AWS Recent Announcements

Amazon SageMaker HyperPod console now validates service quotas for your AWS account before initiating cluster creation, enabling you to confirm sufficient quota availability before provisioning begins. SageMaker HyperPod helps you provision resilient clusters for running AI/ML workloads and developing state-of-the-art models such as large language models (LLMs), diffusion models, and foundation models (FMs). When creating large-scale AI/ML clusters, you need to ensure your account has sufficient quotas for instances, storage, and networking resources, but quota validation previously required manual checks across multiple AWS services, often resulting in failed cluster creation attempts and wasted time if you miss requesting quota limit increases. The new quota validation capability in the SageMaker HyperPod console automatically checks your account-level quotas against your cluster configuration, including instance type limits, EBS volume sizes, and VPC-related quotas when creating new resources. The validation displays a clear table showing expected utilization, applied quota values, and compliance status for each quota. When quotas may be exceeded, you receive a warning alert with direct links to the Service Quotas console to request increases. This feature is available in all AWS Regions where Amazon SageMaker HyperPod is supported. For a complete list of service quota validation checks performed, refer to the Amazon SageMaker HyperPod User Guide.

Amazon Lex now provides three VAD sensitivity levels that can be configured for each bot locale: Default, High, and Maximum. The Default setting is suitable for most environments with typical background noise levels. High is designed for environments with consistent but moderate noise levels, such as busy offices or retail spaces. Maximum provides the highest tolerance for very noisy environments such as manufacturing floors, construction sites, or outdoor locations with significant ambient noise. You can configure VAD sensitivity when creating or updating a bot locale in the Amazon Connect’s Conversational AI designer. This feature is available in all AWS commercial regions where Amazon Connect and Lex operate. To learn more, visit the Amazon Lex documentation or explore the Amazon Connect website to learn how Amazon Connect and Amazon Lex deliver seamless end-customer self-service experiences.

Amazon Connect now offers customers the ability to view status of agent screen recordings in near real time in CloudWatch using Amazon EventBridge. With screen recording, supervisors can identify areas for agent coaching (e.g., non-compliance with business processes) by not only listening to customer calls or reviewing chat transcripts, but also watching agents’ actions while handling a contact (i.e., a voice call, chat and task). Using Amazon EventBridge, customers can see status of each agent screen recording including success/failure, failure codes with description, installed client version, agent web browser version, agent operating system, screen recording start and end times from CloudWatch. Customers can start using Amazon Connect screen recording status tracking by subscribing to Screen Recording Status Changed event type in Amazon EventBridge event bus. Screen recording status tracking is available in all the AWS Regions where Amazon Connect is already available. To learn more about screen recording, please visit the documentation and webpage. For information about screen recording pricing, visit the Amazon Connect pricing page.

Amazon Redshift Serverless, which allows you to run and scale analytics without having to provision and manage data warehouse clusters, is now generally available in the AWS Asia Pacific (New Zealand) region. With Amazon Redshift Serverless, all users, including data analysts, developers, and data scientists, can use Amazon Redshift to get insights from data in seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver high performance for all your analytics. You only pay for the compute used for the duration of the workloads on a per-second basis. You can benefit from this simplicity without making any changes to your existing analytics and business intelligence applications. With a few clicks in the AWS Management Console, you can get started with querying data using the Query Editor V2 or your tool of choice with Amazon Redshift Serverless. There is no need to choose node types, node count, workload management, scaling, and other manual configurations. You can create databases, schemas, and tables, and load your own data from Amazon S3, access data using Amazon Redshift data shares, or restore an existing Amazon Redshift provisioned cluster snapshot. With Amazon Redshift Serverless, you can directly query data in open formats, such as Apache Parquet, Apache Iceberg in Amazon S3 data lakes. Amazon Redshift Serverless provides unified billing for queries on any of these data sources, helping you efficiently monitor and manage costs. To get started, see the Amazon Redshift Serverless feature page, user documentation, and API Reference.

