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Amazon Bedrock now supports server-side custom tools using the Responses API

Amazon Bedrock now supports server-side tools in the Responses API using OpenAI API-compatible service endpoints. Bedrock already supports client-side tool use with the Converse, Chat Completions, and Responses APIs. Now, with the launch of server-side tool use for Responses API, Amazon Bedrock calls the tools directly without going through a client, enabling your AI applications to perform real-time, multi-step actions such as searching the web, executing code, and updating databases within the organizational, governance, compliance, and security boundaries of your AWS accounts. You can either submit your own custom Lambda function to run custom tools or use AWS-provided tools, such as notes and tasks.

Server-side tools using the Responses API is available starting today with OpenAI’s GPT OSS 20B/120B models in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Mumbai), South America (São Paulo), Europe (Ireland), Europe (London), and Europe (Milan) AWS Regions. Support for other regions and models is coming soon.

To get started, visit the service documentation.

 

​Amazon Bedrock now supports server-side tools in the Responses API using OpenAI API-compatible service endpoints. Bedrock already supports client-side tool use with the Converse, Chat Completions, and Responses APIs. Now, with the launch of server-side tool use for Responses API, Amazon Bedrock calls the tools directly without going through a client, enabling your AI applications to perform real-time, multi-step actions such as searching the web, executing code, and updating databases within the organizational, governance, compliance, and security boundaries of your AWS accounts. You can either submit your own custom Lambda function to run custom tools or use AWS-provided tools, such as notes and tasks.
Server-side tools using the Responses API is available starting today with OpenAI’s GPT OSS 20B/120B models in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Mumbai), South America (São Paulo), Europe (Ireland), Europe (London), and Europe (Milan) AWS Regions. Support for other regions and models is coming soon.
To get started, visit the service documentation.  

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Amazon GameLift Servers now supports automatic scaling to and from zero instances

Amazon GameLift Servers now enables automatic scaling to and from zero instances, addressing a critical cost optimization challenge for game developers. Previously, developers had to maintain running instances even during periods of low or no activity in order for Fleet autoscaling to remain active. This resulted in unnecessary infrastructure costs during off-peak hours. With automatic scaling to and from zero instances, game developers using Amazon GameLift Servers can optimize their multiplayer gaming infrastructure costs while maintaining responsive performance.

By eliminating charges for unused instances during inactive periods, while automatically scaling up when game sessions are requested, this new capability delivers significant cost savings for game developers. This is particularly valuable for games with distinct peak and off-peak periods, seasonal or event-based games, new game launches with uncertain traffic patterns, and regional games with time-zone specific activity. Additionally, scaling decisions no longer need manual intervention, as Amazon GameLift Servers intelligently adapts to natural gaming activity patterns.

The automatic scaling to zero instances capability is available in all Amazon GameLift Servers supported regions. To learn more about Amazon GameLift Servers automatic scaling capabilities and implementation details, visit the Amazon GameLift Servers documentation.

 

​Amazon GameLift Servers now enables automatic scaling to and from zero instances, addressing a critical cost optimization challenge for game developers. Previously, developers had to maintain running instances even during periods of low or no activity in order for Fleet autoscaling to remain active. This resulted in unnecessary infrastructure costs during off-peak hours. With automatic scaling to and from zero instances, game developers using Amazon GameLift Servers can optimize their multiplayer gaming infrastructure costs while maintaining responsive performance. By eliminating charges for unused instances during inactive periods, while automatically scaling up when game sessions are requested, this new capability delivers significant cost savings for game developers. This is particularly valuable for games with distinct peak and off-peak periods, seasonal or event-based games, new game launches with uncertain traffic patterns, and regional games with time-zone specific activity. Additionally, scaling decisions no longer need manual intervention, as Amazon GameLift Servers intelligently adapts to natural gaming activity patterns. The automatic scaling to zero instances capability is available in all Amazon GameLift Servers supported regions. To learn more about Amazon GameLift Servers automatic scaling capabilities and implementation details, visit the Amazon GameLift Servers documentation.  

