reads. Glue zero-ETL started tracking which integration owns a target table, which is a way of saying that until Monday two pipelines could write to one table unnoticed. Elastic Beanstalk moved the unit of infrastructure from the application to the pool. And SNS quadrupled its payload ceiling behind an attribute that quietly restricts which protocols may subscribe. Four of the five fail silently if you do nothing.

Covered in depth

Each of these got a full post this week:

Glue zero-ETL ownership and conflict detection — The table had two writers and no owner
Root user sign-in regional resiliency — The root alarm was watching one Region
STS session token size limits and monitoring — The limit you could hit but never measure
Elastic Beanstalk Cluster Mode — The environment per application was the ceiling
SNS 1 MiB payloads — Four times the payload, a fraction of the fan-out

The one that makes a security control stop working without failing

Root user sign-in “is now served across US East (N. Virginia), US East (Ohio), and US West (Oregon), with sign-in traffic distributed across all three Regions.” Unambiguously good: the credential of last resort no longer depends on one Region being healthy.

The consequence is in the same announcement. “ConsoleLogin events for root user sign-ins are recorded in the Region that processed the sign-in request”, and the CloudTrail reference confirms the set: “us-east-1, us-east-2, or us-west-2.”

Alerting on root sign-in is in the CIS benchmark and in essentially every security baseline ever written, and a very large number of those implementations are scoped to us-east-1 because that is where the event reliably landed. Those rules did not break. They simply stop seeing two-thirds of the possible sign-ins, and a quiet root alarm is indistinguishable from a healthy account.

AWS names the action: extend monitoring to all three Regions. The better fix is to alert off a multi-Region or organisation trail so the next routing change does not require another edit.

A limit that became measurable, and a field that became wrong

STS “now enforces a single 4,096-byte size limit on session tokens”, replacing separate limits on the token and on what you passed into it. More usefully, it now returns “response elements indicating session token size and percentage utilization relative to the token size limit”, logged in CloudTrail and published to CloudWatch.

That turns a cliff into a gauge. Session tags, session policies and federation attributes all spend a budget that previously could not be read, and the failure mode — a request rejected by a proxy for an oversized header — never looked like an STS problem.

The action item is not in the announcement. The IAM quota reference carries it: “The PackedPolicySize response element is deprecated. Use SessionTokenUtilization instead.” The two do not measure the same quantity — one was the packed size of policies passed, the other is percentage of the token limit consumed. Anything alarming on the old element is reading a deprecated field about a different thing, and it will keep returning data while it does.

Two services moved the unit of sharing

Beanstalk and SNS made the same kind of change in different places, and both are pooling decisions dressed as features.

Cluster Mode “runs multiple applications on pooled infrastructure in your account, powered by Amazon EKS, instead of a dedicated environment per application.” The constraint it relieves is structural rather than financial: the default quota is 200 environments per account per Region, and “the limit applies across applications, not per application” — so an estate running production and staging per service spends two of those per application.

The economics are conditional, and the pricing sentence says so in both halves: no charge for Cluster Mode, and “you pay for the AWS resources your applications consume, including the EKS cluster and EKS Auto Mode charges.” A standing cluster cost is a fixed component the per-application model never had, which is why the saving is framed as arriving as application count grows.

SNS raised payloads to “1 MiB, a 4x increase from the previous 256 KiB limit” — behind an opt-in attribute that changes what the topic is. “Topics with MaximumMessageSize set above 256 KiB support Amazon SQS, Amazon Data Firehose, and AWS Lambda subscriptions, with up to 100 total subscriptions per topic.” Against a standard-topic default of 12,500,000 subscriptions, and with HTTP/S, email, SMS and mobile push excluded, that is not a bigger topic. It is a different one.

And one that tells you what was previously possible

Glue zero-ETL now “associates the resulting table properties with the owning integration, so two integrations can no longer be pointed at the same target table without your knowledge.”

Read that backwards. Until this week, two managed replication pipelines could write into one table and nothing in the service would mention it — producing wrong data rather than a failed job, which is the category that does not page anyone and surfaces as a figure that will not reconcile.

The detection is described at create-and-modify time, and nothing states what happens to pairs that already overlap. Audit rather than assume.

Quieter, and worth knowing

Also in the feed this week, not covered in depth: AWS Direct Connect flat-rate pricing for dedicated connections and CloudFront flat-rate pricing plans — two pricing model changes in one week is worth a look if either is a material line item. A Detected Anomalies widget arrived in Billing and Cost Management dashboards, which pairs with the anomaly detection covered in Architecture #51. PrivateLink Tunnel Endpoints for reaching network segments, Transfer Family source IP preservation behind an NLB, ECS Express Mode on Graviton, and Resilience Hub added three capabilities.

What I would act on

1. Extend root sign-in alerting to three Regions — this week. It is a named control in most audit scopes, the gap is open now, and the failure is silence. Confirm the trail is multi-Region, then confirm the alarm reads from the trail rather than a Region-scoped log group. Event history covers 90 days and costs nothing to search, so you can also check retrospectively — three searches, since a search returns one Region.

2. Grep for PackedPolicySize. Ten minutes, a definite answer, and it finds every dashboard, log parser and test assertion now reading a deprecated element that measured something else.

3. Audit zero-ETL integrations for duplicate targets. Finite and mechanical. The new detection fires on create and modify, so it does not tell you about the overlap you may already have.

4. Before setting MaximumMessageSize, list subscriptions by protocol. Anything that is not SQS, Firehose or Lambda has to move or stay behind, and the 100-subscription ceiling is low enough for an automated subscriber process to reach.

5. Count your Beanstalk environments, not your applications. If you are near 200, that is the concrete argument for Cluster Mode. If you run a handful of applications, the standing EKS cost will not amortise and this is not for you yet.

Complete inventory: all 47 announcements

Every AWS What’s New announcement published between 14 and 18 September 2026, grouped by day, taken from the raw feed rather than summarised. Each links to AWS’s own page.

