Since the first launch of AWS Elastic Beanstalk in 2011, customers have deployed full-stack applications in Java, .NET, Python, Node.js, PHP, Ruby, and Go, trusting Elastic Beanstalk to manage deployment and infrastructure operations so they could focus on business logic. Fifteen years later, that trust has only deepened, and the service has been rebuilt to match it. Now, AWS Elastic Beanstalk is the application management service on AWS that takes full operational responsibility for your production environments. Bring applications however they exist today: source code, Dockerfiles, or container images. Elastic Beanstalk creates and manages the production environment underneath. You manage your application. AWS manages everything else, deploying, scaling, patching, monitoring, and maintaining it continuously. That operational responsibility stays with AWS, for the life of the application.

We have been rebuilding the operational engine underneath and delivering a series of capabilities that make it more powerful than ever. Elastic Beanstalk now uses AI-powered environment analysis to diagnose health issues and recommend fixes automatically. A new official GitHub Action lets teams deploy directly from their existing CI/CD workflows with a single YAML configuration. And we rebuilt the infrastructure foundation to deliver OpenTelemetry-based observability, traffic-splitting deployments with automatic rollback, event-driven autoscaling, secrets management through AWS Secrets Manager, and HTTPS by default via AWS Certificate Manager.

Today, we’re announcing the next chapter of AWS Elastic Beanstalk: a new fully-managed Cluster Mode that deploys, scales, patches, monitors, and upgrades your applications continuously for the life of the workload. You bring your application. AWS runs it.

A new Cluster Mode is built for teams running a portfolio of applications. Instead of operating each application in isolation, you run multiple applications that share infrastructure powered by Amazon Elastic Kubernetes Service (Amazon EKS), fully managed with a single operational baseline. Multiple applications share resources, so per-application cost decreases as your portfolio grows without adding operational complexity. Whether you run ten applications or a hundred, you manage them through one experience, with the same operational guarantees across every stack.

Elastic Beanstalk Cluster Mode benefits for your workloads:

• Source code to production, any runtime. Upload source code in Java, .NET, Python, Node.js, PHP, Ruby, or Go. Elastic Beanstalk handles containerization automatically through Cloud Native Buildpacks when needed. No Dockerfile and no rearchitecting required. You can bring legacy applications from on-premises or deploy new services in any supported language.
• Enterprise compliance built in. Elastic Beanstalk is HIPAA eligible, PCI DSS compliant, and aligned to SOC 1/2/3 with no additional configuration, so teams in regulated industries can deploy production workloads with the compliance posture they already require.
• Production-grade deployment strategies. All-at-once, rolling, immutable, and traffic-splitting deployments with automatic rollback on failure. Event-driven autoscaling. AWS Secrets Manager integration. All native OpenTelemetry enabling easy integration with most observability backends, including Amazon CloudWatch.
• AI-powered troubleshooting. When something goes wrong, Elastic Beanstalk collects service-side logs and provides AI-generated recommendations to help you resolve issues faster without digging through infrastructure.

A first look of Elastic Beanstalk Cluster Mode
To get started, go to the Elastic Beanstalk console, create a new environment, and choose the Cluster in the Deployment type.

Elastic Beanstalk accepts source code, docker file, or container image to deploy your application. For example, you can provide the application code for your environment by selecting Local file and specifying container image build options. For the rest of the sections, the default values should be good for most scenarios.

Choose Create button and the deployment will begin! Note that the first deployment for a given set of subnets triggers EKS cluster creation, which takes about ten-ish minutes. Subsequent deployments are faster because they reuse an existing EKS cluster.

Here’s what it looks like when deployment is successful:

You can also use AWS Command Line Interface (AWS CLI), the EB CLI, or AWS SDKs. For example, consider deploying an application made up of several microservices to Kubernetes. Create an application first.

aws elasticbeanstalk create-application 
    --application-name "my-microservice" 
    --description "Multi-services demo" 

Each microservice may have pre-built images in Amazon Elastic Container Registry (Amazon ECR). Register them as application versions:

IMAGES=(
    "frontend-v1|public.ecr.aws/my-microservices/frontend:v1"
    "cartservice-v1|public.ecr.aws/my-microservices/cart:v1"
    "paymentservice-v1|public.ecr.aws/my-microservices/payment:v1"
    "shippingservice-v1|public.ecr.aws/my-microservices/shipping:v1"
)

for entry in "${IMAGES[@]}"; do
    IFS='|' read -r label uri <<< "$entry"
    aws elasticbeanstalk create-application-version 
        --application-name $APP_NAME 
        --version-label "$label" 
        --image-configuration Source="{Uri=$uri}" 
	--region "us-west-2
    echo "Registered: $label"
done

You can set and deploy the corresponding service options for each service. For example, the frontend service is the only service that needs a public internet interface such as Application Load Balancer and also sets a health check path since it’s an HTTP service:

[
    {"Namespace": "aws:elasticbeanstalk:eks", "OptionName": "cluster-role", "Value": "arn:aws:iam::0123456789012:rol<...>"},
    {"Namespace": "aws:elasticbeanstalk:eks", "OptionName": "node-role", "Value": "arn:aws:iam::0123456789012:role/E<...>"},
    {"Namespace": "aws:elasticbeanstalk:eks:environment", "OptionName": "observability-role", "Value": "arn:aws:iam::0123456<...>"},
    {"Namespace": "aws:elasticbeanstalk:eks:environment", "OptionName": "subnets", "Value": "subnet-1,subnet-2,subnet-3,<...>"},
    {"Namespace": "aws:elasticbeanstalk:eks:environment:autoscaling", "OptionName": "min-replica", "Value": "1"},
    {"Namespace": "aws:elasticbeanstalk:eks:environment:autoscaling", "OptionName": "max-replica", "Value": "2"},
    {"Namespace": "aws:elasticbeanstalk:eks:environment", "OptionName": "cpu", "Value": "0.5"},
    {"Namespace": "aws:elasticbeanstalk:eks:environment", "OptionName": "memory", "Value": "256Mi"},
    {"Namespace": "aws:elasticbeanstalk:eks:environment", "OptionName": "memory-limit", "Value": "512Mi"},
    {"Namespace": "aws:elasticbeanstalk:eks:environment", "OptionName": "service-port", "Value": "8080"},
    {"Namespace": "aws:elasticbeanstalk:eks:alb", "OptionName": "scheme", "Value": "internet-facing"},
    {"Namespace": "aws:elasticbeanstalk:eks:alb", "OptionName": "healthcheck-path", "Value": "/_healthz"}
] #frontend-options.json namespaces

Now, create the frontend service environment with these options. You can continue to deploy each service environment in a similar manner.

aws elasticbeanstalk create-environment 
    --application-name my-microservice 
    --environment-name frontend 
    --version-label frontend-v1 
    --tier Name=Cluster,Type=EKS 
    --option-settings file:///tmp/frontend-options.json 

Here’s a look at the console once all services are deployed:

Elastic Beanstalk Standard powered by Amazon Elastic Compute Cloud (EC2) continues to be fully supported. Standard and Cluster Mode environments run side by side within the same Elastic Beanstalk application, enabling teams to migrate one environment at a time at their own pace. Validation checks confirm compatibility before any changes are made, so no environment is forced to move.

Elastic Beanstalk Standard Mode remains the best fit for:

• Single applications or single-environment use cases
• Windows/.NET Framework workloads on IIS
• Applications that cannot be containerized
• Workloads spending under $500/month where the EKS control plane fee and EKS Auto Mode premium add overhead that a single application cannot offset through bin-packing

To learn more about how to deploy and manage your applications in the Cluster Mode, visit the Elastic Beanstalk Cluster Mode documentation.

Now available
AWS Elastic Beanstalk Cluster Mode is generally available today in all AWS Regions that Elastic Beanstalk is available. For Regional availability and a future roadmap, visit the AWS Capabilities by Region. If you want to call APIs, search documentation, find regional availability, and troubleshooting about this new feature, try using the AWS MCP Server and plugins with your preferred AI tool.

There is no additional charge for Elastic Beanstalk Cluster Mode. You pay only for the underlying AWS resources your applications consume, including the EKS control plane fee, EKS Auto Mode compute, Amazon ECR, and Amazon CloudWatch. Note Elastic Beanstalk Cluster Mode is not AWS Free Tier eligible. To learn more, visit the AWS Elastic Beanstalk Pricing page.

