We are pleased to announce general availability of Amazon EC2 G6e instances on SageMaker notebook instances.
Amazon EC2 G6e instances are powered by up to 8 NVIDIA L40s Tensor Core GPUs with 48 GB of memory per GPU and third generation AMD EPYC processors. G6e instances deliver up to 2.5x better performance compared to EC2 G5 instances. Customers can use G6e instances to interactively test model deployment and for interactive model training use cases such as generative AI fine-tuning. You can use G6e instances to deploy large language models (LLMs) with up to 13B parameters and diffusion models for generating images, video, and audio.
Amazon EC2 G6e instances are available on SageMaker notebook instances in the AWS US East (N. Virginia and Ohio), US West (Oregon), Asia Pacific (Tokyo), Middle East (Dubai) and Europe (Frankfurt, Sweden, Spain) regions.
We are pleased to announce general availability of Amazon EC2 G6e instances on SageMaker notebook instances.
Amazon EC2 G6e instances are powered by up to 8 NVIDIA L40s Tensor Core GPUs with 48 GB of memory per GPU and third generation AMD EPYC processors. G6e instances deliver up to 2.5x better performance compared to EC2 G5 instances. Customers can use G6e instances to interactively test model deployment and for interactive model training use cases such as generative AI fine-tuning. You can use G6e instances to deploy large language models (LLMs) with up to 13B parameters and diffusion models for generating images, video, and audio.
Amazon EC2 G6e instances are available on SageMaker notebook instances in the AWS US East (N. Virginia and Ohio), US West (Oregon), Asia Pacific (Tokyo), Middle East (Dubai) and Europe (Frankfurt, Sweden, Spain) regions.
Visit developer guides for instructions on setting up and using JupyterLab and CodeEditor applications on SageMaker Studio and SageMaker notebook instances.
Amazon Bedrock AgentCore Memory now enables cross-account access, allowing you to build multi-account architectures where memory resources and consuming agents span multiple AWS accounts. You can grant principals in one account permission to call memory data plane APIs against resources in another account using resource-based policies, and configure memory delivery destinations (Amazon S3, Amazon SNS, Amazon Kinesis Data Streams) that reside in a separate account.
Cross-account access is configured by attaching a resource-based policy to your memory resource. Once configured, principals in the consuming account can create events, write memory records, retrieve records, and perform semantic search by referencing the full memory ARN. Cross-account delivery destinations allow your memory resource to deliver payloads and stream events to S3 buckets, SNS topics, and Kinesis Data Streams in other accounts.
To get started, see Cross-account memory access in the Amazon Bedrock AgentCore Developer Guide. Amazon Bedrock AgentCore Memory cross-account access is available in all AWS Regions where Amazon Bedrock AgentCore Memory is supported.
Amazon Bedrock AgentCore Memory now enables cross-account access, allowing you to build multi-account architectures where memory resources and consuming agents span multiple AWS accounts. You can grant principals in one account permission to call memory data plane APIs against resources in another account using resource-based policies, and configure memory delivery destinations (Amazon S3, Amazon SNS, Amazon Kinesis Data Streams) that reside in a separate account.
Cross-account access is configured by attaching a resource-based policy to your memory resource. Once configured, principals in the consuming account can create events, write memory records, retrieve records, and perform semantic search by referencing the full memory ARN. Cross-account delivery destinations allow your memory resource to deliver payloads and stream events to S3 buckets, SNS topics, and Kinesis Data Streams in other accounts.
To get started, see Cross-account memory access in the Amazon Bedrock AgentCore Developer Guide. Amazon Bedrock AgentCore Memory cross-account access is available in all AWS Regions where Amazon Bedrock AgentCore Memory is supported.
AWS HealthOmics adds ephemeral storage for private workflows, giving bioinformatics workloads dedicated scratch space that delivers more consistent run performance and lower costs. Each workflow task now receives a dedicated local volume mounted at /tmp, and workflows that generate significant scratch data, such as genomic sequence alignment, BAM sorting, and variant calling, can experience faster run times. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed bioinformatics workflows.
