AWS 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.
AWS 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 Monetization Functions in the AWS Elemental MediaTailor User Guide and the MediaTailor pricing page. To get started, sign in to the AWS Elemental MediaTailor console.
Amazon Elastic Container Service (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 documentation.
Amazon Elastic Container Service (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 documentation.
AWS 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.
AWS 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 AWS Security Blogpost, AWS IAM User Guide, and AWS STS API Reference.
Por: Silvia Candiani, vicepresidenta corporativa, Telecomunicaciones, Medios y Entretenimiento a nivel mundial, Microsoft.
Cada conversación que mantengo hoy con líderes de medios vuelve a una pregunta importante: ¿Cómo convertimos la promesa de la IA en valor empresarial medible? Los medios y el entretenimiento pueden tener más que ganar con la IA que casi cualquier industria, pero se mueven más despacio. Solo se estima que un 10% de su plantilla califica como Frontier Professionals, frente al 16% en diferentes sectores y el 28% en tecnología, según el Informe Anual 2026 del Índice de Tendencias Laborales de Microsoft. Al mismo tiempo, el 51% de los usuarios de IA en medios y entretenimiento ya afirman que producen trabajos que no podrían haber creado hace un año, para subir al 80% entre los Frontier Professionals.
Esa brecha es la oportunidad. Las organizaciones que se mueven ahora pueden convertir la capacidad emergente de IA en una ventaja medible en producción, entrega, compromiso con la audiencia y monetización. Los más destacados no solo usan más IA. Rediseñan el trabajo en torno a las fortalezas complementarias de las personas y la IA, comparten prácticas exitosas y aplican el juicio humano donde la calidad, los derechos, la confianza y la rendición de cuentas más importan.
Esto es lo que hace que el momento sea tan emocionante. La IA puede ayudar a empoderar a equipos creativos y empresariales para ampliar lo posible, mientras las personas aportan la imaginación, la experiencia y la responsabilidad que definen grandes experiencias mediáticas.
Desde asistencia aislada hasta inteligencia orquestada
En Microsoft, a esto lo llamamos orquestación de inteligencia mediática: pasar de la asistencia aislada de IA a flujos de trabajo dirigidos por agentes que conectan personas, datos de confianza y sistemas especializados a lo largo del ciclo de vida de los medios. La seguridad y la gobernanza están integradas desde el principio para que las organizaciones puedan escalar la inteligencia sin sacrificar el control.
Las soluciones puntuales pueden optimizar tareas individuales, pero a menudo atrapan datos, contexto y decisiones dentro de herramientas separadas. La orquestación conecta inversiones existentes, por lo que una visión o acción en una etapa puede mejorar lo que ocurre a continuación. Las señales creativas pueden informar la distribución. El rendimiento operativo puede moldear la experiencia de la audiencia. Los datos de compromiso pueden guiar el contenido y las decisiones comerciales futuras.
Cuando estas conexiones funcionan en toda la organización, el valor se acumula. Cada flujo de trabajo exitoso puede informar al siguiente, para ayudar a los equipos a aprender más rápido, colaborar de manera más eficaz y generar impulso para una transformación más amplia de la IA.
Tres casos de uso Frontier
1. Acelerar la creación y producción de contenido
Los equipos creativos se enfrentan a una demanda creciente de más contenido, más formatos y una adaptación más rápida entre mercados. Cuando se conectan los flujos de trabajo de modelos, datos, infraestructura, derechos y aprobación, la IA puede ampliar su capacidad creativa a lo largo del proceso desde la idea hasta la producción. Galleri5 de Collective Artists Network ilustra el potencial. La empresa informa que sus flujos de trabajo nativos de IA pueden reducir los plazos de producción de películas de alrededor de tres años a tres meses, acelerar el tiempo de lanzamiento al mercado de las series de televisión entre 5 y 10 veces y reducir los costes de producción cinematográfica entre un 60 y un 70% en comparación con los flujos de trabajo tradicionales de VFX y acción real.
