AWS Storage Gateway Terraform modules now enable Amazon Linux 2023-based deployments, delivering improved security, reliability, and operational simplicity for Infrastructure as Code (IaC) provisioning. The updated modules support all gateway types including Amazon S3 File Gateway, Tape Gateway, and Volume Gateway in both Amazon EC2 and VMware environments.
You can use the new Terraform modules to deploy AL2023-based gateways that enforce IMDSv2 by default for EC2 deployments, protecting against credential theft and server-side request forgery (SSRF) attacks. The update prevents unexpected gateway replacements during routine Terraform operations and simplifies Active Directory integration with optional domain controller configuration. EC2-based gateways now support optional Elastic IP address (EIP) association, enabling fully private gateway activations.
To get started, download the Terraform Storage Gateway module. To learn more, visit the AWS Storage Gateway product page or the Storage Gateway User Guide. See the AWS Region Table for complete regional availability.
Starting today, the general-purpose Amazon EC2 M8a instances are available in AWS GovCloud (US-West) region. M8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to M7a instances.
M8a instances deliver 45% more memory bandwidth compared to M7a instances, making these instances ideal for even latency sensitive workloads. M8a instances deliver even higher performance gains for specific workloads. M8a instances are up to 60% faster for GroovyJVM benchmark, and up to 39% faster for Cassandra benchmark compared to Amazon EC2 M7a instances. M8a instances are SAP-certified and offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements.
M8a instances are built using the latest sixth generation AWS Nitro Cards and ideal for applications that benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, mid-size data stores, application development environments, and caching fleets.
To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 M8a instance page.
Starting today, the general-purpose Amazon EC2 M8a instances are available in AWS Europe (Ireland) region. M8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to M7a instances.
M8a instances deliver 45% more memory bandwidth compared to M7a instances, making these instances ideal for even latency sensitive workloads. M8a instances deliver even higher performance gains for specific workloads. M8a instances are up to 60% faster for GroovyJVM benchmark, and up to 39% faster for Cassandra benchmark compared to Amazon EC2 M7a instances. M8a instances are SAP-certified and offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements.
M8a instances are built using the latest sixth generation AWS Nitro Cards and ideal for applications that benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, mid-size data stores, application development environments, and caching fleets.
To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 M8a instance page.
Amazon Elastic Compute Cloud (Amazon EC2) R8gd instances with up to 11.4 TB of local NVMe-based SSD block-level storage are now available in US West (N. California), Asia Pacific (Seoul, Hong Kong, Jakarta), Africa (Cape Town), and Canada West (Calgary) AWS Regions. These instances are powered by AWS Graviton4 processors, delivering up to 30% better performance over Graviton3-based instances. They have up to 40% higher performance for I/O intensive database workloads, and up to 20% faster query results for I/O intensive real-time data analytics than comparable AWS Graviton3-based instances. These instances are built on the AWS Nitro System and are a great fit for applications that need access to high-speed, low latency local storage.
Each instance is available in 12 different sizes. They provide up to 50 Gbps of network bandwidth and up to 40 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). Additionally, customers can now adjust the network and
Amazon EBS bandwidth on these instances by 25% using EC2 instance bandwidth weighting configuration, providing greater flexibility with the allocation of bandwidth resources to better optimize workloads. These instances offer Elastic Fabric Adapter (EFA) networking on 24xlarge, 48xlarge, metal-24xl, and metal-48xl sizes.
To learn more, see Amazon R8gd Instances. To explore how to migrate your workloads to Graviton-based instances, see AWS Graviton Fast Start program and Porting Advisor for Graviton. To get started, see the AWS Management Console.
The AWS Advanced JDBC Wrapper now supports automatically caching JDBC queries with Valkey, including Amazon ElastiCache for Valkey caches. Previously, developers who needed to cache JDBC query result sets had to manually write code to store and retrieve data from the cache for each query. Now you can automatically cache result sets from your Aurora and RDS PostgreSQL, MySQL, and MariaDB databases in just a few short steps. Simply add the wrapper dependency, enable the query cache plugin, configure database and cache endpoints, and indicate which queries to cache in your application code.
With this capability, you can store and retrieve query results directly from ElastiCache for Valkey, reducing the number of database reads and lowering read latency for frequently accessed data. Automated query caching can improve performance, lower costs, and increase application resilience by reducing database resource requirements. The AWS Advanced JDBC Wrapper supports annotating queries for caching using popular persistence APIs and frameworks including Hibernate and Spring Data, as well as manual query hinting.