With this launch, workflow tasks can write temporary data to their own local volume, keeping scratch I/O isolated from shared run storage that hosts the working directory. By default, each task includes 16 GiB of ephemeral storage at no additional charge. You can increase the amount of ephemeral storage allocated to individual tasks, up to a maximum of 3,072 GiB per task, using the appropriate directive in your WDL, Nextflow, or CWL workflow definition. You can enable ephemeral storage at runtime with the StartRun API. All ephemeral storage volumes are encrypted and deleted when a task terminates.
You can use ephemeral storage in all AWS Regions where AWS HealthOmics is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more about ephemeral storage, visit the AWS HealthOmics User Guide. For more information on pricing, visit AWS HealthOmics pricing.
AWS HealthOmics adds ephemeral storage for private workflows, giving bioinformatics workloads dedicated scratch space that delivers more consistent run performance and lower costs. Each workflow task now receives a dedicated local volume mounted at /tmp, and workflows that generate significant scratch data, such as genomic sequence alignment, BAM sorting, and variant calling, can experience faster run times. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed bioinformatics workflows.
With this launch, workflow tasks can write temporary data to their own local volume, keeping scratch I/O isolated from shared run storage that hosts the working directory. By default, each task includes 16 GiB of ephemeral storage at no additional charge. You can increase the amount of ephemeral storage allocated to individual tasks, up to a maximum of 3,072 GiB per task, using the appropriate directive in your WDL, Nextflow, or CWL workflow definition. You can enable ephemeral storage at runtime with the StartRun API. All ephemeral storage volumes are encrypted and deleted when a task terminates.
You can use ephemeral storage in all AWS Regions where AWS HealthOmics is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more about ephemeral storage, visit the AWS HealthOmics User Guide. For more information on pricing, visit AWS HealthOmics pricing.
Replantear las operaciones en la nube con observabilidad agéntica
Por: Brendan Burns, investigador técnico y CVP, Azure Cloud Native y Plataforma de Gestión.
Las operaciones en la nube han comenzado a entrar en una nueva era, ya que los agentes autónomos e impulsados por IA se han comenzado a convertir en una parte más importante de los sistemas de software modernos. A medida que el software se vuelve cada vez más agéntico, el reto ya no es solo gestionar una mayor escala y complejidad. Los operadores también deben lidiar con sistemas que evolucionan más rápido, actúan de manera más autónoma e interactúan a través de una red de dependencias en expansión.
A medida que las aplicaciones, modelos, APIs e infraestructuras se vuelven cada vez más interconectados, su comportamiento es más difícil de entender de principio a fin. Los sistemas ya no fallan de manera aislada. Fallan a través de interacciones entre dependencias, servicios y entornos que cambian de manera constante y en tiempo real.
Para ayudar a las organizaciones a operar con eficacia en estos entornos cada vez más dinámicos, hoy anunciamos la disponibilidad general del Agente de Observabilidad Azure Copilot (Azure Copilot Observability Agent). Construido sobre Microsoft Azure Monitor, que correlaciona señales entre agentes, aplicaciones, infraestructuras y servicios para proporcionar el contexto necesario para operar con confianza en este nuevo entorno.
La observabilidad se vuelve fundamental en un mundo agencial
En una encuesta reciente a 250 responsables de la toma de decisiones de TI, Microsoft y Material encontraron que el 84% de las organizaciones informa de un aumento en la complejidad de la nube, y el 69% afirma que supera su modelo operativo actual. El impacto es más agudo en seguridad, gestión de costes y rendimiento, y se extiende a todo el ciclo de vida de las operaciones.
A medida que el ritmo y la escala del cambio se aceleran, ningún individuo o equipo puede mantener de manera realista el contexto completo necesario para diagnosticar y resolver los problemas con suficiente rapidez. Esto impulsa un cambio hacia operaciones agénticas, donde la inteligencia mejora la manera en que se entienden y gestionan los sistemas.
