AWS is announcing the general availability of Amazon EC2 M8in network optimized instances and Amazon EC2 M8ib EBS optimized instances. The new instances are powered by custom sixth generation Intel Xeon Scalable processors, available only on AWS. These instances also feature the latest sixth generation AWS Nitro cards. M8in and M8ib deliver up to 43% higher performance compared to previous generation M6in and M6ib instances.
M8in instances deliver 600 Gbps network bandwidth, the highest network bandwidth among enhanced networking EC2 instances, and are ideal for workloads such as real-time big data analytics, distributed web scale in-memory caches, caching fleets for AI/ML clusters, and Telco applications such as 5G User Plane Function (UPF).
M8ib instances deliver up to 300Gbps EBS bandwidth, the highest among non-accelerated compute EC2 instances, and are best suited for workloads that benefit from high block storage performance, such as high-performance file systems and NoSQL databases.
Amazon EC2 M8in and Amazon EC2 M8ib instances are available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Spain) regions, via Savings Plans, On-Demand, and Spot instances. For more information, visit the Amazon EC2 M8i instance page.
AWS is announcing the general availability of memory optimized Amazon EC2 R8in network optimized instances and Amazon EC2 R8ib EBS optimized instances. These new instances are powered by custom sixth generation Intel Xeon Scalable processors, available only on AWS. These instances also feature the latest sixth generation AWS Nitro cards. M8in and M8ib deliver up to 43% higher performance compared to previous generation M6in and M6ib instances.
R8in instances deliver 600 Gbps network bandwidth, the highest network bandwidth among enhanced networking EC2 instances, and are ideal for workloads such as real-time big data analytics, caching fleets for AI/ML clusters, and distributed web scale in-memory caches.
R8ib instances deliver up to 300Gbps EBS bandwidth, the highest among non-accelerated compute EC2 instances, and are best suited for workloads that benefit from high block storage performance, such as large commercial databases, data lakes, SQL and NoSQL databases, and in-memory databases such as SAP HANA.
Amazon EC2 R8in and Amazon EC2 R8ib instances are available in US East (N. Virginia, Ohio), US West (Oregon), and Europe (Spain) regions, via Savings Plans, On-Demand, and Spot instances. For more information, visit the Amazon EC2 R8i instance page.
AWS is announcing the general availability of Amazon EC2 C8ine and Amazon EC2 M8ine instances, powered by custom sixth generation Intel Xeon Scalable processors, available only on AWS. These also instances feature the latest sixth generation AWS Nitro cards. C8ine and M8ine instances deliver up to 43% higher performance compared to previous generation C6in and M6in instances.
C8ine and M8ine instances offer up to 2.5 times higher packet performance per vCPU versus prior generation network optimized instances. They provide up to 2x higher network throughput for traffic going through Internet gateways compared to existing C6in and M6in network optimized instances.
Both instance families are designed for security and network virtual appliances, including virtual firewalls, load balancers, and Telco 5G UPF workloads.
Amazon EC2 C8ine instances are available in US East (N. Virginia), US West (Oregon), and Asia Pacific (Tokyo), while Amazon EC2 M8ine instances are available in US East (N. Virginia) and US West (Oregon). C8ine and M8ine instances are available via Savings Plans and On-Demand instances. For more information, visit the Amazon EC2 C8i instance and Amazon EC2 M8i instance pages.
Amazon SageMaker Training Plans now supports Amazon CloudWatch metrics to monitor the utilization of capacity reservations associated with your purchased plan. SageMaker Flexible Training Plans helps you create the most cost-efficient training plans that fit within your timeline and AI budget. Once you create and purchase your training plans, SageMaker automatically provisions the infrastructure and runs the AI workloads on these compute resources without requiring any manual intervention.
This feature provides administrators access to both historical and real-time metrics on instance usage—at the individual plan level and across all plans in your account—enabling them to make informed decisions about capacity and cost. To learn more about the Flexible Training Plan reservation monitoring feature, see the Amazon SageMaker Training Plans User Guide.