JDBC query caching with the AWS Advanced JDBC Wrapper works seamlessly with Amazon ElastiCache for Valkey. You can create a new Amazon ElastiCache for Valkey serverless cache with the AWS Management Console, Software Development Kit (SDK), Command Line Interface (CLI), or Model Context Protocol (MCP) server. For more information, see the Advanced JDBC Wrapper and Amazon ElastiCache for Valkey documentation.
AWS AppConfig enhances its deployment capabilities with new controls that allow customers to target feature flag and configuration data values to specific segments or individual users during the lifecycle of a gradual roll-out.
One of AWS AppConfig’s key safety guardrails is the ability for customers to roll out feature flag or configuration data changes slowly, over the course of minutes or hours. This progressive delivery allows customers to move safer, and limit the impact of unexpected changes. AWS AppConfig uses customer-provided entity identifiers to make specific feature flag or dynamic configuration data “sticky” to individual target segments during the lifecycle of these gradual roll-outs. This targeting capability, using AppConfig Agent, ensures fine-grained control, including using an individual user ID or IDs, while updates are being deployed.
AWS Lambda increases the file descriptor limit from 1,024 to 4,096, a 4x increase, for functions running on Lambda Managed Instances (LMI). This capability enables customers to run I/O intensive workloads such as high-concurrency web services, and file-heavy data processing pipelines, without running into file descriptor limits. LMI enables you to run Lambda functions on managed Amazon EC2 instances with built-in routing, load-balancing, and auto-scaling, giving you access to specialized compute configurations including the latest-generation processors and high-bandwidth networking, with no operational overhead.
Customers use Lambda functions to build a wide range of serverless applications such as event-driven workloads, web applications, and AI-driven workflows. These applications rely on file descriptors for operations such as opening files, establishing network socket connections to external services and databases, and managing concurrent I/O streams for data processing. Each open file, network socket, or internal resource consumes one file descriptor. Today, Lambda supports a maximum of 1,024 file descriptors. However, LMI allows multiple requests to be processed simultaneously, which often requires higher number of file descriptors. With this launch, AWS Lambda is increasing the file descriptor limit to 4,096, allowing customers to run I/O intensive workloads, maintain larger connection pools, and effectively utilize multi-concurrency for functions running on LMI.
This feature is available in all AWS Regions where AWS Lambda Managed Instances is generally available. To get started, visit the AWS Lambda Managed Instances documentation.
Today we are announcing the release of the Aurora DSQL Connector for Ruby (pg gem) that makes it easy to build Ruby applications on Aurora DSQL. The Ruby Connector streamlines authentication and eliminates security risks associated with traditional user-generated passwords by automatically generating tokens for each connection, ensuring valid tokens are always used while maintaining full compatibility with existing pg gem features.
The connector handles IAM token generation, SSL configuration, and connection pooling, enabling customers to scale from simple scripts to production workloads without changing their authentication approach. It also provides opt-in optimistic concurrency control (OCC) retry with exponential backoff, custom IAM credential providers, and AWS profile support, giving customers flexibility in how they manage their AWS credentials and handle transient failures.
To get started, visit the Connectors for Aurora DSQL documentation page. For code examples, visit our Github page for the Ruby connector. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about Aurora DSQL, visit the webpage.
Amazon Web Services (AWS) announces the availability of Amazon EC2 I8ge instances in Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Malaysia), Asia Pacific (Singapore), and Asia Pacific (Sydney) AWS regions. I8ge instances are powered by AWS Graviton4 processors to deliver up to 60% better compute performance compared to previous generation Graviton2-based storage optimized Amazon EC2 instances. I8ge instances use the third generation AWS Nitro SSDs, local NVMe storage that delivers up to 55% better real-time storage performance per TB. They offer up to 60% lower storage I/O latency and up to 75% lower storage I/O latency variability compared to previous generation Im4gn instances.
I8ge instances are storage-optimized instances offering up to 120TB of locally attached NVMe storage. They are ideal for workloads that demand rapid local storage with high random read/write performance and consistently low latency for accessing large datasets. These versatile instances are offered in eleven different sizes including two metal sizes, providing flexibility to match customers’ computational needs. They deliver up to 180 Gbps of network performance bandwidth and 60 Gbps of dedicated bandwidth for Amazon Elastic Block Store (EBS), ensuring fast and efficient data transfer for the most demanding applications.
To begin your Graviton journey, visit the Level up your compute with AWS Graviton page. To get started, see AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs. To learn more, visit the I8ge instances page.
AWS Parallel Computing Service (AWS PCS) now supports additional Slurm configuration settings for slurmdbd and cgroups, enabling you to fine-tune accounting behavior and resource isolation directly through the AWS PCS console, CLI, or SDK. This feature helps you implement production-ready HPC environments with enhanced privacy controls, flexible data retention policies, and improved resource management.