Give it a try in the Elastic Beanstalk console and send feedback to AWS re:Post for AWS Elastic Beanstalk or through your usual AWS Support contacts.

— Channy

The post AWS Elastic Beanstalk introduces Cluster Mode appeared first on The NOKIA Blog.

AWS Builder Center turned one year old last week. Launched on July 9, 2025, the platform has grown from a community hub with Wishlist voting, community profiles, and a toolbox into a full ecosystem with sandbox environments, workshops, Spaces, and a Builders’ Library. To mark the anniversary, Rick Suttles published a full feature timeline covering everything shipped over the past year: AWS Capabilities by Region (1,500+ services across 37 Regions), Spaces for community-created groups, workshops with category and complexity filters, badges and streaks, article series, view counts, saved items, student status, availability notifications, sign-in with GitHub and Amazon, and sandbox environments.

Jeff Barr published a retrospective summarizing Builder Center’s first year. Since launch, 5,548 authors have published 6,448 articles with more than 10.4 million page views combined. Builders have earned 99,226 badges since the badge system launched in March 2026. Community members have submitted 565 wishes, 10 of which have shipped with another 20 on the near-term roadmap.

The top community article Building an AWS Study Buddy with MCP + Strands Agents SDK by Dineshraj Dhanapathy reached 50,000+ views. Chris Miller’s Migrating an EOL Linux Server to AWS in 8 Hours with Kiro followed at 45,000+, and Yash Aggarwal’s AIdeas: NeuroVoice – Multimodal AI for Early Screening of Neurological Diseases article reached 38,000+.

The week’s headline addition is Sandbox Environments by Rick Suttles. Sandboxes give you a free, pre-provisioned AWS account to complete a workshop exercise. Each environment is active for 8 hours, after which the account and all its resources are automatically de-provisioned. You can have one active sandbox at a time and request one per week. No personal AWS account, credit card, or manual cleanup required.

Last week’s launches

Here’s what else happened this week.

• AWS Security Hub introduces Network Scanning – Security Hub introduced Network Scanning, a capability that identifies resources in your environment that are reachable from the public internet. Network Scanning probes your resources from the internet to detect actual reachability, complementing the existing network reachability findings in Security Hub that identify configurations that could make a resource reachable. It discovers public IP addresses, virtual machines, and load balancers across your AWS and Azure environments, identifies reachable ports, and determines what services are running behind them. Each reachable port generates a Security Hub finding with evidence of the port and service discovered. Security Hub Exposures then automatically correlates these findings with other findings and resource configurations to determine broader risk. Existing customers can enable Network Scanning in individual accounts and Regions, or across an organization through a configuration policy. For new customers, Network Scanning is on by default. It is included with Security Hub Essentials at no additional cost.
• Security Hub also extends unified security management to Microsoft Azure – Security Hub now monitors Microsoft Azure resources, providing unified posture management, vulnerability management, and security response across both clouds. It automatically discovers Azure VMs, container images, Function Apps, and identities, and evaluates them for misconfigurations, internet exposure, and software vulnerabilities. AWS and Azure findings appear in the same prioritized view with the same formats and automation workflows.
• Amazon SageMaker Studio integrates with Hugging Face for one-click model deployment and customization – You can now go from discovering a model on Hugging Face to working with it in SageMaker Studio in a single click. Select any supported model on Hugging Face and choose “Customize on SageMaker AI” or “Deploy on SageMaker AI” to land directly on the corresponding workflow page with the model pre-loaded. New customers receive a Studio environment created in seconds with pre-configured permissions for serverless model customization (including fine-tuning with custom reward functions for reinforcement learning), model evaluation, and deployment to SageMaker or Bedrock endpoints. Verified customers receive default GPU access to G5, G6, and G4dn instances without requesting quota increases, and quota utilization is visible directly inside the Studio environment.
• Amazon EKS Auto Mode and Amazon ECS Managed Instances reduce GPU management fees by up to 60% – Beginning July 1, 2026, EKS Auto Mode and ECS Managed Instances reduce management fees for accelerated instance types: G-series fees are down 35%, and P-series and AWS Trainium fees are down 60%. The reductions apply automatically to existing clusters and require no action from customers. Both services include capabilities built for accelerated workloads. EKS Auto Mode provides automatic parallel image pulling on GPU instances with local NVMe storage and accelerator-aware node repair. ECS Managed Instances provides GPU metrics through Amazon CloudWatch Container Insights and automatic health monitoring for GPU hardware failures.
• Amazon Aurora DSQL change data capture (CDC) is now generally available – Aurora DSQL CDC streams the results of insert, update, and delete operations as change events to Amazon Kinesis Data Streams. You can use it to synchronize data across microservices, trigger Lambda functions, or deliver changes to S3, Redshift, and OpenSearch Service through Amazon Data Firehose. CDC streaming is designed to have zero impact on database workload performance and requires no infrastructure to manage.

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

Other AWS news

Here are some additional posts you may find useful:

• Building secure AI agents at scale: Introducing Loom for AWS – Loom is an open-source enterprise platform for building agents with AWS Strands Agents and deploying them on Amazon Bedrock AgentCore Runtime. It provides a unified management UI and backend API with identity provider integration, scope-based authorization, multi-persona navigation, and full lifecycle management for agents, memory, MCP servers, and agent-to-agent integrations. Loom enforces automated resource tagging for cost attribution, implements RBAC and ABAC for multi-tenant security, uses paved-path blueprints for agent deployments, manages identity propagation through delegated actor chains, integrates with AWS Agent Registry for discovery and governance, and supports human-in-the-loop review before sensitive actions. The project is available in AWS Labs on GitHub.
• Introducing Claude apps gateway for AWS – The Claude apps gateway is a self-hosted control plane that gives organizations centralized control over access, cost, and policy for Claude Code and Claude Desktop. It connects to any OIDC-compliant identity provider, enforces managed settings on every request, routes inference to Amazon Bedrock or Claude Platform on AWS, and supports per-user and per-group spend caps. The gateway runs as a stateless container in your private network, backed by a PostgreSQL database for short-lived sign-in state. No long-lived secrets are stored on developer machines. Deploy it through Amazon Bedrock to keep data within the AWS security boundary, or through Claude Platform on AWS for the native Claude platform experience.
• Introducing OAuth support for AWS MCP Server – You can now connect agents to the AWS MCP Server using browser-based OAuth with the same credentials you use for the AWS Console or CLI. The new sign-in path supports IAM federation, AWS IAM Identity Center, and root or IAM users. AWS Sign-In issues short-lived access tokens and refresh tokens, with automatic token management so developers stay authenticated across restarts. For headless use cases, a non-interactive flow lets applications with existing AWS credentials obtain OAuth access tokens through the create-oauth2-token-with-iam API. New governance controls include OAuth-specific IAM condition keys, token introspection and revocation, dynamic client registration, and CloudTrail audit elements.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

• AWS Summits – Free in-person events for builders and innovators to learn, think big, and make new connections. Coming up: Taipei (July 15), Bogotá (July 30), Jakarta (August 6), Ciudad de México (August 12), Johannesburg (August 19), and Zurich (September 2).
• AWS Community Days – Community-led conferences planned and delivered by community leaders. Upcoming events include Yaoundé, Cameroon (July 25), Ahmedabad, India (July 25), Belo Horizonte, Brazil (August 22), Ottawa, Canada (August 22), Tulsa, USA (August 22), and Toronto, Canada (August 29).

Visit the AWS Builder Center to meet other builders, contribute solutions, and find resources that help you keep building.

Wishing everyone a restful and enjoyable summer. Whether you’re building, learning, or recharging, I hope you find time for all three. I’ll be heading to Scandinavia for a few weeks to trade the heat for some cooler weather and longer evenings. Come back next week for more news!

— Esra

The post AWS Weekly Roundup: AWS Builder Center at 1 year, Network Scanning in Security Hub, Loom for AWS, and more (July 13, 2026) appeared first on The NOKIA Blog.

A couple of editions ago I wrote about what I find so energizing about working with startups. Last week I got a fresh dose of it: I spent a few days with the AWS Startups team, listening to stories of founders talking about the problems they’re actually solving. One story that stayed with me came from Marco Negreiros, founder of EyeCare Health, a Brazilian healthtech expanding access to eye care. He shared a striking fact: more than 70% of Brazilian municipalities don’t have a single ophthalmologist. His answer was to put a vision test on the one device almost everyone already carries, the smartphone, so a basic eye screening no longer depends on living near a clinic. Watching a founder turn a gap that big into something that concrete is exactly why I love this space.