For a detailed breakdown of Training Plan instance availability by AWS Region, see the SageMaker AI pricing page
Amazon SageMaker HyperPod now supports G7e and r5d.16xlarge instances. SageMaker HyperPod is a purpose-built infrastructure for developing, training, and deploying foundation models at scale. It provides a resilient and performant environment with built-in fault tolerance, automated cluster recovery, and optimized distributed training libraries, reducing the undifferentiated heavy lifting of managing large-scale AI/ML infrastructure.
G7e instances are powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs and deliver up to 2.3x better inference performance than G6e instances, allowing you to process more requests per second while reducing latency. With up to 768 GB of total GPU memory, G7e instances let you deploy larger language models or run multiple models on a single endpoint. You can use these instances for deploying LLMs, agentic AI, multimodal generative AI, and physical AI models. G7e instances are also well suited for cost-efficient single-node fine-tuning or training of NLP, computer vision, and smaller generative AI models, with up to 1.27x the TFLOPs and up to 4x the GPU-to-GPU bandwidth compared to G6e. In addition, HyperPod now supports r5d.16xlarge as well. The r5d.16xlarge instance provides 64 vCPUs, 512 GB of memory, and 5 x 600 GB NVMe SSD instance storage, powered by Intel Xeon Platinum 8000 series processors with a sustained all-core turbo frequency of up to 3.1 GHz. This instance is well suited for distributed training data preprocessing especially with frameworks such as Ray, large-scale feature engineering, and running memory-heavy orchestration services alongside GPU compute.
G7e instances are available in US East (N. Virginia), US East (Ohio), Asia Pacific (Tokyo), and US West (Oregon) and r5d.16xlarge is available in all regions Amazon SageMaker HyperPod is available in.
You can now create Amazon FSx for OpenZFS Single-AZ (HA) file systems in seventeen additional AWS Regions across the South America, Europe, Africa, Asia Pacific, and AWS GovCloud (US).
Amazon FSx for OpenZFS provides fully managed, cost-effective, shared file storage powered by the popular OpenZFS file system. It’s designed to deliver sub-millisecond latencies and multi-GB/s throughput along with rich ZFS-powered data management capabilities (like snapshots, data cloning, and compression). Single-AZ (HA) file systems are a cost-effective solution for workloads that need high availability but don’t need storage redundancy across multiple availability zones, such as data analytics, machine learning, and semiconductor chip design.
With this expansion, FSx for OpenZFS Single-AZ (HA) file systems are now available in the following additional AWS Regions: Africa (Cape Town), Asia Pacific (Hyderabad, Jakarta, Malaysia, Osaka, Taipei, Thailand), Canada West (Calgary), Europe (Milan, Paris, Spain, Zurich), Israel (Tel Aviv), Mexico (Central), South America (São Paulo), and AWS GovCloud (US-East, US-West). To learn more about Amazon FSx for OpenZFS, visit our product page, and see the FSx for OpenZFS Region Table for complete regional availability information.
AWS Billing Conductor now supports the Passthrough Pricing Plan, a new managed pricing plan for Billing Transfer users.
Customers using Billing Transfer can now select the AWS-managed Passthrough Pricing Plan for their billing groups. Under this plan, all accounts in a billing group view billable data that reflects the AWS invoice value through their primary view.
Customers can apply the new Passthrough Pricing Plan by logging into their Bill-Transfer account and selecting a pricing plan in the Billing Transfer page as they configure a new transfer. For existing billing groups, Customers can apply Passthrough Pricing via the AWS Billing Conductor Console. Once configured, the Bill-Transfer account will see the same billing data across both the My View and Showback/Chargeback views associated with billing group's consumption.
Direct Customers or Channel Partners who wish to use Billing Transfer to centralize billing and simplify payments without protecting proprietary discounts or customizing the billing data visible to the accounts in the billing groups, can do so by selecting the Passthrough Pricing plan, free of charge.
This feature is available in the US East (N. Virginia) region. To get started, visit the Billing Transfer page in the AWS Billing and Cost Management Console or the AWS Billing Conductor console. To learn more about Billing Transfer and AWS Billing Conductor visit the Billing Transfer product page, AWS Billing documentation and the AWS Cost Management documentation.
Amazon Connect now supports attachment file sizes up to 100 MB for chat, cases, and tasks, up from the previous 20 MB limit. Administrators can enable these higher limits and configure custom file extensions for attachments across chat, email, cases, and tasks through the Amazon Connect admin website or Amazon Connect APIs.