Amazon Inspector scanning for Lambda functions and Elastic Container Registry (ECR) images now supports Java Gradle inventory and vulnerability scanning. This release also adds coverage for MySQL, MariaDB, PHP, Jenkins-core, 7zip (on Windows), Elasticsearch, and Curl/LibCurl. This update enhances Amazon Inspector’s ability to detect vulnerabilities and misconfigurations across a broader range of applications and environments. Amazon Inspector is an automated vulnerability management service that continually scans AWS workloads for software vulnerabilities and unintended network exposure, helping organizations improve their security posture and meet compliance requirements. The new Java Gradle support allows Inspector to scan Java dependencies based on gradle.lockfile content, providing comprehensive vulnerability assessments for Java applications. When you use Inspector to scan Lambda functions and ECR images, you will now see findings for MySQL, MariaDB, PHP, Jenkins-core, 7zip (on Windows), Elasticsearch, and Curl/LibCurl installations. These enhancements enable more accurate detection of vulnerabilities in packages installed outside of package managers, improving overall security coverage for AWS customers using these technologies. To learn more about Amazon Inspector and how it can help secure your AWS workloads, visit the Amazon Inspector page. For a full list of Amazon Inspector supported operating systems and programming languages, see the user guide. You can start using these new features today in all AWS Regions where Amazon Inspector is available.

Amazon Lightsail now offers two larger database bundles with up to 8 vCPUs, 32GB memory, and 960GB SSD storage. The new database bundles are available in both standard and high-availability plans. You can create MySQL and PostgreSQL databases using the new Lightsail managed database bundles. The new larger database bundles enable you to scale your database workloads and run more data-intensive applications in Lightsail. These higher-performance database bundles are ideal for production workloads that require increased storage capacity and processing power to handle growing datasets and concurrent connections. Using these new bundles, you can run e-commerce platforms, content management systems, business intelligence applications, SaaS products, and more. These new bundles are now available in all AWS Regions where Amazon Lightsail is available. For more information on pricing, or to get started with your free trial, click here.

Amazon EMR Serverless now supports job run-level cost allocation that provides better visibility into charges for individual job runs by allowing you to configure granular billing attribution at the individual job run level. You can get granular cost visibility by filtering and tracking costs in AWS Cost Explorer and Cost and Usage Reports by specific job run IDs and cost allocation tags associated with job runs. Amazon EMR Serverless is a deployment option in Amazon EMR that makes it simple for data engineers and data scientists to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers. Previously, you could assign cost allocation tags to EMR Serverless applications, with cost attribution limited to the application level. With job run-level cost allocation, now you can assign cost allocation tags to each job run, enabling fine-grained billing attribution at the individual job run level. Cost allocation tags at the job run level also allow you to track costs by domains within a single application. For example, a single application could support jobs for finance and marketing domains, allowing you to track costs separately for each domain. Tracking costs for individual job runs makes it easier to conduct benchmarks that assess the costs of each job run as well as focus cost optimization efforts more precisely, allowing deeper insights into resource utilization and spending patterns across different jobs and domains. This feature is available in all AWS Regions where Amazon EMR Serverless is available including AWS GovCloud (US) and China regions. To learn more, see Enabling Job Level Cost Allocation in the Amazon EMR Serverless User Guide

Amazon Relational Database Service (Amazon RDS) for SQL Server now supports setting up cross-region read replicas in 16 additional AWS Regions. Cross-region read replicas enable customers to provide a replica database for read-only applications closer to users in a different region, and scale out read-only workloads. Since a read replica can be “promoted” to a standalone production database, cross-region read replicas can also be used for disaster recovery in case of regional failures. Customers can setup up to fifteen read replicas in the same or different region as the primary database instance. This launch adds support for cross-region read replicas in RDS for SQL Server in the following AWS Regions: Africa (Cape Town), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Asia Pacific (Jakarta), Asia Pacific (Malaysia), Asia Pacific (Melbourne), Asia Pacific (Taipei), Asia Pacific (Thailand), Canada West (Calgary), Europe (Milan), Europe (Spain), Europe (Zurich), Israel (Tel Aviv), Mexico (Central), Middle East (Bahrain), and Middle East (UAE). To get started, visit the Amazon RDS SQL Server User Guide.