This week, I’ll take a closer look at some key launches, and then cover the quarterly AWS Service Availability updates.

Last week’s launches

Here are some of the launches covered from this past week in the AWS News Blog:

Here are some launches and updates that caught my attention:

• Claude Sonnet 5 is now available on AWS – Anthropic’s most capable Sonnet model brings top-tier intelligence at Sonnet pricing for coding, agents, and everyday professional work at scale. It navigates large codebases, calls tools precisely, and holds state across long agentic tasks. To learn more, visit the AI Blog post.
• Amazon WorkSpaces for AI agents is now generally available: AI agents can now securely access and operate desktop applications through managed WorkSpaces environments, without requiring application modernization or custom integrations. To learn more. visit the Desktop and Application Streaming Blog post.
• Amazon OpenSearch Service is now optimized for log analytics: This release introduces a new engine purpose-built for log analytics workloads that delivers up to 4x better price-performance on internal benchmarks, while keeping the full-text search capabilities OpenSearch is known for. Teams can now get aggregations and precise text search in one place. To learn more, visit the Big Data Blog post.
• Amazon SageMaker AI cuts generative AI inference scale-out time by up to half: SageMaker Inference now supports container image caching, enabling up to 2x faster end-to-end scaling for generative AI models during scale-out events. To learn more, visit the AI Blog post.
• Amazon CloudWatch supports creating alarms from log queries : You can now create alarms directly on log query results and set thresholds in a single workflow, eliminating the need to first create metric filters or custom metrics as intermediate steps.

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

AWS Service Availability Updates

When the availability of an AWS service or feature changes, we provide customers guidance in AWS Product Lifecycle Changes on available alternatives and support for migration so that disruptions to your operations are minimized. The following lifecycle changes were updated on June 30, 2026.

Services moving to Maintenance (no longer accessible to new customers starting July 30, 2026):

Services entering Sunset:

Services reaching End of Support (as of June 30, 2026):

• Amazon Chime SDK – Carrier Voice Focus
• Amazon SageMaker AI – Ground Truth Plus

We understand that changes in availability can impact your operations. For specific guidance, consult the relevant service documentation or contact AWS Support.

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

• AWS Summits – AWS Summits are free events that bring the cloud and AI community together to connect, learn, and explore the latest technologies. Browse the full calendar to find a Summit near you in the second half of 2026.
• AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders. If you’re in Latin America, don’t miss AWS Community Day Belo Horizonte on August 22. Registration is open at awscommunityday.com.br.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

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

– Daniel Abib

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The post AWS Weekly Roundup: Claude Sonnet 5 on AWS, Amazon WorkSpaces for AI agents, AWS service availability updates, and more (July 6, 2026) appeared first on The NOKIA Blog.

Upgrading a Kubernetes control plane has long been a one way door. Open source Kubernetes doesn’t support control plane rollback, so once you upgrade, there’s no going back. The community is making real progress here, and KEP-4330 introduces emulated versions to ease rollback. But in practice this constraint has pushed organizations to build elaborate compensating mechanisms like bake periods, stagger groups, automated sign offs, and months long upgrade cycles. With Kubernetes releasing three minor versions per year, teams managing hundreds of clusters, especially in regulated environments, often delay upgrades entirely because they aren’t confident they can recover if something goes wrong. The result is clusters stuck on older versions, missing security patches, and eventually running up against extended support timelines.

Today, we’re announcing Kubernetes version rollbacks for Amazon Elastic Kubernetes Service (Amazon EKS), a new feature that gives cluster administrators a safety net when performing cluster upgrades. With version rollbacks, you can reverse a Kubernetes version upgrade within seven days if you encounter issues after upgrading, returning your cluster to its previous working state.

Where approaches like emulated versions keep a cluster in a transitional holding state, EKS version rollback returns your cluster to a fully validated previous version that ran in production, not an emulation of it. Now, if you upgrade a cluster from, say, Kubernetes 1.34 to 1.35 and discover a compatibility issue, you can roll back to 1.34 within seven days. There’s no need to rebuild your cluster or scramble to troubleshoot under pressure. Think of it as an undo button for Kubernetes version upgrades.

The feature supports rolling back one minor version at a time, matching the same incremental approach EKS uses for upgrades. And to help you roll back safely, EKS automatically evaluates your cluster’s rollback readiness through cluster insights, flagging items like node version compatibility or add-on dependencies before you proceed. If you’ve already assessed the situation and want to move quickly, you can use the --force flag to bypass those checks. The above applies to all EKS clusters, whether you manage your own nodes or let AWS handle them. But for customers who have embraced fully managed infrastructure, rollback goes a step further.

Rollback for EKS Auto Mode

EKS Auto Mode gives you one click deployment of production ready Kubernetes clusters, automating compute, networking, and storage management so you can focus on your applications rather than infrastructure. EKS Auto Mode introduces additional considerations for version rollbacks because both the control plane and managed nodes need to be rolled back together. Since node rollbacks respect your pod disruption budgets, the process can take time depending on your configuration.

To give you control over this process, we’ve introduced a cancel API that lets you stop a node rollback at any point. If you decide the rollback is taking too long or you want to change your approach, you can cancel and adjust your disruption budgets to accelerate things, or choose a different path forward.

By default, EKS never bypasses your disruption budgets during a rollback because we prioritize workload stability. You can always choose to modify or remove disruption budgets yourself to speed up the process if needed.

Let’s try it out

To try version rollbacks, I navigated to the Amazon EKS console and selected one of my clusters that I had recently upgraded.

From the cluster’s configuration page, I can see the option to initiate a version rollback, along with information about my current rollback window.

Before initiating the rollback, I reviewed the rollback insights to check for any potential issues. The insights showed me the status of my nodes and flagged anything I should address before proceeding.

After confirming, the rollback began. My cluster remained functional throughout the process. The control plane rollback took about 20 minutes, similar to a standard upgrade. For my EKS Auto Mode cluster, the nodes rolled back gracefully according to my disruption budget settings.

Once complete, my cluster was back on the previous Kubernetes version, running as expected.

Now available

Kubernetes version rollbacks for Amazon EKS are available today at no additional cost in all commercial AWS Regions where Amazon EKS is available. You pay only for the standard EKS and compute costs you would normally incur. There are no extra charges for using the rollback capability.

Control plane rollbacks are available for all EKS clusters, and node rollbacks are available for clusters running EKS Auto Mode. Version rollbacks support clusters running Kubernetes versions available in EKS standard support and extended support.

To get started, visit the Amazon EKS documentation or try it out directly in the Amazon EKS console.

The post Upgrade Amazon EKS clusters with confidence using Kubernetes version rollbacks appeared first on The NOKIA Blog.

Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) G7 instances, delivering high performance GPU acceleration for AI inference, graphics, and data analytics workloads.

AWS is the first major cloud provider to support NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances are accelerated by these GPUs with custom sixth-generation Intel Xeon Scalable processors, delivering up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6 instances. G7 instances also deliver faster performance for GPU-accelerated analytics on Amazon EMR on Amazon Elastic Kubernetes Service (Amazon EKS). G7 instances are well suited for a broad range of GPU-enabled workloads including AI inference, graphics rendering, video transcoding and analytics, spatial computing, virtual desktop infrastructure (VDI), and data analytics.

Here are improvements of G7 instances compared to previous generation:

• Faster GPU memory – NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs offer 1.33 times the GPU memory capacity and 2.45 times the GPU memory bandwidth compared to G6 instances. With 32 GB of GPU memory per GPU, 5th Gen Tensor Cores, and 4th Gen RT Cores, G7 instances deliver enhanced AI inference and graphics performance.
• High performance networking and storage – G7 instances come with 700 Gbps of EFA-enabled networking throughput (7x compared to G6) enabling the low-latency, high-bandwidth connectivity that AI inference, graphics-intensive applications, and GPU-accelerated data analytics workloads need to perform at their best. G7 instances support up to 7.6 TB local NVMe SSD storage, enabling you to keep large models and datasets close to compute, reduce data transfer overhead, and improve throughput.
• Advanced video encoding and decoding engines – Ninth-generation NVENC and sixth-generation NVDEC engines support 4:2:2 encoding and decoding for high-resolution video workflows, delivering 1.5x concurrent video streams compared to previous-generation G6 instances.