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Amazon Keyspaces (for Apache Cassandra) introduces pre-warming with WarmThroughput for your tables

Amazon Keyspaces (for Apache Cassandra) now supports table pre-warming, allowing you to proactively prepare both new and existing tables to meet future traffic demands. This capability is available for tables in both provisioned and on-demand capacity modes, including multi-Region replicated tables.

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.

While Amazon Keyspaces automatically scales to accommodate growing workloads, certain scenarios like application launches, marketing campaigns, or seasonal events can create sudden traffic spikes that exceed normal scaling patterns. With pre-warming, you can now manually specify your expected peak throughput requirements during table creation or update operations, ensuring your tables are immediately ready to handle large traffic surges without scaling delays or increased error rates.

The pre-warming process is non-disruptive and runs asynchronously, allowing you to continue making other table modifications while pre-warming is in progress. Pre-warming incurs a one-time charge based on the difference between your specified values and the baseline capacity. The feature is now available in all AWS Commercial and AWS GovCloud (US) Regions where Amazon Keyspaces is offered. To learn more, visit the pre-warming launch blog or Amazon Keyspaces documentation.

 

​Amazon Keyspaces (for Apache Cassandra) now supports table pre-warming, allowing you to proactively prepare both new and existing tables to meet future traffic demands. This capability is available for tables in both provisioned and on-demand capacity modes, including multi-Region replicated tables. 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. While Amazon Keyspaces automatically scales to accommodate growing workloads, certain scenarios like application launches, marketing campaigns, or seasonal events can create sudden traffic spikes that exceed normal scaling patterns. With pre-warming, you can now manually specify your expected peak throughput requirements during table creation or update operations, ensuring your tables are immediately ready to handle large traffic surges without scaling delays or increased error rates. The pre-warming process is non-disruptive and runs asynchronously, allowing you to continue making other table modifications while pre-warming is in progress. Pre-warming incurs a one-time charge based on the difference between your specified values and the baseline capacity. The feature is now available in all AWS Commercial and AWS GovCloud (US) Regions where Amazon Keyspaces is offered. To learn more, visit the pre-warming launch blog or Amazon Keyspaces documentation.  

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Amazon Cognito introduces inbound federation Lambda triggers

Amazon Cognito introduces inbound federation Lambda triggers that enable you to transform and customize federated user attributes during the authentication process. You can now modify responses from external SAML and OIDC providers before they are stored in your user pool, providing complete programmatic control over the federation flow without requiring changes to your identity provider configuration..

Inbound federation Lambda trigger addresses current limitations in federated authentication workflows, particularly issues caused by attribute size limits and the need for selective attribute storage from external identity providers. For example, large group attributes from external SAML or OIDC identity providers that exceed Cognito’s 2,048 character limit per attribute can block the authentication flow. This capability allows you to add, override, or suppress attribute values, such as modifying large group attributes, before creating new federated users or updating existing federated user profiles in Cognito.

The new inbound federation Lambda trigger is available through hosted UI (classic) and managed login in all AWS Regions where Amazon Cognito is available. To get started, configure the trigger using the AWS Management Console, AWS Command Line Interface (CLI), AWS Software Development Kits (SDKs), Cloud Development Kit (CDK), or AWS CloudFormation by adding the new parameter to your User Pool LambdaConfig. To learn more, see the Amazon Cognito Developer Guide for implementation examples and best practices.

 

​Amazon Cognito introduces inbound federation Lambda triggers that enable you to transform and customize federated user attributes during the authentication process. You can now modify responses from external SAML and OIDC providers before they are stored in your user pool, providing complete programmatic control over the federation flow without requiring changes to your identity provider configuration..
Inbound federation Lambda trigger addresses current limitations in federated authentication workflows, particularly issues caused by attribute size limits and the need for selective attribute storage from external identity providers. For example, large group attributes from external SAML or OIDC identity providers that exceed Cognito’s 2,048 character limit per attribute can block the authentication flow. This capability allows you to add, override, or suppress attribute values, such as modifying large group attributes, before creating new federated users or updating existing federated user profiles in Cognito.
The new inbound federation Lambda trigger is available through hosted UI (classic) and managed login in all AWS Regions where Amazon Cognito is available. To get started, configure the trigger using the AWS Management Console, AWS Command Line Interface (CLI), AWS Software Development Kits (SDKs), Cloud Development Kit (CDK), or AWS CloudFormation by adding the new parameter to your User Pool LambdaConfig. To learn more, see the Amazon Cognito Developer Guide for implementation examples and best practices.  

