Organizations building event-driven applications on Amazon EventBridge typically start with a single custom event bus in one account. This works well when a single team owns the architecture. As adoption grows across the organization, though, things get complicated. AWS best practices recommend a multi-account structure, which means each team runs in its own account. To route events between them, teams create multiple event buses connected through cross-account rules or bus-to-bus configurations. This workaround reintroduces the operational complexity that serverless architectures are meant to eliminate. Platform teams lose visibility into who is subscribing to which events, cross-account and bus-to-bus routing charges compound quickly, and teams that need capabilities like event ordering are forced to build complex workarounds or adopt entirely different technologies.

Today, we are announcing an enhanced custom event bus in Amazon EventBridge, purpose-built for organizations scaling event-driven applications across teams and accounts. With the new enhanced custom event bus, you can deploy a single, centralized event bus shared across all AWS accounts in your organization, with ordering guarantees, a simplified Subscriber resource, and a new pricing model that delivers improved economics at scale and cost allocation for publishers and subscribers.

Let’s try it out
To get started with an enhanced custom event bus, I navigated to the EventBridge console in the AWS Management Console and opened the Create custom event bus page. I selected Custom event bus, the recommended option labeled New. The page also offered Custom event bus – classic, which continues to receive events and route them with rules and targets. Below the selection, EventBridge showed how the new bus works. One shared bus serves every team in the organization. Publishers send events, subscribers consume only what they need, and EventBridge handles ordering, retention, routing, and delivery.

The Create custom event bus page. Custom event bus is the recommended new option, with ordered delivery, filter patterns, event replay, and sharing across your AWS organization. Custom event bus – classic remains available for existing workloads.

Next, I configured resource sharing. I turned on Enable event bus sharing and selected Allow sharing only within your organization. I chose AWS account ID as the principal type. I could also share with an organization, an organizational unit, or an AWS Identity and Access Management (IAM) role or user. Sharing uses AWS Resource Access Manager (AWS RAM), so I did not have to set up cross-account permissions or bus-to-bus routing myself.

Resource sharing on the new custom event bus. I enabled sharing within my organization through AWS RAM and selected an AWS account as the principal, which is how teams publish and subscribe on the same bus without extra routing.

Organization-wide sharing
With the new enhanced custom event bus, you can create a single event bus and share it across all AWS accounts in your organization. Platform teams deploy one bus and establish it as the central event backbone, eliminating the need to configure cross-account permissions or bus-to-bus routing. Application teams across your organization can publish and subscribe to events on the same bus without waiting for infrastructure provisioning.

Publishers send events without needing to know which teams consume them, and subscribers create their own Subscriptions independently. Platform teams maintain visibility into all event flows and fine-grained control over who can publish and consume events. The new enhanced custom event bus has a default quota of 10,000 Subscribers per bus, and you can request a higher quota. That reduces the fragmentation that occurs when subscriber limits force you to split across multiple buses.

Event ordering
Event-driven architectures work best when consumers are designed around asynchronous patterns, where the order of events does not matter. There are a few cases where order does matter. In a logistics application, driver location updates must arrive in sequence. Out-of-sequence events cause routing algorithms to make decisions based on stale data.

The enhanced custom event bus supports both patterns on the same bus. Publishers can include an EventGroupId when sending events. EventBridge delivers events that share the same EventGroupId in sequence to Subscribers that chose ordered delivery. Other subscribers on that bus can receive the same events without ordering. You can keep events for each driver in the correct order without building complex workarounds, while the rest of your consumers stay fully asynchronous.

To support ordered processing, the enhanced custom event bus includes synchronous invocation for targets like AWS Lambda. Synchronous mode confirms successful processing before acknowledging the event, eliminating the common pattern of placing Amazon Simple Queue Service (Amazon SQS) between an event bus and Lambda to ensure reliability.

Subscriptions
The enhanced custom event bus introduces the Subscriber resource, which combines event filtering, target configuration, retry policies, and dead-letter destinations into a single, manageable unit. Today, achieving the same outcome with EventBridge requires configuring separate rules, targets, and retry settings across multiple resources. Subscribers simplify this by giving each consumer one resource that defines what events they want, where to deliver them, and how to handle failures.

Subscribers also include variable start time options, making it easier for teams to onboard new consumers or replay events to recover from application errors or hydrate new applications.

Event evaluation
Publishers can turn on content-based deduplication so EventBridge detects and drops retries of the same event from the payload itself. You do not have to generate and track a deduplication ID when a timeout or a partial failure sends the same event twice. EventBridge hashes the meaningful parts of the event and collapses matches that arrive within five minutes, which gives those retries exactly-once delivery semantics instead of EventBridge’s usual at-least-once model. If you already stamp your own idempotency token, keep using it. Content-based deduplication is for sources that cannot reliably identify the same event on a retry.

Subscribers can use JSONata expressions to reshape an event before it reaches a target, extracting fields, renaming them, or computing new values when a downstream API expects a different shape. If you already produce Apache Avro or Protocol Buffers events, EventBridge can deserialize those payloads to JSON, allowing subscribers fine grained filtering and routing on the full event payload without having to consume, deserialize, and match or discard on their own.

New pricing model
The enhanced custom event bus uses a new ingress and egress throughput pricing model. Publishers pay for events ingested, and subscribers pay for events delivered. This replaces the per-event model where cross-account and bus-to-bus routing charges compound in multi-bus architectures. For pricing details, visit the EventBridge pricing page.

Existing EventBridge custom event buses continue to work as they do today with no changes required. They now appear as Custom event bus – classic. The enhanced custom event bus is a new resource that you adopt at your own pace. In the console, it appears as Custom event bus.

Now available
The enhanced custom event bus is available today in the US East (N. Virginia, Ohio), US West (Oregon), Europe (Ireland, Frankfurt, Stockholm, Spain), and Asia Pacific (Hong Kong, Malaysia, Mumbai, Singapore, Sydney, Thailand, Tokyo) Regions. You can create your first enhanced custom event bus through the AWS Management Console, AWS Command Line Interface (AWS CLI), or EventBridge APIs. To get started, visit the EventBridge documentation or try it out directly in the EventBridge console.