A technology company supporting enterprise customers can now accept files like diagnostic bundles and log archives up to 100 MB through chat, reducing back-and-forth and helping agents resolve issues faster. A financial services firm can add file extensions for signed contracts or compliance documents, giving customers the ability to attach paperwork directly in chat or email.
You can use these features in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Osaka), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Africa (Cape Town), Canada (Central), Europe (Frankfurt), and Europe (London).
To learn more, visit Amazon Connect and see Enable Attachments in the Amazon Connect Administrator Guide.
Amazon Redshift Serverless, which allows you to run and scale analytics without having to provision and manage data warehouse clusters, is now generally available in the AWS Asia Pacific (Melbourne) and Canada West (Calgary) regions. With Amazon Redshift Serverless, all users, including data analysts, developers, and data scientists, can use Amazon Redshift to get insights from data in seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver high performance for all your analytics. You only pay for the compute used for the duration of the workloads on a per-second basis. You can benefit from this simplicity without making any changes to your existing analytics and business intelligence applications.
With a few clicks in the AWS Management Console, you can get started with querying data using the Query Editor V2 or your tool of choice with Amazon Redshift Serverless. There is no need to choose node types, node count, workload management, scaling, and other manual configurations. You can create databases, schemas, and tables, and load your own data from Amazon S3, access data using Amazon Redshift data shares, or restore an existing Amazon Redshift provisioned cluster snapshot. With Amazon Redshift Serverless, you can directly query data in open formats, such as Apache Parquet, Apache Iceberg in Amazon S3 data lakes. Amazon Redshift Serverless provides unified billing for queries on any of these data sources, helping you efficiently monitor and manage costs.
To get started, see the Amazon Redshift Serverless feature page, user documentation, and API Reference.
AWS Key Management Service (KMS) now provides visibility into the last cryptographic operation performed with your KMS keys, eliminating the need to manually query and analyze logs. This feature helps security administrators and compliance teams quickly determine when their KMS keys were last used for cryptographic operations. You can view the timestamp, the type of operation performed, and the associated AWS CloudTrail event ID from the AWS KMS management console, or via API.
You can use this feature to help identify unused keys for cleanup, verify that keys are actively used, and track down how your keys are used in AWS CloudTrail. In addition, you can use the new condition key (kms:TrailingDaysWithoutKeyUsage) that enables policy-based protection against accidental deletion of recently used keys.
The feature is available in all AWS Regions where AWS KMS is available, including all commercial AWS Regions, AWS GovCloud (US) Regions, and AWS China Regions. For more information, see Determine past usage of a KMS key in the AWS KMS Developer Guide.
Today, we're announcing that Amazon Elastic VMware Service (Amazon EVS) now supports the i7i.metal-24xl Amazon Elastic Cloud Compute (Amazon EC2) bare-metal instance type, offering a lower-core-count option with a newer generation processor to help you realize cost-performance benefits for your VMware-based workloads on AWS.
With this release, you now have more options for running your virtual machines (VMs) on Amazon EVS environments and growing your cloud presence at your own pace, as your business demands. Powered by 5th generation Intel Xeon Scalable processors, i7i instances offer the best compute and storage performance for x86-based storage optimized instances in Amazon EC2, delivering up to 23% better compute performance and more than 10% better price performance over i4i instances.
This latest release is available in AWS Regions where Amazon EVS and Amazon EC2 i7i are both available. See Amazon EVS regional availability and Amazon EC2 i7i regional availability.
Learn more about Amazon EVS by visiting the product detail page and the user guide.
Amazon CloudWatch RUM, which provides real user monitoring for web, iOS, and Android applications, now supports an improved App Monitors overview that surfaces fleet-wide health, SLO breaches, and distributed tracing coverage on a single page. DevOps and SRE teams can now triage critical and degraded monitors, spot worsening trends, and identify gaps in observability setup across their entire fleet without clicking into each monitor individually.
