Generative AI

Amazon Lightsail now offers a new WordPress blueprint, making it easier than ever to launch and manage a WordPress website on the cloud. With just a few clicks, you can create a Lightsail virtual private server (VPS) preinstalled with WordPress, and follow a guided setup wizard to get your site fully configured and running in minutes. This new blueprint has Instance Metadata Service Version 2 (IMDSv2) enforced by default. With Lightsail, you can easily get started on the cloud by choosing a blueprint and an instance bundle to build your web application. Lightsail instance bundles include instances preinstalled with your preferred operating system, storage, and monthly data transfer allowance, giving you everything you need to get up and running quickly. The new WordPress blueprint includes a step-by-step setup workflow that walks you through connecting a custom domain, configuring DNS, attaching a static IP address, and enabling HTTPS encryption using a free Let's Encrypt SSL/TLS certificate — all from within the Lightsail console. This new blueprint is now available in all AWS Regions where Lightsail is available. For more information on blueprints supported on Lightsail, see Lightsail documentation. For more information on pricing, or to get started with your free trial, click here.

Amazon Bedrock batch inference now supports the Converse API as a model invocation type, enabling you to use a consistent, model-agnostic input format for your batch workloads. Previously, batch inference required model-specific request formats using the InvokeModel API. Now, when creating a batch inference job, you can select Converse as the model invocation type and structure your input data using the standard Converse API request format. Output for Converse batch jobs follows the Converse API response format. With this feature, you can use the same unified request format for both real-time and batch inference, simplifying prompt management and reducing the effort needed to switch between models. You can configure the Converse model invocation type through both the Amazon Bedrock console and the API. This capability is available in all AWS Regions that support Amazon Bedrock batch inference. To get started, see Create a batch inference job and Format and upload your batch inference data in the Amazon Bedrock User Guide.

Amazon Elastic Container Service (Amazon ECS) Managed Instances now integrates with Amazon EC2 Capacity Reservations, enabling you to leverage your reserved capacity for predictable workload availability, while ECS handles all infrastructure management. This integration helps you balance reliable capacity scaling with cost efficiency, helping achieve high availability for mission‑critical workloads. Amazon ECS Managed Instances is a fully managed compute option designed to eliminate infrastructure management overhead, dynamically scale EC2 instances to match your workload requirements, and continuously optimize task placement to reduce infrastructure costs. With today’s launch, you can configure your ECS Managed Instances capacity providers to use capacity reservations by setting the capacityOptionType parameter to reserved, in addition to the existing spot and on-demand options. You can also specify reservation preferences to optimize cost and availability: use reservations-only to launch EC2 instances exclusively in reserved capacity for maximum predictability, reservations-first to prefer reservations while maintaining flexibility to fall back to on-demand capacity when needed, or reservations-excluded to prevent your capacity provider from using reservations altogether. To get started, you can use the AWS Management Console, AWS CLI, AWS CloudFormation, or AWS SDKs to configure your ECS Managed Instances capacity provider by choosing capacityOptionType=reserved and providing a capacity reservation group and reservation strategy. This feature is now available in all AWS Regions. For more details, refer to the documentation.

AWS is announcing the general availability of Amazon EC2 Storage Optimized I8g.metal-48xl instances. I8g instances are powered by AWS Graviton4 processors that deliver up to 60% better compute performance compared to previous generation I4g instances. I8g instances use the latest third generation AWS Nitro SSDs, local NVMe storage that deliver up to 65% better real-time storage performance per TB while offering up to 50% lower storage I/O latency and up to 60% lower storage I/O latency variability. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software enhancing the performance and security for your workloads. Amazon EC2 I8g instances are designed for I/O intensive workloads that require rapid data access and real-time latency from storage. These instances excel at handling transactional and real-time databases, including MySQL, PostgreSQL, and NoSQL solutions like ClickHouse, Apache Druid, and MongoDB. They're also optimized for real-time analytics platforms such as Apache Spark. I8g instances are available in 11 different sizes with up to 48xlarge (including 2 metal sizes), 1,536 GiB of memory, and 45 TB local instance storage. They deliver up to 100 Gbps of network performance bandwidth, and 60 Gbps of dedicated bandwidth for Amazon Elastic Block Store (EBS). To learn more, visit EC2 I8g instances. To begin your Graviton journey, visit the Level up your compute with AWS Graviton page.