La observabilidad es fundamental para este cambio. Proporciona la comprensión en tiempo real del comportamiento del sistema de la que dependen los agentes para razonar, adaptarse y actuar. Sin una vista conectada entre señales, incluso los agentes más avanzados carecen del contexto necesario para operar de forma fiable.
Desde las señales hasta la resolución con el Agente de Observabilidad
Diseñamos el Agente de Observabilidad para ayudar a los operadores a avanzar más rápido de la detección a la comprensión. Conecta logs, métricas, trazas, topología y contexto operativo entre entornos, para reducir el tiempo que tarda en identificar la causa raíz de un problema.
A medida que la telemetría se extiende entre sistemas, los operadores a menudo se ven obligados a recomponer el contexto de múltiples herramientas. El Agente de Observabilidad aborda esta fragmentación al razonar a través de señales en tiempo real y unificar ese contexto en una única vista operativa. Estas capacidades agénticas se integran directo en los flujos de trabajo existentes, para ayudar a los equipos a avanzar más rápido de la investigación a la resolución con una visión clara y accionable.
Ya hemos comenzado a ver que los clientes utilizan el Agente de Observabilidad para reducir el esfuerzo manual, acelerar la resolución de incidentes y mejorar la claridad operativa:
«¡El mayor valor es la velocidad! El Agente de Observabilidad [Azure Copilot] nos ayuda a resolver incidentes más rápido y reducir la carga operativa al convertir logs, métricas y trazas en conocimientos en lenguaje sencillo. Estos agentes realizan investigaciones profundas y proporcionan recomendaciones de remediación casi de inmediato, en comparación con horas o incluso días antes.
Desde que adoptamos estas capacidades, hemos recuperado unas 250 horas mensuales de ingeniería que ahora se redirigen a dar soporte a nuevas aplicaciones y funcionalidades. Podemos usar el lenguaje natural para detectar, diagnosticar y remediar problemas más rápido que nunca.»
— Narmada Krishnaswamy, jefe de soporte y operaciones de aplicaciones de auditoría de KPMG
«Azure Copilot Observability Agent nos ayudó a pasar de la búsqueda manual de incidentes a investigaciones más rápidas guiadas por IA. Para PolicyVault, recopila la telemetría de nuestro servicio, la correlaciona con la salud de los recursos de Azure y nos da pasos siguientes accionables basados en la investigación. Eso significa que no solo vemos lo que se rompió; tenemos una idea mucho más clara de por qué ocurrió y qué hacer al respecto, lo que nos ahorra mucho tiempo durante los incidentes.»
— Vladimir Gusarov, fundador y CEO, PolicyVault
«El Agente de Observabilidad de Azure Copilot nos ayuda a avanzar más rápido de la señal a la visión. Al reunir nuestra telemetría y guiarnos hacia las causas raíz probables, se reduce el tiempo y esfuerzo necesarios para investigar incidentes y mantenemos a nuestros equipos centrados en lo que realmente importa.»
— Theus Hossmann, director de tecnología en Ontinue
Más allá de mejorar la respuesta a incidentes, este cambio refleja un nuevo enfoque de las operaciones en la nube, donde los sistemas pueden razonar de manera continua a través de señales y actuar en base a ese entendimiento.
Desde la observabilidad hasta las operaciones agénticas a lo largo del ciclo de vida de la nube
La observabilidad forma parte de un cambio más amplio hacia operaciones agénticas. A medida que los sistemas se vuelven más autónomos, las operaciones pasan de entender lo que ocurre en producción a mejorar de manera continua el comportamiento de esos sistemas con el tiempo.
En un modelo agéntico, esto forma un ciclo de vida. Los sistemas generan señales, los agentes interpretan esas señales, actúan y aprenden de los resultados. Con el tiempo, esto crea un bucle de retroalimentación en el que cada ciclo operativo mejora el siguiente, lo que aumenta la resiliencia y eficiencia del sistema.
