Amazon FSx for Lustre launches new storage class with the lowest-cost and only fully elastic Lustre file storage

Seismic imaging is a geophysical technique used to create detailed pictures of the Earth’s subsurface structure. It works by generating seismic waves that travel into the ground, reflect off various rock layers and structures, and return to the surface where they’re detected by sensitive instruments known as geophones or hydrophones. The huge volumes of acquired data often reach petabytes for a single survey and this presents significant storage, processing, and management challenges for researchers and energy companies.

Customers who run these seismic imaging workloads or other high performance computing (HPC) workloads, such as weather forecasting, advanced driver-assistance system (ADAS) training, or genomics analysis, already store the huge volumes of data on either hard disk drive (HDD)-based or a combination of HDD and solid state drive (SSD) file storage on premises. However, as these on premises datasets and workloads scale, customers find it increasingly challenging and expensive due to the need to make upfront capital investments to keep up with performance needs of their workloads and avoid running out of storage capacity.

Today, we’re announcing the general availability of the Amazon FSx for Lustre Intelligent-Tiering, a new storage class that delivers virtually unlimited scalability, the only fully elastic Lustre file storage, and the lowest cost Lustre file storage in the cloud. With a starting price of less than $0.005 per GB-month, FSx for Lustre Intelligent-Tiering offers the lowest cost high-performance file storage in the cloud, reducing storage costs for infrequently accessed data by up to 96 percent compared to other managed Lustre options. Elasticity means you no longer need to provision storage capacity upfront because your file system will grow and shrink as you add or delete data, and you pay only for the amount of data you store.

FSx for Lustre Intelligent-Tiering automatically optimizes costs by tiering cold data to the applicable lower-cost storage tier based on access patterns and includes an optional SSD read cache to improve performance for your most latency sensitive workloads. Intelligent-Tiering delivers high performance whether you’re starting with gigabytes of experimental data or working with large petabyte-scale datasets for your most demanding artificial intelligence/machine learning (AI/ML) and HPC workloads. With the flexibility to adjust your file system’s performance independent of storage, Intelligent-Tiering delivers up to 34 percent better price performance than on premises HDD file systems. The Intelligent-Tiering storage class is optimized for HDD-based or mixed HDD/SSD workloads that have a combination of hot and cold data. You can migrate and run such workloads to FSx for Lustre Intelligent-Tiering without application changes, eliminating storage capacity planning and management, while paying only for the resources that you use.

Prior to this launch, customers used the FSx for Lustre SSD storage class to accelerate ML and HPC workloads that need all-SSD performance and consistent low-latency access to all data. However, many workloads have a combination of hot and cold data and they don’t need all-SSD storage for colder portions of the data. FSx for Lustre is increasingly used in AI/ML workloads to increase graphics processing unit (GPU) utilization, and now it’s even more cost optimized to be one of the options for these workloads.

FSx for Lustre Intelligent-Tiering
Your data moves between three storage tiers (Frequent Access, Infrequent Access, and Archive) with no effort on your part, so you get automatic cost savings with no upfront costs or commitments. The tiering works as follows:

Frequent Access – Data that has been accessed within the last 30 days is stored in this tier.

Infrequent Access – Data that hasn’t been accessed for 30 – 90 days is stored in this tier, at a 44 percent cost reduction from Frequent Access.

Archive – Data that hasn’t been accessed for 90 or more days is stored in this tier, at a 65 percent cost reduction compared to Infrequent Access.

Regardless of the storage tier, your data is stored across multiple AWS Availability Zones for redundancy and availability, compared to typical on-premises implementations, which are usually confined within a single physical location. Additionally, your data can be retrieved instantly in milliseconds.

Creating a file system
I can create a file system using the AWS Management Console, AWS Command Line Interface (AWS CLI), API, or AWS CloudFormation. On the console, I choose Create file system to get started.

I select Amazon FSx for Lustre and choose Next.

Now, it’s time to enter the rest of the information to create the file system. I enter a name (veliswa_fsxINT_1) for my file system, and for deployment and storage class, I select Persistent, Intelligent-Tiering. I choose the desired Throughput capacity and the Metadata IOPS. The SSD read cache will be automatically configured by FSx for Lustre based on the specified throughput capacity. I leave the rest as the default, choose Next, and review my choices to create my file system.

With Amazon FSx for Lustre Intelligent-Tiering, you have the flexibility to provision the necessary performance for your workloads without having to provision any underlying storage capacity upfront.

I wanted to know which values were editable after creation, so I paid closer attention before finalizing the creation of the file system. I noted that Throughput capacity, Metadata IOPS, Security groups, SSD read cache, and a few others were editable later. After I start running the ML jobs, it might be necessary to increase the throughput capacity based on the volumes of data I’ll be processing, so this information is important to me.

The file system is now available. Considering that I’ll be running HPC workloads, I anticipate that I’ll be processing high volumes of data later, so I’ll increase the throughput capacity to 24 GB/s. After all, I only pay for the resources I use.

The SSD read cache is scaled automatically as your performance needs increase. You can adjust the cache size any time independently in user-provisioned mode or disable the read cache if you don’t need low-latency access.

Good to know

• FSx for Lustre Intelligent-Tiering is designed to deliver up to multiple terabytes per second of total throughput.
• FSx for Lustre with Elastic Fabric Adapter (EFA)/GPU Direct Storage (GDS) support provides up to 12x (up to 1200 Gbps) higher per-client throughput compared to the previous FSx for Lustre systems.
• It can deliver up to tens of millions of IOPS for writes and cached reads. Data in the SSD read cache has submillisecond time-to-first-byte latencies, and all other data has time-to-first-byte latencies in the range of tens of milliseconds.

Now available
Here are a couple of things to keep in mind:

FSx Intelligent-Tiering storage class is available in the new FSx for Lustre file systems in the US East (N. Virginia, Ohio), US West (N. California, Oregon), Canada (Central), Europe (Frankfurt, Ireland, London, Stockholm), and Asia Pacific (Hong Kong, Mumbai, Seoul, Singapore, Sydney, Tokyo) AWS Regions.

You pay for data and metadata you store on your file system (GB/months). When you write data or when you read data that is not in the SSD read cache, you pay per operation. You pay for the total throughput capacity (in MBps/month), metadata IOPS (IOPS/month), and SSD read cache size for data and metadata (GB/month) you provision on your file system. To learn more, visit the Amazon FSx for Lustre Pricing page. To learn more about Amazon FSx for Lustre including this feature, visit the Amazon FSx for Lustre page.