The overview groups monitors into four summary cards: Needs attention by health status, Trending worse, Setup and coverage, and SLOs and Alarms. This helps customers see at a glance how many app monitors are critical or degraded, how many are worsening, and how many are missing SLOs or tracing. Quick filters helps narrow the list so customers can focus on specific app monitors by platform, health, SLI status, and tracing state. Each row in the App Monitors table shows session volume, SLI status, health status primary issue type (such as JavaScript errors on a web monitor or performance regressions on an iOS monitor), trend direction, a direct link to traces in AWS X-Ray, linked-service health from CloudWatch Application Signals, and last event received. A selectable side panel shows additional details like correlated sessions, app monitor health and SLO and alarm details which is particularly useful when troubleshooting a given app monitor on the overview page itself, while also allowing to navigate to per-app monitor page for further deep-dive.
The CloudWatch RUM App Monitors overview is available in all AWS commercial Regions where CloudWatch RUM is available, at no additional cost. To learn more, see the CloudWatch RUM documentation and the pricing page. To get started, open the CloudWatch in AWS Management and select RUM in the left-navigation panel under APM.
Amazon Redshift Serverless now makes AI-driven scaling and optimization the default for all new workgroups. AI-driven scaling uses machine learning to predict compute needs and automatically adjust resources before queries queue, delivering better price-performance without manual tuning. This release also expands support to workloads with a Base RPU range of 8–512 RPU, up from the previous 32–512 RPU, reducing the entry cost for AI-driven scaling.
With AI-driven scaling and optimization, Amazon Redshift monitors your workload patterns and automatically adjusts compute resources based on query complexity, data volume, and expected data scan size. You can use the price-performance slider to choose whether to prioritize cost, performance, or a balance of both. Amazon Redshift also applies additional optimizations, including automatic materialized views and automatic table design optimization, to meet your selected target. To configure price-performance targets, use the AWS Management Console or Amazon Redshift API operations. You can also modify the target after you create the workgroup.
Amazon Redshift Serverless AI-driven scaling and optimization is available in all AWS Regions where Amazon Redshift Serverless is available. For more information, see Amazon Redshift Serverless product page and AI-driven scaling and optimization documentation.
Today, Amazon Quick introduces document and visual creation capabilities, enabling you to produce polished documents, presentations, spreadsheets, and more through natural language without leaving your conversation. No more switching between multiple tools to draft reports, build decks, or format tables. Quick users can now create documents and visuals, refine them in conversation or inline, and download finished files including Word, PDF, PowerPoint, and Excel formats.
Quick also generates images, infographics, charts, and other visuals you can embed in any document or presentation, or export as standalone image files, all from the same conversation. Visual creation is currently available in preview.
Whether you need to generate an executive briefing from meeting notes, create a deck to review quarterly sales trends, build a spreadsheet in Excel or produce an infographic that brings your data to life, Quick handles the end-to-end creation process within your existing chat workflow. This capability is ideal for business analysts, product managers, marketing, finance, and operations teams who need to quickly transform data and insights into shareable, presentation-ready materials without switching tools.
Document creation is available in all AWS Regions where Amazon Quick is currently supported. Visual creation (preview) is available in the US East (N. Virginia) and US West (Oregon) AWS Regions.
You can sign up for an account and start working with Quick for free; no AWS account or credit card is required. To get started with document and visual creation, open a chat conversation and describe whatever you need created. To learn more, see the Amazon Quick User Guide.
Amazon Quick is expanding integrations with 13 new built-in action connectors, all supporting managed authentication so users can securely connect their accounts in just a few clicks without manual credentials setup. Amazon Quick is an AI assistant that turns questions into answers, answers into actions, and actions into outcomes—for you and your entire team. Quick brings all your tools and data together in one place. It learns what matters to you and your team, grounds every answer in your real business data, and goes beyond answers: scheduling, building deliverables, creating dashboards, and acting on your behalf.
With Quick, business users can now take action directly across Gmail, Google Sheets, Google Docs, Google Calendar, Google Drive, Google Slides, Google Meet, Google Analytics, Zoom, QuickBooks, Airtable, and Dropbox. For example, you can draft and send emails in Gmail, update a Google Sheet with the latest data, schedule a meeting in Google Calendar, share files from Google Drive or Dropbox, schedule a Zoom meeting, sync financial records in QuickBooks, manage projects in Airtable, or collaborate with your team in Microsoft Teams, all without leaving Quick. Each connector includes built-in sign-in support, so Quick securely handles the account authorization flow on your behalf, making it easy to get connected in just a few clicks.