Amazon CloudWatch now offers CloudWatch Omni, an AI-powered observability experience for the applications and AI agents you run together. You reach Omni through a dedicated URL for your organization and sign in with the identities you already manage, so working in Omni does not require access to the AWS Management Console. Omni is built on OpenTelemetry: the telemetry you already send to CloudWatch appears in Omni with nothing to reconfigure, and any other workload you instrument with OpenTelemetry sends its telemetry to an OpenTelemetry Protocol (OTLP) endpoint.

CloudWatch Omni offers both agent observability and application observability in a single experience. In our companion post, we introduced the agent observability capabilities of Omni for generative AI and agentic workloads. In this post, we present the application observability experience.

Engineering teams spend a significant portion of their observability time maintaining dashboards, tuning thresholds, and switching between tools to piece together what happened during an incident. When an issue crosses team boundaries, context gets lost in Slack threads and screenshots rather than flowing naturally to the next engineer. CloudWatch Omni changes this by organizing observability around your applications rather than individual signals, and bringing your whole team into the same workspace.

What CloudWatch Omni brings
CloudWatch Omni addresses three problems that engineering teams told us they face today.

One collaborative experience for your whole team. Every engineer accesses CloudWatch Omni through a single URL with enterprise SSO (via IAM Identity Center, supporting Okta, EntraID, and other providers). No AWS Console access is required. SREs, developers, database engineers, and managers share the same data and investigation context. When an investigation escalates, the next person joins the same session with full context already in front of them.

The system adapts as your applications evolve. CloudWatch Omni discovers your services, maps dependencies, and adjusts alarms automatically. Instead of manually curating dashboards and tuning thresholds, you declare what matters (availability targets, latency budgets, error rate thresholds) and Omni adapts as your system changes. When you deploy new services, Omni updates the application topology automatically.

AI-powered investigation with Amazon DevOps Agent. Amazon DevOps Agent participates alongside your team in investigation sessions, correlating signals and suggesting next steps. The agent works from the same telemetry your engineers see, so its suggestions are grounded in the actual state of your application. It identifies correlated events across services, traces root cause paths through your dependency graph, and maintains investigation history for post-incident review.

How an investigation works
When something breaks, CloudWatch Omni opens an investigation session pre-loaded with context. Here is a typical incident workflow:

An alarm fires on elevated error rates in your checkout service. Omni opens a session showing the service topology, correlated signals (a deployment 10 minutes earlier, increased latency from a downstream payment API), and DevOps Agent’s initial analysis.

Your on-call SRE confirms the deployment correlation, pulls in the trace view to identify failing endpoints, and checks if the payment API latency correlates with a capacity limit.

The SRE escalates to the payments team. The payments engineer joins the same session and sees everything found so far, plus DevOps Agent’s correlation with a configuration change in the payment provider’s API gateway. They identify the root cause and roll back.

The entire investigation history is captured automatically. No separate incident report needed.

Walkthrough: setting up your first Space
To set up CloudWatch Omni for your team, open the CloudWatch console and click “Try CloudWatch Omni.”

Next, connect your identity provider through IAM Identity Center (supporting Okta, Azure AD, and other SAML 2.0 providers). Once connected, your team members access Omni directly at your dedicated URL without needing AWS Console credentials.

Create a Space for your team. A Space groups the applications your team owns and the telemetry associated with them.

Once created, Omni discovers your services automatically and maps the dependencies between them. You see your application topology immediately.

You can ask CloudWatch Omni any question about your applications in plain English, and Omni will analyze your telemetry data and surface insights.

You can also set up service health alerts, configure what matters to your team, and trigger an AWS DevOps agent investigation to identify the root cause and develop a mitigation plan.

Application-centric organization
CloudWatch Omni organizes telemetry by application rather than by infrastructure component. The system automatically discovers services from the telemetry data and AWS Config resource discovery, maps dependencies, and lets you see your application as a connected system rather than a collection of isolated resources.

Each team gets a Space that contains the applications they own. A Space points at existing CloudWatch data (logs, metrics, traces, and alarms) with no additional data movement required. Dynamic views replace the maintenance burden of static dashboards, providing ongoing visibility into SLOs and application health.

Getting started
Getting started takes minutes and doesn’t require reconfiguration of your existing CloudWatch setup.

If you’re an existing CloudWatch customer: Click “Try CloudWatch Omni” in the CloudWatch console. All your existing telemetry (logs, metrics, traces, and alarms) is immediately available. Workloads are discovered automatically, and you can start an investigation or browse your application topology right away.

For organization-wide deployment: An administrator configures a domain, connects your identity provider via IAM Identity Center, defines Spaces for teams and environments, and invites users. Each Space points at existing CloudWatch data with no additional data movement required.

For applications in other environments: CloudWatch Omni provides connectors that make it easy to bring in telemetry from additional environments. All ingested telemetry appears alongside your AWS data in the same Spaces and investigation sessions.

For generative AI and agentic workloads: The same CloudWatch Omni experience delivers purpose-built observability for AI agents, including trace exploration, evaluation frameworks, and real-time monitoring. In our companion post, we introduced the agent observability capabilities of Omni; for that walkthrough, see Introducing Amazon CloudWatch Omni: AI-powered observability for generative AI and agentic workloads.

Things to know

• CloudWatch Omni extends CloudWatch. Existing alarms, dashboards, APIs, and console workflows continue unchanged.
• Access is through a dedicated web application with enterprise SSO. Engineers don’t need AWS Console access to use it.
• Once you setup, DevOps Agent is enabled by default in every Omni investigation session.

Pricing and availability
Amazon CloudWatch Omni is now available. Existing CloudWatch customers can try it directly from the CloudWatch console. For pricing details, visit the Amazon CloudWatch pricing page.

To get started, visit Amazon CloudWatch Omni or click “Try CloudWatch Omni” in the Amazon CloudWatch console.

If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Share your feedback on AWS re:Post or reach out through your usual AWS Support contacts.

9/24/2026 – Editor’s note: Azure AD has been renamed to Microsoft Entra ID.

— Daniel Abib

Today, Amazon CloudWatch introduces CloudWatch Omni, a unified observability experience for application and AI workloads that is app-centric, AI-powered, built on open standards, and delivered off-console. CloudWatch Omni is a purpose-built observability, evaluation, and experimentation solution for AI agents. It helps teams design, evaluate, and operate AI agents across any model provider, framework, or runtime, with an eval-driven workflow, support for the tools you already use, and observability delivered where you work: directly in your IDE and through a standalone web experience, separate from the AWS Management Console.