Give Amazon FSx for Lustre Intelligent-Tiering a try in the Amazon FSx console today and send feedback to AWS re:Post for Amazon FSx for Lustre or through your usual AWS Support contacts.

– Veliswa.

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Enhance AI-assisted development with Amazon ECS, Amazon EKS and AWS Serverless MCP server

Today, we’re introducing specialized Model Context Protocol (MCP) servers for Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), and AWS Serverless, now available in the AWS Labs GitHub repository. These open source solutions extend AI development assistants capabilities with real-time, contextual responses that go beyond their pre-trained knowledge. While Large Language Models (LLM) within AI assistants rely on public documentation, MCP servers deliver current context and service-specific guidance to help you prevent common deployment errors and provide more accurate service interactions.

You can use these open source solutions to develop applications faster, using up-to-date knowledge of Amazon Web Services (AWS) capabilities and configurations during the build and deployment process. Whether you’re writing code in your integrated development environment (IDE), or debugging production issues, these MCP servers support AI code assistants with deep understanding of Amazon ECS, Amazon EKS, and AWS Serverless capabilities, accelerating the journey from code to production. They work with popular AI-enabled IDEs, including Amazon Q Developer on the command line (CLI), to help you build and deploy applications using natural language commands.

• The Amazon ECS MCP Server containerizes and deploys applications to Amazon ECS within minutes by configuring all relevant AWS resources, including load balancers, networking, auto-scaling, monitoring, Amazon ECS task definitions, and services. Using natural language instructions, you can manage cluster operations, implement auto-scaling strategies, and use real-time troubleshooting capabilities to identify and resolve deployment issues quickly.
• For Kubernetes environments, the Amazon EKS MCP Server provides AI assistants with up-to-date, contextual information about your specific EKS environment. It offers access to the latest EKS features, knowledge base, and cluster state information. This gives AI code assistants more accurate, tailored guidance throughout the application lifecycle, from initial setup to production deployment.
• The AWS Serverless MCP Server enhances the serverless development experience by providing AI coding assistants with comprehensive knowledge of serverless patterns, best practices, and AWS services. Using AWS Serverless Application Model Command Line Interface (AWS SAM CLI) integration, you can handle events and deploy infrastructure while implementing proven architectural patterns. This integration streamlines function lifecycles, service integrations, and operational requirements throughout your application development process. The server also provides contextual guidance for infrastructure as code decisions, AWS Lambda specific best practices, and event schemas for AWS Lambda event source mappings.

Let’s see it in action
If this is your first time using AWS MCP servers, visit the Installation and Setup guide in the AWS Labs GitHub repository to installation instructions. Once installed, add the following MCP server configuration to your local setup:

Install Amazon Q for command line and add the configuration to ~/.aws/amazonq/mcp.json. If you’re already an Amazon Q CLI user, add only the configuration.

{
  "mcpServers": {
    "awslabs.aws-serverless-mcp":  {
      "command": "uvx",
      "timeout": 60,
      "args": ["awslabs.aws_serverless_mcp_server@latest"],
    },
    "awslabs.ecs-mcp-server": {
      "disabled": false,
      "command": "uv",
      "timeout": 60,
      "args": ["awslabs.ecs-mcp-server@latest"],
    },
    "awslabs.eks-mcp-server": {
      "disabled": false,
      "timeout": 60,
      "command": "uv",
      "args": ["awslabs.eks-mcp-server@latest"],
    }
  }
}

For this demo I’m going to use the Amazon Q CLI to create an application that understands video using 02_using_converse_api.ipynb from Amazon Nova model cookbook repository as sample code. To do this, I send the following prompt:

I want to create a backend application that automatically extracts metadata and understands the content of images and videos uploaded to an S3 bucket and stores that information in a database. I'd like to use a serverless system for processing. Could you generate everything I need, including the code and commands or steps to set up the necessary infrastructure, for it to work from start to finish? - Use 02_using_converse_api.ipynb as example code for the image and video understanding.

Amazon Q CLI identifies the necessary tools, including the MCP serverawslabs.aws-serverless-mcp-server. Through a single interaction, the AWS Serverless MCP server determines all requirements and best practices for building a robust architecture.

I ask to Amazon Q CLI that build and test the application, but encountered an error. Amazon Q CLI quickly resolved the issue using available tools. I verified success by checking the record created in the Amazon DynamoDB table and testing the application with the dog2.jpeg file.

To enhance video processing capabilities, I decided to migrate my media analysis application to a containerized architecture. I used this prompt:

I'd like you to create a simple application like the media analysis one, but instead of being serverless, it should be containerized. Please help me build it in a new CDK stack.

Amazon Q Developer begins building the application. I took advantage of this time to grab a coffee. When I returned to my desk, coffee in hand, I was pleasantly surprised to find the application ready. To ensure everything was up to current standards, I simply asked:

please review the code and all app using the awslabsecs_mcp_server tools 

Amazon Q Developer CLI gives me a summary with all the improvements and a conclusion.

I ask it to make all the necessary changes, once ready I ask Amazon Q developer CLI to deploy it in my account, all using natural language.

After a few minutes, I review that I have a complete containerized application from the S3 bucket to all the necessary networking.

I ask Amazon Q developer CLI to test the app send it the-sea.mp4 video file and received a timed out error, so Amazon Q CLI decides to use the fetch_task_logs from awslabsecs_mcp_server tool to review the logs, identify the error and then fix it.

After a new deployment, I try it again, and the application successfully processed the video file

I can see the records in my Amazon DynamoDB table.

To test the Amazon EKS MCP server, I have code for a web app in the auction-website-main folder and I want to build a web robust app, for that I asked Amazon Q CLI to help me with this prompt:

Create a web application using the existing code in the auction-website-main folder. This application will grow, so I would like to create it in a new EKS cluster

Once the Docker file is created, Amazon Q CLI identifies generate_app_manifests from awslabseks_mcp_server as a reliable tool to create a Kubernetes manifests for the application.

Then create a new EKS cluster using the manage_eks_staks tool.

Once the app is ready, the Amazon Q CLI deploys it and gives me a summary of what it created.

I can see the cluster status in the console.

After a few minutes and resolving a couple of issues using the search_eks_troubleshoot_guide tool the application is ready to use.

Now I have a Kitties marketplace web app, deployed on Amazon EKS using only natural language commands through Amazon Q CLI.