Este cambio requiere más que una mejor visibilidad. Requiere un enfoque coordinado a lo largo de todo el ciclo de vida, desde la observabilidad y el diagnóstico hasta la optimización y remediación, donde la visión y la acción están conectadas de manera estrecha.
A medida que los agentes asumen un papel más importante en ese ciclo de vida, la gobernanza se convierte en central en la forma en que los sistemas son confiables y controlados. La política, la auditoría y los límites aseguran que las acciones tomadas por los agentes estén alineadas con la intención organizativa y operen dentro de límites definidos. La supervisión humana es todavía esencial, no como cuello de botella, sino como mecanismo para generar confianza y garantizar la fiabilidad a medida que la automatización crece.
Aquí es donde Azure está posicionado de forma única. Al combinar observabilidad, automatización y gobernanza dentro de una plataforma conectada, Azure permite a las organizaciones pasar de herramientas aisladas a un modelo operativo integrado que abarca todo el ciclo de vida.
Azure Copilot Observability Agent desempeña un papel clave en este modelo al fundamentar los sistemas agénticos en un contexto operativo en tiempo real. A medida que las organizaciones construyen y despliegan más agentes, esta base se vuelve fundamental para garantizar que esos sistemas funcionen de manera eficaz y responsable.
Las operaciones en la nube pasan de una gestión reactiva a un ciclo de vida continuo e impulsado por agentes, de aprendizaje, adaptación y control. Esta visión de las operaciones en la nube agente ya ha comenzado a tomar forma en Azure. Lean nuestra entrada complementaria en Azure Blog para más detalles.
Brendan Burns es cofundador del proyecto de código abierto Kubernetes y vicepresidente corporativo de Azure cloud-native open source y de la plataforma de gestión de Azure, incluido Azure Arc. También es autor y coautor de varios libros sobre Kubernetes y sistemas distribuidos.
Banco Provincia concretó una alianza estratégica con Microsoft para desarrollar el uso de la Inteligencia Artificial
Banco Provincia y Microsoft firmaron un acuerdo estratégico de colaboración para trabajar en iniciativas de datos, inteligencia artificial y ciberseguridad con el objetivo de modernizar las operaciones del banco, desarrollar capacidades digitales, fortalecer la toma de decisiones e impulsar su transformación digital.
Buenos Aires, Argentina – Banco Provincia y Microsoft firmaron un acuerdo estratégico de colaboración que marca un nuevo hito en el proceso de modernización tecnológica del banco. A través de esta alianza, ambas organizaciones reforzarán el plan de trabajo conjunto en proyectos orientados a potenciar la productividad, fortalecer la ciberseguridad y acelerar la adopción de inteligencia artificial como motor de innovación en distintas áreas clave de la institución.
El acuerdo pone el foco en potenciar el uso estratégico de los datos como motor de innovación, facilitando su acceso y gestión para generar mayor valor, así como para impulsar el uso de inteligencia artificial y tecnologías de IA generativa que permitan analizar grandes volúmenes de información, identificar patrones y acelerar la toma de decisiones.
Además, se avanzará en la incorporación de soluciones de automatización, realidad mixta y aplicaciones de IA para optimizar operaciones, mejorar la eficiencia y reforzar la seguridad, al tiempo que trabajarán en la integración de entornos de TI y operaciones para habilitar análisis en tiempo real y procesos más ágiles, resilientes y sostenibles.
El acuerdo incluye también la colaboración en el desarrollo de capacidades de seguridad, privacidad y ciberseguridad, con foco en la protección de los activos digitales y la construcción de un entorno confiable que apoye la innovación y la colaboración.
“La alianza con Microsoft nos permitirá mejorar aún más la experiencia de nuestros 10,5 millones de clientes y fortalecer la labor de nuestros 10.100 mil trabajadores con herramientas que potencian la productividad, la seguridad y la colaboración”, expresó Juan Cuattromo, presidente de Banco Provincia. Y añadió: “Durante nuestros primeros cuatro años de gestión renovamos todo el ecosistema digital de la institución y, desde 2023, logramos avances muy significativos en la implementación de la inteligencia artificial. Con este acuerdo pensamos expandir aún más nuestras fronteras tecnológicas para seguir construyendo una banca más innovadora, sin perder nuestra impronta de cercanía”.