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Announcing increased 1 MB payload size support in Amazon EventBridge

Amazon EventBridge increases event payload size from 256 KB to 1 MB, enabling developers to ingest richer, complex payloads for their event-driven workloads without the need to split, compress, or externalize data.

Amazon EventBridge is a serverless event router that enables you to create scalable event-driven applications by routing events between your applications, third-party SaaS applications, and AWS services. These applications often need to process rich contextual data, including large-language model prompts, telemetry signals, and complex JSON structures for machine learning outputs. The new 1MB payload support in EventBridge Event Buses enables developers to streamline their architectures by including comprehensive data in a single event, reducing the need for complex data chunking or external storage solutions.

This feature is available in all commercial AWS Regions where Amazon EventBridge is offered, except Asia Pacific (New Zealand), Asia Pacific (Thailand), Asia Pacific (Malaysia), Asia Pacific (Taipei), and Mexico (Central). For a full list, see the AWS Regional Services List. To learn more, visit the EventBridge documentation.

 

​Amazon EventBridge increases event payload size from 256 KB to 1 MB, enabling developers to ingest richer, complex payloads for their event-driven workloads without the need to split, compress, or externalize data. Amazon EventBridge is a serverless event router that enables you to create scalable event-driven applications by routing events between your applications, third-party SaaS applications, and AWS services. These applications often need to process rich contextual data, including large-language model prompts, telemetry signals, and complex JSON structures for machine learning outputs. The new 1MB payload support in EventBridge Event Buses enables developers to streamline their architectures by including comprehensive data in a single event, reducing the need for complex data chunking or external storage solutions. This feature is available in all commercial AWS Regions where Amazon EventBridge is offered, except Asia Pacific (New Zealand), Asia Pacific (Thailand), Asia Pacific (Malaysia), Asia Pacific (Taipei), and Mexico (Central). For a full list, see the AWS Regional Services List. To learn more, visit the EventBridge documentation.  

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Transformación de Frontera en el comercio minorista: Cómo los robots de IA agéntica redefinen la experiencia de las tiendas

Transformación de Frontera en el comercio minorista: Cómo los robots de IA agéntica redefinen la experiencia de las tiendas

Una persona con delantal, usa una tablet. El texto se lee "Comercio minorista personalizado a gran escala".

Por: Ricardo Villarreal, Director de Microsoft AI Co-Innovation Labs.

Por qué las empresas necesitan la Transformación de Frontera

El entorno empresarial actual exige más con menos. Las organizaciones deben ofrecer una mejor personalización, mayor volumen y conocimientos cada vez más complejos, a la vez que operan con mayor eficiencia. La brecha entre las expectativas de los grupos de interés y lo que los equipos pueden ofrecer de forma realista sigue ampliándose.

Los recientes conocimientos de Microsoft sobre la Transformación de Frontera abordan estos desafíos al integrar la IA en el núcleo de las operaciones. Las Empresas Frontera son organizaciones que tratan la IA como una capacidad fundamental y que ya han comenzado a transformar su forma de trabajar.

Exploren los Laboratorios de Co-Innovación de Microsoft AI

Las Empresas Frontera no se limitan a automatizar; se adaptan. Al añadir inteligencia adaptativa a los sistemas existentes, desbloquean tres ventajas:

  • Conciencia: Los sistemas perciben las condiciones en tiempo real.
  • Razonamiento: Priorizan las tareas según las necesidades del negocio.
  • Interacción: Se comunican de forma natural e intuitiva.