EC2 G7 instance specifications

G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with up to 256 GB of total GPU memory (32 GB of memory per GPU) and custom Intel Xeon Scalable processors. They also are available in 7 sizes and support up to 192 vCPUs, up to 700 Gbps of network bandwidth, up to 768 GiB of system memory, and up to 7.6 TB of local NVMe SSD storage.

Here are the specs:

* Coming soon

G7 instances support NVIDIA GPUDirect P2P for multi-GPU sizes, NVIDIA GPUDirect RDMA with EFA, and GPUDirect RDMA with EFA for Amazon FSx for Lustre, enabling low-latency GPU-to-GPU communication for multi-GPU and multi-node workloads.

To get started with G7 instances, you can use the AWS Deep Learning AMIs (DLAMI) or NVIDIA Workstation AMIs with prepackaged GPU drivers for your AI inference and graphics workloads. To use G7 instances with Amazon EKS, build EKS AMIs with NVIDIA driver version R595 with EKS-provided automation. G7 instances support multiple operating systems including Amazon Linux, Ubuntu, RHEL, and Windows Server, with comprehensive NVIDIA driver integration providing compatibility with industry-standard graphics libraries including DirectX, Vulkan, and OpenGL.

Get started today

You can start using Amazon EC2 G7 instances today in two AWS regions: US East (Ohio) and US West (Oregon). To check future Regional expansion plans, look up the instance type in the CloudFormation resources tab on the AWS Capabilities by Region page.

G7 instances are offered through multiple purchasing options, including On-Demand, Savings Plans, and Spot Instances. Dedicated Instances are also supported for the 12xlarge, 24xlarge, and 48xlarge sizes. For detailed pricing, visit the Amazon EC2 Pricing page.

Ready to get started? Launch G7 instances from the Amazon EC2 console. For more details, head over to the Amazon EC2 G7 instances page. We’d love to hear your feedback. Share it on AWS re:Post for EC2 or reach out through your usual AWS Support contacts.

– Daniel Abib

The post Announcing Amazon EC2 G7 instances accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs appeared first on The NOKIA Blog.

This week, the AWS IoT Device SDK for Swift reached general availability. As a member of the Swift Server Workgroup (SSWG), this one caught my attention. The SDK brings production-ready MQTT 5 connectivity, Device Shadow, Jobs, and fleet provisioning to Swift developers on macOS, iOS, tvOS, and Linux.

I’m curious to see what you will build with it. Swift on the server has matured over the past few years, and now it reaches IoT devices too. This connects to a broader trend of running Swift at the edge. WendyOS, for example, is an open-source operating system for physical AI that offers first-class Swift support for deploying apps to NVIDIA Jetson and Raspberry Pi hardware. Between server-side Swift, IoT, and edge computing, the language is showing up in places that would have surprised most people a few years ago.

Now, let’s get into this week’s AWS news.

Headlines

Amazon RDS for SQL Server supports Bring Your Own Media — Customers who migrate SQL Server applications from on-premises environments can now reuse their existing Microsoft SQL Server licenses, including Software Assurance, through Microsoft’s License Mobility program on Amazon RDS. BYOM is integrated with AWS License Manager for tracking license usage and compliance. Read more.

Amazon Cognito now supports multi-Region replication — You can now synchronize user and machine identity data, including credentials, user pool configurations, and federation setups, to a secondary user pool in a standby Region in near real-time. In the event of a disruption in the primary Region, signed-in users continue accessing their applications without re-authenticating, and registered users can sign in with their existing credentials. Multi-Region replication is available as an add-on for user pools in Essentials or Plus feature tiers across 16 Regions. Read more.

GPT-5.5, GPT-5.4, and Codex from OpenAI are now generally available on Amazon Bedrock — You can now use GPT-5.5 and GPT-5.4 in production workloads on Amazon Bedrock and build with Codex for AI-powered software development, with the same security, governance, and operational controls you already use across AWS. GPT-5.5 is the most capable model from OpenAI, excelling at agentic coding, data analysis, and multi-step autonomous tasks. Codex is available through the Codex App, the Codex CLI, and IDE integrations with Visual Studio Code, JetBrains, and Xcode. Pricing matches OpenAI first-party rates, and usage counts toward existing AWS commitments. Read more.

Last week’s launches

Here are some launches and updates from this past week that caught my attention:

• Amazon Bedrock adds CloudWatch metrics for OpenAI- and Anthropic-compatible APIs — You can now monitor inference traffic to the bedrock-mantle endpoint with CloudWatch metrics, including inference counts, input and output token totals, and client error counts at account, project, model, and project-and-model granularity.
• Amazon Bedrock launches a redesigned console optimized for OpenAI- and Anthropic-compatible APIs — A refreshed console workflow with a model catalog, side-by-side comparison, project-based organization, and project-aware documentation with pre-filled code snippets.
• Amazon Bedrock AgentCore Identity now supports bring-your-own secrets with AWS Secrets Manager — Customers can now reference existing AWS Secrets Manager secret ARNs in AgentCore Identity Credential Providers, enabling full ownership of secrets governance including custom CMKs, tagging strategies, and automatic rotation.
• AWS Step Functions adds AgentCore-powered agentic reasoning step — You can now add AI agent reasoning steps to your Step Functions workflows through an integration with the managed harness in Amazon Bedrock AgentCore. Run multiple agents in parallel or sequence, add human approval, and trace every agent decision.
• Amazon EKS and Amazon EKS Distro now support Kubernetes version 1.36 — Kubernetes 1.36 promotes User Namespaces to GA, introduces Mutating Admission Policies, In-Place Pod-Level Resources Vertical Scaling, and Resource Health Status reporting. Available in all Regions where EKS is available.
• Amazon ECS Managed Instances now supports AWS Trainium and AWS Inferentia — You can now create an ECS Managed Instances capacity provider with Inferentia2, Trainium1, and Trainium2 instance types and have Amazon ECS automatically allocate all accelerator resources to your workload.
• Amazon Quick now supports VPC connectivity for MCP connections — Enterprise customers can now connect privately hosted Model Context Protocol (MCP) servers to Amazon Quick through VPC, enabling secure access to proprietary applications and internal tools without exposing them to the internet.
• AWS Cost and Usage Report 2.0 now supports Athena and Redshift integration — CUR 2.0 exports are automatically delivered in the optimal format for your chosen query engine, with supporting infrastructure templates, table definitions, and data loading instructions.
• Amazon Location Service announces public transit and intermodal routing — The Routes API now supports two new travel modes, Transit and Intermodal, to plan journeys combining public transportation with walking, driving, taxi, and rental segments across 13 Regions.

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

Upcoming AWS events

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events as well as AWS Summits and AWS Community Days. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

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

— seb

The post AWS Weekly Roundup: BYOM for Amazon RDS for SQL Server, AWS IoT Device SDK for Swift, and more (June 8, 2026) appeared first on The NOKIA Blog.

Late March took me to Seattle for the Specialist Tech Conference, one of the most energizing gatherings of AWS specialists from around the world. It was an incredible opportunity to connect with peers, exchange experiences, and go deep on the latest advancements in Generative AI and Amazon Bedrock — and a powerful reminder of something I truly believe in: when specialists come together to challenge each other, explore edge cases, and co-create solutions, the impact goes far beyond the meeting room. In a fast-moving space like AI, having a strong internal community isn’t a nice-to-have — it’s a competitive advantage.

Now, let’s get into this week’s AWS news…

Headlines

Anthropic partnership: Claude on AWS Trainium and Graviton, and Claude Cowork in Amazon Bedrock – This week, AWS and Anthropic deepened their product collaboration in meaningful ways for builders. Anthropic is now training its most advanced foundation models on AWS Trainium and Graviton infrastructure, co-engineering directly at the silicon level with Annapurna Labs to maximize computational efficiency from the hardware up through the full stack.

Claude Cowork is now available in Amazon Bedrock — Claude Cowork brings Anthropic’s collaborative AI capabilities directly to enterprise builders within the AWS ecosystem, enabling teams to work alongside Claude as a true collaborator, not just a tool. You can now deploy Claude Cowork within your existing Amazon Bedrock environment, keeping your data secure within AWS while leveraging the full power of Claude for team-based AI workflows.

Claude Platform on AWS (Coming soon) — A unified developer experience to build, deploy, and scale Claude-powered applications without leaving AWS. If you’re building with Generative AI on AWS, this is a significant step forward in what you’ll be able to do with Claude directly through Amazon Bedrock.