Friday 18 September — 8 announcements

AWS Continuum now supports credential testing and accessible domain suggestionsAWS Continuum for penetration testing is a frontier agent that proactively secures applications throughout the development lifecycle by offering on-demand, customized penetration testing with real exploitability testing. Developers and security teams can now test login credentials and receive suggested domains before a penetration test runs. This makes it easier to configure an accurate network scope from the start, reducing misconfiguration and wasted test cycles. Previously, identifying all the URLs your application reaches required manual effort, and authentication failures were only discovered after a full test cycle completed, costing time and resources. With this launch, when you add login credentials during test configuration, AWS Continuum authenticates into your application exactly as a real user would, capturing every accessible domain reached during login and surfacing them as in-scope URL suggestions. You can review accessible domains, validate credentials, and confirm the agent covers the right endpoints, all before the real test begins. Accessible domains are returned regardless of whether the credential test succeeds, fails, or times out. You can learn more about this feature in our updated <a href="https://docs.aws.amazon.com/securityagent/latest/userguide/perform-penetration-test.html#_configure_authentication_credentials_optional">documentaiton</a>. This capability is available in all regions where AWS Continuum for penetration testing is available and is detailed on the <a href="https://aws.amazon.com/continuum/?trk=77f1fbea-4ad5-4c1d-b16e-e5634ca5086e&sc_channel=ps&ef_id=CjwKCAjwwrPVBhA1EiwAv_YO-dtnJZvxRUPyj3N8BdcJLvN4fjq5kQwIththbsuo7ZvdNlCIcxxmSRoCX2kQAvD_BwE:G:s&gclid=CjwKCAjwwrPVBhA1EiwAv_YO-dtnJZvxRUPyj3N8BdcJLvN4fjq5kQwIththbsuo7ZvdNlCIcxxmSRoCX2kQAvD_BwE">AWS Continuum product page</a>.
Amazon ECS Express Mode now supports AWS Graviton (ARM64) workloads<a href="https://aws.amazon.com/ecs/" target="_blank">Amazon Elastic Container Service</a> Express Mode now supports specifying ARM64 as the CPU architecture for your service, making it easy to deploy ARM-based container images on AWS Graviton-powered compute and benefit from up to 40% better price-performance compared to x86-based instances. Amazon ECS Express Mode makes it easy to rapidly launch containerized applications, including web applications and APIs, by orchestrating and managing the cloud architecture for your application. You provide your container image, and ECS Express Mode handles the rest, configuring networking, load balancing, auto scaling, and deployments, and providing your application with an auto-generated URL. With ARM64 architecture support, you can choose the compute architecture that fits your workload, whether you're optimizing for cost with Graviton, matching your existing fleet, or deploying images built natively for ARM. You can configure the CPU architecture for new and existing Amazon ECS Express Mode services using the AWS Management Console, AWS CLI, AWS SDKs, and infrastructure as code (IaC) tools. This capability is available in all AWS commercial Regions and AWS GovCloud (US) <a href="https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/" target="_blank">Regions</a>. To learn more, see the <a href="https://docs.aws.amazon.com/AmazonECS/latest/developerguide/express-service-overview.html" target="_blank">ECS Express Mode documentation</a>.
AWS Resilience Hub adds three new capabilitiesThe next generation of AWS Resilience Hub is a central location in the AWS that helps platform engineering and site reliability teams assess and strengthen the resilience of their workloads running on AWS. It provides automated dependency discovery, generative AI-powered failure mode analysis, modular resilience policies, resilience testing, and organization-wide resilience posture reporting. Today, AWS Resilience Hub adds three new capabilities: EKS labels support for service input sources, dependency insights, and resilience policy sharing through AWS Organizations. EKS labels support for service input sources. Resilience Hub now supports EKS labels within a namespace as a service input source, allowing customers to use their existing Kubernetes labeling conventions to scope exactly which resources Resilience Hub discovers and uses during failure mode analysis. This ensures assessments stay aligned with how teams organize their EKS workloads. Dependency insights. Customers who enable dependency discovery can now generate dependency insights, a new generative AI-powered capability that analyzes discovered application dependencies and highlights meaningful patterns. Dependency insights surfaces new dependencies, identifies cross-Region dependencies, and flags unusual usage patterns. This helps teams accelerate dependency analysis and identify potential new risks. Resilience policy sharing through AWS Organizations. Resilience Hub now supports sharing resilience policies through AWS Organizations, enabling central teams to build and manage policies that can be applied across multiple accounts. This provides organization-wide observability into which services are using each policy, making it easier to monitor adoption and ensure consistent resilience posture across the organization. To get started, visit the  <a href="https://console.aws.amazon.com/resiliencehub/v2/home">AWS console</a>. To learn more, see the <a href="https://aws.amazon.com/resilience-hub/">product page</a> or visit the <a href="https://docs.aws.amazon.com/resilience-hub/latest/userguide/next-gen.html">documentation</a>.
AWS RTB Fabric now supports configurable Availability Zone affinityAWS RTB Fabric now supports <a href="https://docs.aws.amazon.com/rtb-fabric/latest/userguide/working-with-responder-gateways.html#configuring-availability-zone-affinity" target="_blank" rel="noopener noreferrer">configurable Availability Zone (AZ) affinity</a> for responder gateways. With this capability, you can configure how your partners connect to your responder gateway: either in their own Availability Zone or to any Availability Zone that the gateway spans. This launch helps advertising technology (AdTech) companies use their infrastructure more efficiently—with no extra charges on RTB Fabric. Demand-side platforms (DSPs) and supply-side platforms (SSPs) run their bidding systems across multiple Availability Zones. Previously, AWS RTB Fabric sent each request to available gateway capacity in the requester's own Availability Zone (AZ), so capacity in other AZs could go unused. Now you can set a client routing policy to control this: prefer the requester's own AZ to avoid added latency of crossing a boundary, or use every AZ that the responder gateway spans to give each requester access to more gateway capacity. Configurable Availability Zone affinity is available in all AWS Regions where AWS RTB Fabric is available. See the AWS RTB Fabric <a href="https://docs.aws.amazon.com/rtb-fabric/latest/userguide/working-with-responder-gateways.html#configuring-availability-zone-affinity" target="_blank" rel="noopener noreferrer">User Guide</a> for fleet requirements before enabling. AWS RTB Fabric helps you connect with your AdTech partners such as Amazon Ads, GumGum, Kargo, MobileFuse, Sovrn, TripleLift, Viant, Yieldmo, and more in three steps while delivering single-digit millisecond latency through a private, high-performance network environment. RTB Fabric reduces standard cloud networking costs by up to 80% and does not require upfront commitments. AWS RTB Fabric is generally available in the following <a href="https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/" target="_blank" rel="noopener noreferrer">AWS Regions</a>: US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (Ireland). See the AWS RTB Fabric <a href="https://aws.amazon.com/rtb-fabric/" target="_blank" rel="noopener noreferrer">Product Page</a> to learn more.
Kimi K3 by Moonshot AI is now generally available on Amazon BedrockAmazon 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. 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. 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/">launch blog</a> post.
Amazon SNS now supports message payloads up to 1 MiB<a href="https://aws.amazon.com/sns/">Amazon Simple Notification Service (Amazon SNS)</a> now supports message payloads up to 1 MiB, a 4x increase from the previous 256 KiB limit, so you can publish larger messages to your SNS topics. Amazon SNS is a fully managed pub/sub messaging service that enables you to decouple and scale microservices, distributed systems, and serverless applications. As workloads such as application integration, IoT, and generative AI increasingly exchange larger volumes of data in a single message, the previous 256 KiB limit required customers to offload or split payloads before publishing. With this launch, you can publish message payloads up to 1 MiB by setting the new MaximumMessageSize topic attribute on both SNS Standard and SNS FIFO topics. Topics with MaximumMessageSize set above 256 KiB support Amazon SQS, Amazon Data Firehose, and AWS Lambda subscriptions, with up to 100 total subscriptions per topic. Amazon SNS 1 MiB support is available today in all AWS Regions where Amazon SNS is available. To learn more about sending large payloads with Amazon SNS, see the <a href="https://docs.aws.amazon.com/sns/latest/dg/large-message-payloads.html">Amazon SNS Developer Guide</a>.
The new AgentCore Runtime is now available in Amazon Bedrock AgentCoreToday, AWS announces the availability of the next generation of <a href="https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/agents-tools-runtime.html">AgentCore Runtime</a>, the serverless microVM compute within Amazon Bedrock AgentCore. The new Runtime delivers elastic memory management that reclaims unused memory throughout the session so you pay for actual usage rather than the peak, and consistent cold start times regardless of container image size or concurrency. You get the serverless model you already rely on: no pre-provisioning, scale to zero, hardware-enforced session isolation, and pay only for what you use - now with lower costs and faster starts. With the new Runtime, each session starts with a small, efficient memory profile. Additional memory is allocated on demand as the workload needs it, and memory that is no longer actively used is reclaimed rather than held until the session ends. For cold starts, the new Runtime prepares the agent environment once and snapshots it. Every new instance restores from that snapshot instead of repeating the full startup sequence, keeping start times consistent regardless of image size. In testing, the new Runtime delivered a P75 cold start of 1.9 to 2.0 seconds for container images from 200 MB to 2 GB, compared to 5.4–30 seconds with V1. The new AgentCore Runtime is available in the following regions: us-east-1, us-east-2, us-west-2, eu-west-1, and ap-northeast-1. To get started, set platformVersion to V2 when creating or updating a runtime. To learn more, visit the <a href="https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/runtime-how-it-works.html#runtime-platform-versions">AgentCore Runtime documentation</a> or the <a href="https://aws.amazon.com/blogs/machine-learning/the-new-agentcore-runtime-elastic-optimized-and-consistently-fast-starts/">AWS News Blog</a>. For pricing details, visit <a href="https://aws.amazon.com/bedrock/agentcore/pricing/">AgentCore pricing</a>.
AWS PrivateLink announces Tunnel Endpoints to access network segmentsAWS 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. 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>.   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), Europe (Zurich), Mexico (Central), South America (São Paulo). To learn more about this capability and get started, please refer to the <a href="https://docs.aws.amazon.com/vpc/latest/privatelink/what-is-privatelink.html">AWS PrivateLink documentation</a>.