Por su parte Sebastián Aveille, Country Manager de Microsoft Argentina, señaló: “Vivimos una redefinición profunda del trabajo y de los servicios financieros, y la inteligencia artificial es uno de los grandes catalizadores de ese cambio. Estamos orgullosos de este acuerdo por el que podremos acompañar a Banco Provincia en su proceso de transformación, siempre poniendo la tecnología al servicio de las personas.”
El acuerdo fortalece el vínculo entre ambas organizaciones, que ya trabajaron juntas en diferentes proyectos. El más relevante fue la implementación de PIA, un agente de IA que funciona dentro del home banking y les permite a las personas realizar diversas operaciones y consultas utilizando lenguaje natural desde un chat.
Además de Juan Cuattromo y Sebastián Aveille, participaron de la firma por parte de Microsoft Natalia García, Directora para Servicios Financieros de Microsoft Argentina y Natalia Torres, Directora de Comunicaciones para Sudamérica. Asimismo, por parte del Banco estuvieron presentes integrantes del Directorio y la Gerencia General.
The post Banco Provincia concretó una alianza estratégica con Microsoft para desarrollar el uso de la Inteligencia Artificial appeared first on Source LATAM.
Today, AWS announces new automated refinement workflows for Automated Reasoning checks in Amazon Bedrock Guardrails. Automated Reasoning checks use formal logic to mathematically validate the accuracy of generative AI responses against a policy you define, helping detect hallucinations and provide verifiable explanations. The quality of validation results depends on how well a policy is defined. The new workflows help customers improve their policies with less manual effort, leading to more reliable Guardrail validation results.
The launch introduces two refinement workflows. With the iterative policy improvement workflow, customers who have created natural language tests for a policy can start an iterative refinement run, letting the system deduce the changes needed for the policy to pass those tests. With the ambiguity reduction workflow, customers who frequently encounter ambiguous translation results can run the resolve policy ambiguities workflow to automatically refine variable descriptions and type definitions, reducing how often ambiguous translations occur. Both workflows are available through the Amazon Bedrock APIs and in the AWS Management Console, where customers can start a workflow by choosing Refine policy on the policy page.
Today, AWS announces new automated refinement workflows for Automated Reasoning checks in Amazon Bedrock Guardrails. Automated Reasoning checks use formal logic to mathematically validate the accuracy of generative AI responses against a policy you define, helping detect hallucinations and provide verifiable explanations. The quality of validation results depends on how well a policy is defined. The new workflows help customers improve their policies with less manual effort, leading to more reliable Guardrail validation results.
The launch introduces two refinement workflows. With the iterative policy improvement workflow, customers who have created natural language tests for a policy can start an iterative refinement run, letting the system deduce the changes needed for the policy to pass those tests. With the ambiguity reduction workflow, customers who frequently encounter ambiguous translation results can run the resolve policy ambiguities workflow to automatically refine variable descriptions and type definitions, reducing how often ambiguous translations occur. Both workflows are available through the Amazon Bedrock APIs and in the AWS Management Console, where customers can start a workflow by choosing Refine policy on the policy page.
These workflows are available in all AWS Regions where Automated Reasoning checks in Amazon Bedrock Guardrails are available. To learn more, visit the Amazon Bedrock Guardrails product page and the Automated Reasoning checks User Guide.
AWS Transform for migrations now supports all AWS commercial regions as migration targets. A migration target region is the AWS region where migrated resources are deployed, including landing zones, network infrastructure, and server rehosting. Customers can now deploy workloads in any commercial region, making it easier to meet data residency requirements.