Get started today
Visit the AWS Labs GitHub repository to start using these AWS MCP servers and enhance your AI-powered developmen there. The repository includes implementation guides, example configurations, and additional specialized servers to run AWS Lambda function, which transforms your existing AWS Lambda functions into AI-accessible tools without code modifications, and Amazon Bedrock Knowledge Bases Retrieval MCP server, which provides seamless access to your Amazon Bedrock knowledge bases. Other AWS specialized servers in the repository include documentation, example configurations, and implementation guides to begin building applications with greater speed and reliability.

To learn more about MCP Servers for AWS Serverless and Containers and how they can transform your AI-assisted application development, visit the Introducing AWS Serverless MCP Server: AI-powered development for modern applications, Automating AI-assisted container deployments with the Amazon ECS MCP Server, and Accelerating application development with the Amazon EKS MCP server deep-dive blogs.

— Eli

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Amazon Aurora DSQL is now generally available

Today, we’re announcing the general availability of Amazon Aurora DSQL, the fastest serverless distributed SQL database with virtually unlimited scale, the highest availability, and zero infrastructure management for always available applications. You can remove the operational burden of patching, upgrades, and maintenance downtime and count on an easy-to-use developer experience to create a new database in a few quick steps.

When we introduced the preview of Aurora DSQL at AWS re:Invent 2024, our customers were excited by this innovative solution to simplify complex relational database challenges. In his keynote, Dr. Werner Vogels, CTO of Amazon.com, talked about managing complexity upfront in the design of Aurora DSQL. Unlike most traditional databases, Aurora DSQL is disaggregated into multiple independent components such as a query processor, adjudicator, journal, and crossbar.

These components have high cohesion, communicate through well-specified APIs, and scale independently based on your workloads. This architecture enables multi-Region strong consistency with low latency and globally synchronized time. To learn more about how Aurora DSQL works behind the scenes, watch Dr. Werner Vogels’ keynote and read about an Aurora DSQL story.

The architecture of Amazon Aurora DSQL
Your application can use the fastest distributed SQL reads and writes and scale to meet any workload demand without any database sharding or instance upgrades. With Aurora DSQL, its active-active distributed architecture is designed for 99.99 percent availability in a single Region and 99.999 percent availability across multiple Regions. This means your applications can continue to read and write with strong consistency, even in the rare case an application is unable to connect to a Region cluster endpoint.

In a single-Region configuration, Aurora DSQL commits all write transactions to a distributed transaction log and synchronously replicates all committed log data to user storage replicas in three Availability Zones. Cluster storage replicas are distributed across a storage fleet and automatically scale to ensure optimal read performance.

Multi-Region clusters provide the same resilience and connectivity as single-Region clusters while improving availability through two Regional endpoints, one for each peered cluster Region. Both endpoints of a peered cluster present a single logical database and support concurrent read and write operations with strong data consistency. A third Region acts as a log-only witness which means there is is no cluster resource or endpoint. This means you can balance applications and connections for geographic locations, performance, or resiliency purposes, making sure readers consistently see the same data.

Aurora DSQL is an ideal choice to support applications using microservices and event-driven architectures, and you can design highly scalable solutions for industries such as banking, ecommerce, travel, and retail. It’s also ideal for multi-tenant software as a service (SaaS) applications and data-driven services like payment processing, gaming platforms, and social media applications that require multi-Region scalability and resilience.

Getting started with Amazon Aurora DSQL
Aurora DSQL provides a easy-to-use experience, starting with a simple console experience. You can use familiar SQL clients to leverage existing skillsets, and integration with other AWS services to improve managing databases.

To create an Aurora DSQL cluster, go to the Aurora DSQL console and choose Create cluster. You can choose either Single-Region or Multi-Region configuration options to help you establish the right database infrastructure for your needs.

1. Create a single-Region cluster

To create a single-Region cluster, you only choose Create cluster. That’s all.

In a few minutes, you’ll see your Aurora DSQL cluster created. To connect your cluster, you can use your favorite SQL client such as PostgreSQL interactive terminal,  DBeaver, JetBrains DataGrip, or you can take various programmable approaches with a database endpoint and authentication token as a password. You can integrate with AWS Secrets Manager for automated token generation and rotation to secure and simplify managing credentials across your infrastructure.

To get the authentication token, choose Connect and Get Token in your cluster detail page. Copy the endpoint from Endpoint (Host) and the generated authentication token after Connect as admin is chosen in the Authentication token (Password) section.

Then, choose Open in CloudShell, and with a few clicks, you can seamlessly connect to your cluster.

After you connect the Aurora DSQL cluster, test your cluster by running sample SQL statements. You can also query SQL statements for your applications using your favorite programming languages: Python, Java, JavaScript, C++, Ruby, .NET, Rust, and Golang. You can build sample applications using a Django, Ruby on Rails, and AWS Lambda application to interact with Amazon Aurora DSQL.

2. Create a multi-Region cluster

To create a multi-Region cluster, you need to add the other cluster’s Amazon Resource Name (ARN) to peer the clusters.

To create the first cluster, choose Multi-Region in the console. You will also be required to choose the Witness Region, which receives data written to any peered Region but doesn’t have an endpoint. Choose Create cluster. If you already have a remote Region cluster, you can optionally enter its ARN.

Next, add an existing remote cluster or create your second cluster in another Region by choosing Create cluster.

Now, you can create the second cluster with your peer cluster ARN as the first cluster.

When the second cluster is created, you must peer the cluster in us-east-1 in order to complete the multi-Region creation.

Go to the first cluster page and choose Peer to confirm cluster peering for both clusters.

Now, your multi-Region cluster is created successfully. You can see details about the peers that are in other Regions in the Peers tab.

To get hands-on experience with Aurora DSQL, you can use this step-by-step workshop. It walks through the architecture, key considerations, and best practices as you build a sample retail rewards point application with active-active resiliency.

You can use the AWS SDKs, AWS Comand Line Interface (AWS CLI), and Aurora DSQL APIs to create and manage Aurora DSQL programmatically. To learn more, visit Setting up Aurora DSQL clusters in the Amazon Aurora DSQL User Guide.

What did we add after the preview?
We used your feedback and suggestions during the preview period to add new capabilities. We’ve highlighted a few of the new features and capabilities:

• Console experience –We improved your cluster management experience to create and peer multi-Region clusters as well as easily connect using AWS CloudShell.
• PostgreSQL features – We added support for views, unique secondary indexes for tables with existing data and launched Auto-Analyze which removes the need to manually maintain accurate table statistics. Learn about Aurora DSQL PostgreSQL-compatible features.
• Integration with AWS services –We integrated various AWS services such as AWS Backup for a full snapshot backup and Aurora DSQL cluster restore, AWS PrivateLink for private network connectivity, AWS CloudFormation for managing Aurora DSQL resources, and AWS CloudTrail for logging Aurora DSQL operations.