Thursday 17 September — 10 announcements

AWS Transfer Family now supports source IP preservation for SFTP servers behind a Network Load Balancer (NLB)AWS Transfer Family now preserves the client's source IP address using Proxy Protocol v2 (PPv2) when you place a Network Load Balancer (NLB) in front of your SFTP server that uses a VPC-hosted endpoint. You can now retain visibility of the client's source IP for IP-based auditing, access controls, and compliance when you use your own NLB. Previously, an NLB replaced the client's source IP with its own private IP address, so your Transfer Family logs and events recorded the NLB's address instead of the client's source IP. Because the NLB's private IP was the address presented to your custom identity provider during authentication, you couldn't authorize users based on their true source IP. With this launch, you can enable source IP preservation on your SFTP server so that the client's source IP is preserved. The preserved source IP is recorded in your logs and events and presented to your custom identity provider during authentication. You can enable the feature on each Transfer Family server individually through the console, CLI, or API. Source IP preservation for SFTP servers is available in all <a href="https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/" target="_blank" rel="noopener noreferrer">AWS Regions where AWS Transfer Family is available</a>. To get started, visit the AWS Transfer Family console or use the AWS CLI/SDK. To learn more, visit the <a href="https://docs.aws.amazon.com/transfer/latest/userguide/working-with-nlb.html#nlb-sftp-source-ip">Transfer Family User Guide</a>.
AWS HealthOmics now supports IAM session policiesAWS HealthOmics now supports IAM session policies, enabling you to restrict permissions for individual runs without creating and managing multiple IAM roles. Until now, there was no way to dynamically scope down permissions for a single run, requiring you to create a separate IAM role for each tenant or run. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed bioinformatics workflows. An IAM session policy is an inline policy that limits the maximum permissions of a run without modifying the underlying service role. Your effective permissions during a run are the intersection of permissions allowed by both the underlying identity-based policy and the temporary session policy. For example, if you operate a multi-tenant application, you can pass a session policy that limits a run's access to only that tenant's Amazon S3 buckets, without provisioning a dedicated IAM role for that tenant. You can also use IAM session policies to grant temporary access to specific Amazon S3 objects for a single run, and isolate access to sensitive resources on a per-run basis. IAM session policy support is available in all AWS Regions where AWS HealthOmics is available: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Tokyo, Singapore, Seoul). To learn how to configure IAM session policies for your runs, visit the Permissions section of the  <a href="https://docs.aws.amazon.com/omics/latest/dev/permissions-service.html">AWS HealthOmics User Guide</a>. To learn more about the service, visit  <a href="https://aws.amazon.com/healthomics/">AWS HealthOmics</a>.
AWS Batch now supports bulk job cancellation and terminationAWS Batch now supports bulk job cancellation and termination, enabling you to cancel or terminate up to 50 jobs with a single API call. The new CancelJobs, TerminateJobs, and TerminateServiceJobs APIs reduce the operational complexity of managing large-scale batch workloads, letting you act on groups of jobs at once and receive per-job results in a single response. Additionally, ListJobs now returns isCancelled and isTerminated fields, and ListServiceJobs returns isTerminated, making it easier to track the lifecycle state of your jobs. To get started, call CancelJobs for jobs in SUBMITTED, PENDING, or RUNNABLE states, or TerminateJobs and TerminateServiceJobs for jobs in any state, including STARTING and RUNNING. All three APIs accept up to 50 job IDs and work with individual and array jobs. You can access them through the AWS CLI or AWS SDKs. Bulk job cancellation and termination is available in all <a href="https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/">AWS Regions</a> where AWS Batch is available. For more information, see <a href="https://docs.aws.amazon.com/batch/latest/APIReference/API_CancelJobs.html">CancelJobs</a>, <a href="https://docs.aws.amazon.com/batch/latest/APIReference/API_TerminateJobs.html">TerminateJobs</a>, and <a href="https://docs.aws.amazon.com/batch/latest/APIReference/API_TerminateServiceJobs.html">TerminateServiceJobs</a> pages in the AWS Batch API Reference Guide.
Introducing Amazon EC2 T8i instancesAWS is announcing the general availability of new low-cost burstable Amazon EC2 T8i instances. These instances are powered by custom sixth generation Intel Xeon 6 processors, available exclusively on AWS, and built on the latest sixth generation AWS Nitro cards. T8i instances deliver up to 30% better price performance compared to previous generation T3 instances, and offer four sizes: nano, micro, small, and medium. These instances are designed for workloads requiring low to moderate CPU utilization, including data processing, login gateways, small databases, batch processing, event driven functions, CI/CD pipelines, microservices, and low traffic websites.   T8i instances deliver up to 70% higher compute performance, 1.25x higher network bandwidth, and 2.4x higher EBS bandwidth compared to T3 instances. They use the same CPU credit system as T3 with Standard and Unlimited modes, so existing T3 customers can upgrade to T8i and immediately benefit from better price performance, enabling them to lower their TCO. For customers new to AWS or migrating from on-premises, T8i instances provide one of the most cost-effective entry points to run a variety of workloads. T8i.micro and T8i.small are available under the <a href="https://aws.amazon.com/free/" target="_blank" rel="noopener noreferrer">AWS Free Tier</a>. T8i instances are available in US East (N. Virginia, Ohio), US West (Oregon, N. California), Europe (Frankfurt, Ireland, London, Paris), Asia Pacific (Hyderabad, Malaysia, Mumbai, Seoul, Singapore, Sydney, Tokyo), and Canada (Central) regions. T8i instances are available to purchase via On-Demand instances, and Spot instances with Savings Plan option coming soon. To learn more, visit the <a href="https://aws.amazon.com/ec2/instance-types/t8i/" target="_blank" rel="noopener noreferrer">Amazon EC2 T8i instances page</a>.
AWS Elastic Beanstalk introduces Cluster Mode to run multiple applications on shared infrastructureAWS Elastic Beanstalk introduces Cluster Mode, a new deployment mode that lets you run and manage multiple applications on shared infrastructure in your account. Provide your source code, a Dockerfile, or a container image in Amazon ECR, and Elastic Beanstalk handles containerization, provisioning, and ongoing operations through the console, CLI, and APIs you already use. A new Elastic Beanstalk GitHub Action deploys your application directly from your repository as part of your CI/CD pipeline. AWS Elastic Beanstalk deploys, scales, and manages your applications for their full lifecycle while handling the infrastructure, so you can focus on your code. It now offers two deployment modes. Standard Mode is the existing Elastic Beanstalk experience and continues to support all currently available platforms, including .NET, Node.js, and Python. Cluster Mode runs multiple applications on pooled infrastructure in your account, powered by Amazon EKS, instead of a dedicated environment per application. This can lower your per-application compute cost as your applications scale or your application count grows. Cluster Mode supports event-driven autoscaling, OpenTelemetry-based observability to Amazon CloudWatch and third-party providers, AWS Secrets Manager integration, and HTTPS by default via AWS Certificate Manager. To get started, create an environment in the Elastic Beanstalk console and select Cluster Mode. You can also deploy with the AWS CLI, the GitHub Action, or the agent skills. Cluster Mode is available in all commercial AWS Regions where Elastic Beanstalk is available. Elastic Beanstalk is HIPAA eligible and in scope for programs including PCI DSS, SOC, FedRAMP, and IRAP. There is no additional charge for Cluster Mode. You pay for the AWS resources your applications consume, including the EKS cluster and EKS Auto Mode charges for the infrastructure Elastic Beanstalk provisions on your behalf. See the Elastic Beanstalk and EKS pricing pages for details. To learn more, see the <a href="https://aws.amazon.com/blogs/aws/aws-elastic-beanstalk-introduces-cluster-mode/">AWS News blog</a>, the <a href="https://docs.aws.amazon.com/elasticbeanstalk/latest/dg/">Elastic Beanstalk Developer Guide</a>, and the <a href="https://aws.amazon.com/elasticbeanstalk/">Elastic Beanstalk product page</a>, or download the <a href="https://github.com/aws/agent-toolkit-for-aws/blob/main/skills/core-skills/aws-containers/SKILL.md">agent skills</a>.