Meta signs agreement with AWS to power agentic AI on Amazon’s Graviton chips — Meta has signed an agreement to deploy AWS Graviton processors at scale, starting with tens of millions of Graviton cores to power CPU-intensive agentic AI workloads — including real-time reasoning, code generation, search, and multi-step task orchestration.

Last week’s launches

Here are some launches and updates from this past week that caught my attention:

• AWS Lambda functions can now mount Amazon S3 buckets as file systems with S3 Files — You can now mount Amazon S3 buckets as file systems in AWS Lambda using S3 Files, enabling your functions to perform standard file operations without downloading data for processing. Built on Amazon EFS, S3 Files provides the simplicity of a file system with the scalability, durability, and cost-effectiveness of S3 — and multiple Lambda functions can connect to the same file system simultaneously, sharing data through a common workspace. This is particularly valuable for AI and machine learning workloads where agents need to persist memory and share state across pipeline steps.
• Amazon EKS Hybrid Nodes gateway for hybrid Kubernetes networking — Amazon Elastic Kubernetes Service now offers the Amazon EKS Hybrid Nodes gateway, which automates networking between your EKS cluster VPC and Kubernetes Pods running on EKS Hybrid Nodes. You can now eliminate the need to make on-premises pod networks routable or coordinate network infrastructure changes, greatly simplifying hybrid Kubernetes environments. The gateway automatically enables pod-to-pod traffic across cloud and on-premises environments, control plane-to-webhook communication, and connectivity for AWS services like Application Load Balancers, and is available at no additional charge.
• Amazon Aurora Serverless: Up to 30% better performance, smarter scaling, and still scales to zero — Amazon Aurora Serverless just got faster and smarter, with up to 30% better performance than the previous version and an enhanced scaling algorithm designed to handle workloads where multiple tasks compete for resources — like busy APIs and agentic AI applications with bursts of activity and long idle windows. You can now run even more demanding workloads serverlessly, paying only for what you use, and automatically scaling to zero when not in use. All improvements are available in platform version 4 at no additional cost.
• Amazon Bedrock AgentCore adds new features to help developers build agents faster — Amazon Bedrock AgentCore introduces a managed harness (preview), the AgentCore CLI, and AgentCore skills for coding assistants, helping developers go from idea to working agent prototype faster. The managed harness lets you define an agent by specifying a model, system prompt, and tools and run it immediately with no orchestration code required — and when you’re ready for full control, you can export the harness orchestration as Strands-based code. The AgentCore CLI deploys your agents with the governance and auditability of infrastructure-as-code (AWS CDK today, Terraform coming soon), and is available in 14 AWS Regions at no additional charge.

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

Other AWS news

Here are some additional posts and resources that you might find interesting:

• Introducing granular cost attribution for Amazon Bedrock — This post walks through how Amazon Bedrock’s granular cost attribution works and covers practical example cost tracking scenarios. You can now tag and track Bedrock usage costs at a finer level of detail — useful for organizations running multiple teams or projects on Bedrock who need precise cost visibility and chargeback capabilities.
• Automating Incident Investigation with AWS DevOps Agent and Salesforce MCP Server — This post (co-written with Salesforce) shows how AWS DevOps Agent, integrated with the Salesforce MCP Server, automates the full lifecycle of infrastructure incident investigation — from identifying issues and diagnosing root causes to notifying customers through Salesforce Service Cloud. It’s a compelling real-world example of how AI agents and MCP-based tool connectivity are reshaping DevOps workflows in production, dramatically reducing mean time to resolution.
• Microcredentials from AWS are now free — Here’s why that matters — You can now access AWS microcredentials at no cost through AWS Skill Builder in all countries where the platform is offered. Unlike traditional multiple-choice certifications, microcredentials are hands-on assessments that place builders in simulated business scenarios where they configure, troubleshoot, and optimize directly in a live AWS environment — the same way they would on the job. A great opportunity to validate real cloud skills without a cost barrier.
• Amazon SageMaker AI now supports optimized generative AI inference recommendations — You can now use Amazon SageMaker AI to automatically identify optimized deployment configurations for your generative AI models, including instance type, container, and inference parameters. This new capability takes the guesswork out of tuning inference infrastructure, helping you reduce costs and improve latency for your AI applications in production.

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

• What’s Next with AWS — Tune in on April 28 for What’s Next with AWS, a virtual event featuring the latest announcements and product updates directly from AWS teams. A great opportunity to get up to speed on what’s new before diving into the week’s launches.
• AWS Summits — AWS Summits are free in-person events where you can explore the latest in cloud and AI innovation, learn best practices, and network with builders and experts. Coming up in May: Singapore (May 6), Tel Aviv (May 6), Warsaw (May 6), Stockholm (May 7), Sydney (May 13–14), Hamburg (May 20), Seoul (May 20), Amsterdam (May 27), Bangkok (May 28), Milan (May 28), and Mumbai (May 28). And in June, join us in Los Angeles (June 10). Check the full schedule and register at the link above.
• AWS Community Days — Community-led conferences where content is planned, sourced, and delivered by community leaders, featuring technical discussions, workshops, and hands-on labs. Upcoming events include Athens, Greece (April 28), Vancouver, Canada (May 1), İstanbul, Türkiye (May 9), and Panama City, Panama (May 23). If you’re in Latin America, mark your calendar for the AWS Community Day Belo Horizonte (August 22) — registration is open at awscommunityday.com.br.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

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

— Daniel Abib

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The post AWS Weekly Roundup: Anthropic & Meta partnership, AWS Lambda S3 Files, Amazon Bedrock AgentCore CLI, and more (April 27, 2026) appeared first on The NOKIA Blog.

I’m excited to announce Amazon S3 Files, a new file system that seamlessly connects any AWS compute resource with Amazon Simple Storage Service (Amazon S3).

More than a decade ago, as an AWS trainer, I spent countless hours explaining the fundamental differences between object storage and file systems. My favorite analogy was comparing S3 objects to books in a library (you can’t edit a page, you need to replace the whole book) versus files on your computer that you can modify page by page. I drew diagrams, created metaphors, and helped customers understand why they needed different storage types for different workloads. Well, today that distinction becomes a bit more flexible.

With S3 Files, Amazon S3 is the first and only cloud object store that offers fully-featured, high-performance file system access to your data. It makes your buckets accessible as file systems. This means changes to data on the file system are automatically reflected in the S3 bucket and you have fine-grained control over synchronization. S3 Files can be attached to multiple compute resources enabling data sharing across clusters without duplication.

Until now, you had to choose between Amazon S3 cost, durability, and the services that can natively consume data from it or a file system’s interactive capabilities. S3 Files eliminates that tradeoff. S3 becomes the central hub for all your organization’s data. It’s accessible directly from any AWS compute instance, container, or function, whether you’re running production applications, training ML models, or building agentic AI systems.

You can access any general purpose bucket as a native file system on your Amazon Elastic Compute Cloud (Amazon EC2) instances, containers running on Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS), or AWS Lambda functions. The file system presents S3 objects as files and directories, supporting all Network File System (NFS) v4.1+ operations like creating, reading, updating, and deleting files.

As you work with specific files and directories through the file system, associated file metadata and contents are placed onto the file system’s high-performance storage. By default, files that benefit from low-latency access are stored and served from the high performance storage. For files not stored on high performance storage such as those needing large sequential reads, S3 Files automatically serves those files directly from Amazon S3 to maximize throughput. For byte-range reads, only the requested bytes are transferred, minimizing data movement and costs.

The system also supports intelligent pre-fetching to anticipate your data access needs. You have fine-grained control over what gets stored on the file system’s high performance storage. You can decide whether to load full file data or metadata only, which means you can optimize for your specific access patterns.

Under the hood, S3 Files uses Amazon Elastic File System (Amazon EFS) and delivers ~1ms latencies for active data. The file system supports concurrent access from multiple compute resources with NFS close-to-open consistency, making it ideal for interactive, shared workloads that mutate data, from agentic AI agents collaborating through file-based tools to ML training pipelines processing datasets.

Let me show you how to get started.

Creating my first Amazon S3 file system, mounting, and using it from an EC2 instance is straightforward.

I have an EC2 instance and a general purpose bucket. In this demo, I configure an S3 file system and access the bucket from an EC2 instance, using regular file system commands.

For this demo, I use the AWS Management Console. You can also use the AWS Command Line Interface (AWS CLI) or infrastructure as code (IaC).

Here is the architecture diagram for this demo.