These connectors are now available in all AWS Regions where Amazon Quick is available.
Start working with Amazon Quick by signing up for an account. To learn more about integrations, visit the integrations webpage and documentation.
Starting today, new Free and Plus pricing plans for Amazon Quick allow you to sign up in minutes using your personal email address or existing Google, Apple, Github, or Amazon credentials—no AWS account required. A guided onboarding experience helps you find value in less than 5 minutes, with role-specific workflows for sales, marketing, finance, operations, and more.
Amazon Quick is an AI assistant that turns questions into answers, answers into actions, and actions into outcomes—for you and your entire team. Quick connects with all your applications, tools, and data, creating your own personal knowledge graph that learns your priorities, preferences, and network. It doesn't just answer your questions; it knows how you want to work. Give it a task and it takes action—scheduling meetings, sending emails, and following up on action items. Whether you’re a seller looking to prioritize leads and generate personalized outreach to top prospects or a marketing manager looking to optimize campaign performance, Quick learns what matters to you and your team, grounds every answer in your real business data, and goes beyond answers: scheduling, building deliverables, and acting on your behalf.
You can sign up for an account and start working in Amazon Quick in minutes. By the end of the day, you'll wonder how you ever worked without it. Amazon Quick is also available through Professional and Enterprise plans that include additional agentic/business intelligence capabilities, enterprise governance, support for any number of users, and more. To compare plans, visit the Amazon Quick pricing plans page. Visit Signing up at quick.aws.com documentation.
Today, AWS announces the general availability of Amazon Connect Decisions, an agentic AI planning and intelligence solution that helps supply chain teams shift from firefighting to proactive operations. Combining 30 years of Amazon operational science and 25+ specialized supply chain tools, AI teammates adapt to your business, learn from your team's decisions, and continuously improve operations. Amazon Connect Decisions can be used by businesses across retail, CPG, automotive, and industrial manufacturing industries, among others, that want to transform their supply chain operations without having to replace their existing systems.
AI teammates work 24/7 to harmonize demand signals into consensus forecasts, generate constraint-aware supply plans, and monitor operations across your supply chain — detecting variances, performing automated root cause analysis, and triaging thousands of exceptions, surfacing only what matters most based on your business priorities as actionable recommendations.
Click here to start a free trial or learn more about how Amazon Connect Decisions can help you make better decisions, faster, so your organization can prevent stockouts, reduce working capital waste, and transform supply chain performance.
Amazon Quick is now available as a native desktop application for macOS and Windows in preview. The desktop application extends Quick beyond your browser and utilizes the capabilities on your computer– including direct access to local files, proactive OS-level notifications, and native desktop control. Teams and individuals who want an AI assistant that understands their full work context across files, calendar, communications, and applications can now run Quick directly on their desktop.
With Quick on your desktop, you can read and work with files on your computer without uploading them, receive notifications when action items, calendar conflicts, or messages need your attention, and automate browser-based tasks and desktop applications. Quick builds a personal knowledge graph that learns your people, projects, and relationships across every interaction–compounding context over time. For builders, the desktop application supports local Model Context Protocol (MCP) connections to coding agents. Memory, knowledge graph, and agents are shared across web and desktop, so your context travels with you across surfaces.
The Amazon Quick desktop application is available in preview to all Quick subscribers on macOS and Windows in all US East (N. Virginia).
To get started, download the Quick desktop application here. Start working with Amazon Quick by signing up for an account. To learn more, visit our website and Amazon Quick documentation.
Today, AWS announces new features in preview for Amazon Quick, allowing users to create custom web applications in minutes using natural language. Creating internal tools and web applications typically requires developer resources or technical skills, but with this new capability, any user can simply describe what they need and get a fully interactive application—no coding required. These applications connect to live data sources, implement complex workflows, embed AI-powered features, and can be published and shared with your team in one click.