Using slurmdbd settings, you can configure how Slurm accounting operates on your cluster—including privacy controls, data retention policies, and workload tracking capabilities. With cgroups support, you can prevent resource oversubscription by binding CPU cores, enforce memory limits to maintain node stability, and control device access to ensure workloads run within defined boundaries.
AWS PCS is a managed service that simplifies running and scaling HPC workloads on AWS using Slurm. You can build complete, elastic environments that integrate compute, storage, networking, and visualization tools, while the service handles cluster operations with managed updates and built-in observability features.
This feature is available in all AWS Regions where AWS PCS is available. You can configure these settings when creating a new cluster or by modifying an existing cluster. To learn more, see the AWS PCS User Guide.
Today we're announcing Research and Engineering Studio (RES) on AWS 2026.03, which introduces new administrator controls, expanded filesystem support, and session management improvements.
Research and Engineering Studio on AWS (RES) is an open source, easy-to-use web-based portal for administrators to create and manage secure cloud-based research and engineering environments. Using RES, scientists and engineers can visualize data and run interactive applications without the need for cloud expertise.
RES 2026.03 gives administrators more flexibility in configuring and managing their environments. Admins can now onboard multiple individual FSx for ONTAP volumes as RES filesystems. Admins can also configure DCV token expiration time, which is useful for enabling session files with longer durations, and add up to three custom links on the RES login page for resources such as account management pages, help documentation, or usage policy pages.
Version 2026.03 also improves the experience for both admins and users around virtual desktop sessions. Admins can now restart VDIs in an error state directly from the Sessions page, helping resolve launch issues with less user intervention. Users can reset a VDI session schedule back to the system default with a single button. This version also includes assorted bug fixes and performance improvements.
This release is available in all AWS Regions where RES is available. To learn more about RES 2026.03, including detailed release notes and deployment instructions, visit the Research and Engineering Studio documentation or check out the RES GitHub repository.
Today we're announcing Research and Engineering Studio (RES) on AWS 2026.03, which introduces new administrator controls, expanded filesystem support, and session management improvements.
Research and Engineering Studio on AWS (RES) is an open source, easy-to-use web-based portal for administrators to create and manage secure cloud-based research and engineering environments. Using RES, scientists and engineers can visualize data and run interactive applications without the need for cloud expertise.
RES 2026.03 gives administrators more flexibility in configuring and managing their environments. Admins can now onboard multiple individual FSx for ONTAP volumes as RES filesystems. Admins can also configure DCV token expiration time, which is useful for enabling session files with longer durations, and add up to three custom links on the RES login page for resources such as account management pages, help documentation, or usage policy pages.
Version 2026.03 also improves the experience for both admins and users around virtual desktop sessions. Admins can now restart VDIs in an error state directly from the Sessions page, helping resolve launch issues with less user intervention. Users can reset a VDI session schedule back to the system default with a single button. This version also includes assorted bug fixes and performance improvements.
This release is available in all AWS Regions where RES is available. To learn more about RES 2026.03, including detailed release notes and deployment instructions, visit the Research and Engineering Studio documentation or check out the RES GitHub repository.
Starting today, customers can deploy their Graviton-based and GPU-accelerated workloads on Amazon Elastic Container Service (Amazon ECS) Managed Instances in a Federal Information Processing Standard (FIPS) compliant mode in the AWS GovCloud (US) Regions. FIPS is a U.S. and Canadian government standard that specifies the security requirements for cryptographic modules that protect sensitive information.
In the AWS GovCloud (US) Regions, Amazon ECS Managed Instances automatically enable FIPS compliance by default. ECS Managed Instances communicate through FIPS-compliant endpoints, use appropriately configured cryptographic modules, and boot the underlying kernel in FIPS mode. Customers with federal compliance requirements can run workloads with FIPS-validated cryptographic modules across a broad range of instance types, including Graviton-based, GPU-accelerated, network-optimized, and burstable performance instances.
To learn more about FIPS, refer to FIPS on AWS and AWS Fargate Federal Information Processing Standard (FIPS-140). To get started with ECS Managed Instances, use the AWS Console, Amazon ECS MCP Server, ECS Express Mode, or your favorite infrastructure-as-code tooling to enable it in a new or existing Amazon ECS cluster. You will be charged for the management of compute provisioned, in addition to your regular Amazon EC2 costs. To learn more about ECS Managed Instances, visit the feature page, documentation, and AWS News launch blog.