Amazon MQ now supports the ability for RabbitMQ brokers to perform authentication (determining who can log in) using X.509 client certificates with mutual TLS (mTLS). The RabbitMQ auth_mechanism_ssl plugin can be configured on brokers running RabbitMQ version 4.2 and above on Amazon MQ by making changes to the associated configuration file. To start using certificate based authentication on Amazon MQ, simply select RabbitMQ 4.2 when creating a new broker using the M7g instance type through the AWS Management console, AWS CLI, or AWS SDKs, and then edit the associated configuration file with the required values. To learn more about the plugin, see the Amazon MQ release notes and the Amazon MQ developer guide. This plugin is available in all regions where Amazon MQ RabbitMQ 4 instances are available today. 

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8i and R8i-flex instances are available in the Asia Pacific (Mumbai, Hyderabad) and Europe (Paris) regions. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. The R8i and R8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver 20% higher performance than R7i instances, with even higher gains for specific workloads. They are up to 30% faster for PostgreSQL databases, up to 60% faster for NGINX web applications, and up to 40% faster for AI deep learning recommendation models compared to R7i. R8i-flex, our first memory-optimized Flex instances, are the easiest way to get price performance benefits for a majority of memory-intensive workloads. They offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources. R8i instances are a great choice for all memory-intensive workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. R8i instances offer 13 sizes including 2 bare metal sizes and the new 96xlarge size for the largest applications. R8i instances are SAP-certified and deliver 142,100 aSAPS, delivering exceptional performance for mission-critical SAP workloads. To get started, sign in to the AWS Management Console. For more information about the R8i and R8i-flex instances visit the AWS News blog.

Amazon DocumentDB (with MongoDB compatibility) is now available in the Asia Pacific (Jakarta) region adding to the list of available regions where you can use Amazon DocumentDB. Amazon DocumentDB is a fully managed, native JSON database that makes it simple and cost-effective to operate critical document workloads at virtually any scale without managing infrastructure. Amazon DocumentDB is designed to give you the scalability and durability you need when operating mission-critical MongoDB workloads. Storage scales automatically up to 128TiB without any impact to your application. In addition, Amazon DocumentDB natively integrates with AWS Database Migration Service(DMS), Amazon CloudWatch, AWS CloudTrail, AWS Lambda, AWS Backup and more. Amazon DocumentDB supports millions of requests per second and can be scaled out to 15 low latency read replicas in minutes with no application downtime. To learn more about Amazon DocumentDB, please visit the Amazon DocumentDB product page and pricing page. You can create a Amazon DocumentDB cluster from the AWS Management console, AWS Command Line Interface (CLI), or SDK.

Amazon Quick is expanding its third-party integrations by adding AI agents and growing its built-in actions library. Quick is Amazon’s new AI-powered workspace and agentic teammate that helps organizations get answers from their business data and move quickly from insights to action. As organizations navigate newly adopted AI agents and work with existing enterprise tools for CRM, support, collaboration, and more, users face fragmented experiences. Users are forced to switch between different interfaces, repeat context, and manually stitch together outputs. Quick enables users to work with third-party agents and enterprise tools from a single interface, eliminating the wasted time and cognitive load of constantly switching between applications. With Quick, business users can now invoke specialized agents from Box, Canva, and PagerDuty to accomplish chat and automation tasks. For example, you can pull incident insights from PagerDuty, generate a presentation in Canva, and query documents stored in Box – all directly from Quick. Additionally, Quick has expanded its built-in actions to include integrations with GitHub, Notion, Canva, Box, Linear, Hugging Face, Monday.com, HubSpot, Intercom, and more. This enables Quick users to accomplish tasks like creating GitHub issues, summarizing meeting notes in Notion, managing their CRM, and more. Beyond our new built-in integrations, customers can continue to leverage custom Model Context Protocol (MCP) and OpenAPI connectors to connect Quick to thousands of additional applications. These features are now available in all AWS Regions where Amazon Quick is available. To learn more, visit the Amazon Quick Supported Integrations Guide and Integration Specific Guide.