Amazon S3 Express One Zone is now available in 7 additional AWS RegionsThe Amazon S3 Express One Zone storage class is now available in 7 additional AWS Regions: Asia Pacific (Singapore), South America (Sao Paulo), US West (N. California), Canada (Central), Europe (Paris), Asia Pacific (Sydney), and Asia Pacific (Seoul). Amazon S3 Express One Zone is a high-performance, single-Availability Zone storage class purpose-built to deliver consistent single-digit millisecond data access for your most frequently accessed data and latency-sensitive applications. S3 Express One Zone delivers data access speed up to 10x faster and request costs up to 80% lower than S3 Standard. It enables workloads such as machine learning training, interactive analytics, and key-value caching in AI search engines to achieve fast data access with high durability and availability. With this expansion, S3 Express One Zone is now available in 15 <a href="https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-express-Endpoints.html">AWS Regions</a>. For pricing details, visit the <a href="https://aws.amazon.com/s3/pricing/">S3 pricing page</a>. To learn more, visit the <a href="https://aws.amazon.com/s3/storage-classes/express-one-zone/">product page</a> and <a href="https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-express-getting-started.html">documentation</a>.
AWS Builder Center now available as mobile app on iOS and AndroidAWS Builder Center is a platform that brings together the global builder community, offering articles, workshops, sandbox environments, events, and learning resources in one place. Previously limited to desktop and web access, the experience is now available as a mobile app on iOS and Android. Builders worldwide can stay connected and engaged with the community from anywhere, using their AWS Builder ID to remain signed in across sessions. The AWS Builder Center mobile app enables builders to browse trending articles, access 600+ AWS Skill Builder courses, and manage hands-on workshops with free sandbox environments directly from their mobile devices. Builders can follow AWS Heroes, Community Builders, and User Group Leaders, check Builder Loft event calendars on the go, and receive push notifications for subscribed topics and communities. The app also supports the Wishlist feature, allowing builders to submit and manage product feedback directly to AWS teams. The AWS Builder Center mobile app is available worldwide on the Apple App Store and Google Play Store. To learn more about the AWS Builder Center mobile app, visit the <a href="https://builder.aws.com/mobile">AWS Builder Center mobile page</a>.
Amazon SES now supports tenant-level deliverability insights<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. 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. Virtual Deliverability Manager is available in all AWS Regions where Amazon SES is available. 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>.
Amazon Quick now generates individual sheets and builds analyses from an imageAmazon Quick now expands Generate Analysis with two new ways to create dashboards faster. You can generate a single sheet inside an existing analysis by describing it in natural language, and you can generate a new analysis from an image of an existing dashboard.   With Generate Sheet, you describe the sheet you want and Amazon Quick adds it to your current analysis with visuals selected for your data, filter controls, and calculated fields such as year-over-year growth and month-over-month comparisons. You can extend an analysis without building each visual by hand.   With generate an analysis from an image, you attach an image of a dashboard, including dashboards from other BI tools, to your prompt when you generate an analysis. Amazon Quick recreates it as an editable analysis, building what is supported in Amazon Quick. Both capabilities work with existing publishing workflows, embedding, CI/CD pipelines, and point-and-click editing.   At launch, Generate Sheet and generate analysis from an image are available to Enterprise subscription/Author Pro users. Authors also have promotional access to this capability through December 2026 as part of Amazon Quick Enterprise, provided their organization has not restricted access.   To learn more, see <a title="Generating an analysis with natural language prompts in the Amazon Quick User Guide" href="https://docs.aws.amazon.com/quick/latest/userguide/generating-an-analysis.html">Generating an analysis with natural language prompts</a> in the Amazon Quick User Guide. To get started, open an analysis and choose Generate Sheet, or attach an image to your prompt and choose Generate analysis.
Amazon Corretto 27 is now generally availableAmazon Corretto 27, a Feature Release (FR) version, is now available for download. <a href="https://aws.amazon.com/corretto/">Amazon Corretto</a> is a no-cost, multi-platform, production-ready distribution of <a href="https://aws.amazon.com/blogs/opensource/amazon-corretto-no-cost-distribution-openjdk-long-term-support/">OpenJDK</a>. You can download Corretto 27 for Linux, Windows, and macOS from our <a href="https://docs.aws.amazon.com/corretto/latest/corretto-27-ug/downloads-list.html">downloads page</a>. Corretto 27 will be supported through April 2027. Noticeable features:  <ul> <li>G1 as the Default Garbage Collector in All Environments - Makes G1 the default garbage collector across all environments, delivering more consistent performance and predictable pause times regardless of where your application runs (JEP 523)</li> <li>Post-Quantum Hybrid Key Exchange for TLS 1.3 - Adds protection against future quantum computing threats by combining classical and post-quantum key exchange algorithms in TLS 1.3 connections (JEP 527)</li> <li>Compact Object Headers by Default - Reduces the memory footprint of Java objects by enabling smaller object headers by default, improving memory efficiency and cache utilization (JEP 534)</li> <li>JFR In-Process Data Redaction - Allows sensitive data to be redacted from Java Flight Recorder recordings before they leave the JVM, helping protect confidential information during profiling and diagnostics (JEP 536)</li> <li>Enhanced Pattern Matching (continued preview) - Extends support for primitive types in patterns, instanceof, and switch, letting developers write cleaner type and value checks including for types like int and boolean (JEP 532)</li> <li>Structured Concurrency (continued preview) - Refines the API for structured concurrency, treating groups of related tasks running in different threads as single units of work, streamlining error handling and cancellation, improving reliability, and enhancing observability (JEP 533)</li> <li>Lazy Constants (continued preview) - Provides an API to defer initialization of immutable data until it is actually needed, combining the performance benefits of final fields with the flexibility of lazy initialization (JEP 531)</li> <li>Vector API (continued incubator) - Improves support for high-performance mathematical operations that can take advantage of modern CPU vector capabilities (JEP 537)</li> </ul> A detailed description of these features can be found on the <a href="https://openjdk.org/projects/jdk/27/">OpenJDK 27 Project page</a>. Amazon Corretto 27 is distributed by Amazon under an <a href="https://openjdk.org/legal/gplv2+ce.html">open source license</a>.