Los primeros usuarios ven cómo pequeñas mejoras se acumulan con rapidez. Estos incluyen un servicio más rápido, recomendaciones más precisas, menos sorpresas en el equipo y una visión más clara de las horas pico y cuellos de botella. A medida que la IA agéntica madura, las empresas pueden ofrecer orientación y asistencia que resulten intuitivas. Los empleados ganan más tiempo para trabajos de alto valor y los líderes obtienen una mayor visibilidad de las operaciones.

La Transformación de Frontera es mucho más que una simple actualización tecnológica. Representa un cambio en el modelo operativo. Las organizaciones que traten la IA como base liderarán la próxima ola de innovación empresarial.

La IA agéntica transforma la experiencia del cliente

Este cambio ya es visible en el sector minorista, donde los robots agentes transforman la experiencia del cliente y mejoran el rendimiento operativo. Los clientes esperan un servicio rápido y personalizado, pero los minoristas a menudo se enfrentan a limitaciones de personal, carencias de formación y una demanda impredecible.

Los estudios del sector muestran:

  • El 75% de los consumidores es más propenso a comprar cuando las recomendaciones resultan relevantes.
  • Casi el 40% de las quejas en tienda están relacionadas con los tiempos de espera.
  • Las inexactitudes en el inventario representan entre el 4 y el 8% de las ventas perdidas.

Estos desafíos reflejan un patrón más amplio en sectores con gran presencia de primera línea. Las expectativas de los clientes siguen en aumento y las cargas de trabajo de los empleados se vuelven más complejas.

El Índice de Tendencias Laboralesde Microsoftrefuerza esta dinámica. Los empleados de primera línea afirman que las herramientas de IA que reducen tareas repetitivas, muestran información en tiempo real y agilizan las interacciones con los clientes tienen el mayor impacto en la satisfacción y el rendimiento. A medida que las organizaciones integran la inteligencia adaptativa en los flujos de trabajo diarios, estos beneficios se complementan y ayudan a acelerar la Transformación de Frontera. 

Investigaciones recientes del sector muestran que las organizaciones minoristas y de bienes de consumo envasados generan un valor empresarial significativo a partir de la IA generativa y agéntica, con despliegues iniciales que proporcionan de manera constante un retorno de inversión múltiple y un impacto acelerado en las operaciones de primera línea.

La IA agéntica crea nuevas posibilidades para las tiendas. En lugar de depender de una automatización rígida, combina conciencia ambiental, razonamiento adaptativo e interacción conversacional para ayudar a los equipos a responder en tiempo real.

ADAM: Desde el servicio de bebidas hasta la atención al cliente

El robot de bebidas ADAM de Richtech Robotics ilustra lo rápido que los sistemas agentes pueden mejorar la experiencia del cliente. Richtech, con sede en Las Vegas, diseña y comercializa soluciones robóticas autónomas para hostelería, comercio minorista, logística y fabricación. A través de una estrecha colaboración práctica entre el equipo de ingeniería de Richtech y los Microsoft AI Co-Innovation Labs, ambas empresas desarrollaron en conjunto una nueva inteligencia adaptativa para ADAM, transformándolo en un asistente conversacional y consciente del contexto impulsado por Microsoft Azure AI. Estas mejoras permitieron a ADAM ir más allá de la preparación rutinaria de bebidas y apoyar interacciones más enriquecidas con los clientes.

Hoy, ADAM:

  • Ajusta las recomendaciones según el tiempo, la hora del día y las promociones.
  • Responde de forma natural a peticiones de clientes como «menos dulce», «extra hielo» o «¿qué es de temporada?»
  • Notifica al personal sobre problemas con los ingredientes o equipos antes de que surjan problemas.
  • Utiliza modelos de visión para mantener la velocidad y la calidad durante los periodos de mayor actividad.

Los minoristas informan de operaciones más fluidas y mejores comentarios de los clientes. ADAM es consciente del contexto, conversacional y fiable—cualidades que los clientes recompensan de manera constante y áreas donde la IA ha tenido dificultades a nivel histórico.

Aunque ADAM es un ejemplo minorista, el patrón va mucho más allá de la automatización de bebidas. En logística, sanidad, hostelería y manufactura, las Empresas Frontera añaden inteligencia ambiental y flujos de trabajo agentivos a las operaciones físicas, para ver así ganancias significativas como resultado.