Step 1: Create an S3 file system.

On the Amazon S3 section of the console, I choose File systems and then Create file system.

I enter the name of the bucket I want to expose as a file system and choose Create file system.

Step 2: Discover the mount target.

A mount target is a network endpoint that will live in my virtual private cloud (VPC). It allows my EC2 instance to access the S3 file system.

The console creates the mount targets automatically. I take notes of the Mount target IDs on the Mount targets tab.

When using the CLI, two separate commands are necessary to create the file system and its mount targets. First, I create the S3 file system with create-file-system. Then, I create the mount target with create-mount-target.

Step 3: Mount the file system on my EC2 instance.

After it’s connected to an EC2 instance, I type:

sudo mkdir /home/ec2-user/s3files sudo mount -t s3files fs-0aa860d05df9afdfe:/ /home/ec2-user/s3files

I can now work with my S3 data directly through the mounted file system in ~/s3files, using standard file operations.

When I make updates to my files in the file system, S3 automatically manages and exports all updates as a new object or a new version on an existing object back in my S3 bucket within minutes.

Changes made to objects on the S3 bucket are visible in the file system within a few seconds but can sometimes take a minute or longer.

# Create a file on the EC2 file system 
echo "Hello S3 Files" > s3files/hello.txt 

# and verify it's here 
ls -al s3files/hello.txt
 -rw-r--r--. 1 ec2-user ec2-user 15 Oct 22 13:03 s3files/hello.txt 

# See? the file is also on S3 
aws s3 ls s3://s3files-aws-news-blog/hello.txt 
2025-10-22 13:04:04 15 hello.txt 

# And the content is identical! 
aws s3 cp s3://s3files-aws-news-blog/hello.txt . && cat hello.txt
Hello S3 Files

Things to know

Let me share some important technical details that I think you’ll find useful.

Another question I frequently hear in customer conversations is about choosing the right file service for your workloads. Yes, I know what you’re thinking: AWS and its seemingly overlapping services, keeping cloud architects entertained during their architecture review meetings. Let me help demystify this one.

S3 Files works best when you need interactive, shared access to data that lives in Amazon S3 through a high performance file system interface. It’s ideal for workloads where multiple compute resources—whether production applications, agentic AI agents using Python libraries and CLI tools, or machine learning (ML) training pipelines—need to read, write, and mutate data collaboratively. You get shared access across compute clusters without data duplication, sub-millisecond latency, and automatic synchronization with your S3 bucket.

For workloads migrating from on-premises NAS environments, Amazon FSx provides the familiar features and compatibility you need. Amazon FSx is also ideal for high-performance computing (HPC) and GPU cluster storage with Amazon FSx for Lustre. It’s particularly valuable when your applications require specific file system capabilities from Amazon FSx for NetApp ONTAP, Amazon FSx for OpenZFS, or Amazon FSx for Windows File Server.

Pricing and availability

S3 Files is available today in all commercial AWS Regions.

You pay for the portion of data stored in your S3 file system, for small file read and all write operations to the file system, and for S3 requests during data synchronization between the file system and the S3 bucket. The Amazon S3 pricing page has all the details.

From discussions with customers, I believe S3 Files helps simplify cloud architectures by eliminating data silos, synchronization complexity, and manual data movement between objects and files. Whether you’re running production tools that already work with file systems, building agentic AI systems that rely on file-based Python libraries and shell scripts, or preparing datasets for ML training, S3 Files lets these interactive, shared, hierarchical workloads access S3 data directly without choosing between the durability of Amazon S3 and cost benefits and a file system’s interactive capabilities. You can now use Amazon S3 as the place for all your organizations’ data, knowing the data is accessible directly from any AWS compute instance, container, and function.

To learn more and get started, visit the S3 Files documentation.

I’d love to hear how you use this new capability. Feel free to share your feedback in the comments below.

— seb

The post Launching S3 Files, making S3 buckets accessible as file systems appeared first on The NOKIA Blog.

AWS has reached its 20th anniversary! With a steady pace of innovation, AWS has grown to offer over 240 comprehensive cloud services and continues to launch thousands of new features annually for millions of customers. During this time, over 4,700 posts have been published on this blog—more than double the number since Jeff Barr wrote the 10th anniversary post.

AWS changed my life

Reflecting on what I was doing 20 years ago, I met Jeff in Seoul on March 13, 2006, when he came as the keynote speaker for the Korea NGWeb conference. At that time, Amazon was one of the first pioneers to initiate an API economy, introducing ecommerce API services. After the keynote speech, he returned home that evening, and I believe he wrote the Amazon S3 launch blog post on the flight back to the United States.

That short meeting with him brought significant changes to my life. He became my role model as a blogger, and I began building API-based services in my company and opening them to third-party developers. When I was a PhD student while taking a break from work, I realized that for individual researchers like me, AWS Cloud services are powerful tools for conducting large-scale research projects. After returning to work, my company became one of the first AWS customers in Korea in 2014. Countless developers—myself included—have embraced cloud computing and actively used its capabilities to accomplish what was previously impossible.

Over the past decade, the technology landscape has transformed dramatically. Deep learning emerged as a breakthrough in AI, evolving through generative AI based on large language models (LLMs) to today’s agentic AI technology. Jeff wrote, “When looking into the future, you need to be able to distinguish between flashy distractions and genuine trends, while remaining flexible enough to pivot if yesterday’s niche becomes today’s mainstream technology.” This principle guides how AWS approaches innovation—we start by listening to what customers truly need. The real trend isn’t pursuing every emerging technology, but rather reimagining solutions that address customers’ most critical challenges.

20 years of AWS

For the first 10 years, Jeff selected his favorite AWS launches and blog posts. Amazon S3, Amazon EC2 (2006), Amazon Relational Database Service, Amazon Virtual Private Cloud (2009), Amazon DynamoDB, Amazon Redshift (2012), Amazon WorkSpaces, Amazon Kinesis (2013), AWS Lambda (2014), and AWS IoT (2015).

While I also hate to play favorites, I want to choose some of my favorite AWS blog posts of the past decade.

• Deploying containers easily (2014) – Amazon Elastic Container Service makes it straightforward for you to run any number of containers across a managed cluster of Amazon EC2 instances using powerful APIs and other tools. In 2017, we launched Amazon Elastic Kubernetes Service as a fully managed Kubernetes service and AWS Fargate as a serverless deployment option.
• High availability database at global scale (2017) – Amazon Aurora is a modern relational database service offering performance and high availability at scale. In 2018, we launched Amazon Aurora Serverless v1, and this serverless database evolved to Amazon Aurora Serverless v2 to scale down to zero. In 2025, we also launched Amazon Aurora DSQL is the fastest serverless distributed SQL database for always available applications.
• Machine learning (ML) at your fingertips (2017) – Amazon SageMaker is a fully managed end-to-end ML service that data scientists, developers, and ML experts can use to quickly build, train, and host machine learning models at scale. In 2024, we launched the next generation of Amazon SageMaker, a unified platform for data, analytics, and AI and introduced Amazon SageMaker AI to focus specifically on building, training, and deploying AI and ML models at scale.
• Best price performance for cloud workloads (2018) – We launched Amazon EC2 A1 instances powered by the first generation of Arm-based AWS Graviton Processors designed to deliver the best price performance for your cloud workloads. Last year, we previewed EC2 M9g instances powered by AWS Graviton5 processors. Over 90,000 AWS customers have reaped the benefits of Graviton supporting popular AWS services such as Amazon ECS and Amazon EKS, AWS Lambda, Amazon RDS, Amazon ElastiCache, Amazon EMR, and Amazon OpenSearch Service.
• Run AWS Cloud in your data center (2019) – AWS Outposts is a family of fully managed services delivering AWS infrastructure and services to virtually any on-premises or edge location for a truly consistent hybrid experience. Now, AWS Outposts is available in a variety of form factors, from 1U and 2U Outposts servers to 42U Outposts racks, and multiple rack deployments. Customers such as DISH, Fanduel, Morningstar, Philips, and others use Outposts in workloads requiring low latency access to on-premises systems, local data processing, data residency, and application migration with local system interdependencies.
• Best price performance for ML workloads (2019) – We launched Amazon EC2 Inf1 instances powered by the first generation of AWS Inferentia chips designed to provide fast, low-latency inferencing. In 2022, we launched Amazon EC2 Trn1 instances powered by the first generation of AWS Trainium chips optimized for high performance AI training. Last year, we launched Amazon EC2 Trn3 UltraServers powered by Trainium3 to deliver the best token economics for next-generation generative AI applications. Customers such as Anthropic, Decart, poolside, Databricks, Ricoh, Karakuri, SplashMusic, and others are realizing performance and cost benefits of Trainium-based instances and UltraServers.
• Build your generative AI apps on AWS (2023) – Amazon Bedrock is a fully managed service that offers a choice of industry leading AI models along with a broad set of capabilities that you need to build generative AI applications, simplifying development with security, privacy, and responsible AI. Last year, we introduced Amazon Bedrock AgentCore, an agentic platform for building, deploying, and operating effective agents securely at scale. Now, more than 100,000 customers worldwide choose Amazon Bedrock to deliver personalized experiences, automate complex workflows, and uncover actionable insights.
• Your AI coding companion (2023) – We launched Amazon CodeWhisperer as the industry’s first cloud-based AI coding assistant service. The service delivered code generation from comments, open-source code reference tracking, and vulnerability scanning capabilities. In 2024, we rebranded the service to Amazon Q Developer and expanded its features to include a chat-based assistant in the console, project-based code generation, and code transformation tools. In 2025, this service evolved into Kiro, a new agentic AI development tool that brings structure to AI coding through spec-driven development, taking projects from prototype to production. Recently, Kiro previewed an autonomous agent, a frontier agent that works independently on development tasks, maintaining context and learning from every interaction.
• Broaden your AI model choices (2024) – We launched Amazon Titan models further increasing cost-effective AI model choice for text and multimodal needs in Amazon Bedrock. At AWS re:Invent 2024, we announced Amazon Nova models that delivers frontier intelligence and industry leading price performance. Now Amazon Nova has a portfolio of AI offerings—including Amazon Nova models, Amazon Nova Forge, a new service to build your own frontier models; and Amazon Nova Act, a new service to build agents that automate browser-based UI workflows powered by a custom Amazon Nova 2 Lite model.