Wednesday 16 September — 12 announcements

Amazon SageMaker AI now supports serverless model customization for NVIDIA Nemotron 3.5 LightningAmazon SageMaker AI now supports serverless model customization for the NVIDIA Nemotron 3.5 Lightning model using supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and reinforcement fine-tuning (RFT). This is one of the latest open-weight models from NVIDIA and employs a hybrid Mixture-of-Experts architecture with 3B active parameters and 30B parameters in total. In addition to deploying this model on SageMaker AI, you can now adapt it to your specific domains and workflows. Model customization enables you to tailor foundation models with your proprietary data so a smaller, right-sized model can match frontier-model quality on your tasks, 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 enhance 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 for NVIDIA Nemotron 3.5 Lightning 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, or use the <a href="https://sagemaker.readthedocs.io/en/stable/model_customization/index.html">SageMaker Python SDK</a> for programmatic access. To learn more, see the Amazon SageMaker AI <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/customizing-models.html">model customization documentation</a>.
Amazon ECS deployment observability for Amazon ECS Managed Daemons in AWS Management Console<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. 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. 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>.
AWS Client VPN is now supporting MacOS 27 Golden GateAWS Client VPN now supports MacOS 27 Golden Gate client with versions 6.0+ You can now run the AWS supplied VPN client on the latest MacOS versions. AWS Client VPN desktop clients are available free of charge, and can be downloaded <a href="https://aws.amazon.com/vpn/client-vpn-download/">here</a>. AWS Client VPN is a managed service that securely connects your remote workforce to AWS or on-premises networks. It supports desktop clients for MacOS, Windows x64, Windows Arm64 and Ubuntu-Linux. With client version 6.0 onwards, Client VPN now supports the latest MacOS  Golden Gate 27.0. It already supports Mac OS version 13.0, 14.0, 15.0, and 26.0, Windows 10 (x64) and Windows 11 (Arm64 and x64), and Ubuntu Linux 22.04 and 24.04 LTS versions. To learn more about Client VPN: <ul> <li>Visit the AWS Client VPN <a href="https://aws.amazon.com/vpn/">product page</a></li> <li>Read the AWS Client VPN <a href="https://docs.aws.amazon.com/vpn/latest/clientvpn-admin/what-is.html">documentation</a></li> <li>Read the AWS Client VPN <a href="https://docs.aws.amazon.com/vpn/latest/clientvpn-user/client-vpn-user-what-is.html">user guide</a></li> </ul>
New AWS experience helps builders get started and ship fasterBuilders can now sign up for a new simplified experience that makes it faster to turn ideas into running code on AWS. AWS services provide an array of configuration options so that the largest enterprises can customize settings for their distinct needs. Until now, builders with a new project idea had to decide among these options and configure their AWS environment before beginning to build. The new experience reduces setup effort by simplifying sign-up and automatically configuring an initial project with sensible defaults so builders can begin implementing their idea immediately. When you sign up, you can use your existing Google, GitHub, Apple, or Amazon credentials. For most new customers, no credit card is required and you will receive up to $200 in free credits as part of the <a href="https://aws.amazon.com/free/">AWS Free Tier</a>. Once signup is complete, you can connect your coding agent to AWS with a single prompt that automatically installs the AWS CLI and the <a href="https://aws.amazon.com/products/developer-tools/agent-toolkit-for-aws/">Agent Toolkit for AWS</a>. Your coding agent connects to your project and uses built-in guidance to deploy resources using best practices. As your project expands, you can invite team members to collaborate with you by email. When a team member accepts your invitation, their project access is configured automatically. Similarly, many of the AWS resources you create can access resources in other AWS services automatically because the new <a href="https://aws.amazon.com/blogs/security/how-aws-iam-role-manager-rethinks-the-starting-point-for-iam-roles/">IAM role manager</a> capability is enabled for your projects. When you're ready to upgrade to a paid plan, you can set a monthly spend limit for your project based on your usage patterns so that you stay within your budget. If a project's usage reaches its spend limit, your project is paused for that month. You can create additional projects with one click, each with its own spend limit and distinct team members. At any time, you can activate advanced features without migration or downtime, enabling you to use additional features such as multiple AWS Regions and custom governance controls. To try the new experience, <a href="https://signin.aws.amazon.com/signup?request_type=builderId">create a new AWS account</a>. To learn more, see <a href="http://aws.amazon.com/blogs/aws/aws-reimagines-the-getting-started-experience">AWS reimagines the getting started experience</a> on the AWS News Blog.
Amazon WorkSpaces adds support for NVIDIA Blackwell GPU instancesAmazon WorkSpaces Personal and Amazon WorkSpaces Core now support Graphics G7 bundles, built on NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs and Intel Xeon 6 processors. Graphics G7 delivers up to 2.1x better performance for graphics-intensive workloads compared to previous generation Graphics G6 bundles. Four bundle sizes are available, ranging from 8 vCPUs, 32 GB of memory, and one GPU up to 48 vCPUs, 192 GB of memory, and two GPUs. With Graphics G7, you can run demanding professional applications such as CAD/CAM, 3D rendering, scientific visualization, video editing, and AI-assisted design workflows at higher fidelity and frame rates. Each GPU provides 32 GB of GDDR7 memory, enabling larger and more complex 3D scenes and models. Graphics G7 bundles are available with Windows, including bring-your-own-license, and support both AlwaysOn and AutoStop running modes. Graphics G7 bundles are available for both WorkSpaces Personal and WorkSpaces Core in US East (N. Virginia), US East (Ohio), and US West (Oregon). Additional Regions will be added as availability expands. To get started, select a Graphics G7 bundle when creating a workspace in the <a href="https://aws.amazon.com/console/">Amazon WorkSpaces console</a>, or through your Workspaces Core partner solution. For more information on available instance types, see <a href="https://docs.aws.amazon.com/workspaces/latest/adminguide/bundle-options.html">WorkSpaces</a><a href="https://docs.aws.amazon.com/workspaces/latest/adminguide/bundle-options.html"> Personal Instance </a><a href="https://docs.aws.amazon.com/workspaces/latest/adminguide/bundle-options.html">Families</a>. To learn more about G7 GPU capabilities, visit the <a href="https://aws.amazon.com/ec2/instance-types/g7/">EC2 G7 Instance Types page</a>. For pricing details, see <a href="https://aws.amazon.com/workspaces/desktop-as-a-service/pricing/">Amazon WorkSpaces Personal Pricing</a> or <a href="https://aws.amazon.com/workspaces/vdi-partners/pricing/">Core Bundles Pricing</a>.
Amazon Connect Customer can now import evaluation form PDFs using AIAmazon 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. 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.