The new migration target regions are: US East (N. California), Africa (Cape Town), Asia Pacific (Bangkok), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Asia Pacific (Jakarta), Asia Pacific (Kuala Lumpur), Asia Pacific (Melbourne), Asia Pacific (New Zealand), Asia Pacific (Taipei), Canada (Calgary), Europe (Milan), Europe (Spain), Europe (Zurich), Mexico (Querétaro) and Middle East (Tel Aviv).
Target region selection is available in the AWS Transform for migrations workflow. For the most up-to-date availability information, see the supported migration target region list.
AWS Transform for migrations now supports all AWS commercial regions as migration targets. A migration target region is the AWS region where migrated resources are deployed, including landing zones, network infrastructure, and server rehosting. Customers can now deploy workloads in any commercial region, making it easier to meet data residency requirements. The new migration target regions are: US East (N. California), Africa (Cape Town), Asia Pacific (Bangkok), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Asia Pacific (Jakarta), Asia Pacific (Kuala Lumpur), Asia Pacific (Melbourne), Asia Pacific (New Zealand), Asia Pacific (Taipei), Canada (Calgary), Europe (Milan), Europe (Spain), Europe (Zurich), Mexico (Querétaro) and Middle East (Tel Aviv). Target region selection is available in the AWS Transform for migrations workflow. For the most up-to-date availability information, see the supported migration target region list.
AWS HealthOmics now supports Nextflow profiles, enabling customers to activate predefined execution settings at run time. Nextflow profiles allow customers to define reusable settings and select them at the point of execution, making it easy to switch between execution settings without modifying workflow source code. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
With Nextflow profiles, you can cleanly separate platform-specific settings such as resource limits or execution options from core workflow logic. You can switch between development and production settings without creating separate workflow definitions. This reduces errors from manual edits, accelerates workflow portability, and saves time when scaling from development to production. If you use nf-core workflows, you can now activate the built-in and institutional profiles those pipelines already ship with.
You can now specify one or more Nextflow profiles in your workflow runs in all AWS HealthOmics Regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more, visit the Nextflow Profiles section on HealthOmics Nextflow engine settings documentation.
AWS HealthOmics now supports Nextflow profiles, enabling customers to activate predefined execution settings at run time. Nextflow profiles allow customers to define reusable settings and select them at the point of execution, making it easy to switch between execution settings without modifying workflow source code. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
With Nextflow profiles, you can cleanly separate platform-specific settings such as resource limits or execution options from core workflow logic. You can switch between development and production settings without creating separate workflow definitions. This reduces errors from manual edits, accelerates workflow portability, and saves time when scaling from development to production. If you use nf-core workflows, you can now activate the built-in and institutional profiles those pipelines already ship with.
You can now specify one or more Nextflow profiles in your workflow runs in all AWS HealthOmics Regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more, visit the Nextflow Profiles section on HealthOmics Nextflow engine settings documentation.
AWS introduces Lambda MicroVMs, a new serverless compute primitive that provides VM-level isolation, near-instant launch and resume speeds, and state preservation for executing user or AI-generated code. You can now give each user or job their own compute environment to securely run code without managing virtualization infrastructure or choosing between isolation, speed, and state retention.
Developers are increasingly building multi-tenant applications that execute code supplied by end users or AI for use cases such as interactive coding environments, data analytics platforms, coding assistants, and vulnerability scanning platforms. For these applications, developers need to allocate a separate, isolated execution environment per user or session to limit the impact of incorrect or malicious code on other concurrently running users or jobs. Previously, developers needed to choose between strong isolation, fast launch times, and state retention when building these applications. Starting today, Lambda MicroVMs provides you these capabilities without any trade-offs. You get VM-level isolation, near-instant launch speeds, and the ability to suspend and resume execution for up to 8 hours. Lambda MicroVMs is built on Firecracker virtualization, the technology powering more than 15 trillion monthly Lambda Function invocations.
To get started, create a MicroVM image from your Dockerfile, then launch MicroVMs from that image. Give each user or job their own MicroVM with a dedicated HTTPS URL that supports popular connectivity protocols such as HTTP/2, gRPC, and WebSockets.