Organizations deploying agentic AI systems face observability challenges that traditional monitoring can’t address. Agent behavior is non-deterministic: a prompt change can degrade response quality even when standard metrics show no errors. Teams spend hours manually reviewing logs across multiple systems, unable to pinpoint what changed or why. Existing tools force teams to choose between siloed generative AI monitoring or fragmented solutions requiring constant context-switching between their coding environment and browser-based dashboards.

CloudWatch Omni captures every trace and includes built-in evaluators for correctness, coherence, retrieval quality, and tool selection, among others. You can compare prompt versions side by side in the playground, build test datasets from production traffic, run experiments across different configurations, and detect regressions automatically.

Two surfaces for development and operations
CloudWatch Omni delivers observability through two complementary surfaces. Developers get a native extension inside VS Code and Kiro (the currently supported IDEs), where traces appear as you run your agent with a playground and evaluators a click away. Operators get a standalone web experience, separate from the AWS Management Console to monitor the fleet, accessible through SSO with no AWS console needed. Both share the same data: the trace a developer debugs is the trace an operator investigates.

The Cloud Login feature connects your local IDE environment to your AWS account, enabling you to send telemetry data to Amazon CloudWatch for persistent storage, share traces with your team, and access production dashboards. This connection is optional. You can use CloudWatch Omni entirely locally during development, then connect to the cloud when you are ready to monitor agents in production.

Getting started
CloudWatch Omni offers two ways to get started: through the IDE extension (for VS Code and Kiro) or directly through the cloud experience, where you can start sending telemetry data to CloudWatch without installing any IDE extension. In this walkthrough, I install the extension, create an agent, run it, and explore the traces and evaluation tools from my IDE.

After installing the CloudWatch Omni extension from the VS Code Marketplace, the CloudWatch Omni icon appears in the Activity Bar. From the welcome screen, I selected Get started with Sample Project to load a pre-configured agent with sample trace data or use shortcut to Command Palette using Command + Shift + P (on macOS) or Ctrl + Shift + P (on Windows/Linux) and select Omni: Create a new Project

The sample project comes with an agent implementation and example datasets. Part of the getting-started experience is adding OpenTelemetry instrumentation, and CloudWatch Omni guides you through each step. You can also create a new agent from scratch. CloudWatch Omni walks you through the process using an interactive chat where you define the agent’s purpose, select a model provider, and configure tools. All data is stored locally by default. You can optionally connect to AWS to send data to Amazon CloudWatch.

After verifying the configuration, I started the local dev server and sent a question to the agent. What makes this different from a typical chatbot interface is what happens next: selecting View Trace shows exactly how the agent processed the request.

CloudWatch Omni integrates with AI code assistants such as Kiro, Claude Code, and Codex to streamline the setup process. These assistants can configure the Dev Server, install dependencies, and set up instrumentation on your behalf, so you can go from installation to running your first traced agent session in minutes without manual configuration.

Traces are essential for understanding AI agent behavior. Unlike traditional request-response systems, agents make multiple decisions per invocation: choosing tools, composing prompts, and chaining sub-calls. Without full trace visibility, diagnosing why an agent produced an incorrect answer or took an unexpected path becomes guesswork. CloudWatch Omni records every step in a structured timeline so you can pinpoint exactly where behavior diverged.

The Trace Explorer shows a detailed breakdown of every step the agent took (LLM calls, tool invocations, and reasoning steps) in a structured, hierarchical timeline. I could drill into any span to inspect inputs, outputs, token usage, and latency.

The Trace Explorer also supports Compare mode, which places two traces side by side to see how different prompts or configurations affect behavior. Compare mode is especially helpful when debugging regressions. And with Ask Assistant, an AI agent analyzes your traces to surface patterns and anomalies, answering questions like “Why did the agent call this tool twice?”

Evaluation is what turns observability into actionable quality improvement for generative AI. Traditional metrics like latency and error rate cannot tell you whether an agent’s response was helpful, coherent, or factually correct. Evaluators score each response against quality dimensions, letting you measure what users actually experience and catch regressions that standard monitoring misses entirely.

CloudWatch Omni includes 17 built-in evaluators for metrics like coherence, helpfulness, faithfulness, and routing correctness. I selected traces from the Trace Explorer, chose evaluators, and ran an evaluation, getting per-example scores and aggregate metrics without building any custom evaluation framework.

From there, I used the Playground to test different system prompts side by side, comparing multiple model and prompt configurations in real time to see how each variation affects output quality before committing changes. With the Experiments view, I could run the same dataset against two agent variants and compare their evaluation scores, latency, and token usage side by side to pick the best-performing configuration.

Figure 7. Comparing evaluations across agent variants in the Omni Experiments console

With Prompt Management, you can version and track prompt configurations over time, making it easy to roll back when a new version underperforms.

CloudWatch Omni also provides a Session Explorer to review full conversation histories and understand how agents handle multi-turn interactions, along with an Agent Topology view that visualizes the architecture of your agent system, including sub-agents, tools, and their interconnections. You can drill into any node to inspect performance and identify bottlenecks.

CloudWatch Omni also offers a dedicated web experience accessible from any browser without an IDE. Teams can access all capabilities collaboratively, including application monitoring, analytics, agent observability, and AI-powered investigations.

I curated traces into golden datasets for structured experimentation. The Experiment function runs the agent against a dataset and automatically scores results, creating benchmarks for regression testing whenever prompts or agent logic change.

If you already have an agent built with a supported framework, CloudWatch Omni provides two paths to add instrumentation: Auto-instrument with Kiro, which detects your framework and configures tracing automatically, or manual instrumentation with ready-to-use code snippets for Python and TypeScript. For detailed instrumentation guides, see the CloudWatch Omni documentation.

Supported frameworks and open standards
The walkthrough above uses the sample project, but CloudWatch Omni works with the agent frameworks teams are already using: LangChain, LangGraph, CrewAI, OpenAI SDK, Strands, Vercel AI SDK, and more, in both Python and TypeScript. It also provides native observability for agents built with Amazon Bedrock AgentCore, and uses AgentCore’s evaluation capabilities to assess agent quality directly within the Omni workflow.