In this post, we explain how we implemented multi-LoRA inference for Mixture of Experts (MoE) models in vLLM, describe the kernel-level optimizations we performed, and show you how you can benefit from this work. We use GPT-OSS 20B as our primary example throughout this post.

This post demonstrates how to quickly deploy a production-ready event assistant using the components of Amazon Bedrock AgentCore. We'll build an intelligent companion that remembers attendee preferences and builds personalized experiences over time, while Amazon Bedrock AgentCore handles the heavy lifting of production deployment: Amazon Bedrock AgentCore Memory for maintaining both conversation context and long-term preferences without custom storage solutions, Amazon Bedrock AgentCore Identity for secure multi-IDP authentication, and Amazon Bedrock AgentCore Runtime for serverless scaling and session isolation. We will also use Amazon Bedrock Knowledge Bases for managed RAG and event data retrieval.

Today we are announcing the release of the Aurora DSQL Driver for SQLTools and the Aurora DSQL Plugin for DBeaver Community Edition. These integrations allow customers to leverage popular database tools to run queries against Aurora DSQL clusters, explore database schemas, and manage their data. Both integrations simplify database connectivity by automatically handling IAM authentication and transparently managing access tokens, eliminating the need to write token generation code or manually supply IAM tokens. The SQLTools driver integrates Aurora DSQL with Visual Studio Code and is also available on Open VSX Registry for use with VS Code-compatible editors such as Cursor and Kiro. The DBeaver plugin is built on top of the Aurora DSQL Connector for JDBC. Both integrations eliminate security risks associated with traditional user-generated passwords by using AWS IAM credentials for secure, password-free authentication. To get started, visit the Aurora DSQL documentation page for VSCode and DBeaver. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about Aurora DSQL, visit the webpage.

Today, Amazon Location launched curated AI Agent context as a Kiro power, Claude Code plugin, and agent skill in the open Agent Skills format, usable by any compatible agent. Developers can use this context with generative AI tools such as Kiro, Claude Code, and Cursor to improve code accuracy, accelerate feature implementation, and reduce iteration time when adding Amazon Location-enabled capabilities to their applications. Amazon Location Service is a mapping service that offers geospatial data and location functionality such as maps, places search and geocoding, route planning, device tracking, and geofencing. Once loaded by AI development tools, the curated Amazon Location context accelerates development of common location-based solutions such as address entry forms for delivery applications, map display, nearest-store lookup, and route visualization. The context includes pre-validated implementation patterns and step-by-step instructions for these use cases, allowing developers to focus on application-specific logic rather than API integration details. Amazon Location Service is available in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Malaysia), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Spain), Europe (Stockholm), South America (São Paulo), and AWS GovCloud (US-West). To get started, download and install the context to your agent of choice from the amazon-location-agent-context repository on GitHub, or learn more about using AI and LLMs to accelerate development with Amazon Location Service.

Today, AWS announces the general availability of metal-24xl and metal-48xl sizes for Amazon Elastic Compute Cloud (Amazon EC2) M8gn and M8gb instances. These instances are powered by AWS Graviton4 processors to deliver up to 30% better compute performance than AWS Graviton3 processors. M8gn instances feature the latest 6th generation AWS Nitro Cards, and offer up to 600 Gbps network bandwidth, the highest network bandwidth among network optimized EC2 instances. M8gb offers up to 300 Gbps of EBS bandwidth to provide higher EBS performance compared to same-sized equivalent Graviton4-based instances. M8gn and M8gb instances offer instance sizes up to 48xlarge and metal-48xl, with up to 768 GiB of memory. M8gn instances offer up to 600 Gbps of networking bandwidth, up to 60 Gbps of bandwidth to Amazon Elastic Block Store (EBS), and are ideal for network-intensive workloads such as high-performance file systems, distributed web scale in-memory caches, caching fleets, real-time big data analytics, Telco applications such as 5G User Plane Function (UPF). M8gb instances offer up to 300 Gbps of EBS bandwidth, up to 400 Gbps of networking bandwidth, and are ideal for workloads requiring high block storage performance such as high-performance databases and NoSQL databases. M8gn and M8gb instances support Elastic Fabric Adapter (EFA) networking on 16xlarge, 24xlarge, 48xlarge, metal-24xl, and metal-48xl sizes. EFA networking enables lower latency and improved cluster performance for workloads deployed on tightly coupled clusters. The new metal-24xl and metal-48xl sizes are available in the AWS US East (N. Virginia) region.  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.