Starting today, Amazon EC2 M8i instances are now available in Europe (Frankfurt) and Asia Pacific (Malaysia) Regions. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. The M8i offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver up to 20% better performance than M7i instances, with even higher gains for specific workloads. The M8i instances are up to 30% faster for PostgreSQL databases, up to 60% faster for NGINX web applications, and up to 40% faster for AI deep learning recommendation models compared to M7i instances. M8i instances are a great choice for all general purpose workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. The SAP-certified M8i instances offer 13 sizes including 2 bare metal sizes and the new 96xlarge size for the largest applications. To get started, sign in to the AWS Management Console. For more information about the new instances, visit the M8i instance page or visit the AWS News blog.

AWS Lambda now supports creating serverless applications using .NET 10. Developers can use .NET 10 as both a managed runtime and a container base image, and AWS will automatically apply updates to the managed runtime and base image as they become available. .NET 10 is the latest long-term support release of .NET and is expected to be supported for security and bug fixes until November 2028. This release provides Lambda developers with access to the latest .NET features, including file-based apps. It also includes support for Lambda Managed Instances, enabling you to run Lambda functions on Amazon EC2 instances while maintaining serverless operational simplicity, providing cost efficiency and specialized compute options. Powertools for AWS Lambda (.NET), a developer toolkit to implement serverless best practices and increase developer velocity, also supports .NET 10. You can use the full range of AWS deployment tools, including the Lambda console, AWS CLI, AWS Serverless Application Model (AWS SAM), AWS CDK, and AWS CloudFormation to deploy and manage serverless applications written in .NET 10. The .NET 10 runtime is available in all Regions, including the AWS GovCloud (US) Regions and China Regions. For more information, including guidance on upgrading existing Lambda functions, see our blog post. For more information about AWS Lambda, visit our product page.

Today, AWS announces a simplified onboarding experience for AWS Client VPN, introducing a new Quickstart setup method that streamlines the process of creating and configuring Client VPN endpoints. AWS Client VPN allows you to securely connect remote users to AWS resources and on-premises networks. The new Quickstart setup, reduces the number of steps required to set up an Client VPN endpoint. You can now easily set up Client VPN endpoints with pre-defined default configurations, requiring only three key inputs: IPv4 CIDR, server certificate ARN, and subnet selection. For example, development teams in large organizations who use Client VPN for remote access to their VPC resources for quick testing can now create endpoints quickly with the new simplified setup process. The Quickstart method is available alongside the existing Standard Setup option, giving you the flexibility to choose the approach that best fits your deployment needs. Additionally, when you create a VPC, the Client VPN Quickstart workflow is automatically suggested as a follow up step. Once endpoint creation is complete, you can immediately download the client configuration file to connect using your VPN client. You can later modify and enhance your endpoint configuration using the Client VPN standard console or API as your usage patterns evolve. This enhancement is available at no additional cost in all AWS Regions where AWS Client VPN is generally available. To learn more about Client VPN: Visit the AWS Client VPN product page Read the AWS Client VPN documentation

AWS is announcing starting today, Amazon EC2 I7ie instances are now available in AWS Asia Pacific (Mumbai), Canada West (Calgary) and Europe (Paris) regions. Designed for large storage I/O intensive workloads, I7ie instances are powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance over existing I3en instances. I7ie instances offer up to 120TB local NVMe storage density for storage optimized instances and offer up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, I7ie instances deliver up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances. I7ie are high density storage optimized instances, ideal for workloads requiring fast local storage with high random read/write performance at very low latency consistency to access large data sets. These instances are available in 9 different virtual sizes and deliver up to 100Gbps of network bandwidth and 60Gbps of bandwidth for Amazon Elastic Block Store (EBS). To learn more, visit the I7ie instances page.