Instrumentation uses open standards (OpenInference and ADOT), whether your agents run on Lambda, ECS, EKS, or other clouds. For evaluation, Omni integrates with third-party evaluators including AutoEval and DeepEval alongside built-in datasets, a playground, and batch experiments. No re-platforming required.

CloudWatch Omni brings agent observability and application observability together in a single experience. For the application observability experience, read the companion post Introducing Amazon CloudWatch Omni: collaborative AI-powered observability for your applications.

Pricing and availability
Amazon CloudWatch Omni is now generally available. The IDE extension is free to use. You don’t need an AWS account to get started. You only need AWS credentials for Amazon Bedrock models, or API keys for other providers like OpenAI or Anthropic. Get started today by installing the extension from the VS Code Marketplace.

To explore all capabilities and get started quickly, visit CloudWatch on AWS Builder Center.

If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Share your feedback on AWS re:Post or reach out through your usual AWS Support contacts.

Happy building!

— Daniel Abib

Today, we’re announcing the general availability of new low-cost burstable Amazon EC2 T8i instances powered by custom sixth generation Intel Xeon Scalable Processors (Granite Rapids), available only on AWS. T8i instances are among the lowest-cost EC2 instances and deliver up to 30% better price performance over previous generation T3 instances. These instances are designed to run a variety of low-to-moderate CPU utilization workloads such as freemium services, training and demo environments, staging and development, data processing, microservices, low-traffic websites, and login gateways.

T8i instances
Thousands and thousands of customers run various lightweight workloads on T3 instances that require small, cost-effective compute configurations. These include microservices architectures, low-traffic websites, development and testing environments, small databases, data processing jobs, and short-duration compute tasks. Many of these customers like T family’s burstable performance model, which provides a baseline level of CPU performance with the ability to burst above the baseline when needed using CPU credits.

As customers modernize their infrastructure, migrate from on-premises environments, adopt event-driven and microservices architectures, and experiment with AI inference workloads, they have asked for newer generation cost-optimized small instances, better price performance to reduce their total cost of ownership, and a seamless migration path that leverages their existing knowledge and tooling.

T8i instances address each of these requests:

• Up to 30% better price performance. Powered by the AWS Nitro System and custom sixth generation Intel Xeon Scalable Processors (Granite Rapids), T8i instances enable customers to lower their total cost of ownership with up to 30% better price performance.
• Up to 70% higher compute performance. T8i instances deliver up to 70% higher compute performance, up to 1.25x higher network bandwidth, and up to 2.4x higher EBS bandwidth compared to T3 instances.
• Seamless upgrade from T3. For existing T3 customers, upgrading to T8i is straightforward. The instances offer the same CPU credit system and the same familiar lightweight compute options customers already know. Customers simply select T8i instead of T3 and immediately benefit from improved price performance.
• Cost-effective entry point for new customers. For customers new to AWS or migrating from on-premises, T8i instances provide one of the most cost-effective entry points to run workloads that need low-to-moderate CPU utilization or for running short-duration compute tasks such as batch processing, event-driven functions, or CI/CD pipelines.

Instance specifications
T8i instances offer four sizes, each with two vCPU offered as a single core. The following table summarizes the specifications.

Instance size	vCPUs	Memory (GiB)	Baseline Performance /vCPU (%)	CPU credits earned / hour	Network burst bandwidth (Gbps)
t8i.nano	2	0.5	5	3	Up to 6.25
t8i.micro	2	1	10	6	Up to 6.25
t8i.small	2	2	20	12	Up to 6.25
t8i.medium	2	4	20	12	Up to 6.25

Like T3, T8i instances offer unique vCPU-to-memory ratios such as 1:0.25, 1:0.5, and 1:1 that are not offered by other EC2 instances. Like T3, T8i instances utilize the CPU credit system along with the Standard and Unlimited credit configuration modes. Unlimited mode is the default on T8i.

For workloads that need larger instance sizes above T8i offerings (nano, micro, small, and medium), I recommend M8i Flex instances that offer up to 30% better price performance than equivalent previous generation T3 instances along with the flexibility to scale up to 16xlarge.

Now available
Amazon EC2 T8i instances are available today in the following AWS Regions: US East (N. Virginia, Ohio), US West (Oregon, N. California), Asia Pacific (Hyderabad, Malaysia, Mumbai, Seoul, Singapore, Sydney, Tokyo), Canada (Central), and Europe (Frankfurt, Ireland, London, Paris). For Regional availability and upcoming Region expansion, search the instance type in the CloudFormation resources tab of AWS Capabilities by Region.

You can purchase T8i instances via On-Demand instances, and Spot instances with Savings Plan option coming soon. T8i instances support shared tenancy only and do not support Dedicated tenancy or Dedicated Hosts. t8i.micro and t8i.small instances are also available under the AWS Free Tier. To learn more, visit the Amazon EC2 Pricing page.

Try T8i instances in the Amazon EC2 console and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— Channy

Updated on September 18 — Corrected the memory size for each instance type.

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

Amazon Web Services (AWS) started with a handful of foundational infrastructure services such as Amazon Simple Storage Service (Amazon S3), Amazon Elastic Compute Cloud (Amazon EC2), and Amazon Simple Queue Service (Amazon SQS), so that anyone with an idea could start building. As the world’s largest companies and governments adopted AWS, they asked for features to optimize their configuration for a range of global business contexts, security requirements, and operational needs. To meet these needs, AWS expanded globally through new Regions and added breadth and depth of services in security, networking, governance, and cost controls, so those customers could operate wherever they needed and at the scale they require. That combination of global reach, breadth, and depth remains essential for those customers, but if you are at the start of a new idea, every configuration option is effort standing in the way of shipping your dream product fast.

Today, we’re announcing a new simplified experience on AWS for builders who are working at the pace of AI. Instead of having to complete configuration tasks before you can work on your project, you start with sensible defaults and simple administration. You sign up using an existing identity from providers including Google, GitHub, and Apple. For most new customers, no credit card is required to start and you receive $100 in free credits as part of the AWS Free Tier. You can build immediately in your first project. As you continue to work, you can invite collaborators with just an email address, without learning about AWS Identity and Access Management (IAM) or AWS IAM Identity Center. When your project grows beyond the free credits, you can set a spend limit so you stay within your budget on the paid plan. If you grow to need additional customization, you can activate advanced AWS features to access the full breadth and depth of AWS without migrating.