AWS Elemental Inference, a fully managed Artificial Intelligence (AI) service that enables broadcasters and streamers to automatically generate vertical content and highlight clips for mobile and social platforms in real time, is now generally available. The service applies AI capabilities to live and on-demand video in parallel with encoding and helps companies and creators to reach audiences in any format without requiring AI expertise or dedicated production teams. With Elemental Inference you can process video once and optimize it everywhere—creating main broadcasts while simultaneously generating vertical versions for TikTok, Instagram Reels, YouTube Shorts, Snapchat, and other mobile platforms in parallel with live video. For example, sports broadcasters can automatically generate vertical highlight clips during live games and distribute them to social platforms in real-time, capturing viral moments as they happen rather than hours later.  The service launches with two AI features: vertical video cropping that transforms live and on-demand landscape broadcasts into mobile-optimized formats, and advanced metadata analysis that identifies key moments to generate highlight clips from live content. Using an agentic AI application that requires no prompts or human-in-the-loop intervention, broadcasters can scale content production without adding manual workflows or production staff—the system automatically adapts content for each platform. In beta testing, large media companies achieved 34% or more savings on AI-powered live video workflows compared to using multiple point solutions. AWS Elemental Inference is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), and Europe (Ireland). For more information, visit the AWS News Blog or explore the AWS Elemental Inference documentation.

In this post, we show you how to build a comprehensive photo search system using the AWS Cloud Development Kit (AWS CDK) that integrates Amazon Rekognition for face and object detection, Amazon Neptune for relationship mapping, and Amazon Bedrock for AI-powered captioning.

AWS is announcing Amazon EC2 I7ie instances are now available in AWS Africa (Cape Town) region. Designed for large storage I/O intensive workloads, I7ie instances are powered by 5th Gen Intel Xeon Processors with an all-core turbo frequency of 3.2 GHz, offering up to 40% better compute performance and 20% better price performance over existing I3en instances. I7ie instances offer up to 120TB local NVMe storage density for storage optimized instances and offer up to twice as many vCPUs and memory compared to prior generation instances. Powered by 3rd generation AWS Nitro SSDs, I7ie instances deliver up to 65% better real-time storage performance, up to 50% lower storage I/O latency, and 65% lower storage I/O latency variability compared to I3en instances. I7ie are high density storage optimized instances, ideal for workloads requiring fast local storage with high random read/write performance at very low latency consistency to access large data sets. These instances are available in 9 different virtual sizes and deliver up to 100Gbps of network bandwidth and 60Gbps of bandwidth for Amazon Elastic Block Store (EBS). To learn more, visit the I7ie instances page.

Amazon Elastic Kubernetes Service (Amazon EKS) Node Monitoring Agent is now open source. You can access the Amazon EKS Node Monitoring Agent source code and contribute to its development on GitHub. Running workloads reliably in Kubernetes clusters can be challenging. Cluster administrators often have to resort to manual methods of monitoring and repairing degraded nodes in their clusters. The Amazon EKS Node Monitoring Agent simplifies this process by automatically monitoring and publishing node-level system, storage, networking, and accelerator issues as node conditions, which are used by Amazon EKS for automatic node repair. With the Amazon EKS Node Monitoring Agent’s source code available on GitHub, you now have visibility into the agent’s implementation, can customize it to fit your requirements, and can contribute directly to its ongoing development. The Amazon EKS Node Monitoring Agent is included in Amazon EKS Auto Mode and is available as an Amazon EKS add-on in all AWS Regions where Amazon EKS is available. To learn more about the Amazon EKS Node Monitoring Agent and node repair, visit the Amazon EKS documentation.