Desbloquear la transformación del comercio minorista a gran escala

Una vez que los minoristas ven cómo la inteligencia mejora la interacción con un solo cliente, de manera natural surge la siguiente pregunta: ¿dónde más puede ayudar esto? Basándose en los avances logrados con ADAM, Richtech Robotics amplía estas capacidades a través de su iniciativa Agentic Store. Al aplicar visión, voz y razonamiento agénticos a tareas comunes en tienda, la iniciativa ayuda a los minoristas a abordar puntos de fricción que ralentizan la experiencia de compra.

Ejemplos en desarrollo incluyen:

  • Robots que guían a los clientes hacia productos.
  • Sistemas que detectan estanterías vacías u objetos extraviados.
  • Asistencia en el pasillo con voz activada.
  • Ajustes en tiempo real basados en el tráfico de personas o eventos locales.

Este enfoque no requiere grandes inversiones en hardware. Estos flujos de trabajo son impulsados por software y se basan en la infraestructura existente de la tienda. Refleja cómo las Empresas Frontera impulsan la transformación al difundir la inteligencia a lo largo del ecosistema, en lugar de actualizar un solo proceso a la vez.

Los minoristas obtienen una visión más clara de la demanda pico, el comportamiento del cliente, el movimiento del producto y la calidad del servicio sin aumentar el seguimiento manual. Como lo describió un encargado de tienda, «se siente como tener un segundo par de ojos que nunca se cansa.»

Un servicio cómodo y de alta calidad se convierte en un modelo para la inteligencia a nivel de tienda. En los próximos años, surgirá una diferencia clara entre los minoristas que tratan la IA como una herramienta y aquellos que la consideran una base. Esto último marcará el ritmo de la industria.

Pasos hacia la Transformación de Frontera

La IA agéntica ofrece a los minoristas un camino práctico y alcanzable hacia adelante. Eleva la experiencia del cliente, reduce la carga operativa y crea las bases para tiendas más inteligentes y adaptativas. Las organizaciones que adoptan la Transformación de Frontera se posicionan como Empresas Frontera, listas para escalar más rápido, trabajar de forma más inteligente y desbloquear nuevo valor mediante la combinación de juicio humano y visión impulsada por IA.

El camino comienza con pequeños pasos estratégicos y una visión audaz de lo que es posible. Para explorar el impacto empresarial más amplio de la IA tanto en puestos de primera línea como de cara al cliente, consulten el Índice de Tendencias Laborales de Microsoft: El año en que nació la Empresa Frontera.

Descubran cómo las organizaciones se transforman con la IA y aprendan cómo pueden crear su propia prueba de concepto de IA generativa con los Microsoft AI Co-Innovation Labs.

Descubran más sobre Empresas Frontera

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AWS announces Deployment Agent SOPs in AWS MCP Server (preview)

AWS announces the launch of deployment Standard Operating Procedures (SOPs) available in the AWS MCP Server. SOPs are structured, natural language instructions that guide AI agents through complex, multi-step tasks to ensure consistent, reliable, and efficient behavior. With these automated procedures, customers can deploy web applications to their AWS account using natural language prompts from any MCP-compatible IDE or CLI, including Kiro, Kiro CLI, Cursor, and Claude Code. Deployment works by generating AWS CDK infrastructure, deploying CloudFormation stacks, and creating CI/CD pipelines with recommended AWS security best practices.

Previously, developers struggled to take their vibe-coded applications to production with DevOps best practices in place. Now, developers can move quickly from prototype to production in as little as one prompt. When you ask your AI assistant configured with AWS MCP Server to deploy your web application, your AI agent will follow the multi-step plan defined in Agent SOPs to analyze the project structure, generate CDK infrastructure, and deploy a preview environment hosted on Amazon S3 and Amazon CloudFront. Once you are ready, it can configure AWS CodePipeline for automated production deployments from source repositories, setting up CI/CD automatically for your application. The Agent SOPs support web applications built with popular frameworks including React, Vue.js, Angular, and Next.js. Deployment documentation is automatically created in the repository, enabling agents to handle future deployments, query logs for troubleshooting and resume work across sessions.