Kantar muestra cómo este cambio va más allá de la creación hacia una evaluación rápida a gran escala. Tras reconstruir su plataforma de eficacia creativa Link AI en Azure, Kantar redujo la puntuación publicitaria de una duración de solo una hora a unos pocos minutos y evaluó decenas de miles de anuncios para un cliente global de bebidas en cuestión de horas. El objetivo no es la automatización por sí misma, sino eliminar fricciones para que los equipos puedan producir, probar y perfeccionar más posibilidades creativas mientras la gente se centra en las ideas que hacen que el contenido sea distintivo.
El viejo mundo de pasar meses en un puñado de anuncios ha desaparecido. El nuevo imperativo es crear y evaluar cientos o miles de versiones personalizadas con rapidez para diferentes plataformas digitales.
Ashok Kalidas Ph.D., Científico Jefe de IA, Kantar
2. Entregar contenido de manera fiable a través de todas las plataformas
Las organizaciones mediáticas deben llevar la intención creativa, los derechos, los metadatos y el contexto operativo a través de plataformas de difusión, streaming, digital, social y partners. La inteligencia conectada puede ayudar a los equipos a monitorizar flujos de trabajo, identificar problemas, adaptar las rutas de entrega y actuar más rápido a medida que cambian las condiciones. Sanoma utilizó Azure AI para sustituir una única actualización meteorológica nacional por pronósticos automatizados adaptados a 26 regiones, lo que hizo a la localización escalable a nivel económico, para proporcionar información más oportuna y relevante, y crear nuevas oportunidades para la publicidad local.
En las pruebas de audiencia, el 70% de los encuestados dijo que la voz sintética era muy adecuada para las noticias. Esto demuestra el valor empresarial de la orquestación: mayor resiliencia, consistencia y control, junto con nuevas oportunidades para servir a audiencias y mercados de forma más eficaz. También muestra cómo la innovación puede empoderar a los equipos para escalar experiencias de alta calidad sin crear más complejidad para quienes las gestionan.
3. Aumentar la inteligencia de audiencia y contenido
Conectar contenido, identidad, interacción, publicidad, comercio y señales de derechos puede mejorar el descubrimiento, la retención, las decisiones de campaña, el rendimiento de inventario y las propuestas directas a los fans. La Premier League ha comenzado a reunir más de 30 temporadas de estadísticas, 300.000 artículos, 9.000 vídeos y datos de partidos en directo para impulsar experiencias personalizadas. El Premier League Companion, impulsado por Microsoft Copilot, lleva ese contexto a una base global de aficionados de 1.800 millones de personas en 189 países.
Esta es una de las oportunidades más inspiradoras que se avecinan. Las organizaciones mediáticas pueden transformar archivos extensos y datos en vivo en experiencias más ricas y relevantes, lo que ayuda a las audiencias a conectar más a profundidad con los clubes, historias, creadores y momentos que les importan.
La oportunidad también va más allá de la personalización. Cuando las organizaciones conectan de manera responsable la inteligencia de audiencia y contenido, pueden fortalecer la participación, desbloquear nuevos enfoques de monetización y crear experiencias que continúan con el aprendizaje y mejoran con el tiempo.
En estos escenarios, Microsoft proporciona una capa abierta de orquestación a través de la producción, datos, contenido, distribución, audiencia y entornos empresariales existentes. Las organizaciones pueden conectar contexto y aprendizaje sin reemplazar todos los sistemas especializados que ya impulsan sus operaciones. La infraestructura en la nube, los datos, la IA, la seguridad, la gobernanza, las herramientas de productividad y un amplio ecosistema de socios trabajan juntos como base para flujos de trabajo coordinados a gran escala.
La colaboración es esencial para dar vida a esta base. Al trabajar junto a clientes, socios tecnológicos, creadores y líderes del sector, podemos conectar la experiencia especializada y las inversiones existentes con la innovación de Microsoft Cloud y la IA. Este enfoque colaborativo ayuda a las organizaciones a modernizarse a su propio ritmo, mientras construyen sobre las herramientas, el talento y las relaciones de confianza que ya poseen.