Amazon Connect Customer custom metrics now supports tag-based access controlAmazon Connect Customer now supports tag-based access control for custom metrics, so you can govern which users and roles can view, create, or modify each custom metric. As more teams across your organization adopt custom metrics, they end up editing the same shared library. Without controls, one person can change a metric that other teams count on. The usual fix is to give only a handful of people permissions to custom metrics, but that shuts out every other team that wants to start using them.   Now you can tag each metric, then use those tags to set access: a team gets full control of their own metrics, view-only access to another team’s, and no visibility into the metrics you restrict. Search follows the same access rules, so people only see the metrics they’re allowed to view or manage.
AWS Elemental MediaTailor Monetization Functions adds ad response hooksAWS Elemental MediaTailor now supports two additional lifecycle hooks for monetization functions: post ad decision server (ADS) response, and pre manifest insertion. MediaTailor is a video service that personalizes and inserts ads into live and on-demand streams. Monetization functions run customer-defined logic at defined points in an ad-personalized playback session, removing the need for a middleware tier between MediaTailor and the ADS. The post ADS response hook runs after MediaTailor parses an ADS response and resolves all Video Ad Serving Template (VAST) wrappers, before ads are selected and transcoded. Customers use it to call a secondary ad source when the primary ADS returns less demand than the ad break can hold, remove ads that their content or brand policies do not permit, and add house ads or promotions from their own marketing systems. The pre manifest insertion hook runs at the last point before MediaTailor returns the ad pod to the viewer, and receives every newly personalized ad break in a single invocation. Customers use it as a final check on what is about to play, including inserting a personalized slate when a break cannot be filled any other way. Pre-built recipes in the MediaTailor console give customers a starting point for each of these use cases. Both hooks are fail-open: on a timeout, expression error, or resource limit, MediaTailor discards the function output and continues with default ad insertion, so viewer playback is unaffected. The new hooks are available in all AWS Regions where AWS Elemental MediaTailor is available. To learn more, see <a href="https://docs.aws.amazon.com/mediatailor/latest/ug/monetization-functions.html" target="_blank">Monetization Functions</a> in the AWS Elemental MediaTailor User Guide and the <a href="https://aws.amazon.com/mediatailor/pricing/" target="_blank">MediaTailor pricing page</a>. To get started, sign in to the <a href="https://console.aws.amazon.com/mediatailor/" target="_blank">AWS Elemental MediaTailor console</a>.
Amazon ECS extends Amazon S3 Files support to the Amazon EC2 compute type<a href="https://aws.amazon.com/ecs/">Amazon Elastic Container Service</a> (Amazon ECS) now supports Amazon S3 Files for tasks running on the Amazon EC2 launch type, enabling customers to connect their containerized applications directly to data in Amazon S3 as a shared file system. With this launch, customers running ECS workloads on EC2 instances can work with their S3 data using standard file system semantics, so file-based applications, AI agents, and data processing workloads run on S3 data with no code changes and without duplicating or staging files first. S3 Files support was previously available for ECS tasks on AWS Fargate and ECS Managed Instances, and now extends to the EC2 launch type, giving customers consistent access to S3 Files across all three ECS launch types. Amazon S3 Files, built on Amazon EFS, delivers a shared file system that connects AWS compute resources directly with data in Amazon S3, providing full file system semantics and low-latency performance without data leaving S3. S3 Files works with new and existing data in S3 buckets, with no migration required. Customers can mount an Amazon S3 Files volume in their Amazon ECS tasks to read and write data in an S3 bucket using standard file system operations, with no application code changes and no need to copy or stage data. Amazon S3 Files support is available in all AWS commercial Regions and the AWS GovCloud (US) Regions. To learn more, refer our <a href="https://docs.aws.amazon.com/AmazonECS/latest/developerguide/s3files-volumes.html">documentation</a>.
Amazon Connect Customer now lets you create, manage, and search custom metrics through APIsAmazon Connect Customer provides a library of standard metrics. Custom metrics let you go further; defining the measurements unique to your business. But managing them across various instances manually means definitions slowly drift until the numbers no longer align. A metric you can't trust is worse than no metric at all, because people still make decisions on it. Now you define a metric once and manage it programmatically through seven new API operations (CreateMetric, DeleteMetric, DescribeMetric, ListMetrics, SearchMetrics, UpdateMetricContent, and UpdateMetricMetadata) so your definitions stay consistent as you scale across queues, teams, and environments. For example, a customer experience leader noticing a drop in self-service completion rates can define a custom metric that tracks the percentage of interactions where customers abandon and request a live agent within 90 seconds. Using the API, an AI-powered operations assistant can automatically create the metric and publish it to a monitoring dashboard — no manual console work, no definition drift across environments. Every change is tracked in AWS CloudTrail, giving you a complete audit trail.
Amazon Keyspaces (for Apache Cassandra) is now generally available in 11 additional Regions<a href="https://aws.amazon.com/keyspaces/">Amazon Keyspaces (for Apache Cassandra)</a> is now generally available in 11 additional Regions, allowing customers in those Regions to build Cassandra-compatible applications with lower latency while keeping their data within the Region to meet data residency requirements..  Amazon Keyspaces (for Apache Cassandra) is a scalable, highly available, and managed Apache Cassandra–compatible database service. Amazon Keyspaces is serverless, so you pay for only the resources that you use and you can build applications that serve thousands of requests per second with virtually unlimited throughput and storage.  To find the list of new available Regions, see the following resources: <a href="https://builder.aws.com/build/capabilities">AWS Capabilities by Region</a>.
Amazon Connect Talent is now generally availableAmazon Connect Talent is now generally available, giving talent acquisition leaders an AI-powered hiring solution that accelerates candidate selection at scale. Informed by decades of Amazon's hiring science, Amazon Connect Talent uses AI agents to conduct structured voice interviews, administer evidence based assessments, and score candidates consistently, freeing recruiters to focus on strategic decisions. Candidates interview 24/7 from any device. Recruiters review scores, transcripts, and detailed candidate evaluations generated by their AI teammate, empowering them to make faster hiring decisions with consistent objectivity. GA capabilities include competency based assessments, AI-led voice interviews with adaptive questioning, a brand-customizable mobile-first candidate portal, and system admin onboarding tools for easy deployment. Amazon Connect Talent scales to handle hiring surges, evaluating hundreds of candidates simultaneously. Amazon Connect Talent is now available in AWS US East (N. Virginia) and US West (Oregon) regions. To learn more, visit the <a href="https://aws.amazon.com/products/connect/talent/">Amazon Connect Talent page</a>.