Today, AWS announces the ability to remotely connect from Kiro and Cursor IDEs to Amazon SageMaker Studio. This new capability allows data scientists, ML engineers, and developers to leverage their Kiro and Cursor setup - including its spec-driven development, conversational coding, and automated feature generation capabilities - while accessing the scalable compute resources of Amazon SageMaker Studio. By connecting Kiro and Cursor to SageMaker Studio using the AWS Toolkit extension, you can eliminate context switching between your local IDE and cloud infrastructure, maintaining your existing agentic development workflows within a single environment for all your AWS analytics and AI/ML services.
SageMaker Studio, offers a broad set of fully managed cloud interactive development environments (IDE), including JupyterLab and Code Editor based on Code-OSS (Open-Source Software), and VS Code IDE as remote IDE. Starting today, you can also use your customized local Kiro and Cursor setup - complete with specs, steering files, and hooks - while accessing your compute resources and data on Amazon SageMaker. You can authenticate using the AWS Toolkit extension in Kiro or Cursor or through SageMaker Studio's web interface. Once authenticated, connect to any of your SageMaker Studio development environments in a few simple clicks. You maintain the same security boundaries as SageMaker Studio’s web-based environments while developing AI models and analyzing data in local IDE of your choice - Kiro or Cursor.
To learn more, refer to the SageMaker user guide.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies via a single API. Starting today, customers can use Palmyra Vision 7B from Writer on Amazon Bedrock to build generative AI applications that interpret and generate text from images.
With Palmyra Vision 7B on Bedrock, customers can build generative AI applications for visual understanding tasks without managing inference infrastructure. The model has been trained on PixMo, a dataset of 1 million high-quality image-text pairs, and excels in visual question answering and image-text comprehension for enterprise applications. It enables visual understanding tasks such as document analysis, chart interpretation, and image-based question answering. Palmyra Vision 7B can extract handwritten text, classify objects and colors, interpret plots and dashboards, and answer natural-language questions about image content. Typical applications include accessibility features such as alt-text and image descriptions, document and report ingestion including handwritten forms, claims, and clinical notes, product and UX analysis from screenshots, and multimodal assistants that let users converse about images and text in a single interface.
Palmyra Vision 7B is now available in Amazon Bedrock across select AWS Regions. To get started, visit the Amazon Bedrock and see our documentation for more details.
AWS Step Functions expands its AWS SDK integrations with 28 additional services and over 1,100 new API actions across new and existing AWS services, including Amazon Bedrock AgentCore and Amazon S3 Vectors. This expansion enables you to orchestrate a broader set of AWS services directly from your workflows without writing integration code.
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 Amazon Bedrock AgentCore service integration, you can invoke AI agent runtimes with built-in retries, run multiple agents in parallel using Map states, and automate agent provisioning workflows that create, update, and tear down agent infrastructure as workflow steps. This expansion also includes Amazon S3 Vectors for automating document ingestion pipelines that populate knowledge bases for AI applications. It also adds support for AWS Lambda durable execution APIs, allowing you to pass an execution name for idempotent invocations of Lambda durable functions and manage durable executions directly from your workflows.
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.
Amazon EC2 High Memory U7i-8TB instances (u7i-8tb.112xlarge) and U7i-12TB instances (u7i-12tb.224xlarge) are now available in AWS Europe (Milan). U7i instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-8tb instances offer 8TiB of DDR5 memory, and U7i-12tb instances offer 12TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment.
U7i-8tb instances deliver 448 vCPUs; U7i-12tb instances deliver 896 vCPUs. Both instances support up to 100 Gbps of Amazon EBS bandwidth for faster data loading and backups, 100 Gbps of network bandwidth, and ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.
To learn more about U7i instances, visit the High Memory instances page.
AWS introduces visual customization capability in AWS Management Console that enables selective display of relevant AWS Regions and services for your team members. By hiding unused Regions and services, you can reduce cognitive load and eliminate unnecessary clicks and scrolling, helping you focus better and work faster.