Aurora DSQL now provides a Model Context Protocol (MCP) server to improve developer productivity by making it easy for your generative AI models and database to interact through natural language. For example, install Amazon Q Developer CLI and configure Aurora DSQL MCP server. Amazon Q Developer CLI now has access to an Aurora DSQL cluster. You can easily explore the schema of your database, understand the structure of the tables, and even execute complex SQL queries, all without having to write any additional integration code.

Now available
Amazon Aurora DSQL is available today in the AWS US East (N. Virginia), US East (Ohio), US West (Oregon) Regions for single- and multi-Region clusters (two peers and one witness Region), Asia Pacific (Osaka) and Asia Pacific (Tokyo) for single-Region clusters, and Europe (Ireland), Europe (London), and Europe (Paris) for single-Region clusters.

You’re billed on a monthly basis using a single normalized billing unit called Distributed Processing Unit (DPU) for all request-based activity such as read/write. Storage is based on the total size of your database and measured in GB-months. You are only charged for one logical copy of your data per single-Region cluster or multi-Region peered cluster. As a part of the AWS Free Tier, your first 100,000 DPUs and 1 GB-month of storage each month is free. To learn more, visit Amazon Aurora DSQL Pricing.

Give Aurora DSQL a try for free in the Aurora DSQL console. For more information, visit the Aurora DSQL User Guide and send feedback to AWS re:Post for Aurora DSQL or through your usual AWS support contacts.

— Channy

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AWS Weekly Roundup: Claude 4 in Amazon Bedrock, EKS Dashboard, community events, and more (May 26, 2025)

As the tech community we continue to have many opportunities to learn and network with other like-minded folks. This past week AWS customers attended the AWS Summit Dubai for an action-packed day featuring live demos, hands-on experiences with cutting-edge AI/ML tools, and more. Right here in South Africa I attended the Data & AI Community in Durban for a day of inspiration and learning from the community. In India, the AWS Community Day Bengaluru brought together hundreds of passionate tech enthusiasts for a day of learning and networking.

Last week’s launches
Here are the launches that got my attention:

• Claude 4 in Amazon Bedrock, the most powerful models for coding from Anthropic – Anthropic launched the next generation of Claude models today—Opus 4 and Sonnet 4—designed for coding, advanced reasoning, and the support of the next generation of capable, autonomous AI agents. Both models are now generally available in Amazon Bedrock, giving you immediate access to both the model’s advanced reasoning and agentic capabilities.
• Centralize visibility of Kubernetes clusters across AWS Regions and accounts with EKS Dashboard – We announced EKS Dashboard, a centralized display that enables cloud architects and cluster administrators to maintain organization-wide visibility across their Kubernetes clusters.
• AWS Product Lifecycle page and AWS service availability updates – We introduced the AWS Product Lifecycle page to consolidate all service availability information in one convenient location. With the AWS Product Lifecycle page, you get detailed visibility into services closing access to new customers, services that have announced end of support, and services that have reached their end of support date.
• AWS Cost Anomaly Detection integrates with AWS User Notifications – You can configure sophisticated alert rules based on service, account, or other cost dimensions to identify and respond to unexpected spending changes faster using AWS User Notifications via Amazon EventBridge.
• Amazon DynamoDB local on AWS CloudShell – You can now start DynamoDB local just by using dynamodb-local alias in CloudShell to develop and test your DynamoDB tables anywhere in the console without downloading or installing the AWS CLI nor DynamoDB local.
• We are accelerating IPv6 support across AWS services. Last week, we started to support publicly access EC2 instances over IPv6 using EC2 Public DNS names, our new dual-stack endpoints to connect to AWS Organizations, and a dual-stack PrivateLink interface VPC endpoints to Amazon Lightsail. To lean more, visit What’s New for IPv6 launches.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS? page.

Additional updates
Here are some additional projects, blog posts, and news items that you might find interesting:

• Building AI Agents with Strands – Earlier this month we announced Strands Agents, an open source SDK that takes a model-driven approach to building and running AI agents in just a few lines of code. To help you get started with this SDK, my colleague Dennis Traub has started a series of guides on how to build AI agents with Strands.
• Build Games with Amazon Q CLI campaign – If you’re located in the Asia Pacific, Japan or China region (APJC), join the campaign and create a game. Share your experience between 20th May – 20th June 2025 and a T-shirt will be shipped to you. Here is an example to build a chess game with Amazon Q CLI.
• How to secure your EC2 instances with multi-factor authentication (MFA) – You can learn how to enhance your Amazon Linux 2023 EC2 instance security by implementing MFA with Google Authenticator. This setup requires users to provide both their SSH key pair and a time-based one-time password from their application when connecting to instances, adding an essential extra layer of protection.

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

• AWS Summits – Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Tel Aviv (May 28), Singapore (May 29), Stockholm (June 4), Sydney (June 4–5), Washington (June 10-11), and Madrid (June 11).
• AWS re:Inforce – Mark your calendars for AWS re:Inforce (June 16–18) in Philadelphia, PA. AWS re:Inforce is a learning conference focused on AWS security solutions, cloud security, compliance, and identity.
• AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Milwaukee, USA (June 5), and Nairobi, Kenya (June 14).

That’s all for this week. Check back next Monday for another Weekly Roundup!

– Veliswa.

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Introducing Claude 4 in Amazon Bedrock, the most powerful models for coding from Anthropic

Anthropic launched the next generation of Claude models today—Opus 4 and Sonnet 4—designed for coding, advanced reasoning, and the support of the next generation of capable, autonomous AI agents. Both models are now generally available in Amazon Bedrock, giving developers immediate access to both the model’s advanced reasoning and agentic capabilities.

Amazon Bedrock expands your AI choices with Anthropic’s most advanced models, giving you the freedom to build transformative applications with enterprise-grade security and responsible AI controls. Both models extend what’s possible with AI systems by improving task planning, tool use, and agent steerability.

With Opus 4’s advanced intelligence, you can build agents that handle long-running, high-context tasks like refactoring large codebases, synthesizing research, or coordinating cross-functional enterprise operations. Sonnet 4 is optimized for efficiency at scale, making it a strong fit as a subagent or for high-volume tasks like code reviews, bug fixes, and production-grade content generation.