Tuesday 15 September — 9 announcements

AWS STS simplifies session token size limits and adds session token size monitoringAWS Security Token Service (STS) now enforces a single 4,096-byte size limit on session tokens. Previously, STS enforced separate limits on session token size and passed-in parameters (i.e., inline policies, managed policies, and session tags). STS has removed that separation, providing more flexibility for larger combinations of session policies and session tags. Additionally, STS now returns response elements indicating session token size and percentage utilization relative to the token size limit. STS logs these values in AWS CloudTrail and publishes corresponding metrics in Amazon CloudWatch. A new optional API parameter also lets you generate larger session tokens (up to the 4,096-byte limit) to test whether your applications and infrastructure can handle them. These capabilities are available in all commercial AWS Regions, the AWS GovCloud (US) Regions, and the AWS European Sovereign Cloud Region. To learn more, please read the <a href="https://aws.amazon.com/blogs/security/aws-sts-simplifies-session-token-size-limits-and-adds-session-token-size-monitoring/" target="_blank">AWS Security Blogpost</a>, <a href="https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_request.html" target="_blank">AWS IAM User Guide</a>, and <a href="https://docs.aws.amazon.com/STS/latest/APIReference/Welcome.html" target="_blank">AWS STS API Reference</a>.
Monitor cost anomalies directly in Billing and Cost Management Dashboards with the new Detected Anomalies widgetToday, AWS Billing and Cost Management (BCM) announces support for the Detected Anomalies widget in BCM Dashboards. You can now review cost anomalies alongside cost and usage, budgets, cost efficiency, and reports for Savings Plans and Reserved Instance coverage and utilization. This provides a unified view of your spending, commitments, and cost anomalies in a single, tailored dashboard. The Detected Anomalies widget displays the number of anomalies detected and their total cost impact relative to your month-to-date spend, giving you immediate context on each anomaly's scale. For each anomaly, you can review the cost impact, root cause, and duration. Choose a look-back period of 30, 60, or 90 days, and filter by severity, service, account, and region so that each dashboard shows the anomalies most relevant to that account. By adding one or more Detected Anomalies widget to a BCM Dashboard, finance teams and cloud administrators can monitor anomalies from their existing cost management workflows. The widget links directly to the AWS Cost Anomaly Detection console so you can easily investigate an anomaly and record your assessment. It is fully integrated with dashboard exports and can be included in scheduled email reports or downloaded as a CSV or PDF for offline analysis. It is also included with cross-account dashboard sharing. The Detected Anomalies widget for BCM Dashboards is available in all commercial AWS Regions at no additional charge. To learn more, visit our <a href="https://docs.aws.amazon.com/cost-management/latest/userguide/dashboards.html">User Guide</a>.
AWS Direct Connect announces flat-rate pricing for dedicated connectionsAWS Direct Connect announces flat-rate pricing for 10G and 100G dedicated connections AWS Direct Connect provides dedicated network connectivity from on-premises environments to AWS. Customers value the flexibility of AWS Direct Connect's pay-as-you-go pricing, paying only for the port hours and data transfer out (DTO) they use, with no upfront commitment or long-term contracts. For workloads with large, sustained DTO, the per-gigabyte charges vary from month to month, making monthly costs challenging to predict as traffic grows. Flat-rate pricing for 10 Gbps and 100 Gbps dedicated connections introduces a single fixed monthly price that eliminates DTO charges within the selected pricing tier. This pricing model is designed for network architects, enterprise IT teams, and FinOps practitioners who need predictable, fixed-line-item network budgeting. With a fixed monthly price,  you can grow your use of AWS without worrying about increasing data transfer bills. With flat-rate pricing, you pay a single fee based on your connection bandwidth and the geographical scope of your connectivity. With five geographic tiers ranging from same-metro to global coverage, customers can select the scope that matches their traffic patterns and pay one consistent monthly rate regardless of data volume. Flat-rate pricing also introduces the port-pair concept, a new way to provision dedicated connections with inbuilt resiliency. A port-pair is two dedicated connections on different devices or locations that share the same bandwidth and geographical connectivity scope. One connection carries your traffic while the other stands by for redundancy. The second connection is included at no additional charge. Port-pairs are recommended but optional. Flat-rate pricing is available at all AWS Direct Connect locations across all commercial AWS Regions (China Regions excluded) and applies exclusively to Dedicated Connections. The pricing model is configured per connection and gives customers the flexibility to switch billing mode on connections at any time. To learn more about flat-rate pricing tiers, port-pair configurations, and eligibility requirements, visit the <a href="http://aws.amazon.com/directconnect/pricing/flat-rate/">AWS Direct Connect Flat-Rate Pricing</a>, <a href="https://docs.aws.amazon.com/directconnect/latest/UserGuide/flat-rate-connections.html">User Guide</a> and <a href="https://docs.aws.amazon.com/directconnect/latest/PricingGuide/pricing-flat-rate.html">Pricing Guide</a> pages.
AWS Billing Conductor now supports custom rates and usage tier pricing configurationsAWS Billing Conductor now lets you define custom rate pricing for AWS services, including defining custom usage tiers to configure rates by the desired usage volume.  Customers and Partners using AWS Billing Conductor to model commercial agreements with subsidiaries, affiliates, or end customers can now more easily reflect their negotiated pricing on pro forma billing data. Using SKU-scoped pricing rules, you can enter a custom rate and configure usage tier thresholds — instead of marking up or down from the public on-demand rates tied to pre-defined AWS usage tiers.  By configuring exact rates and tier breaks directly in a pricing rule, you no longer need to calculate percentage-based markups or markdowns against public on-demand pricing to model your commercial agreements. This gives you precise control over your pro forma billing configuration. Custom rates and custom usage tiers configuration via SKU-scoped pricing rules is available in all commercial AWS Regions, excluding the Amazon Web Services China (Beijing) Region, operated by Sinnet, and the Amazon Web Services China (Ningxia) Region, operated by NWCD. To learn more, visit the AWS Billing Conductor <a href="https://aws.amazon.com/aws-cost-management/aws-billing-conductor/">product page</a>, or review the <a href="https://docs.aws.amazon.com/billingconductor/latest/userguide/what-is-billingconductor.html">User Guide</a>.
AWS Step Functions adds new AWS service integrations automatically, starting with AWS Lambda MicroVMs<a href="https://aws.amazon.com/step-functions/">AWS Step Functions</a> now automatically adds AWS SDK integrations for new AWS services and capabilities within weeks of their release, starting with AWS Lambda MicroVMs, AWS Lambda Core and others. Now, you can orchestrate the latest AWS services from your workflows without waiting for updates.    AWS Step Functions is a visual workflow service capable of orchestrating over 220 AWS services to help customers build distributed applications at scale. With the AWS Lambda Core and AWS Lambda MicroVMs service integrations, you can orchestrate agentic workflows without writing custom coordination code. You can use Step Functions to launch Lambda MicroVMs, which provide isolated, secure execution environments for each agent task with built-in retries if an environment fails to start. You can run multiple tasks simultaneously using Step Functions Parallel or Map states and automatically terminate environments when the task completes. You can use Lambda Core to configure the private networking those MicroVM environments need to securely reach your internal databases or APIs in the same workflow.   This expansion also includes AWS Partner Central Revenue Measurement, AWS Resilience Hub V2, AWS Support Authorization and Amazon SageMaker Job Runtime. Going forward, new AWS services will automatically appear as Step Functions integrations within weeks of their release with no additional configuration or action required. As updates will be continuous, we will no longer publish What's New posts to highlight new AWS SDK service integration updates.   These enhancements are now generally available in all AWS Regions where AWS Step Functions is available. Specific services and API actions are subject to the availability of the target service in the AWS Region. To learn more about AWS Step Functions SDK integrations, visit the <a href="https://docs.aws.amazon.com/step-functions/latest/dg/integrate-services.html">Developer Guide</a>, or see the full list of supported services at <a href="https://docs.aws.amazon.com/step-functions/latest/dg/supported-services-awssdk.html">AWS SDK service integrations,</a> and <a href="https://docs.aws.amazon.com/step-functions/latest/dg/awssdk-release-history.html">integration release history</a>.
Amazon SageMaker AI now supports instance preference lists for training and processing jobsToday, 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. 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. 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 href="https://docs.aws.amazon.com/sagemaker/latest/dg/train-instance-preferences.html">documentation</a> or our <a href="https://aws.amazon.com/blogs/machine-learning/announcing-instance-preference-lists-for-amazon-sagemaker-ai-training-jobs/">launch blog</a>.
Analyze your CloudTrail events using natural language in Amazon Q ConsoleAWS CloudTrail, a service that records API activity across your AWS account for security auditing, compliance, and operational troubleshooting, now integrates with Amazon Q Console to help you investigate your AWS account activity using natural language. You can ask Amazon Q Console questions about your CloudTrail configuration, query your logged events for security investigations, and troubleshoot operational issues without writing queries or manually parsing log files. With this integration, you can ask Amazon Q Console to check whether your CloudTrail trails are properly configured, identify gaps in your logging coverage, and confirm which data event sources you are tracking. You can investigate security concerns by asking who accessed a specific IAM role, what changes were made to your VPC configuration, or whether there were unauthorized access attempts in the past week. For operational troubleshooting, you can ask Amazon Q Console to find who created or deleted specific resources, identify which API calls are generating errors, trace activity from a specific IP address, or determine why your bill spiked. Amazon Q Console can query your CloudTrail trails, associated CloudWatch log groups, and event data stores on your behalf, providing answers grounded in your actual account activity rather than generic documentation.  This integration is available in all AWS commercial regions where Amazon Q Console is supported. To get started, open Amazon Q in AWS Management Console and ask questions about your CloudTrail configuration or account activity. For more information, visit the <a href="https://docs.aws.amazon.com/awscloudtrail/latest/userguide/">AWS CloudTrail documentation</a>.
Amazon Connect Customer now enables agents to bid on preferred shiftsAmazon Connect Customer now enables agents to bid on preferred shifts, giving them more control over their work schedules. Schedulers first establish agent ranking either by uploading a CSV file or generating a randomized ranking. Connect Customer then uses forecasted demand and shift profiles to generate available shifts and presents them to agents to rank. For example, in a Monday–Friday 6AM–10PM shift profile with 9-hour shifts, required shifts are 6AM–3PM (500 agents), 9AM–6PM (800 agents), and 1PM–10PM (600 agents). Once the bidding window closes, Connect Customer automatically assigns each agent to their highest-ranked available shift while using agent ranking as a tiebreaker. Shift bidding gives agents a structured way to influence their own schedules, while reducing the time schedulers spend on manual shift assignments, improving both agent satisfaction and scheduling efficiency. This feature is available in all <a href="https://docs.aws.amazon.com/connect/latest/adminguide/regions.html#optimization_region">AWS Regions</a> where Amazon Connect Customer agent scheduling is available. To learn more about Amazon Connect Customer agent scheduling, click <a href="https://docs.aws.amazon.com/connect/latest/adminguide/forecasting-capacity-planning-scheduling.html">here</a>.
AWS Glue zero-ETL now captures archived Salesforce recordsAWS Glue zero-ETL integrations for Salesforce now capture archived records in your target tables, giving you a complete replica of your Salesforce data for analytics and reporting. Archived records are identified and reflected accurately through the isArchived field, so your analytical data stays fully in sync with your Salesforce source. This is especially valuable for high-volume entities that Salesforce archives automatically, such as Events, Tasks, and Activities, giving you complete visibility into this data in your analytical data stores. This feature applies to your zero-ETL integrations with no action required. New integrations capture archived records automatically from the moment the integration is created. For existing integrations, AWS Glue automatically backfills archived record status, with no re-sync, reconfiguration, or schema change needed. Your existing pipelines continue running uninterrupted while the isArchived field is populated accurately on your behalf. This feature is available in all AWS Commercial and AWS GovCloud (US) Regions where AWS Glue zero-ETL integrations for Salesforce are supported. To get started, create or continue using an AWS Glue zero-ETL integration for Salesforce using the <a href="https://console.aws.amazon.com/glue/">AWS Glue console</a>, CLI, or SDK. To learn more, see AWS Glue zero-ETL <a href="https://docs.aws.amazon.com/glue/latest/dg/zero-etl-sources.html">documentation</a>.