How it works
When you sign up, AWS organizes your work in a project. A project contains an AWS account, where you create resources, and settings for sharing with team members. AWS creates that structure for you and applies additional security controls so you can start building your idea. After signing in, you get a prompt to paste into your coding agent that configures it to work with your new AWS environment. From there, your agent can deploy resources, run workloads, and iterate on your application following best practices for working with AWS.

You can create another project with a click. When you want to work with an additional team member, you send an invitation to their email address. Identity permissions are handled for you, so there are no IAM users to create; each person you invite only gets access to the projects you specify. Console workflows and coding agents also configure permissions between supported services and resources automatically, so you do not have to set up or troubleshoot resource permissions by hand.

When you’re ready to move beyond free credits, you can upgrade to a paid plan by entering your payment method. You can set a monthly spend limit on a project based on your usage trends, starting at $20 per month. The spend limit is the ceiling for that project’s costs, and you pay for what you actually use up to that amount. For example, if you set a $50 spend limit and your project incurs $32 in charges that month, you pay $32 (plus taxes). AWS will suggest a spend limit based on your usage, and you can accept that recommendation or set a custom amount if you are planning to further scale your usage. If your project approaches the limit, you first receive notifications. If spend reaches the limit, AWS pauses your project rather than accumulating charges, and you can resume working on it when you raise the limit. Each project has its own spend limit so you can give a larger budget to a workload that is gaining traction while keeping a smaller budget on an experimental idea.

Let’s try it out
To get started, I went to aws.amazon.com and chose Create account. I signed in with my Google account and within seconds had a new project ready to go, as shown in the following screenshot.

The Sign up for AWS page, with options to continue with email or sign in using Google, GitHub, Apple, or Amazon.

The first thing I saw was a prompt to configure my coding agent. I copied the prompt and pasted it into my agent. The agent set up the AWS Command Line Interface (AWS CLI) and the Agent Toolkit for AWS, logged me into AWS, and created a CLAUDE.md file in my project with guidance for the new experience.

The Setup Agent Toolkit for AWS dialog, with a prompt to copy and paste into your coding agent.

With the agent connected, I gave it a short prompt: build an API that returns a new unique sequential ID on every request. The agent created an AWS Lambda function, an Amazon DynamoDB table, and an Amazon API Gateway API, then deployed them for me. I did not have to configure resource permissions by hand. Within a few minutes I had a public endpoint that returned a newly minted ID on each request. My project started with $100 in free credits, and I received an additional $20 when the Lambda function was deployed.

A coding agent prompt to build an API that returns a unique sequential ID on every request.

The coding agent presents architecture options for the sequential ID API, with AWS Lambda and Amazon DynamoDB selected.

The coding agent confirms the API is live and lists the Amazon DynamoDB table, AWS Lambda function, and Amazon API Gateway API it deployed.

From the project, I could manage settings, invite team members by email, and monitor billing, as shown in the following screenshots.

The Projects page, showing remaining free-plan days, credits, and a project.

The project Members page, with the option to invite a new team member by email.

The Billing page, showing a $0.00 balance on the free plan, remaining credits, and cost by project.

Activating advanced features
If you reach the point where you need multiple Regions, or governance features like custom policies in AWS Organizations, you can activate advanced features at no additional cost. You’ll find yourself in a fully configured AWS Organization built according to best practices, with no migration and no downtime. Everything you configured previously is preserved and reflected in the underlying AWS services.

Now rolling out
We’ve heard from builders that they do not want to spend their first hours configuring an AWS environment. They want to build what they came to build, and we listened. AWS began as a place where anyone with an idea could start building, and this new simplified experience brings that starting point back, with sensible defaults so you can begin immediately, and with the global reach, breadth, and depth of AWS still there when your idea needs it. We are gradually rolling this experience out to new customers. We cannot wait to see what you build, and we want your feedback on the experience.

To try the new experience, create a new AWS account. To learn more, see the AWS Sign up user documentation.

Last year, we introduced Volume Clones of Amazon Elastic Block Store (Amazon EBS), a new capability that lets you create instant point-in-time copies of your EBS volumes within the same Availability Zone.

Today, we are extending Volume Clones with cross-account copy, so you can create copies of your EBS volumes into other AWS accounts and optionally re-encrypt them with an AWS Key Management Service (AWS KMS) key in the target account.

With this new feature, you can use your latest application data to develop, test, and experiment in a secondary environment, while protecting and isolating the information in the production environment. For example, you can create copies of a production environment to refresh test and development environments set up in separate accounts with the desired EBS encryption.

Copy EBS volumes across AWS accounts in action
To create a copy of an EBS volume across accounts, the owners of the volume can first grant the target account access to their volume in AWS Resource Access Manager (RAM), which provides a way to share resources across AWS accounts or within an AWS Organization. Then, from the target account, they can locate the volume they have access to create a copy of it.

To get started, choose Share volume for the volume you want to share with the target account in the Amazon EBS console.

Share the volume with other AWS accounts by adding it to existing resource shares, or create a new resource share in the AWS RAM console. For more details, refer to the AWS RAM User Guide.

You can now see confirmation that the volume has been shared in the Volume sharing tab of the volume detail page.

A target account must accept the resource share on the RAM console.

Once they accept the resource share, they can see the volumes in the EBS volume page of the target account. Choose Copy volume for any shared volume.

To share and copy EBS volumes across AWS accounts programmatically, including calling APIs and searching documentation, try the AWS MCP Server and plugins with your preferred AI coding tool. To learn more, visit the Amazon EBS User Guide.

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

• Encryption: You can share unencrypted volumes and volumes encrypted with a customer managed key (CMK). Volumes encrypted with the default AWS managed key (AMK) cannot be shared. When copying a shared volume encrypted with a CMK, the CMK must also be shared with the target account. You can specify a different CMK to re-encrypt the copy in the target account.
• Monitoring: You can monitor SharedVolumeCopyInitiated through AWS CloudTrail event in your account. You will also receive events in Amazon EventBridge at the start of the copy operation when the state of the copied volume is initializing, and at the end of the operation when the state of the copied volume changes to completed. You can see the shared volume ID, consuming account ID, and event time.
• Pricing: Once a copy is initiated, you’ll pay a one-time fee based on your volume size, charged to the account where the copy will reside. There’s no cost for sharing EBS volumes through AWS RAM. The copied volume will incur regular EBS volume charges upon creation.
• Availability Zone: The volume copy must be created in the same Availability Zone as the source volume. Use Availability Zone IDs (such as use1-az1) to identify the same physical location across accounts.