When building with generative AI, many developers work on long-horizon tasks. These workflows require deep, sustained reasoning, often involving multistep processes, planning across large contexts, and synthesizing diverse inputs over extended timeframes. Good examples of these workflows are developer AI agents that help you to refactor or transform large projects. Existing models may respond quickly and fluently, but maintaining coherence and context over time—especially in areas like coding, research, or enterprise workflows—can still be challenging.

Claude Opus 4
Claude Opus 4 is the most advanced model to date from Anthropic, designed for building sophisticated AI agents that can reason, plan, and execute complex tasks with minimal oversight. Anthropic benchmarks show it is the best coding model available on the market today. It excels in software development scenarios where extended context, deep reasoning, and adaptive execution are critical. Developers can use Opus 4 to write and refactor code across entire projects, manage full-stack architectures, or design agentic systems that break down high-level goals into executable steps. It demonstrates strong performance on coding and agent-focused benchmarks like SWE-bench and TAU-bench, making it a natural choice for building agents that handle multistep development workflows. For example, Opus 4 can analyze technical documentation, plan a software implementation, write the required code, and iteratively refine it—while tracking requirements and architectural context throughout the process.

Claude Sonnet 4
Claude Sonnet 4 complements Opus 4 by balancing performance, responsiveness, and cost, making it well-suited for high-volume production workloads. It’s optimized for everyday development tasks with enhanced performance, such as powering code reviews, implementing bug fixes, and new feature development with immediate feedback loops. It can also power production-ready AI assistants for near real-time applications. Sonnet 4 is a drop-in replacement from Claude Sonnet 3.7. In multi-agent systems, Sonnet 4 performs well as a task-specific subagent—handling responsibilities like targeted code reviews, search and retrieval, or isolated feature development within a broader pipeline. You can also use Sonnet 4 to manage continuous integration and delivery (CI/CD) pipelines, perform bug triage, or integrate APIs, all while maintaining high throughput and developer-aligned output.

Opus 4 and Sonnet 4 are hybrid reasoning models offering two modes: near-instant responses and extended thinking for deeper reasoning. You can choose near-instant responses for interactive applications, or enable extended thinking when a request benefits from deeper analysis and planning. Thinking is especially useful for long-context reasoning tasks in areas like software engineering, math, or scientific research. By configuring the model’s thinking budget—for example, by setting a maximum token count—you can tune the tradeoff between latency and answer depth to fit your workload.

How to get started
To see Opus 4 or Sonnet 4 in action, enable the new model in your AWS account. Then, you can start coding using the Bedrock Converse API with model IDanthropic.claude-opus-4-20250514-v1:0 for Opus 4 and anthropic.claude-sonnet-4-20250514-v1:0 for Sonnet 4. We recommend using the Converse API, because it provides a consistent API that works with all Amazon Bedrock models that support messages. This means you can write code one time and use it with different models.

For example, let’s imagine I write an agent to review code before merging changes in a code repository. I write the following code that uses the Bedrock Converse API to send a system and user prompts. Then, the agent consumes the streamed result.

private let modelId = "us.anthropic.claude-sonnet-4-20250514-v1:0"

// Define the system prompt that instructs Claude how to respond
let systemPrompt = """
You are a senior iOS developer with deep expertise in Swift, especially Swift 6 concurrency. Your job is to perform a code review focused on identifying concurrency-related edge cases, potential race conditions, and misuse of Swift concurrency primitives such as Task, TaskGroup, Sendable, @MainActor, and @preconcurrency.

You should review the code carefully and flag any patterns or logic that may cause unexpected behavior in concurrent environments, such as accessing shared mutable state without proper isolation, incorrect actor usage, or non-Sendable types crossing concurrency boundaries.

Explain your reasoning in precise technical terms, and provide recommendations to improve safety, predictability, and correctness. When appropriate, suggest concrete code changes or refactorings using idiomatic Swift 6
"""
let system: BedrockRuntimeClientTypes.SystemContentBlock = .text(systemPrompt)

// Create the user message with text prompt and image
let userPrompt = """
Can you review the following Swift code for concurrency issues? Let me know what could go wrong and how to fix it.
"""
let prompt: BedrockRuntimeClientTypes.ContentBlock = .text(userPrompt)

// Create the user message with both text and image content
let userMessage = BedrockRuntimeClientTypes.Message(
    content: [prompt],
    role: .user
)

// Initialize the messages array with the user message
var messages: [BedrockRuntimeClientTypes.Message] = []
messages.append(userMessage)

// Configure the inference parameters
let inferenceConfig: BedrockRuntimeClientTypes.InferenceConfiguration = .init(maxTokens: 4096, temperature: 0.0)

// Create the input for the Converse API with streaming
let input = ConverseStreamInput(inferenceConfig: inferenceConfig, messages: messages, modelId: modelId, system: [system])

// Make the streaming request
do {
    // Process the stream
    let response = try await bedrockClient.converseStream(input: input)

    // Iterate through the stream events
    for try await event in stream {
        switch event {
        case .messagestart:
            print("AI-assistant started to stream"")

        case let .contentblockdelta(deltaEvent):
            // Handle text content as it arrives
            if case let .text(text) = deltaEvent.delta {
                self.streamedResponse + = text
                print(text, termination: "")
            }

        case .messagestop:
            print("\n\nStream ended")
            // Create a complete assistant message from the streamed response
            let assistantMessage = BedrockRuntimeClientTypes.Message(
                content: [.text(self.streamedResponse)],
                role: .assistant
            )
            messages.append(assistantMessage)

        default:
            break
        }
    }

To help you get started, my colleague Dennis maintains a broad range of code examples for multiple use cases and a variety of programming languages.

Available today in Amazon Bedrock
This release gives developers immediate access in Amazon Bedrock, a fully managed, serverless service, to the next generation of Claude models developed by Anthropic. Whether you’re already building with Claude in Amazon Bedrock or just getting started, this seamless access makes it faster to experiment, prototype, and scale with cutting-edge foundation models—without managing infrastructure or complex integrations.

Claude Opus 4 is available in the following AWS Regions in North America: US East (Ohio, N. Virginia) and US West (Oregon). Claude Sonnet 4 is available not only in AWS Regions in North America but also in APAC, and Europe: US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Hyderabad, Mumbai, Osaka, Seoul, Singapore, Sydney, Tokyo), and Europe (Spain). You can access the two models through cross-Region inference. Cross-Region inference helps to automatically select the optimal AWS Region within your geography to process your inference request.

Opus 4 tackles your most challenging development tasks, while Sonnet 4 excels at routine work with its optimal balance of speed and capability.