Lambda MicroVMs is available today in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To learn more, visit the AWS Lambda MicroVMs developer guide and the launch blog post. Get started with MicroVMs through the AWS Lambda console, AWS CloudFormation, AWS Cloud Development Kit, or use the Agent Toolkit for AWS with your preferred Agentic development tools. You pay for baseline compute resources while your MicroVM is running, and only for the active duration of additional resources consumed when your workload exceeds the baseline. To learn more about pricing, see Lambda MicroVMs pricing.
AWS introduces Lambda MicroVMs, a new serverless compute primitive that provides VM-level isolation, near-instant launch and resume speeds, and state preservation for executing user or AI-generated code. You can now give each user or job their own compute environment to securely run code without managing virtualization infrastructure or choosing between isolation, speed, and state retention.
Developers are increasingly building multi-tenant applications that execute code supplied by end users or AI for use cases such as interactive coding environments, data analytics platforms, coding assistants, and vulnerability scanning platforms. For these applications, developers need to allocate a separate, isolated execution environment per user or session to limit the impact of incorrect or malicious code on other concurrently running users or jobs. Previously, developers needed to choose between strong isolation, fast launch times, and state retention when building these applications. Starting today, Lambda MicroVMs provides you these capabilities without any trade-offs. You get VM-level isolation, near-instant launch speeds, and the ability to suspend and resume execution for up to 8 hours. Lambda MicroVMs is built on Firecracker virtualization, the technology powering more than 15 trillion monthly Lambda Function invocations.
To get started, create a MicroVM image from your Dockerfile, then launch MicroVMs from that image. Give each user or job their own MicroVM with a dedicated HTTPS URL that supports popular connectivity protocols such as HTTP/2, gRPC, and WebSockets.
Lambda MicroVMs is available today in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). To learn more, visit the AWS Lambda MicroVMs developer guide and the launch blog post. Get started with MicroVMs through the AWS Lambda console, AWS CloudFormation, AWS Cloud Development Kit, or use the Agent Toolkit for AWS with your preferred Agentic development tools. You pay for baseline compute resources while your MicroVM is running, and only for the active duration of additional resources consumed when your workload exceeds the baseline. To learn more about pricing, see Lambda MicroVMs pricing.
AWS Outposts now provides self-service capabilities for configuration, quoting, ordering, subscription management, renewal, and decommissioning directly from the AWS Management Console, CLI, and API. Previously, customers relied on AWS teams for managing their Outposts lifecycle, from evaluation through end of term.
A new configuration and quoting tool generates real-time cost estimates across payment options and term lengths, and proactively surfaces account and regional constraints before order submission. Quotes are generated in seconds and can be converted to orders directly in the console, for both new deployments and capacity additions. Subscription details, including term dates and billing, are now available in the console and programmatically, eliminating the need to contact AWS for contract information. When your term approaches its end date, self-service workflows let you renew with a new term and payment option, or decommission your Outpost through a guided workflow that handles resource cleanup.
These features are available in all commercial AWS Regions that support AWS Outposts. To learn more, refer to the Launch Blog.
AWS Outposts now provides self-service capabilities for configuration, quoting, ordering, subscription management, renewal, and decommissioning directly from the AWS Management Console, CLI, and API. Previously, customers relied on AWS teams for managing their Outposts lifecycle, from evaluation through end of term. A new configuration and quoting tool generates real-time cost estimates across payment options and term lengths, and proactively surfaces account and regional constraints before order submission. Quotes are generated in seconds and can be converted to orders directly in the console, for both new deployments and capacity additions. Subscription details, including term dates and billing, are now available in the console and programmatically, eliminating the need to contact AWS for contract information. When your term approaches its end date, self-service workflows let you renew with a new term and payment option, or decommission your Outpost through a guided workflow that handles resource cleanup. These features are available in all commercial AWS Regions that support AWS Outposts. To learn more, refer to the Launch Blog.