Build with AI: Your path forward

A decade ago, AWS responded to the emergence of deep learning by launching the broadest and deepest ML services, such as Amazon SageMaker, democratizing AI for a wide range of customers—from individual developers and startups to large enterprises—regardless of their technical expertise.

AI technology has advanced significantly, but building and deploying AI models and applications still remains complex for many developers and organizations. AWS offers the broadest selection of AI models through Amazon Bedrock, including leading providers such as Anthropic and OpenAI. By using our model training and inference infrastructure and responsible AI both practical and scalable, you can accelerate trusted AI innovation while maintaining control of your data and costs—all built on our global infrastructure’s operational excellence.

Reinvent your idea, keep on learning, build confidently with AI you can trust, and share your successes with us! New AWS customers receive up to $200 in credits to try AWS AI for free. If you’re a student, start building with Kiro for free using 1,000 credits per month for one year.

— Channy

The post 20 years in the AWS Cloud – how time flies! appeared first on The NOKIA Blog.

This past week, I’ve been deep in the trenches helping customers transform their businesses through AI-DLC (AI-Driven Lifecycle) workshops. Throughout 2026, I’ve had the privilege of facilitating these sessions for numerous customers, guiding them through a structured framework that helps organizations identify, prioritize, and implement AI use cases that deliver measurable business value.

AI-DLC is a methodology that takes companies from AI experimentation to production-ready solutions by aligning technical capabilities with business outcomes. If you’re interested in learning more, check out this blog post that dives deeper into the framework, or watch as Riya Dani teaches me all about AI-DLC on our recent GenAI Developer Hour livestream!

Now, let’s get into this week’s AWS news…

OpenAI and Amazon announced a multi-year strategic partnership to accelerate AI innovation for enterprises, startups, and end consumers around the world. Amazon will invest $50 billion in OpenAI, starting with an initial $15 billion investment and followed by another $35 billion in the coming months when certain conditions are met. AWS and OpenAI are co-creating a Stateful Runtime Environment powered by OpenAI models, available through Amazon Bedrock, which allows developers to keep context, remember prior work, work across software tools and data sources, and access compute.

AWS will serve as the exclusive third-party cloud distribution provider for OpenAI Frontier, enabling organizations to build, deploy, and manage teams of AI agents. OpenAI and AWS are expanding their existing $38 billion multi-year agreement by $100 billion over 8 years, with OpenAI committing to consume approximately 2 gigawatts of Trainium capacity, spanning both Trainium3 and next-generation Trainium4 chips.

Last week’s launches

Here are some launches and updates from this past week that caught my attention:

• AWS Security Hub Extended offers full-stack enterprise security with curated partner solutions — AWS launched Security Hub Extended, a plan that simplifies procurement, deployment, and integration of full-stack enterprise security solutions including 7AI, Britive, CrowdStrike, Cyera, Island, Noma, Okta, Oligo, Opti, Proofpoint, SailPoint, Splunk, Upwind, and Zscaler. With AWS as the seller of record, customers benefit from pre-negotiated pay-as-you-go pricing, a single bill, no long-term commitments, unified security operations within Security Hub, and unified Level 1 support for AWS Enterprise Support customers.
• Transform live video for mobile audiences with AWS Elemental Inference — AWS launched Elemental Inference, a fully managed AI service that automatically transforms live and on-demand video for mobile and social platforms in real time. The service uses AI-powered cropping to create vertical formats optimized for TikTok, Instagram Reels, and YouTube Shorts, and automatically extracts highlight clips with 6-10 second latency. Beta testing showed large media companies achieved 34% or more savings on AI-powered live video workflows. Deep dive into the Fox Sports implementation.
• MediaConvert introduces new video probe API — AWS Elemental MediaConvert introduced a free Probe API for quick metadata analysis of media files, reading header metadata to return codec specifications, pixel formats, and color space details without processing video content.
• OpenAI-compatible Projects API in Amazon Bedrock — Projects API provides application-level isolation for your generative AI workloads using OpenAI-compatible APIs in the Mantle inference engine in Amazon Bedrock. You can organize and manage your AI applications with improved access control, cost tracking, and observability across your organization.
• Amazon Location Service introduces LLM Context — Amazon Location launched curated AI Agent context as a Kiro power, Claude Code plugin, and agent skill in the open Agent Skills format, improving code accuracy and accelerating feature implementation for location-based capabilities.
• Amazon EKS Node Monitoring Agent is now open source — The Amazon EKS Node Monitoring Agent is now open source on GitHub, allowing visibility into implementation, customization, and community contributions.
• AWS AppConfig integrates with New Relic — AWS AppConfig launched integration with New Relic Workflow Automation for automated, intelligent rollbacks during feature flag deployments, reducing detection-to-remediation time from minutes to seconds.

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

Other AWS news

Here are some additional posts and resources that you might find interesting:

From AWS community

Here are my personal favorite posts from AWS community:

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

• AWS at NVIDIA GTC 2026 — Join us at our AWS sessions, booths, demos, ancillary events in NVIDIA GTC 2026 on March 16 – 19, 2026 in San Jose. You can receive 20% off event passes through AWS and request a 1:1 meeting at GTC.
• AWS Summits — Join AWS Summits in 2026, free in-person events where you can explore emerging cloud and AI technologies, learn best practices, and network with industry peers and experts. Upcoming Summits include Paris (April 1), London (April 22), and Bengaluru (April 23–24).
• AWS Community Days — Community-led conferences where content is planned, sourced, and delivered by community leaders. Upcoming events include JAWS Days in Tokyo (March 7), Chennai (March 7), Slovakia (March 11), and Pune (March 21).

Browse here for upcoming AWS led in-person and virtual events, startup events, and developer-focused events.

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

The post AWS Weekly Roundup: OpenAI partnership, AWS Elemental Inference, Strands Labs, and more (March 2, 2026) appeared first on The NOKIA Blog.

I joined AWS in 2021, and since then I’ve watched the Amazon Elastic Compute Cloud (Amazon EC2) instance family grow at a pace that still surprises me. From AWS Graviton-powered instances to specialized accelerated computing options, it feels like every few months there’s a new instance type landing that pushes performance boundaries further. As of February 2026, AWS offers over 1,160 Amazon EC2 instance types, and that number keeps climbing.

This week’s opening news is a good example: The general availability of Amazon EC2 M8azn instances. These are general purpose, high-frequency, high-network instances powered by fifth generation AMD EPYC processors, offering the highest maximum CPU frequency in the cloud at 5 GHz. Compared to the previous generation M5zn instances, M8azn instances deliver up to 2x compute performance, 4.3x higher memory bandwidth, and a 10x larger L3 cache. They also provide up to 2x networking throughput and up to 3x Amazon Elastic Block Store (Amazon EBS) throughput compared with M5zn.