Learn more about the pricing and how to use these new models in Amazon Bedrock today!

— seb

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Deprecation of Catalog Items v0 API

via Ecommerce Data Analytics

Centralize visibility of Kubernetes clusters across AWS Regions and accounts with EKS Dashboard

Today, we are announcing EKS Dashboard, a centralized display that enables cloud architects and cluster administrators to maintain organization-wide visibility across their Kubernetes clusters. With EKS Dashboard, customers can now monitor clusters deployed across different AWS Regions and accounts through a unified view, making it easier to track cluster inventory, assess compliance, and plan operational activities like version upgrades.

As organizations scale their Kubernetes deployments, they often run multiple clusters across different environments to enhance availability, ensure business continuity, or maintain data sovereignty. However, this distributed approach can make it challenging to maintain visibility and control, especially in decentralized setups spanning multiple Regions and accounts. Today, many customers resort to third-party tools for centralized cluster visibility, which adds complexity through identity and access setup, licensing costs, and maintenance overhead.

EKS Dashboard simplifies this experience by providing native dashboard capabilities within the AWS Console. The Dashboard provides insights into 3 different resources including clusters, managed node groups, and EKS add-ons, offering aggregated insights into cluster distribution by Region, account, version, support status, forecasted extended support EKS control plane costs, and cluster health metrics. Customers can drill down into specific data points with automatic filtering, enabling them to quickly identify and focus on clusters requiring attention.

Setting up EKS Dashboard

Customers can access the Dashboard in EKS console through AWS Organizations’ management and delegated administrator accounts. The setup process is straightforward and includes simply enabling trusted access as a one-time setup in the Amazon EKS console’s organizations settings page. Trusted access is available from the Dashboard settings page. Enabling trusted access will allow the management account to view the Dashboard. For more information on setup and configuration, see the official AWS Documentation.

A quick tour of EKS Dashboard

The dashboard provides both graphical, tabular, and map views of your Kubernetes clusters, with advanced filtering, and search capabilities. You can also export data for further analysis or custom reporting.

EKS Dashboard overview with key info about your clusters.

There is a wide variety of available widgets to help visualize your clusters.

You can visualize your managed node groups by instance type distribution, launch templates, AMI versions, and more

There is even a map view where you can see all of your clusters across the globe.

Beyond EKS clusters

EKS Dashboard isn’t limited to just Amazon EKS clusters; it can also provide visibility into connected Kubernetes clusters running on-premises or on other cloud providers. While connected clusters may have limited data fidelity compared to native Amazon EKS clusters, this capability enables truly unified visibility for organizations running hybrid or multi-cloud environments.

Available now

EKS Dashboard is available today in the US East (N. Virginia) Region and is able to aggregate data from all commercial AWS Regions. There is no additional charge for using the EKS Dashboard. To learn more, visit the Amazon EKS documentation.

This new capability demonstrates our continued commitment to simplifying Kubernetes operations for our customers, enabling them to focus on building and scaling their applications rather than managing infrastructure. We’re excited to see how customers use EKS Dashboard to enhance their Kubernetes operations.

— Micah;

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Configure System Integrity Protection (SIP) on Amazon EC2 Mac instances

I’m pleased to announce developers can now programmatically disable Apple System Integrity Protection (SIP) on their Amazon EC2 Mac instances. System Integrity Protection (SIP), also known as rootless, is a security feature introduced by Apple in OS X El Capitan (2015, version 10.11). It’s designed to protect the system from potentially harmful software by restricting the power of the root user account. SIP is enabled by default on macOS.

SIP safeguards the system by preventing modification of protected files and folders, restricting access to system-owned files and directories, and blocking unauthorized software from selecting a startup disk. The primary goal of SIP is to address the security risk linked to unrestricted root access, which could potentially allow malware to gain full control of a device with just one password or vulnerability. By implementing this protection, Apple aims to ensure a higher level of security for macOS users, especially considering that many users operate on administrative accounts with weak or no passwords.

While SIP provides excellent protection against malware for everyday use, developers might occasionally need to temporarily disable it for development and testing purposes. For instance, when creating a new device driver or system extension, disabling SIP is necessary to install and test the code. Additionally, SIP might block access to certain system settings required for your software to function properly. Temporarily disabling SIP grants you the necessary permissions to fine-tune programs for macOS. However, it’s crucial to remember that this is akin to briefly disabling the vault door for authorized maintenance, not leaving it permanently open.

Disabling SIP on a Mac requires physical access to the machine. You have to restart the machine in recovery mode, then disable SIP with the csrtutil command line tool, then restart the machine again.

Until today, you had to operate with the standard SIP settings on EC2 Mac instances. The physical access requirement and the need to boot in recovery mode made integrating SIP with the Amazon EC2 control plane and EC2 API challenging. But that’s no longer the case! You can now disable and re-enable SIP at will on your Amazon EC2 Mac instances. Let me show you how.

Let’s see how it works
Imagine I have an Amazon EC2 Mac instance started. It’s a mac2-m2.metal instance, running on an Apple silicon M2 processor. Disabling or enabling SIP is as straightforward as calling a new EC2 API: CreateMacSystemIntegrityProtectionModificationTask. This API is asynchronous; it starts the process of changing the SIP status on your instance. You can monitor progress using another new EC2 API: DescribeMacModificationTasks. All I need to know is the instance ID of the machine I want to work with.

Prerequisites
On Apple silicon based EC2 Mac instances and more recent type of machines, before calling the new EC2 API, I must set the ec2-user user password and enable secure token for that user on macOS. This requires connecting to the machine and typing two commands in the terminal.