The Agent SOPs are available in preview as part of the AWS MCP Server at no additional cost in the US East (N. Virginia) Region. You pay only for AWS resources you create and applicable data transfer costs. To get started, see the AWS MCP Server documentation.

 

​AWS announces the launch of deployment Standard Operating Procedures (SOPs) available in the AWS MCP Server. SOPs are structured, natural language instructions that guide AI agents through complex, multi-step tasks to ensure consistent, reliable, and efficient behavior. With these automated procedures, customers can deploy web applications to their AWS account using natural language prompts from any MCP-compatible IDE or CLI, including Kiro, Kiro CLI, Cursor, and Claude Code. Deployment works by generating AWS CDK infrastructure, deploying CloudFormation stacks, and creating CI/CD pipelines with recommended AWS security best practices. Previously, developers struggled to take their vibe-coded applications to production with DevOps best practices in place. Now, developers can move quickly from prototype to production in as little as one prompt. When you ask your AI assistant configured with AWS MCP Server to deploy your web application, your AI agent will follow the multi-step plan defined in Agent SOPs to analyze the project structure, generate CDK infrastructure, and deploy a preview environment hosted on Amazon S3 and Amazon CloudFront. Once you are ready, it can configure AWS CodePipeline for automated production deployments from source repositories, setting up CI/CD automatically for your application. The Agent SOPs support web applications built with popular frameworks including React, Vue.js, Angular, and Next.js. Deployment documentation is automatically created in the repository, enabling agents to handle future deployments, query logs for troubleshooting and resume work across sessions. The Agent SOPs are available in preview as part of the AWS MCP Server at no additional cost in the US East (N. Virginia) Region. You pay only for AWS resources you create and applicable data transfer costs. To get started, see the AWS MCP Server documentation.  

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Amazon EKS and Amazon EKS Distro now supports Kubernetes version 1.35

Kubernetes version 1.35 introduced several new features and bug fixes, and AWS is excited to announce that you can now use Amazon Elastic Kubernetes Service (EKS) and Amazon EKS Distro to run Kubernetes version 1.35. Starting today, you can create new EKS clusters using version 1.35 and upgrade existing clusters to version 1.35 using the EKS console, the eksctl command line interface, or through an infrastructure-as-code tool.

Kubernetes version 1.35 introduces several key improvements, including In-Place Pod Resource Updates allowing CPU and memory adjustments without restarting Pods, and PreferSameNode Traffic Distribution prioritizing local endpoints before routing to remote nodes for reduced latency. The release brings Node Topology Labels via Downward API enabling Pods to access region and zone information without API server queries, alongside Image Volumes delivering data artifacts like AI models using OCI container images. To learn more about the changes in Kubernetes version 1.35, see our documentation and the Kubernetes project release notes.

EKS now supports Kubernetes version 1.35 in all the AWS Regions where EKS is available, including the AWS GovCloud (US) Regions.

You can learn more about the Kubernetes versions available on EKS and instructions to update your cluster to version 1.35 by visiting EKS documentation. You can use EKS cluster insights to check if there are any issues that can impact your Kubernetes cluster upgrades. EKS Distro builds of Kubernetes version 1.35 are available through ECR Public Gallery and GitHub. Learn more about the EKS version lifecycle policies in the documentation.

 

​Kubernetes version 1.35 introduced several new features and bug fixes, and AWS is excited to announce that you can now use Amazon Elastic Kubernetes Service (EKS) and Amazon EKS Distro to run Kubernetes version 1.35. Starting today, you can create new EKS clusters using version 1.35 and upgrade existing clusters to version 1.35 using the EKS console, the eksctl command line interface, or through an infrastructure-as-code tool. Kubernetes version 1.35 introduces several key improvements, including In-Place Pod Resource Updates allowing CPU and memory adjustments without restarting Pods, and PreferSameNode Traffic Distribution prioritizing local endpoints before routing to remote nodes for reduced latency. The release brings Node Topology Labels via Downward API enabling Pods to access region and zone information without API server queries, alongside Image Volumes delivering data artifacts like AI models using OCI container images. To learn more about the changes in Kubernetes version 1.35, see our documentation and the Kubernetes project release notes. EKS now supports Kubernetes version 1.35 in all the AWS Regions where EKS is available, including the AWS GovCloud (US) Regions. You can learn more about the Kubernetes versions available on EKS and instructions to update your cluster to version 1.35 by visiting EKS documentation. You can use EKS cluster insights to check if there are any issues that can impact your Kubernetes cluster upgrades. EKS Distro builds of Kubernetes version 1.35 are available through ECR Public Gallery and GitHub. Learn more about the EKS version lifecycle policies in the documentation.  