La confianza debe mantenerse central en ese camino. A medida que la IA se integra más a lo largo del ciclo de vida de los medios, la responsabilidad humana, la seguridad, los derechos, la privacidad y las prácticas responsables de IA deben avanzar junto con la innovación.
Esa base importa porque la transformación exitosa de la IA no es un conjunto de pilotos. Es un modelo operativo que puede reutilizar datos y gobernanza confiables, escalar prácticas exitosas, medir resultados a lo largo del ciclo de vida y aprender de forma continua.
Empiecen con un flujo de trabajo prioritario
En IBC 2026, Microsoft mostrará cómo clientes y socios conectan la inteligencia a través de la creación, operaciones, entrega y compromiso con la audiencia. Animo a los líderes a tener en mente un flujo de trabajo de alto valor, identificar dónde los datos y decisiones fragmentados limitan el rendimiento hoy en día y explorar cómo una capa de orquestación abierta y gobernada podría mejorar la velocidad, la resiliencia, el control, los ingresos o el valor de la audiencia.
Empezar con un reto empresarial significativo crea un camino práctico a seguir. Ofrece a los equipos la oportunidad de medir el impacto, aprender de la experiencia y generar la confianza y el impulso necesarios para transformar flujos de trabajo adicionales.
Lo que más me entusiasma es la oportunidad que tenemos por delante. Las organizaciones mediáticas siempre han transformado la manera en que las personas se conectan con las historias, la información, el deporte y el entretenimiento. La IA nos da la oportunidad de hacerlo de formas nuevas, mientras empodera a las personas cuya creatividad y experiencia hacen avanzar la industria.
La innovación y la responsabilidad deben avanzar juntas. Cuando combinamos creatividad humana, datos de confianza, IA y alianzas sólidas, podemos crear experiencias de audiencia más enriquecedoras, desbloquear nuevo valor empresarial y moldear un futuro más dinámico para los medios y el entretenimiento.
AWS Step Functions 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 Developer Guide, or see the full list of supported services at AWS SDK service integrations, and integration release history.
AWS Step Functions 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 Developer Guide, or see the full list of supported services at AWS SDK service integrations, and integration release history.
AWS 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.
AWS 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 AWS Direct Connect Flat-Rate Pricing, User Guide and Pricing Guide pages.
Today, 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 User Guide.
Today, 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 User Guide.
AWS 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 product page, or review the User Guide.
AWS 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 product page, or review the User Guide.
Today, Amazon SageMaker AI announces instance preference lists for training and processing jobs, making it easier and faster to find compute capacity for your workloads. Many AI training, fine-tuning, and data processing workloads run comparably well on any of several instance types or sizes. However, before now, you had to name only one instance type at the time of job submission and wait for SageMaker to find that specific instance for your job. For high-demand GPUs during peak periods, where wait times can be unpredictable, customers sometimes had to build complex retry logic or concurrently submit multiple jobs with different instance types to find the first available option. Now you can simply provide a prioritized list of the instance types your workload accepts, and SageMaker automatically runs your job on the first available configuration from your preferences. With this solution, your training or processing job will likely start sooner.
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 documentation or our launch blog.
Today, Amazon SageMaker AI announces instance preference lists for training and processing jobs, making it easier and faster to find compute capacity for your workloads. Many AI training, fine-tuning, and data processing workloads run comparably well on any of several instance types or sizes. However, before now, you had to name only one instance type at the time of job submission and wait for SageMaker to find that specific instance for your job. For high-demand GPUs during peak periods, where wait times can be unpredictable, customers sometimes had to build complex retry logic or concurrently submit multiple jobs with different instance types to find the first available option. Now you can simply provide a prioritized list of the instance types your workload accepts, and SageMaker automatically runs your job on the first available configuration from your preferences. With this solution, your training or processing job will likely start sooner.
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 documentation or our launch blog.
AWS 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 AWS CloudTrail documentation.
AWS 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 AWS CloudTrail documentation.