# on the target EC2 Mac instance
# Set a password for the ec2-user user
~ % sudo /usr/bin/dscl . -passwd /Users/ec2-user
New Password: (MyNewPassw0rd)

# Enable secure token, with the same password, for the ec2-user
# old password is the one you just set with dscl
~ % sysadminctl -newPassword MyNewPassw0rd -oldPassword MyNewPassw0rd
2025-03-05 13:16:57.261 sysadminctl[3993:3033024] Attempting to change password for ec2-user…
2025-03-05 13:16:58.690 sysadminctl[3993:3033024] SecKeychainCopyLogin returned -25294
2025-03-05 13:16:58.690 sysadminctl[3993:3033024] Failed to update keychain password (-25294)
2025-03-05 13:16:58.690 sysadminctl[3993:3033024] - Done

# The error about the KeyChain is expected. I never connected with the GUI on this machine, so the Login keychain does not exist
# you can ignore this error.  The command below shows the list of keychains active in this session
~ % security list
    "/Library/Keychains/System.keychain"

# Verify that the secure token is ENABLED
~ % sysadminctl -secureTokenStatus ec2-user
2025-03-05 13:18:12.456 sysadminctl[4017:3033614] Secure token is ENABLED for user ec2-user

Change the SIP status
I don’t need to connect to the machine to toggle the SIP status. I only need to know its instance ID. I open a terminal on my laptop and use the AWS Command Line Interface (AWS CLI) to retrieve the Amazon EC2 Mac instance ID.

 aws ec2 describe-instances \
         --query "Reservations[].Instances[?InstanceType == 'mac2-m2.metal' ].InstanceId" \
         --output text

i-012a5de8da47bdff7

Now, still from the terminal on my laptop, I disable SIP with the create-mac-system-integrity-protection-modification-task command:

echo '{"rootVolumeUsername":"ec2-user","rootVolumePassword":"MyNewPassw0rd"}' > tmpCredentials
aws ec2 create-mac-system-integrity-protection-modification-task \
--instance-id "i-012a5de8da47bdff7" \
--mac-credentials fileb://./tmpCredentials \
--mac-system-integrity-protection-status "disabled" && rm tmpCredentials

{
    "macModificationTask": {
        "instanceId": "i-012a5de8da47bdff7",
        "macModificationTaskId": "macmodification-06a4bb89b394ac6d6",
        "macSystemIntegrityProtectionConfig": {},
        "startTime": "2025-03-14T14:15:06Z",
        "taskState": "pending",
        "taskType": "sip-modification"
    }
}

After the task is started, I can check its status with the aws ec2 describe-mac-modification-tasks command.

{
    "macModificationTasks": [
        {
            "instanceId": "i-012a5de8da47bdff7",
            "macModificationTaskId": "macmodification-06a4bb89b394ac6d6",
            "macSystemIntegrityProtectionConfig": {
                "debuggingRestrictions": "",
                "dTraceRestrictions": "",
                "filesystemProtections": "",
                "kextSigning": "",
                "nvramProtections": "",
                "status": "disabled"
            },
            "startTime": "2025-03-14T14:15:06Z",
            "tags": [],
            "taskState": "in-progress",
            "taskType": "sip-modification"
        },
...

The instance initiates the process and a series of reboots, during which it becomes unreachable. This process can take 60–90 minutes to complete. After that, when I see the status in the console becoming available again, I connect to the machine through SSH or EC2 Instance Connect, as usual.

➜  ~ ssh ec2-user@54.99.9.99
Warning: Permanently added '54.99.9.99' (ED25519) to the list of known hosts.
Last login: Mon Feb 26 08:52:42 2024 from 1.1.1.1

    ┌───┬──┐   __|  __|_  )
    │ ╷╭╯╷ │   _|  (     /
    │  └╮  │  ___|\___|___|
    │ ╰─┼╯ │  Amazon EC2
    └───┴──┘  macOS Sonoma 14.3.1

➜  ~ uname -a
Darwin Mac-mini.local 23.3.0 Darwin Kernel Version 23.3.0: Wed Dec 20 21:30:27 PST 2023; root:xnu-10002.81.5~7/RELEASE_ARM64_T8103 arm64

➜ ~ csrutil --status 
System Integrity Protection status: disabled.

When to disable SIP
Disabling SIP should be approached with caution because it opens up the system to potential security risks. However, as I mentioned in the introduction of this post, you might need to disable SIP when developing device drivers or kernel extensions for macOS. Some older applications might also not function correctly when SIP is enabled.

Disabling SIP is also required to turn off Spotlight indexing. Spotlight can help you quickly find apps, documents, emails and other items on your Mac. It’s very convenient on desktop machines, but not so much on a server. When there is no need to index your documents as they change, turning off Spotlight will release some CPU cycles and disk I/O.

Things to know
There are a couple of additional things to know about disabling SIP on Amazon EC2 Mac:

• Disabling SIP is available through the API and AWS SDKs, the AWS CLI, and the AWS Management Console.
• On Apple silicon, the setting is volume based. So if you replace the root volume, you need to disable SIP again. On Intel, the setting is Mac host based, so if you replace the root volume, SIP will still be disabled.
• After disabling SIP, it will be enabled again if you stop and start the instance. Rebooting an instance doesn’t change its SIP status.
• SIP status isn’t transferable between EBS volumes. This means SIP will be disabled again after you restore an instance from an EBS snapshot or if you create an AMI from an instance where SIP is enabled.

These new APIs are available in all Regions where Amazon EC2 Mac is available, at no additional cost. Try them today.

— seb

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Introducing the AWS Product Lifecycle page and AWS service availability updates

Today, we’re introducing the AWS Product Lifecycle page, a centralized resource that provides comprehensive information about service availability changes across AWS.

The new AWS Product Lifecycle page consolidates all service availability information in one convenient location. This dedicated resource offers detailed visibility into three key categories of changes: 1) services closing access to new customers, 2) services that have announced end of support, and 3) services that have reached their end of support date. For each service listed, you can access specific end-of-support dates, recommended migration paths, and links to relevant documentation, enabling more efficient planning for service transitions.

The AWS Product Lifecycle page helps you stay informed about changes that may affect your workloads and enables more efficient planning for service transitions. The centralized nature of this resource reduces the time and effort needed to track service lifecycle information, allowing you to focus more on your core business objectives and less on administrative overhead.

Today on the new Product Lifecycle page, you will see updates about the following changes to services and capabilities:

AWS service availability updates in 2025
After careful consideration, we’re announcing availability changes for a select group of AWS services and features. We understand that the decision to end support for a service or feature significantly impacts your operations. We approach such decisions only after thorough evaluation, and when end of support is necessary, we provide detailed guidance on available alternatives and comprehensive support for migration.

Services closing access to new customers
We’re closing access to new customers after June 20, 2025, for the following services or capabilities listed. Existing customers will be able to continue to use the service.

• Amazon Timestream for LiveAnalytics

Services that have announced end of support
The following services will no longer be supported. To find out more about service specific end-of-support dates, as well as detailed migration information, please visit individual service documentation pages.

• Amazon Pinpoint
• AWS DMS Fleet Advisor
• AWS IQ
• AWS IoT Analytics
• AWS IoT Events
• AWS SimSpace Weaver
• AWS Panorama
• Amazon Inspector Classic
• Amazon Connect Voice ID

Services that have reached their end of support
The following services have reached their end of support date and can no longer be accessed:

• AWS Private 5G
• AWS DataSync Discovery

The AWS Product Lifecycle page is available and all the changes described in this post are listed on the new page now. We recommend that you bookmark this page and check out What’s New with AWS? for upcoming AWS service availability updates. For more information about using this new resource, contact us or your usual AWS Support contacts for specific guidance on transitioning affected workloads.