Today, AWS WAF announced a new AI activity dashboard that provides centralized visibility into AI bot and agent traffic reaching your applications. With this launch, AWS WAF Bot Control expands its detection coverage to track more than 650 unique bots and agents, offering one of the most comprehensive AI bot detection catalogs available. AI-powered bots and autonomous agents are rapidly reshaping web traffic patterns. AI search crawlers index content, retrieval-augmented generation (RAG) systems fetch data in real time, and autonomous agents execute multi-step tasks across APIs and web applications. Without clear visibility, this traffic can increase infrastructure costs, affect application performance, and access content in ways that may not align with your organization’s security or business policies. The AI traffic analysis dashboard provides a centralized view of AI bot and agent traffic across your protected resources. You can visualize AI traffic trends over time, identify the most active bots and frequently accessed paths, analyze request volumes by bot category and verification status, and take action directly using AWS WAF Bot Control rules, such as allowing verified AI search crawlers while rate-limiting or blocking unverified agents. AWS WAF Bot Control's detection catalog now covers more than 650 unique bots and agents spanning categories including AI search engine crawlers, AI data collectors, AI assistants, and large language model training crawlers. The catalog is continuously updated, enabling customers to identify newly emerging AI bots as they appear. For customers on flat-rate pricing plans, the dashboard is included with all paid plans. For WAF customers not subscribed to flat-rate plans, the AI traffic analysis dashboard is available at no additional cost. Refer to WAF pricing for details. The new dashboard and expanded detection capabilities are available in all AWS Regions where AWS WAF is available. To get started, visit the AWS WAF console or explore the AWS WAF Bot Control documentation.

In this post, we examine how Bedrock Robotics tackles this challenge. By joining the AWS Physical AI Fellowship, the startup partnered with the AWS Generative AI Innovation Center to apply vision-language models that analyze construction video footage, extract operational details, and generate labeled training datasets at scale, to improve data preparation for autonomous construction equipment.

Today, AWS announces the general availability of Amazon Q Developer artifacts in the AWS Management Console. Amazon Q artifacts is a generative AI-based user experience that enables customers to visualize resource data in tables and cost data in charts. The launch also moves the Q icon to the navigation bar and the chat panel to the left, making Amazon Q easier to access from anywhere in the AWS Management Console. Customers can access Amazon Q artifacts by selecting the Amazon Q icon and asking questions about their AWS resources to understand the state of their resources and costs using Amazon Q artifacts. For example, on asking “List S3 buckets with tag value production", Amazon Q displays the S3 buckets that has a tag value of production in a tabular format. Customers can then select the hyperlinks on the bucket name to view the bucket details in the S3 console. Customers can also visualize cost and billing information with charts. For example, on entering "Show me RDS costs by instance type over the last 6 months", Q will render the response in a Q artifacts using a chart (e.g., bar graph, line chart, pie chart, or area chart). Customers can also use sample prompts in the Prompt Library in the Amazon Q chat panel to get started quickly. The artifacts are displayed in an artifact panel to the right of the Amazon Q chat panel. Users can expand Amazon Q to full-screen for a dedicated focus mode experience. The Amazon Q Developer artifacts are available in all AWS Regions where Amazon Q Developer is available. To get started visit Amazon Q Developer documentation.

Hugging Face smolagents is an open source Python library designed to make it straightforward to build and run agents using a few lines of code. We will show you how to build an agentic AI solution by integrating Hugging Face smolagents with Amazon Web Services (AWS) managed services. You'll learn how to deploy a healthcare AI agent that demonstrates multi-model deployment options, vector-enhanced knowledge retrieval, and clinical decision support capabilities.

Amazon S3 Tables are now available in AWS GovCloud (US-East) and AWS GovCloud (US-West). Amazon S3 Tables deliver the first cloud object store with built-in Apache Iceberg support, offering optimized tabular data storage at scale. S3 Tables are designed to perform continual table maintenance to automatically optimize query efficiency and storage cost over time, even as your data lake scales and evolves. With S3 Tables support for the Apache Iceberg standard, your tabular data can be easily queried by popular AWS and third-party query engines. Additionally, with the Intelligent-Tiering storage class, S3 Tables automatically manage costs based on access patterns, without performance impact or operational overhead. For a full list of AWS Regions where S3 Tables are available, see S3 Tables AWS Regions and endpoints. To learn more, visit the product page, documentation, and the Amazon S3 pricing page.