Now available
Cross-account volume clones for Amazon EBS are available in all AWS Regions that support Amazon EBS Volume Clones. For Regional availability and a future roadmap, visit the AWS Capabilities by Region.

Give this feature a try in the Amazon EC2 console today and send feedback to AWS re:Post for Amazon EBS or through your usual AWS Support contacts.

— Channy

Today, Amazon EC2 R9g and R9gd instances are generally available, powered by AWS Graviton5 processors. R9g instances are memory-optimized and deliver up to 25% better compute performance compared to Graviton4-based R8g instances, powered by the most energy efficient processor AWS has ever built.

R9g instances are ideal for memory-intensive workloads including databases, in-memory caches (Valkey, Redis, MemCached), real-time big data analytics, Linux-based workloads including containerized and micro-service-based applications (e.g. Kubernetes, Docker, EKS, ECS), as well as applications written in popular programming languages such as C/C++, Rust, Go, Java, Python, .NET Core, Node.js, Ruby, and PHP.

R9gd instances include local NVMe-based SSD block-level storage, ideal for memory-intensive workloads requiring fast, low-latency local storage such as open-source databases, distributed real-time big data analytics, large in-memory databases, and large caching workloads.

If you’re running workloads on R8g instances today, R9g gives you more performance per vCPU with faster memory, higher network and Amazon EBS bandwidth, and a larger L3 cache, all while using less energy.

What makes R9g different
Graviton5 processors bring several hardware improvements over Graviton4:

• Up to 25% higher compute performance per vCPU
• DDR5 8800 MT/s memory (up from 5600 MT/s in Graviton4), the fastest memory available in the cloud
• 5x larger L3 cache for better data locality
• Up to 2x higher network and EBS bandwidth for the largest instance sizes (up to 100 Gbps network, up to 72 Gbps EBS on the 48xlarge)
• Up to 3x higher packet-processing performance

R9g and R9gd instances support Instance Bandwidth Configuration (IBC), which lets you adjust the allocation of bandwidth between Amazon EBS and Amazon VPC networking by 25%. This helps optimize performance for workloads with specific bandwidth requirements such as databases and caching.

All R9g and R9gd instances run on the AWS Nitro System, which offloads virtualization, storage, and networking to dedicated hardware. This gives your applications near-bare-metal performance while maintaining strong security isolation between instances.

R9g and R9gd instances feature the Nitro Isolation Engine (NIE), the same enhancement to the Nitro System introduced with C9g and M9g instances earlier this year, which enforces isolation of instances and harnesses formal verification to provide assurances of isolation with mathematical precision. Nitro Isolation Engine is a purpose-built component that is responsible for enforcing isolation between virtual machines, including mediation of all access to virtual machine memory, CPU register state, and I/O devices through a minimal set of APIs. Nitro Isolation Engine leverages formal verification, a technique to mathematically demonstrate that the hardware or software behaves as intended, and not just in specific test cases. This intensive verification technique establishes Nitro as the first formally verified cloud hypervisor, pioneering a new standard for mathematically proven cloud security. To learn more about the Nitro Isolation Engine, visit the blog post. For details on the formal verification results, including scope and assumptions, see the technical white paper.

EC2 R9g and R9gd instance specifications
R9g and R9gd instances are each available in 11 sizes, from medium to metal-48xl. The following tables show the full specifications for each size.

Instance size	vCPUs	Memory (GiB)	Instance Storage	Network Bandwidth (Gbps)	EBS Bandwidth (Gbps)
r9g.medium	1	8	EBS-Only	Up to 15	Up to 12
r9g.large	2	16	EBS-Only	Up to 15	Up to 12
r9g.xlarge	4	32	EBS-Only	Up to 15	Up to 12
r9g.2xlarge	8	64	EBS-Only	Up to 17	Up to 12
r9g.4xlarge	16	128	EBS-Only	Up to 17	Up to 12
r9g.8xlarge	32	256	EBS-Only	17	12
r9g.12xlarge	48	384	EBS-Only	25	18
r9g.16xlarge	64	512	EBS-Only	34	24
r9g.24xlarge	96	768	EBS-Only	50	36
r9g.48xlarge	192	1536	EBS-Only	100	72
r9g.metal‑48xl	192	1536	EBS-Only	100	72

R9gd instances offer the same compute and networking performance as R9g, with the addition of local NVMe-based SSD storage for workloads that need fast, low-latency scratch space or temporary caches.

Instance size	vCPUs	Memory (GiB)	Instance Storage (NVMe SSD)	Network Bandwidth (Gbps)	EBS Bandwidth (Gbps)
r9gd.medium	1	8	1 x 59 GB	Up to 15	Up to 12
r9gd.large	2	16	1 x 118 GB	Up to 15	Up to 12
r9gd.xlarge	4	32	1 x 237 GB	Up to 15	Up to 12
r9gd.2xlarge	8	64	1 x 474 GB	Up to 17	Up to 12
r9gd.4xlarge	16	128	1 x 950 GB	Up to 17	Up to 12
r9gd.8xlarge	32	256	1 x 1900 GB	17	12
r9gd.12xlarge	48	384	3 x 950 GB	25	18
r9gd.16xlarge	64	512	1 x 3800 GB	34	24
r9gd.24xlarge	96	768	3 x 1900 GB	50	36
r9gd.48xlarge	192	1536	3 x 3800 GB	100	72
r9gd.metal‑48xl	192	1536	3 x 3800 GB	100	72

Getting started
You can launch R9g and R9gd instances from the Amazon EC2 console using any supported Arm-based AMI. R9g instances support Amazon Linux 2023, Amazon Linux 2, Ubuntu 22.04+, RHEL 8.4+, SUSE Linux Enterprise Server 15 SP3+, Debian 12+, and other major Linux distributions.

If you’re migrating from R8g, no code changes are required for most applications. Select the equivalent R9g instance size and your application runs with better performance. For containerized workloads, R9g works with Amazon EKS, Amazon ECS, and standard Kubernetes deployments. Multi-arch container images built for Arm64 run without changes.