Monday 14 September — 8 announcements

AWS improves regional resiliency for root user sign-inAWS root user sign-in is now served across US East (N. Virginia), US East (Ohio), and US West (Oregon), with sign-in traffic distributed across all three Regions. This change reduces reliance on US East (N. Virginia) and improves resiliency during service disruptions. AWS automatically routes your root user sign-in to a supported Region without requiring you to select a Region or change how you sign in. This improvement is available now for all AWS accounts. In AWS CloudTrail, ConsoleLogin events for root user sign-ins are recorded in the Region that processed the sign-in request. To maintain full visibility into root user sign-in activity, update your monitoring and alerting to cover US East (N. Virginia), US East (Ohio), and US West (Oregon). To learn more, see the <a href="https://docs.aws.amazon.com/signin/latest/userguide/introduction-to-root-user-sign-in-tutorial.html">AWS Sign-In documentation</a> and the <a href="https://docs.aws.amazon.com/awscloudtrail/latest/userguide/cloudtrail-event-reference-aws-console-sign-in-events.html#cloudtrail-event-reference-aws-console-sign-in-events-root">CloudTrail ConsoleLogin event reference</a>.
Qwen3.6-35B-A3B-NVFP4 and Wan2.1-T2V-1.3B-Diffusers models now available on Amazon SageMaker JumpStartNVIDIA's Qwen3.6-35B-A3B-NVFP4 and Alibaba's Wan2.1-T2V-1.3B-Diffusers models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models bring specialized capabilities spanning agentic coding with long-context reasoning and lightweight text-to-video generation, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure. These models address different enterprise AI challenges with specialized capabilities: Qwen3.6-35B-A3B-NVFP4 is optimized for agentic coding, multimodal reasoning, and long-context understanding as the NVIDIA-quantized variant of Alibaba's Qwen3.6-35B-A3B. This Mixture-of-Experts model contains 35B total parameters with only 3B activated per token (8 of 256 experts), supporting a 262K-token context window extendable to ~1M via YaRN scaling. Quantized to NVFP4 using NVIDIA's ModelOpt framework, it preserves thinking across conversation turns, multi-token prediction, and tool calling for multi-step agent pipelines—all at a significantly reduced memory footprint. Wan2.1-T2V-1.3B-Diffusers excels in text-to-video generation on consumer-grade hardware. Built on the diffusion transformer paradigm with a novel Video Variational Autoencoder (VAE), this 1.3B-parameter model generates high-quality, physics-consistent video clips from text prompts while requiring only 8.19 GB of VRAM. It can produce a 5-second 480p video on an RTX 4090 in approximately 4 minutes, making it one of the most accessible open-source video generation models available. With SageMaker JumpStart, customers can deploy any of these models 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 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html">Amazon SageMaker JumpStart documentation</a>.
Ministral-3-3B-Instruct-2512 and Ministral-3-8B-Instruct-2512 models now available on Amazon SageMaker JumpStartMistral AI's Ministral-3-3B-Instruct-2512 and Ministral-3-8B-Instruct-2512 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models from the Ministral 3 family bring compact, vision-capable language models purpose-built for edge deployment and resource-constrained environments, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure. These models address different enterprise AI challenges with specialized capabilities: Ministral-3-3B-Instruct-2512 is engineered for ultra-lightweight edge deployment with multimodal understanding. Comprising a 3.4B language model and a 0.4B vision encoder, it fits in just 8GB of VRAM in FP8 while supporting a 256K-token context window. It offers vision analysis, multilingual instruction following across dozens of languages (including English, French, Spanish, German, Chinese, Japanese, Korean, and Arabic), strong system-prompt adherence, and native function calling with structured JSON output—all under the Apache 2.0 license. Ministral-3-8B-Instruct-2512 delivers frontier-class capabilities comparable to its larger Mistral Small 3.2 24B counterpart in a compact 8B form factor. Built with an 8.4B language model and a 0.4B vision encoder, it fits in 12GB of VRAM in FP8 and features an interleaved sliding-window attention pattern for faster, memory-efficient inference. It shares the same vision, multilingual, agentic, and function-calling capabilities as its 3B sibling while offering stronger reasoning and generation performance. With SageMaker JumpStart, customers can deploy any of these models 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 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html">Amazon SageMaker JumpStart documentation</a>.
Gemma-4-31B-it-assistant and Gemma-4-31B-IT-NVFP4 models now available on Amazon SageMaker JumpStartGoogle DeepMind's Gemma-4-31B-it-assistant and NVIDIA's Gemma-4-31B-IT-NVFP4 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models bring the flagship Gemma 4 31B dense architecture to enterprise workloads in both full-precision and optimized quantized variants, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure. These models address different enterprise AI challenges with specialized capabilities: Gemma-4-31B-it-assistant is built for multimodal reasoning, coding, and agentic workflows as the assistant-tuned variant of Google's flagship 31B dense model. It handles text and image inputs (including video as frame sequences) and generates text output, with a 256K-token context window and support for over 140 languages. Ranked #3 among open models on the Arena AI text leaderboard—outcompeting models 20x its size—it features a hybrid attention mechanism interleaving local sliding-window and full global attention with native function calling for building autonomous agents. Gemma-4-31B-IT-NVFP4 delivers the same Gemma 4 31B capabilities at a fraction of the memory footprint. Quantized with NVIDIA's ModelOpt framework to 4-bit FP4 precision, it reduces memory usage to ~18.5 GB (68% smaller than the base model) and achieves approximately 2.5x faster inference while retaining 97–99% of the original model's quality. Ideal for cost-efficient, high-throughput production deployments on NVIDIA RTX, DGX Spark, and data center GPUs. With SageMaker JumpStart, customers can deploy any of these models 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 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html">Amazon SageMaker JumpStart documentation</a>.
granite-speech-4.1-2b, kanana-2-30b-a3b-instruct, and OpenFold3 models now available on Amazon SageMaker JumpStartBM's granite-speech-4.1-2b, Kakao's kanana-2-30b-a3b-instruct, and the OpenFold Consortium's OpenFold3 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models bring specialized capabilities spanning multilingual speech recognition, bilingual agentic AI, and biomolecular structure prediction, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure. These models address different enterprise AI challenges with specialized capabilities: granite-speech-4.1-2b is purpose-built for multilingual automatic speech recognition (ASR) and bidirectional speech translation (AST) across English, French, German, Spanish, Portuguese, and Japanese. This compact 2B-parameter speech-language model delivers a word error rate of 5.33% with a real-time factor of ~231, making it one of the most efficient ASR models in its class. Released under Apache 2.0, it integrates seamlessly into enterprise voice workflows for transcription, translation, and audio processing at scale. kanana-2-30b-a3b-instruct excels in bilingual Korean-English instruction following and agentic AI workflows. Developed by Kakao, it adopts a cutting-edge architecture featuring Multi-head Latent Attention (MLA) and Mixture-of-Experts (MoE), activating only 3B of its 30B total parameters per forward pass for superior throughput. Post-trained with supervised fine-tuning and reinforcement learning, it supports up to 128K tokens via YaRN scaling and is designed to function as an AI collaborator that understands context and acts proactively. OpenFold3 provides all-atom biomolecular complex structure prediction for proteins, DNA, RNA, and small-molecule ligands. Developed by the OpenFold Consortium and the AlQuraishi Lab at Columbia University, this diffusion-based model extends structure prediction beyond single proteins to model multi-chain complexes and heterogeneous biomolecular interactions. It supports computer-aided drug design and is applicable across academic and pharmaceutical research labs. With SageMaker JumpStart, customers can deploy any of these models 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 <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html">Amazon SageMaker JumpStart documentation</a>.
AWS Glue zero-ETL adds target table property ownership and conflict detectionAWS Glue zero-ETL integrations now detect table property conflicts and track integration ownership. When you configure a source table and target catalog, Glue associates the resulting table properties with the owning integration, so two integrations can no longer be pointed at the same target table without your knowledge. This works across Amazon S3 Tables and SageMaker Lakehouse catalogs. Data teams running multiple zero-ETL integrations gain predictable control over where each source table lands. If you attempt to create or modify an integration whose table properties are owned by another integration, Glue identifies the owning integration and guides you to choose a different target or update the existing one, so your pipelines stay isolated and your data lands exactly where you intend. This feature is available in all AWS Commercial and AWS GovCloud (US) Regions where AWS Glue zero-ETL integrations are supported. To learn more, refer to the <a href="https://docs.aws.amazon.com/glue/latest/dg/zero-etl-target.html">AWS Glue Developer Guide</a> and get started in the <a href="https://console.aws.amazon.com/glue/">AWS Glue console</a>.
AWS End User Messaging now supports Dynamic Flows in WhatsAppAWS End User Messaging now supports dynamic WhatsApp flows so you can turn WhatsApp conversations into rich, interactive experiences that end-users complete directly in WhatsApp. Instead of pushing end-users to a website or a separate form to complete an interactive experience, you can instead guide them through an appointment booking, lead generation, sign-up, survey or a transaction directly in the chat. Customers can start building a flow using one of Meta's stater templates with familiar building blocks like text inputs, date pickers, drop downs, and buttons, or customize the flow from scratch. With dynamic flows, each screen can call your own HTTPS endpoint in real time, so you can show live availability, and personalize each step in the flow for your end-users. You build and manage flows entirely in AWS End User Messaging Social, through the console or APIs. Define a flow with a JSON schema, then deliver it with a message template to reach customers where they are. WhatsApp flows are available in all regions where End User Messaging Social is available. To get started, see the <a href="https://docs.aws.amazon.com/social-messaging/latest/userguide/what-is-service.html">User Guide.</a>
AWS End User Messaging strengthens SMS deliverability with automatic failoverAWS End User Messaging is advancing its deliverability capabilities in phone pools. Now, customers sending through phone pools automatically benefit from automatic failover to the next best performing number in the pool when SMS delivery is impacted from downstream disruptions. SMS deliverability can be affected by drops or delays when trying to reach the end recipient while it's being routed from AWS to messaging providers and eventually mobile carriers. These disruptions can hit the most important parts of your messaging program, including delivery notifications, one-time passcodes, reminders, and other time-sensitive messages. AWS End User Messaging watches for delivery delays, message failures, or drops in conversion and reroutes traffic to a more optimized delivery path. Customers do not need to take any action to receive this benefit for phone pools. If you are not sending through phone pools today, you can create one by following these <a href="https://docs.aws.amazon.com/sms-voice/latest/userguide/phone-pool-create.html">instructions</a>. This capability is available in all AWS Regions where AWS End User Messaging is available. To learn more, see the AWS End User Messaging SMS <a href="https://docs.aws.amazon.com/sms-voice/latest/userguide/what-is-sms-mms.html">User Guide</a>.

14 – 18 September 2026