In this post, you’ll use a six-step checklist to build a new MCP server or validate and adjust an existing MCP server for Amazon Quick integration. The Amazon Quick User Guide describes the MCP client behavior and constraints. This is a “How to” guide for detailed implementation required by 3P partners to integrate with Amazon Quick with MCP.

You can now deploy AWS IAM Identity Center in 38 AWS Regions, including Asia Pacific (New Zealand). IAM Identity Center is the recommended service for managing workforce access to AWS applications. It enables you to connect your existing source of workforce identities to AWS once and offer your users single sign on experience across AWS. It powers the personalized experiences offered by AWS applications, such as Amazon Q, and the ability to define and audit user-aware access to data in AWS services, such as Amazon Redshift. It can also help you manage access to multiple AWS accounts from a central place. IAM Identity Center is available at no additional cost in these AWS Regions. To learn more about IAM Identity Center, visit the product detail page. To get started, see the IAM Identity Center user guide.

Starting today, Amazon EC2 G7e instances accelerated by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs are now available in  Asia Pacific (Tokyo) region. G7e instances offer up to 2.3x inference performance compared to G6e. Customers can use G7e instances to deploy large language models (LLMs), agentic AI models, multimodal generative AI models, and physical AI models. G7e instances offer the highest performance for spatial computing workloads as well as workloads that require both graphics and AI processing capabilities. G7e instances feature up to 8 NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, with 96 GB of memory per GPU, and 5th Generation Intel Xeon processors. They support up to 192 virtual CPUs (vCPUs) and up to 1600 Gbps of networking bandwidth. G7e instances support NVIDIA GPUDirect Peer to Peer (P2P) that boosts performance for multi-GPU workloads. Multi-GPU G7e instances also support NVIDIA GPUDirect Remote Direct Memory Access (RDMA) with EFA in EC2 UltraClusters, reducing latency for small-scale multi-node workloads. You can use G7e instances for Amazon EC2 in the following AWS Regions: US West (Oregon), US East (N. Virginia, Ohio) and Asia Pacific (Tokyo). You can purchase G7e instances as On-Demand Instances, Spot Instances, or as part of Savings Plans. To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit G7e instances.

Amazon MQ now supports ActiveMQ minor version 5.19, which introduces several improvements and fixes compared to the previous version of ActiveMQ supported by Amazon MQ. Amazon MQ manages the patch version upgrades for your brokers. All brokers on ActiveMQ version 5.19 will be automatically upgraded to the next compatible and secure patch version in your scheduled maintenance window. If you are utilizing prior versions of ActiveMQ, such as 5.18, we strongly recommend you to upgrade to ActiveMQ 5.19. You can easily perform this upgrade with just a few clicks in the AWS Management Console. To learn more about upgrading, consult the ActiveMQ Version Management section in the Amazon MQ Developer Guide. To learn more about the changes in ActiveMQ 5.19, see the Amazon MQ release notes. This version is available across all AWS Regions where Amazon MQ is available.

In this post, we explain how you can use the Flyte Python SDK to orchestrate and scale AI/ML workflows. We explore how the Union.ai 2.0 system enables deployment of Flyte on Amazon Elastic Kubernetes Service (Amazon EKS), integrating seamlessly with AWS services like Amazon Simple Storage Service (Amazon S3), Amazon Aurora, AWS Identity and Access Management (IAM), and Amazon CloudWatch. We explore the solution through an AI workflow example, using the new Amazon S3 Vectors service.

In this blog post, we will guide you through establishing data source connectivity between Amazon Quick Sight and Snowflake through secure key pair authentication.

In this post, we demonstrate how to build unified intelligence systems using Amazon Bedrock AgentCore through our real-world implementation of the Customer Agent and Knowledge Engine (CAKE).