Built on the AWS Nitro System using sixth generation Nitro Cards, M8azn instances target workloads such as real-time financial analytics, high-performance computing, high-frequency trading, CI/CD pipelines, gaming, and simulation modeling across automotive, aerospace, energy, and telecommunications. The instances feature a 4:1 ratio of memory to vCPU and are available in 9 sizes ranging from 2 to 96 vCPUs with up to 384 GiB of memory, including two bare metal variants. For more information visit the Amazon EC2 M8azn instance page.

Last week’s launches

Here are some of the other announcements from last week:

• Amazon Bedrock adds support for six fully managed open weights models – Amazon Bedrock now supports DeepSeek V3.2, MiniMax M2.1, GLM 4.7, GLM 4.7 Flash, Kimi K2.5, and Qwen3 Coder Next. These models span frontier reasoning and agentic coding workloads. DeepSeek V3.2 and Kimi K2.5 target reasoning and agentic intelligence, GLM 4.7 and MiniMax M2.1 support autonomous coding with large output windows, and Qwen3 Coder Next and GLM 4.7 Flash provide cost-efficient alternatives for production deployment. These models are powered by Project Mantle and provide out-of-the-box compatibility with OpenAI API specifications. With the launch, you can also use new open weight models–DeepSeek v3.2 , MiniMax 2.1, and Qwen3 Coder Next in Kiro, a spec-driven AI development tool.
• Amazon Bedrock expands support for AWS PrivateLink – Amazon Bedrock now supports AWS PrivateLink for the bedrock-mantle endpoint, in addition to existing support for the bedrock-runtime endpoint. The bedrock-mantle endpoint is powered by Project Mantle, a distributed inference engine for large-scale machine learning model serving on Amazon Bedrock. Project Mantle provides serverless inference with quality of service controls, higher default customer quotas with automated capacity management, and out-of-the-box compatibility with OpenAI API specifications. AWS PrivateLink support for OpenAI API-compatible endpoints is available in 14 AWS Regions. To get started, visit the Amazon Bedrock console or the OpenAI API compatibility documentation.
• Amazon EKS Auto Mode announces enhanced logging for managed Kubernetes capabilities – You can now configure log delivery sources using Amazon CloudWatch Vended Logs in Amazon EKS Auto Mode. This helps you collect logs from Auto Mode’s managed Kubernetes capabilities for compute autoscaling, block storage, load balancing, and pod networking. Each Auto Mode capability can be configured as a CloudWatch Vended Logs delivery source with built-in AWS authentication and authorization at a reduced price compared to standard CloudWatch Logs. You can deliver logs to CloudWatch Logs, Amazon S3, or Amazon Data Firehose destinations. This feature is available in all Regions where EKS Auto Mode is available.
• Amazon OpenSearch Serverless now supports Collection Groups – You can use new Collection Groups to share OpenSearch Compute Units (OCUs) across collections with different AWS Key Management Service (AWS KMS) keys. Collection Groups reduce overall OCU costs through a shared compute model while maintaining collection-level security and access controls. They also introduce the ability to specify minimum OCU allocations alongside maximum OCU limits, providing guaranteed baseline capacity at startup for latency-sensitive applications. Collection Groups are available in all Regions where Amazon OpenSearch Serverless is currently available.
• Amazon RDS now supports backup configuration when restoring snapshots – You can view and modify the backup retention period and preferred backup window before and during snapshot restore operations. Previously, restored database instances and clusters inherited backup parameter values from snapshot metadata and could only be modified after restore was complete. You can now view backup settings as part of automated backups and snapshots, and specify or modify these values when restoring, eliminating the need for post-restoration modifications. This is available for all Amazon RDS database engines (MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, and Db2) and Amazon Aurora (MySQL-Compatible and PostgreSQL-Compatible editions) in all AWS commercial Regions and AWS GovCloud (US) Regions at no additional cost.

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

Upcoming AWS events

Check your calendar and sign up for upcoming AWS events:

AWS Summits – Join AWS Summits in 2026, free in-person events where you can explore emerging cloud and AI technologies, learn best practices, and network with industry peers and experts. Upcoming Summits include Paris (April 1), London (April 22), and Bengaluru (April 23–24).

AWS AI and Data Conference 2026 – A free, single-day in-person event on March 12 at the Lyrath Convention Centre in Ireland. The conference covers designing, training, and deploying agents with Amazon Bedrock, Amazon SageMaker, and QuickSight, integrating them with AWS data services, and applying governance practices to operate them at scale. The agenda includes strategic guidance and hands-on labs for architects, developers, and business leaders.

AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders, featuring technical discussions, workshops, and hands-on labs. Upcoming events include Ahmedabad (February 28), Slovakia (March 11), and Pune (March 21).

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS led in-person and virtual events and developer-focused events.

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

— Esra

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The post AWS Weekly Roundup: Amazon EC2 M8azn instances, new open weights models in Amazon Bedrock, and more (February 16, 2026) appeared first on The NOKIA Blog.

Today, we’re announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) G7e instances that deliver cost-effective performance for generative AI inference workloads and the highest performance for graphics workloads.

G7e instances are accelerated by the NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs and are well suited for a broad range of GPU-enabled workloads including spatial computing and scientific computing workloads. G7e instances deliver up to 2.3 times inference performance compared to G6e instances.

Improvements made compared to predecessors:

• NVIDIA RTX PRO 6000 Blackwell GPUs — NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs offer two times the GPU memory and 1.85 times the GPU memory bandwidth compared to G6e instances. By using the higher GPU memory offered by G7e instances, you can run medium-sized models of up to 70B parameters with FP8 precision on a single GPU.
• NVIDIA GPUDirect P2P — For models that are too large to fit into the memory of a single GPU, you can split the model or computations across multiple GPUs. G7e instances reduce the latency of your multi-GPU workloads with support for NVIDIA GPUDirect P2P, which enables direct communication between GPUs over PCIe interconnect. These instances offer the lowest peer to peer latency for GPUs on the same PCIe switch. Additionally, G7e instances offer up to four times the inter-GPU bandwidth compared to L40s GPUs featured in G6e instances, boosting the performance of multi-GPU workloads. These improvements mean you can run inference for larger models across multiple GPUs offering up to 768 GB of GPU memory in a single node.
• Networking — G7e instances offer four times the networking bandwidth compared to G6e instances, which means you can use the instance for small-scale multi-node workloads. Additionally, multi-GPU G7e instances support NVIDIA GPUDirect Remote Direct Memory Access (RDMA) with Elastic Fabric Adapter (EFA), which reduces the latency of remote GPU-to-GPU communication for multi-node workloads. These instance sizes also support NVIDIA GPUDirectStorage with Amazon FSx for Lustre, which increases throughput by up to 1.2 Tbps to the instances compared to G6e instances, which means you can quickly load your models.

EC2 G7e specifications

G7e instances feature up to 8 NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs with up to 768 GB of total GPU memory (96 GB of memory per GPU) and Intel Emerald Rapids processors. They also support up to 192 vCPUs, up to 1,600 Gbps of network bandwidth, up to 2,048 GiB of system memory, and up to 15.2 TB of local NVMe SSD storage.

Here are the specs:

To get started with G7e instances, you can use the AWS Deep Learning AMIs (DLAMI) for your machine learning (ML) workloads. To run instances, you can use AWS Management Console, AWS Command Line Interface (AWS CLI) or AWS SDKs. For a managed experience, you can use G7e instances with Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), and AWS Parallel Computing Service (AWS PCS). Support for Amazon SageMaker AI is also coming soon.

Now available

Amazon EC2 G7e instances are available today in the US East (N. Virginia) and US East (Ohio) AWS Regions. For Regional availability and a future roadmap, search the instance type in the CloudFormation resources tab of AWS Capabilities by Region.

The instances can be purchased as On-Demand Instances, Savings Plan, and Spot Instances. G7e instances are also available in Dedicated Instances and Dedicated Hosts. To learn more, visit the Amazon EC2 Pricing page.

Give G7e instances a try in the Amazon EC2 console. To learn more, visit the Amazon EC2 G7e instances page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

The post Announcing Amazon EC2 G7e instances accelerated by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs appeared first on The NOKIA Blog.