You can now create Apache Airflow version 2.11 environments on Amazon Managed Workflows for Apache Airflow (MWAA). Apache Airflow 2.11 introduces several changes to help you prepare for upgrading to Apache Airflow 3.  Amazon MWAA is a managed orchestration service for Apache Airflow that makes it easier to set up and operate end-to-end data pipelines in the cloud. Apache Airflow 2.11 introduces several notable enhancements, such as new trigger-based scheduling for delta intervals, consistent reporting of metrics in milliseconds and other changes that will make it easy to migrate to Apache Airflow 3. In addition, MWAA now provides support for Python 3.12 that you can leverage in your workflows. You can launch a new Apache Airflow 2.11 environment on Amazon MWAA with just a few clicks in the AWS Management Console in all currently supported Amazon MWAA regions. To learn more about Apache Airflow 2.11 visit the Amazon MWAA documentation, and the Apache Airflow 2.11 change log in the Apache Airflow documentation. Apache, Apache Airflow, and Airflow are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C8i and C8i-flex instances are available in the Asia Pacific (Mumbai), Asia Pacific (Seoul), and Asia Pacific (Tokyo) regions. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. These C8i and C8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver up to 20% higher performance than C7i and C7i-flex instances, with even higher gains for specific workloads. The C8i and C8i-flex are up to 60% faster for NGINX web applications, up to 40% faster for AI deep learning recommendation models, and 35% faster for Memcached stores compared to C7i and C7i-flex. C8i-flex are the easiest way to get price performance benefits for a majority of compute intensive workloads like web and application servers, databases, caches, Apache Kafka, Elasticsearch, and enterprise applications. They offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources. C8i instances are a great choice for all memory-intensive workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. C8i instances offer 13 sizes including 2 bare metal sizes and the new 96xlarge size for the largest applications. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information about the new C8i and C8i-flex instances visit the AWS News blog.

Amazon Elastic Container Service (Amazon ECS) now supports tmpfs mounts for Linux tasks running on AWS Fargate and Amazon ECS Managed Instances, extending beyond the EC2 launch type. With tmpfs, you can now create memory‑backed file systems for your containerized workloads without writing this data to task storage. tmpfs mounts provide a temporary file system that is backed by memory and exposed inside the container at a path you choose. This is ideal for performance‑sensitive workloads that need fast access to scratch files, caches, or temporary working sets, and for security‑sensitive data such as short‑lived secrets or credentials, because the data does not persist after the task stops. tmpfs also lets you keep the container root file system read‑only using the readonlyRootFilesystem setting while still allowing applications to write to specific in‑memory directories. To get started, update your task definition so that the container definitions include a linuxParameters block with one or more tmpfs entries. For each tmpfs mount, specify the containerPath, size, and optional mountOptions. You can register or update task definitions using the Amazon ECS console, AWS CLI, AWS CloudFormation, or AWS CDK. This feature is available in all AWS Regions where Amazon ECS, AWS Fargate, and Amazon ECS Managed Instances are supported. To learn more, see the LinuxParameters and Tmpfs sections in the Amazon ECS API Reference and the Amazon ECS Developer Guide.

Today, AWS announces collection visibility in AWS Marketplace Seller Reporting, which adds up-to-date payment collection status to the Billed Revenue Dashboard and Billing Event Data Feed. This enhancement enables sellers to distinguish between invoiced, collected, and disbursed amounts, eliminating the visibility gap between invoice creation and disbursement. With this feature, sellers can make informed business decisions and reduce unnecessary follow-ups with customers about payment status. Collection visibility particularly benefits sellers using monthly disbursement who previously waited up to 30 days to understand payment collection status. All AWS Marketplace sellers can now improve payment forecasting accuracy and detect collection issues earlier. This enhanced visibility streamlines seller operations and improves customer relationships by providing clarity on payment status. Collection visibility is available in all AWS Regions where AWS Seller Reporting is available. The feature launches on January 6th, 2026 for all AWS sellers. To access collection visibility, log into the AWS Marketplace Management Portal and navigate to Insights → Finance Operations

Amazon MQ now supports the ability for RabbitMQ brokers to perform authentication (determining who can log in) and authorization (determining what permissions they have) by making requests to an HTTP server. This plugin can be configured on brokers running RabbitMQ 4.2 and above on Amazon MQ by making changes to the associated configuration file. To start using HTTP based authentication and authorization on Amazon MQ, simply select RabbitMQ 4.2 when creating a new broker using the m7g instance type through the AWS Management console, AWS CLI, or AWS SDKs, and then edit the associated configuration file. To learn more about the plugin, see the Amazon MQ release notes and the Amazon MQ developer guide. This plugin is available in all regions where Amazon MQ RabbitMQ 4 instances are available today. 