CyberArk is a global leader in identity security. Centered on intelligent privilege controls, it provides comprehensive security for human, machine, and AI identities across business applications, distributed workforces, and hybrid cloud environments. In this post, we show you how CyberArk redesigned their support operations by combining Iceberg’s intelligent metadata management with AI-powered automation from Amazon Bedrock. You’ll learn how to simplify data processing flows, automate log parsing for diverse formats, and build autonomous investigation workflows that scale automatically.

Today, AWS announces Amazon Aurora DSQL integration with Kiro powers and AI agent skills, enabling developers to build Aurora DSQL-backed applications faster with AI agent-assisted development. These integrations bundle the Aurora DSQL Model Context Protocol (MCP) server with development best practices, so AI agents can help you with Aurora DSQL schema design, performance optimization, and database operations out of the box. Kiro powers is a registry of curated and pre-packaged MCP servers, steering files, and agent hooks to accelerate specialized software development and deployment use cases. With the Kiro power for Aurora DSQL, agents have instant access to specialized knowledge, so developers can work confidently without any prior context, reducing trial-and-error development cycles. The power is available within the Kiro IDE for one-click installation. The Aurora DSQL skill extends the same capabilities to additional AI coding agents through the Skills CLI. Developers can install the skill with a single command and select their preferred agents including Kiro CLI, Claude Code, Gemini, Codex, Cursor, Copilot, Cline, Windsurf, Roo, OpenCode, and more. When developers work on database tasks, the agent dynamically loads relevant skill guidance, including Aurora DSQL Postgres-compatible SQL patterns, distributed database design, and IAM authentication, eliminating the need to repeatedly provide the same context across conversations. As Aurora DSQL adds new features, future skill releases will include updated patterns and guidance, ensuring that agents always have current best practices. For more information on the Aurora DSQL Kiro power and agent skills, visit the Aurora DSQL steering documentation and GitHub page. Get started with Aurora DSQL for free with the AWS Free Tier.

Amazon OpenSearch Service now supports latest generation x86 based high performance Storage Optimized i7i instances. Powered by 5th generation Intel Xeon Scalable processors, I7i instances deliver up to 23% better compute performance and more than 10% better price performance over previous generation I4i instances. I7i instances have 3rd generation AWS Nitro SSDs with up to 50% better real-time storage performance, up to 50% lower storage I/O latency, and up to 60% lower storage I/O latency variability compared to I4i instances. Built on the AWS Nitro System, these instances offload CPU virtualization, storage, and networking functions to dedicated hardware and software enhancing the performance and security for your workloads. Amazon OpenSearch Service supports i7i instances in following AWS Regions US East (N. Virginia, Ohio), US West (N. California, Oregon), Canada (Central), Canada West (Calgary), Europe (Frankfurt, Ireland, London, Milan, Spain, Stockholm, Zurich ), Africa (Cape Town), Asia Pacific (Hong Kong, Hyderabad, Jakarta, Malaysia, Melbourne, Mumbai, Osaka, Seoul, Singapore, Sydney, Tokyo), Middle East (UAE), South America (São Paulo) & AWS GovCloud (US-West). For region specific availability & pricing, visit our pricing page. To learn more about Amazon OpenSearch Service and its capabilities, visit our product page.

Amazon Connect now includes agent time-off requests in draft schedules, making it easier for you to view why an agent was not scheduled on a particular day or part of the day. For example, when generating schedules for next month, you can see that an agent who typically works Monday to Friday wasn't scheduled for the first week because they're on leave without needing to check the published schedules or troubleshooting configuration as to why agent was not scheduled. This launch helps schedulers quickly identify coverage gaps and adjust schedules before publishing them to agents. This feature is available in all AWS Regions where Amazon Connect agent scheduling is available. To learn more about Amazon Connect agent scheduling, click here.

Amazon Connect now supports larger, multi-line text fields on case templates allowing agents to capture detailed free-form notes and structured data directly within cases. These fields expand vertically to accommodate multiple paragraphs, making it easier to document root cause analysis, transaction details, investigation findings, or customer-facing updates. Amazon Connect Cases is available in the following AWS regions: US East (N. Virginia), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (London), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Africa (Cape Town) AWS regions. To learn more and get started, visit the Amazon Connect Cases webpage and documentation.