Several resources help you get started: the AWS Graviton Getting Started Guide covers how to build, run, and optimize workloads on Graviton-based instances. The Graviton Savings Dashboard helps you track cost savings. AWS Transform automates code transformations for migrating Java applications from x86 to Graviton. To learn more, visit AWS Graviton Processors or Level up your compute with AWS Graviton.

Pricing and availability
Amazon EC2 R9g and R9gd instances are available in US East (N. Virginia, Ohio), US West (Oregon), and Europe (Frankfurt) Regions.

R9g and R9gd instances are available for purchase through Savings Plans, On-Demand, Spot Instances, Dedicated Instances, or Dedicated Hosts. For detailed pricing, visit the Amazon EC2 pricing page.

Ready to get started? Launch R9g instances from the Amazon EC2 console. For more details, visit the Amazon EC2 R9g instances page.

If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this feature, try using the AWS MCP Server and plugins with your preferred AI tool. Share your feedback on AWS re:Post for Amazon EC2 or reach out through your usual AWS Support contacts.

— Daniel Abib

During my time at AWS, I have always looked for opportunities to work with students. I have delivered over 50 talks at universities across the region, and watching the potential in the room is always a strong motivator. It reminds me of why I do this work, and that the students I meet today may well become our customers and collaborators tomorrow. That is why I am happy to open this week with Student Rewards on AWS Builder Center.

Rick Suttles published Introducing Student Rewards on AWS Builder Center, a new benefit for verified higher education students. When you verify your enrollment through SheerID and complete your Builder Center profile, you unlock 12 months of premium AWS Skill Builder access (900+ courses, hands-on labs, certification exam prep, and game-based learning). From there, you earn badges through actions on Builder Center: publishing articles, commenting, and maintaining engagement. At 7 badges, you unlock $10 in AWS Credits. At 14 badges, another $20 in credits. At 21 badges, you earn an AWS Foundational Certification exam voucher ($100 value).

This represents a commitment of over $500 million in resources during this back-to-school season, providing students with the training, tools, and certification needed to start building their careers in cloud and AI. Student Rewards is available to students 18 years or older and enrolled at accredited higher education institutions worldwide, subject to verification and applicable terms.

Verify your student status and start learning, earning badges, and unlocking rewards!

Last week’s launches
Here’s what else happened this week.

• A new AWS Local Zone in Las Vegas, Nevada – This new Local Zone supports Amazon EC2 C7i, M7i, R7i, and C8gn instances, Amazon EBS, Amazon ECS, Amazon EKS, Application Load Balancer, and AWS Direct Connect. AWS Local Zones are now available in more than 30 metropolitan areas worldwide. In addition, AWS added a fourth Availability Zone to the Europe (London) Region, delivering next-generation AI and ML capacity with Trn3 and P6 accelerated instances alongside general-purpose compute.
• Amazon EC2 Auto Scaling now supports batch instance termination – You can now pass up to 100 instance IDs to the TerminateInstanceInAutoScalingGroup API to terminate them as a batch, reducing the number of API calls needed to scale down your Auto Scaling groups. Batch termination is designed for workloads that need to rapidly scale down, such as AI/ML training jobs, container orchestrators, or event-driven architectures that spin up large fleets temporarily.
• AWS CloudShell now includes a built-in visual file editor – CloudShell now includes a visual file editor that you can launch directly from your shell session using a single edit command. The editor supports syntax highlighting, find-and-replace, multi-line selection, copy-paste, and undo-redo in a single browser session. Whether you are updating a deployment script, modifying an agent steering file, editing a CloudFormation template, or fixing a Lambda function, the editor provides a seamless edit-and-run experience without leaving CloudShell.
• Amazon Bedrock now supports SpaceXAI Grok 4.6 with cross-Region inference – Grok 4.6, a frontier model built for coding, agentic tasks, and knowledge work, is now available on Amazon Bedrock. The model runs on the bedrock-runtime endpoint with support for the Responses, Chat Completions, and Converse APIs, and works with existing account-level controls including model invocation logging, Amazon CloudWatch metrics, and cost itemization in AWS Cost Explorer.
• Amazon Bedrock expands API support and introduces cross-Region inference for OpenAI models – Amazon Bedrock now supports OpenAI GPT-5.6 models (Sol, Terra, and Luna) with the Responses, Converse, and Chat Completions APIs, and adds cross-Region inference. Geo cross-Region inference routes requests within a predefined geography (including new US Geo support with this launch), while Global cross-Region inference serves requests from any commercial AWS Region at a lower per-token cost.
• AgentCore payments is now generally available in Amazon Bedrock AgentCore – At general availability, AgentCore payments includes Quick Create for Coinbase credential provisioning directly within the AgentCore console, a curated Coinbase Bazar MCP server of pay-per-use x402 endpoints via AgentCore gateway, support for the Machine Payment Protocol (MPP), and the “upto” scheme in the x402 protocol for pay-per-inference and dynamic pricing use cases. To learn more, visit the AI Blog post.
• AWS Glue 6.0 delivers 30% price reduction and Iceberg v3 support – AWS Glue 6.0 is built on a fully modernized runtime, Apache Spark 4.1, Python 3.13, and Scala 2.13, delivering 30% lower pricing than previous AWS Glue versions. With Iceberg v3, Glue 6.0 adds the VARIANT data type with automatic shredding for faster reads on semi-structured data, deletion vectors for high-performance row-level updates, geometry and geography data types for spatial processing, and flexible schema evolution.