AWS Config now supports 21 additional AWS resource types across key services including Amazon EC2, Amazon SageMaker, and Amazon S3 Tables. This expansion provides greater coverage over your AWS environment, enabling you to more effectively discover, assess, audit, and remediate an even broader range of resources. With this launch, if you have enabled recording for all resource types, then AWS Config will automatically track these new additions. The newly supported resource types are also available in Config rules and Config aggregators. You can now use AWS Config to monitor the following newly supported resource types in all AWS Regions where the supported resources are available: Resource Types: AWS::AppStream::AppBlockBuilder AWS::IoT::ThingGroup AWS::B2BI::Capability AWS::IoTSiteWise::Asset AWS::CleanRoomsML::TrainingDataset AWS::Location::APIKey AWS::CloudFront::KeyValueStore AWS::MediaPackageV2::OriginEndpoint AWS::Connect::SecurityProfile AWS::PCAConnectorAD::Connector AWS::Deadline::Monitor AWS::Route53::DNSSEC AWS::EC2::SubnetCidrBlock AWS::S3Tables::TableBucketPolicy AWS::ECR::ReplicationConfiguration AWS::SageMaker::UserProfile AWS::GameLift::Build AWS::SecretsManager::ResourcePolicy AWS::GuardDuty::MalwareProtectionPlan       AWS::SSMContacts::Contact AWS::ImageBuilder::LifecyclePolicy  

Starting today, the Amazon Elastic Compute Cloud (Amazon EC2) G5 instances powered by NVIDIA A10G Tensor Core GPUs are now available in the Asia Pacific (Hong Kong) region. G5 instances can be used for a wide range of graphics intensive and machine learning use cases. Customers can use G5 instances for graphics-intensive applications such as remote workstations, video rendering, and cloud gaming to produce high fidelity graphics in real time. Machine learning customers can use G5 instances for high performance and cost-efficient training and inference for natural language processing, computer vision, and recommender engine use cases. G5 instances feature up to 8 NVIDIA A10G Tensor Core GPUs and 2nd generation AMD EPYC processors. They also support up to 192 vCPUs, up to 100 Gbps of network bandwidth, and up to 7.6 TB of local NVMe SSD storage. With eight G5 instance sizes that offer access to single or multiple GPUs, customers have the flexibility to pick the right instance size for their applications. Customers can easily optimize G5 instances for their workloads with NVIDIA drivers specific to compute, gaming or workstation workloads. Customers can purchase G5 instances as On-Demand Instances or Reserved Instances.

Today, AWS Resource Explorer has expanded the availability of resource search and discovery to the Asia Pacific (New Zealand) Region. With AWS Resource Explorer you can search for and discover your AWS resources across AWS Regions and accounts in your organization, either using the AWS Resource Explorer console, the AWS Command Line Interface (AWS CLI), the AWS SDKs, or the unified search bar from wherever you are in the AWS Management Console. For more information about the Regions where AWS Resource Explorer is available, see the AWS Region table. To turn on AWS Resource Explorer, visit the AWS Resource Explorer console. Read about getting started in our AWS Resource Explorer documentation, or explore the AWS Resource Explorer product page.

Customers in AWS Asia Pacific (New Zealand) Region can now use AWS Transfer Family for file transfers over Secure File Transfer Protocol (SFTP), File Transfer Protocol (FTP), FTP over SSL (FTPS) and Applicability Statement 2 (AS2). AWS Transfer Family provides fully managed file transfers for Amazon Simple Storage Service (Amazon S3) and Amazon Elastic File System (Amazon EFS) over SFTP, FTP, FTPS and AS2 protocols. In addition to file transfers, Transfer Family enables common file processing and event-driven automation for managed file transfer (MFT) workflows, helping customers to modernize and migrate their business-to-business file transfers to AWS. To learn more about AWS Transfer Family, visit our product page and user guide. See the AWS Region Table for complete regional availability information.