AWS HealthImaging has launched additional metrics through Amazon CloudWatch that enable monitoring storage at the account and data store levels. These new metrics help customers better understand their medical imaging storage and growth trends over time. HealthImaging now provides customers with granular CloudWatch metrics to monitor their data stores. Customers can track storage by volume, number of image sets, and the number of DICOM studies, series, and instances. These metrics provide the insights needed to manage both single-tenant and multi-tenant workloads at petabyte scale. To learn more, visit Using Amazon CloudWatch with HealthImaging. AWS HealthImaging is a HIPAA-eligible service that empowers healthcare providers and their software partners to store, analyze, and share medical images. AWS HealthImaging is generally available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Ireland).

Verisk, a catastrophe modeling SaaS provider serving insurance and reinsurance companies worldwide, cut processing time from hours to minutes-level aggregations while reducing storage costs by implementing a lakehouse architecture with Amazon Redshift and Apache Iceberg. If you’re managing billions of catastrophe modeling records across hurricanes, earthquakes, and wildfires, this approach eliminates the traditional compute-versus-cost trade-off by separating storage from processing power. In this post, we examine Verisk’s lakehouse implementation, focusing on four architectural decisions that delivered measurable improvements.

Kiro brings agentic AI development capabilities to workloads with elevated compliance needs in AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. Kiro is an agentic AI with an integrated development environment (IDE) and command-line interface (CLI) that helps you go from prototype to production with spec-driven development. From simple to complex tasks, Kiro works alongside you to turn prompts into detailed specs, then into working code, docs, and tests—so what you build is exactly what you want and ready to share with your team. Kiro's agents help you solve challenging problems and automate tasks like generating documentation and unit tests. With native Model Context Protocol (MCP) support, Kiro connects to documentation, databases, APIs, and other enterprise resources, providing capability for mission-critical development workflows. Kiro in AWS GovCloud (US) Regions uses enterprise authentication via AWS IAM Identity Center. To learn more about building with Kiro in AWS GovCloud (US), read the blog post. For more details about Kiro in AWS GovCloud (US), visit the GovCloud documentation or contact your AWS account team for more information. To learn more about Kiro, visit the Kiro product page.

Starting today, customers can create nested environments within virtualized Amazon EC2 instances. Previously, customers could only create and manage virtual machines inside bare metal EC2 instances. With this launch, customers can create nested virtual machines by running KVM or Hyper-V on virtual EC2 instances. Customers can leverage this capability for use cases such as running emulators for mobile applications, simulating in-vehicle hardware for automobiles, and running Windows Subsystem for Linux on Windows workstations.

Amazon EC2 High Memory instances are now available in new regions - U7i-6tb.112xlarge instances in AWS South America (Sao Paulo) and Europe (Milan), U7i-12tb.224xlarge in AWS GovCloud (US-East), and U7in-16tb.224xlarge instances in Europe (London). U7i instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-6tb instances offer 6TiB of DDR5 memory, U7i-12tb instances offer 12TiB of DDR5 memory, and U7in-16tb instances offer 16TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment. U7i-6tb instances offer 448 vCPUs and support up to 100Gbps Elastic Block Storage (EBS) and deliver up to 100Gbps of network bandwidth. U7i-12tb instances offer 896 vCPUs, support up to 100Gbps Elastic Block Storage (EBS) and deliver up to 100Gbps of network bandwidth. U7in-16tb instances offer 896 vCPUs, support up to 100Gbps Elastic Block Storage (EBS) and deliver up to 200Gbps of network bandwidth for faster data loading and backups. All U7i instances support 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.

Amazon Connect now supports in-app notifications in the workspace header, visible from any page, so your team can stay informed without interrupting their workflow— whether configuring, analyzing data, or servicing customers. A notification icon appears in the header of every workspace page, with a badge indicating unread messages. Click the icon to view messages, access relevant resources through embedded links, and manage read/unread status—all without navigating away from your current task. For example, if all supervisors need to complete a certain training by end of week, a notification can be published to non-compliant users to remind them. The new notification APIs enable you to programmatically send targeted messages to specific audiences within your organization, ensuring teams stay aware of urgent updates, policy changes, and action items requiring immediate attention. Amazon Connect will also leverage this capability to deliver system updates and important announcements. In-app notifications are available in all AWS regions where Amazon Connect is available and offer public API and AWS CloudFormation support. To learn more about in-app notifications, see the Amazon Connect Administrator Guide. To learn more about Amazon Connect, please visit the Amazon Connect website.