Whether you’re a sales leader wanting to create an application for pipeline review by pulling data from a CRM and other business applications in real time, or a finance manager looking to simplify monthly close by aggregating information from QuickBooks, Excel, and internal systems, Quick allows anyone to create applications that will drive their business forward using a simple prompt.
Amazon Quick is an AI assistant for work that turns questions into answers, answers into actions, and actions into outcomes — for you and your entire team. You can sign up for an account and start working with Amazon Quick for free; no AWS account or credit card is required. A guided onboarding experience helps you find value in less than 5 minutes, with role-specific workflows for sales, marketing, finance, HR, and more. To learn more about building applications in Quick, visit the product documentation or Amazon Quick product page.
Late March took me to Seattle for the Specialist Tech Conference, one of the most energizing gatherings of AWS specialists from around the world. It was an incredible opportunity to connect with peers, exchange experiences, and go deep on the latest advancements in Generative AI and Amazon Bedrock — and a powerful reminder of something […]
製造業のお客様を支援しているソリューションアーキテクトの澤、大前、池田です。 2026 年 3 月 31 日に […]
こんにちは。AWS ソリューションアーキテクトの松井です。 2026 年 3 月 18 日、富士通株式会社様( […]
Understanding what AWS Identity and Access Management (IAM) policies can control helps you build better security controls and avoid spending time on approaches that won’t work. You’ve likely encountered questions like: Can I use AWS Organizations service control policies (SCPs) to prevent the creation of security groups that allow traffic from 0.0.0.0/0? Can I block […]
April 27, 2026: This post was first published in September 2025 when the enhanced AWS Security Hub was in public preview. It has since been updated to reflect the general availability of Security Hub. This revision also provides a more detailed, step-by-step framework for planning your POC. AWS Security Hub prioritizes your critical security issues […]
Bulletin ID: 2026-020-AWS
Scope: AWS
Content Type: Important (requires attention)
Publication Date: 2026/04/27 13:15 PM PDT
Description:
QnABot on AWS is an open-source solution that provides a multi-channel, multi-language conversational interface powered by Amazon Lex, Amazon OpenSearch Service, and optionally Amazon Bedrock.
We identified CVE-2026-7191, where the improper use of the static-eval npm package may allow an authenticated administrator to execute arbitrary code within the fulfillment Lambda execution context. By injecting a crafted conditional chaining expression via the Content Designer interface, an actor with Admin access could bypass the intended expression sandbox through JavaScript prototype manipulation. Successful exploitation may grant direct access to backend resources, including Lambda environment variables, OpenSearch indices, S3 objects, and DynamoDB tables, that are not exposed through normal administrative interfaces.
Impacted versions: <=7.2.4
Please refer to the article below for the most up-to-date and complete information related to this AWS Security Bulletin.
In this post, we explore how Deloitte used Amazon EKS and vCluster to transform their testing infrastructure.
In this post, we share how we applied Amazon Bedrock and the Amazon Nova family of models to reimagine our Title Suggestion feature. By combining metadata, computer vision, and retrieval-augmented generative AI, we now automatically generate creative, brand-aligned titles and subtitles across 12 languages. Using the unified API of Amazon Bedrock, Anthropic’s Claude 3 Haiku, and Amazon Nova Lite and Pro, we improved quality, reduced cost, and cut response times. This resulted in higher customer satisfaction, measurable uplifts in engagement and purchase rates, and over 5.5 million personalised titles generated in 2025.
In this post, we demonstrate how to build AI agents using Strands Agents SDK with models deployed on SageMaker AI endpoints. You will learn how to deploy foundation models from SageMaker JumpStart, integrate them with Strands Agents, and establish production-grade observability using SageMaker Serverless MLflow for agent tracing. We also cover how to implement A/B testing across multiple model variants and evaluate agent performance using MLflow metrics and show how you can build, deploy, and continuously improve AI agents on infrastructure you control.
In this post, we explore an automated solution that detects S3 events and triggers ingestion jobs while respecting service quotas and providing comprehensive monitoring. This serverless solution uses an event-driven architecture to keep your knowledge base current without overwhelming the Amazon Bedrock APIs.
This post shows you how to build your first AI-powered workflow, using Amazon Quick, starting with a financial analysis tool and progressing to an advanced employee onboarding automation.