Today, AWS announces new Spot interruption metrics for Amazon EC2 Capacity Manager that allow you to better understand Spot capacity across your organization. EC2 Capacity Manager helps you monitor, analyze, and manage your EC2 capacity across On-Demand, Spot, and Capacity Reservations from a single location. With this new capability, you can now track how many Spot instances are running, monitor interruption counts, and calculate interruption rates across regions, availability zones, and accounts. This enables you to make data-driven decisions about your Spot instance strategy. EC2 Capacity Manager now includes three new metrics: ‘Spot Total Count’, ‘Spot Total Interruptions’, and ‘Spot Interruption Rate. ‘Spot Total Count’ shows the total number of distinct Spot instances or vCPUs that ran during a selected period, ‘Spot Total Interruptions’ tracks how many were interrupted, and ‘Spot Interruption Rate’ calculates the percentage of running instances that experienced interruptions. This data helps you identify patterns, compare across different regions and availability zones, and optimize your Spot instance strategy by diversifying instance types, expanding across availability zones, or using Spot placement score to identify optimal capacity pools with higher availability. EC2 Capacity Manager with Spot interruption metrics is available in all commercial AWS Regions enabled by default at no additional cost. To get started, visit EC2 Capacity Manager in the AWS console.

Today, AWS Clean Rooms announces the launch of detailed monitoring for SQL queries in a collaboration. This new capability publishes detailed metrics to CloudWatch for operational monitoring of collaborations, including query performance and resource utilization. You can choose to publish detailed monitoring metrics for SQL queries run in a Clean Rooms collaboration to CloudWatch, helping you improve the observability for your workloads at scale. The collaboration creator can enable detailed monitoring for a collaboration, and the analysis runner or configured payor can enable detailed monitoring when configuring their collaboration membership. For example, advertisers can monitor their campaign lift analysis queries in CloudWatch to identify performance issues and optimize costs. With AWS Clean Rooms, customers can create a secure data clean room in minutes and collaborate with any company on AWS or Snowflake to generate unique insights about advertising campaigns, investment decisions, and research and development. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms.

Amazon Connect now enables you to store and work with complex data structures in your flows, making it easy to build dynamic automated experiences that use rich information returned from your internal business systems. You can save complete data records, including nested JSON objects and lists, and reference specific elements within them, such as a particular order from a list of orders returned in JSON format. Additionally, you can automatically loop through lists of items in your customer service flows, moving through each entry in sequence while tracking the current position in the loop. This allows you to easily access item-level details and present relevant information to end-customers. For example, a travel agency can retrieve all of a customer’s itineraries in a single request and guide the caller through each booking to review or update their reservations. A bank can similarly walk customers through recent transactions one by one using data retrieved securely from its systems. These capabilities reduce the need for repeated calls to your business systems, simplify workflow design, and make it easier to deliver advanced automated experiences that adapt as your business requirements evolve. To learn more about these features, see the Amazon Connect Administrator Guide. These features are available in all AWS regions where Amazon Connect is available. To learn more about Amazon Connect, AWS’s AI-native customer experience solution, please visit the Amazon Connect website.

Starting today, AWS WAF is available in the AWS Asia Pacific (New Zealand) Region. AWS WAF is a web application firewall that helps you protect your web application resources against common web exploits and bots that can affect availability, compromise security, or consume excessive resources. With AWS WAF, you can control access to your content. Based on conditions that you specify, such as the IP addresses that requests originate from or the values of query strings, your protected resource responds to requests either with the requested content, with an HTTP 403 status code (Forbidden), or with a custom response. To see the full list of regions where AWS WAF is currently available, visit the AWS Region Table. For more information about the service, visit the AWS WAF page. For more information about pricing, visit the AWS WAF Pricing page.

Today, AWS launches simplified import of CloudTrail Lake data in Amazon CloudWatch, a data management and analytics service that allows you to unify operational, security, and compliance data across your AWS environment and third-party sources. With this launch, you can now import your historical CloudTrail Lake data into CloudWatch with a few steps enabling you to easily consolidate operational, security, and compliance data in one place. In CloudWatch, you simply specify the CloudTrail Lake event data store (EDS), and the date range to initiate import of your CloudTrail data. Simplified import of CloudTrail Lake data is supported via the AWS console, CLI, and SDK. While simplified import of CloudTrail Lake data is available at no additional cost, you incur CloudWatch fees based on custom logs pricing. To learn more about simplified import of CloudTrail Lake data and supported AWS regions, visit the Amazon CloudWatch documentation.