Amazon Connect now provides AI-powered Task overviews with suggested next actions so agents can understand work items faster and resolve them more quickly. For example, when an agent receives a Task to process a refund request submitted through an online form, Amazon Connect summarizes earlier activities such as verifying order details, checking return eligibility, and confirming the payment method, and then presents recommended next steps to complete the refund. To enable this feature, add the Connect assistant flow block to your flows before a Task contact is assigned to your agent. You can guide the recommendations of your generative AI-powered Tasks assistant by adding knowledge bases. This new feature is available in all AWS regions where Amazon Connect real time agent assistance is available. To learn more and get started, refer to the help documentation, pricing page, or visit the Amazon Connect website.

The new multipart download support in AWS SDK for .NET Transfer Manager improves the performance of downloading large objects from Amazon Simple Storage Service (Amazon S3). Customers are looking for better performance and parallelization of their downloads, especially when working with large files or datasets. The AWS SDK for .NET Transfer Manager (version 4 only) […]

In this post, we present an observability agent using OpenSearch Service and Amazon Bedrock AgentCore that can help surface root cause and get insights faster, handle multiple query-correlation cycles, and ultimately reduce MTTR even further.

In this post, we show you how to implement real-time data correlation using Apache Flink to join streaming order data with historical customer and product information, enabling you to make informed decisions based on comprehensive, up-to-date analytics. We also introduce an optimized solution to automatically load Hive dimension table data into Alluxio Universal Flash Storage (UFS) through the Alluxio cache layer. This enables Flink to perform temporal joins on changing data, accurately reflecting the content of a table at specific points in time.

To support cloud applications that increasingly depend on rich contextual data, AWS is raising the maximum payload size from 256 KB to 1 MB for asynchronous AWS Lambda function invocations, Amazon Amazon SQS, and Amazon EventBridge. Developers can use this enhancement to build and maintain context-rich event-driven systems and reduce the need for complex workarounds such as data chunking or external large object storage.

In healthcare, generative AI is transforming how medical professionals analyze data, summarize clinical notes, and generate insights to improve patient outcomes. From automating medical documentation to assisting in diagnostic reasoning, large language models (LLMs) have the potential to augment clinical workflows and accelerate research. However, these innovations also introduce significant privacy, security, and intellectual property challenges.

In this post, we walk through building a generative AI–powered troubleshooting assistant for Kubernetes. The goal is to give engineers a faster, self-service way to diagnose and resolve cluster issues, cut down Mean Time to Recovery (MTTR), and reduce the cycles experts spend finding the root cause of issues in complex distributed systems.

e are pleased to announce the Developer Preview release of the Amazon S3 Transfer Manager for Swift —a high-level file and directory transfer utility for Amazon Simple Storage Service (Amazon S3) built with the AWS SDK for Swift.

Modern generative AI applications often need to stream large language model (LLM) outputs to users in real-time. Instead of waiting for a complete response, streaming delivers partial results as they become available, which significantly improves the user experience for chat interfaces and long-running AI tasks. This post compares three serverless approaches to handle Amazon Bedrock LLM streaming on Amazon Web Services (AWS), which helps you choose the best fit for your application.

Generative AI agents in production environments demand resilience strategies that go beyond traditional software patterns. AI agents make autonomous decisions, consume substantial computational resources, and interact with external systems in unpredictable ways. These characteristics create failure modes that conventional resilience approaches might not address. This post presents a framework for AI agent resilience risk analysis […]

Software development is far more than just writing code. In reality, a developer spends a large amount of time maintaining existing applications and fixing bugs. For example, migrating a Go application from the older AWS SDK for Go v1 to the newer v2 can be a significant undertaking, but it’s a crucial step to future-proof […]