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Amazon DynamoDB global tables with multi-Region strong consistency now supports application resiliency testing with AWS Fault Injection Service

Amazon DynamoDB global tables with multi-Region strong consistency (MRSC) now supports application resiliency testing with AWS Fault Injection Service (FIS), a fully managed service for running controlled fault injection experiments to improve application performance, observability, and resilience. With this launch, you can create real-world failure scenarios to MRSC global tables, such as during regional failures, enabling you to observe how your applications respond to these disruptions and validate your resilience mechanisms.

MRSC global tables replicate your DynamoDB tables automatically across your choice of AWS Regions to achieve fast, strongly consistent read and write performance, providing you 99.999% availability, increased application resiliency, and improved business continuity. FIS is a fully managed service for running controlled fault injection experiments to improve an application’s performance, observability, and resilience. You can use the new FIS action to observe how their application responds to a pause in regional replication and tune their monitoring and recovery process to improve resiliency and application availability.

MRSC global tables support for FIS is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Osaka), Asia Pacific (Seoul), Europe (Ireland), Europe (London), Europe (Frankfurt), and Europe (Paris). To get started, visit the DynamoDB FIS actions documentation.

 

​Amazon DynamoDB global tables with multi-Region strong consistency (MRSC) now supports application resiliency testing with AWS Fault Injection Service (FIS), a fully managed service for running controlled fault injection experiments to improve application performance, observability, and resilience. With this launch, you can create real-world failure scenarios to MRSC global tables, such as during regional failures, enabling you to observe how your applications respond to these disruptions and validate your resilience mechanisms. MRSC global tables replicate your DynamoDB tables automatically across your choice of AWS Regions to achieve fast, strongly consistent read and write performance, providing you 99.999% availability, increased application resiliency, and improved business continuity. FIS is a fully managed service for running controlled fault injection experiments to improve an application’s performance, observability, and resilience. You can use the new FIS action to observe how their application responds to a pause in regional replication and tune their monitoring and recovery process to improve resiliency and application availability. MRSC global tables support for FIS is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Osaka), Asia Pacific (Seoul), Europe (Ireland), Europe (London), Europe (Frankfurt), and Europe (Paris). To get started, visit the DynamoDB FIS actions documentation.  

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Amazon EC2 R7gd instances are now available in Europe (Paris) Region

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R7gd instances with up to 3.8 TB of local NVMe-based SSD block-level storage are available in Europe (Paris) Region.

R7gd are powered by AWS Graviton3 processors with DDR5 memory are built on the AWS Nitro System. They are ideal for memory-intensive workloads such as open-source databases, in-memory caches, and real-time big data analytics and are a great fit for applications that need access to high-speed, low latency local storage, including those that need temporary storage of data for scratch space, temporary files, and caches. 

To learn more, see Amazon R7gd Instances. To get started, see the AWS Management Console.

 

​Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R7gd instances with up to 3.8 TB of local NVMe-based SSD block-level storage are available in Europe (Paris) Region. R7gd are powered by AWS Graviton3 processors with DDR5 memory are built on the AWS Nitro System. They are ideal for memory-intensive workloads such as open-source databases, in-memory caches, and real-time big data analytics and are a great fit for applications that need access to high-speed, low latency local storage, including those that need temporary storage of data for scratch space, temporary files, and caches.  To learn more, see Amazon R7gd Instances. To get started, see the AWS Management Console.