— seb

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AWS Weekly Roundup: Strands Agents, AWS Transform, Amazon Bedrock Guardrails, AWS CodeBuild, and more (May 19, 2025)

Many events are taking place in this period! Last week I was at the AI Week in Italy. This week I’ll be in Zurich for the AWS Community Day – Switzerland. On May 22, you can join us remotely for AWS Cloud Infrastructure Day to learn about cutting-edge advances across compute, AI/ML, storage, networking, serverless technologies, and global infrastructure. Look for events near you for an opportunity to share your knowledge and learn from others.

What got me particularly excited last Friday was the introduction of Strands Agents, an open source SDK that you can use to build and run AI agents in just a few lines of code. It can scale from simple to complex use cases, including local development and production deployment. By default, it uses Amazon Bedrock as model provider, but many others are supported, including Ollama (to run models locally), Anthropic, Llama API, and LiteLLM (to provide a unified interface for other providers such as Mistral). With Strands, you can use any Python function as a tool for your agent with the @tool decorator. Strands provides many example tools for manipulating files, making API requests, and interacting with AWS APIs. You can also choose from thousands of published Model Context Protocol (MCP) servers, including this suite of specialized MCP servers that help you get the most out of AWS. Multiple teams at AWS already use Strands for their AI agents in production, including Amazon Q Developer, AWS Glue, and VPC Reachability Analyzer. Read it all in Clare’s post.

Last week’s launches
Here are the other launches that got my attention:

• AWS Transform for .NET, the first agentic AI service for modernizing .NET applications at scale – Compared to the preview, we added new capabilities to support projects with private NuGet packages, porting model-view-controller (MVC) Razor views to ASP .NET Core Razor views, and running the ported unit tests.
• Accelerate the modernization of Mainframe and VMware workloads with AWS Transform – To automate assessment, planning, and transformation of both mainframe and VMware workloads into cloud-based architectures, streamlining the entire process.
• Amazon Bedrock Guardrails now supports cross-Region inference – Amazon Bedrock Guardrails provides configurable safeguards when invoking any model including those hosted in Amazon Bedrock, self-hosted models, and third-party models outside Bedrock using the ApplyGuardrail API, providing a consistent experience to help standardize safety and privacy controls. With this new capability, you get consistent throughput and enhanced resilience during periods of peak demand.
• Amazon VPC adds CloudTrail logging for VPC resources created by default – Now, at the time of creation or deletion of the VPC, you can con view events that trigger the creation or deletion of default resources such as security group, network access control list (ACL), and route table. This provides improved visibility of VPC resources and can help you in auditing and governance.
• AWS EC2 instances now support ENA queue allocation for your network interfaces – Elastic network adapter (ENA) queues are key components of elastic network interfaces (ENIs) to help efficiently manage network traffic by load balancing sent and received data across available queues. This flexible ENA queue allocation enables maximum vCPU utilization through optimized resource distribution. Network-intensive applications can be allocated more queues, and CPU-intensive applications can operate with fewer queues.
• New Amazon EC2 P6-B200 instances powered by NVIDIA Blackwell GPUs to accelerate AI innovations – These instances are especially well-suited for large-scale distributed AI training and inferencing for foundation models (FMs) with reinforcement learning (RL) and distillation, multimodal training and inference, and high performance computing (HPC) applications such as climate modeling, drug discovery, seismic analysis, and insurance risk modeling.
• AWS Control Tower introduces account-level reporting for baseline APIs – Now you can use baseline status to view enrollment for your accounts and use drift status to identify when account and organizational unit (OU) baseline configurations are out of sync.
• Simplify AWS AppSync Events integration with Powertools for AWS Lambda – Powertools for AWS is a developer toolkit that includes observability, batch processing, AWS Systems Manager Parameter Store integration, idempotency, feature flags, Amazon CloudWatch metrics, structured logging, and more. Powertools for AWS now supports AppSync Events through the new resolver, available in Python, TypeScript, and .NET.
• Accelerate CI/CD pipelines with the new AWS CodeBuild Docker Server capability – You can now provision a fully managed Docker server that reduces wait times, increases overall efficiency, and can maintain a persistent cache across builds.
• AWS CodePipeline now supports deploying to AWS Lambda with traffic shifting – To publish Lambda function updates using either linear or canary deployment patterns.
• Amazon Cognito now supports OIDC prompt parameter – To choose if users should reauthenticate explicitly (maintaining their existing authenticated sessions) or have a silent check on their authentication state.

Additional updates
Here are some additional projects, blog posts, and news items that you might find interesting:

• Securing Amazon S3 presigned URLs for serverless applications – Focusing on the security ramifications of using Amazon S3 presigned URLs, explaining mitigation steps that developers can take to improve the security of their systems using S3 presigned URLs, and walking through an AWS Lambda function that adheres to the provided recommendations.
  
• Running GenAI Inference with AWS Graviton and Arcee AI Models – While large language models (LLMs) are capable of a wide variety of tasks, they require compute resources to support hundreds of billions and sometimes trillions of parameters. Small language models (SLMs) in contrast typically have a range of 3 to 15 billion parameters and can provide responses more efficiently. In this post, we share how to optimize SLM inference workloads using AWS Graviton based instances.
  

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

• AWS Summits – Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Dubai (May 21), Tel Aviv (May 28), Singapore (May 29), Stockholm (June 4), Sydney (June 4–5), Washington (June 10-11), and Madrid (June 11)
• AWS Cloud Infrastructure Day – On May 22, discover the latest innovations in AWS Cloud infrastructure technologies at this exclusive technical event.
• AWS re:Inforce – Mark your calendars for AWS re:Inforce (June 16–18) in Philadelphia, PA. AWS re:Inforce is a learning conference focused on AWS security solutions, cloud security, compliance, and identity.
• AWS Partners Events – You’ll find a variety of AWS Partner events that will inspire and educate you, whether you’re just getting started on your cloud journey or you’re looking to solve new business challenges.
• AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Zurich, Switzerland (May 22), Bengaluru, India (May 23), Yerevan, Armenia (May 24), Milwaukee, USA (June 5), and Nairobi, Kenya (June 14)

That’s all for this week. Check back next Monday for another Weekly Roundup!

– Danilo

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