本日、Amazon Quick が AWS アジアパシフィック (東京) リージョンで利用可能になったことをお […]
こんにちは! 今回初めて AWS Weekly Roundup を担当する Daniel Abib です。私は […]
本記事は、三菱電機グループの社内 AWS ユーザーグループ「MAWS（Mitsubishi AWS User Group）」シリーズの第 3 弾です。第 1 弾では一人のエンジニアの小さな行動から 300 人を超えるコミュニティへと成長した誕生ストーリーを、第 2 弾では実務への展開や経営層との対話、次世代への継承といった MAWS の進化をお伝えしました。2026 年 3 月 6 日、755 名に成長した MAWS のリーダーたちが AWS Tokyo Executive Briefing Center に集まり、AWS VP / Chief Evangelist の Jeff Barr とのセッションが実現しました。Jeff の 23 年間の AWS での経験をもとに、AI 時代における開発組織の変化、生産性のパラダイムシフト、そして人材育成の課題について議論が交わされました。本記事では、セッションで共有されたインサイトと、MAWS メンバーとの対話から見えてきた AI 時代の組織変革の姿をお伝えします。
インシデント発生時の根本原因分析は、クラウド運用において最も時間がかかる作業の一つです。AWS DevOps Agent は、自律的な調査能力により平均復旧時間 (MTTR) を数時間から数分に短縮します。本記事では、調査能力と運用効率のバランスを取る Agent Space のセットアップに関するベストプラクティスを紹介します。最適な調査精度を実現するための Agent Space の構成方法、適切なリソースアクセス範囲の決定方法、そして Infrastructure as Code を活用したデプロイの効率化について解説します。
Amazon CloudFront は相互TLS（mTLS）機能をカスタマーオリジンに拡張しました。これにより、ビューワーからカスタマーオリジンまでの接続パス全体を通じた、真のエンドツーエンド認証が可能になります。CloudFront はこれまで、ビューワーと CloudFront 間のビューワー mTLS をサポートしており、トラフィックが境界に入る前にクライアントを強力に認証することができました。今回のリリースにより、同じトラフィックが CloudFront からオリジンへも mTLS 経由で継続できるようになり、すべてのホップにわたって暗号化されたアイデンティティと信頼が維持されます。その結果、完全に認証されたリクエストパスが実現し、暗黙の信頼を排除し、エッジでのパフォーマンスを犠牲にすることなくゼロトラストの多層防御アーキテクチャを実現します。
インシデント発生時の根本原因分析は、クラウド運用において最も時間がかかる作業の一つです。AWS DevOps Agent は、自律的な調査能力により平均復旧時間 (MTTR) を数時間から数分に短縮します。本記事では、調査能力と運用効率のバランスを取る Agent Space のセットアップに関するベストプラクティスを紹介します。最適な調査精度を実現するための Agent Space の構成方法、適切なリソースアクセス範囲の決定方法、そして Infrastructure as Code を活用したデプロイの効率化について解説します。
Deploying agentic AI in financial services requires additional security controls that address AI-specific risks. This post walks you through comprehensive observability and fine-grained access controls—two critical capabilities for maintaining explainability and accountability in AI systems. You will learn seven design principles and get implementation guidance for meeting regulatory requirements while deploying secure AI solutions. Financial […]
In this post, we demonstrate how to architect AWS systems that enable AI agents to iterate rapidly through design patterns for both system architecture and code base structure. We first examine the architectural problems that limit agentic development today. We then walk through system architecture patterns that support rapid experimentation, followed by codebase patterns that help AI agents understand, modify, and validate your applications with confidence.
Today, we’re excited to announce the new Bidirectional Streaming API for Amazon Polly, enabling streamlined real-time text-to-speech (TTS) synthesis where you can start sending text and receiving audio simultaneously. This new API is built for conversational AI applications that generate text or audio incrementally, like responses from large language models (LLMs), where users must begin synthesizing audio before the full text is available.
Last year, AWS announced an integration between Amazon SageMaker Unified Studio and Amazon S3 general purpose buckets. This integration makes it straightforward for teams to use unstructured data stored in Amazon Simple Storage Service (Amazon S3) for machine learning (ML) and data analytics use cases. In this post, we show how to integrate S3 general purpose buckets with Amazon SageMaker Catalog to fine-tune Llama 3.2 11B Vision Instruct for visual question answering (VQA) using Amazon SageMaker Unified Studio.
In this post, we walk you through how to implement a fully automated, context-aware AI solution using a serverless architecture on AWS. This solution helps organizations looking to deploy responsible AI systems, align with compliance requirements for vulnerable populations, and help maintain appropriate and trustworthy AI responses across diverse user groups without compromising performance or governance.
Today, we’re excited to announce that Amazon Bedrock is now available in the Asia Pacific (New Zealand) Region (ap-southeast-6). Customers in New Zealand can now access Anthropic Claude models (Claude Opus 4.5, Opus 4.6, Sonnet 4.5, Sonnet 4.6, and Haiku 4.5) and Amazon (Nova 2 Lite) models directly in the Auckland Region with cross region inference. In this post, we explore how cross-Region inference works from the New Zealand Region, the models available through geographic and global routing, and how to get started with your first API call. We