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:

• Updates to your AWS Sign-In experience – AWS is gradually introducing updates to the sign-in and sign-up experience. The redesigned sign-in page introduces a unified email entry point for root users and customers using the new email-based sign-in method, while IAM users continue signing in with their account ID, username, and password. The page also includes sign-in options for customers whose AWS account was created using a supported identity provider (Google, GitHub, Apple, or Amazon.com). A redesigned session selection page simplifies viewing and managing multiple active account and role sessions. If your organization relies on browser automation or scripted workflows that interact with the sign-in page, review the post to understand how these changes might affect your configuration.
• In the works: AWS Builder Lofts in Berlin, Hyderabad, and São Paulo – My colleague Channy announced plans to open new Builder Lofts in three cities. Since the first Builder Loft opened in San Francisco in July 2025, it has welcomed more than 22,500 developers through its doors. Each new location will be a permanent community space offering free workshops, networking events, pitch nights, content creation spaces, and co-working areas. Berlin will focus on digital sovereignty and security-readiness, Hyderabad on AI and cloud-native architecture, and São Paulo on supporting Latin America’s developer ecosystem.
• AWS and Amazon WorkSpaces recognized as a Leader in the 2026 Gartner Magic Quadrant for Desktop as a Service – AWS has been named a Leader in the 2026 Gartner Magic Quadrant for Desktop as a Service (DaaS) for the third consecutive year, evaluated on Completeness of Vision and Ability to Execute. Gartner noted strengths in operations, geographic strategy, and overall viability. This is also the first year the evaluation includes Amazon WorkSpaces for AI agents, a capability that runs AI agents within the same desktop environment, security perimeter, and audit trail as human users.

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, connect, and explore the latest in cloud. Coming up: Zurich (September 2), São Paulo (September 3), Tel Aviv (September 10), and Dubai (September 30). You can also watch keynotes and sessions on the AWS Summits Global Livestream + On-Demand Hub. To access live and on-demand content for a particular Summit, you need to be registered for that event.
• AWS Community Days – Community-led conferences planned and delivered by community leaders. Upcoming events include Bolivia, Santa Cruz (August 29), Toronto, Canada (August 29), JAWS SONIC, Tokyo (September 5), Warsaw, Poland (September 8).

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

Summer is slowly coming to an end, and I am already planning a few days off in the coming months to keep me motivated through the rainy autumn ahead. I hope you are doing the same. Come back next week for more!

— Esra

Today, we are announcing the general availability of AWS Glue 6.0, delivering 30% lower pricing than previous AWS Glue versions and introducing full support for Apache Iceberg v3 features. AWS Glue 6.0 is built on a fully modernized runtime, Apache Spark 4.1, Python 3.13, and Scala 2.13, delivering faster performance.

With this release, AWS Glue provides the most complete Iceberg v3 implementation on any fully serverless managed Spark service, along with new capabilities that simplify ETL authoring, improve PySpark performance, and enable real-time streaming with single-digit millisecond latency.

What is new in AWS Glue 6.0
AWS Glue 6.0 delivers the complete Apache Iceberg v3 specification, built on Iceberg 1.11.0. The headline feature is the VARIANT data type with shredding support, which achieves faster query read performance compared to traditional string data type columns for semi-structured data.

With VARIANT shredding, you can store and query JSON, logs, and event data without flattening schemas, eliminating duplicate data copies, custom parsing code, and pipeline breakage when schemas change. This capability transforms how teams handle semi-structured data at scale.

Additional Iceberg v3 capabilities include:

• Geometry and Geography data types: Enable native spatial processing for GIS analytics, location intelligence, and geospatial data pipelines directly on managed Spark.
• Nanosecond-precision timestamps: Support IoT sensor data, scientific computing, and high-frequency financial workloads that require precision beyond standard milliseconds.
• Unknown type handling: Process data with unexpected or evolving schemas without pipeline failures, providing resilience against upstream schema changes.

AWS Glue 6.0 also includes most significant upgrade in Spark 4.1, the modern runtime engine:

• Spark declarative pipelines: Spark Declarative Pipelines introduces a simplified approach to ETL authoring. Data engineers declare transformations, specifying what data should look like, while the engine automatically determines execution order and optimization. This reduces the complexity of pipeline development and eliminates manual orchestration overhead.
• Arrow-native Python UDFs and UDTFs: AWS Glue 6.0 introduces Arrow-native execution for Python User-Defined Functions (UDFs) and User-Defined Table Functions (UDTFs). This eliminates serialization overhead between Python and the JVM, improving PySpark performance for complex transformations.
• Real-time streaming mode: For stateless streaming use cases, AWS Glue 6.0 introduces a real-time streaming mode that achieves single-digit millisecond latency. Built on Spark 4.1’s Real-Time Mode with Glue-optimized execution, this capability supports real-time event processing, low-latency data transformation pipelines, and time-sensitive data routing.

Getting started with AWS Glue 6.0
No API changes are required to use AWS Glue 6.0. You can select the new version using the existing --glue-version parameter in the create-job or update-job APIs through AWS Command Line Interface (AWS CLI), AWS SDK, AWS Glue Studio, Amazon SageMaker Unified Studio, and your preferred IDE.

To get started with AWS Glue 6.0 jobs in the AWS Glue Studio console, open the AWS Glue job and on the Job Details tab, choose the version Glue 6.0 – Supports Spark 4.1, Scala 2, Python 3. You can create new AWS Glue jobs on AWS Glue 6.0 to get the benefit from the improvements, or migrate your existing AWS Glue jobs.

To start using AWS Glue 6.0 on an AWS Glue Studio notebook or an interactive session through a Jupyter notebook, set 6.0 in the %glue_version magic. You can also upgrade existing jobs to Glue 6.0 using the Spark upgrade agent on AWS Glue Studio or use the auto-upgrade feature in their existing Glue jobs to automatically upgrade them to Glue 6.0.

To learn more, visit the AWS Glue 6.0 version detail and Migrating AWS Glue for Spark jobs to AWS Glue version 6.0 in the AWS documentation.

Now available
AWS Glue 6.0 is generally available today in all AWS Regions where AWS Glue operates. 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 check troubleshooting about this new feature, try using the AWS MCP Server and plugins with your preferred AI tool.

You pay an hourly rate, billed by the second, for crawlers (discovering data) and extract, transform, and load (ETL) jobs (processing and loading data). For the AWS Glue Data Catalog, you pay a simplified monthly fee for storing and accessing the metadata. The first million objects stored are free, and the first million accesses are free. To learn more, visit AWS Glue Pricing page.

Give it a try in the AWS Glue Studio console, and send feedback to AWS re:Post for AWS Glue or through your usual AWS support contacts.

— Channy

When you move AI agents from prototype to production, the infrastructure challenges multiply. Your agents need to persist state across multi-step workflows that run for hours or days. They need to coordinate with other agents, share context, and sometimes access GPUs for specialized tasks. Amazon Bedrock AgentCore runtime microVMs provide a fully managed environment for invocations that can run for up to 8 hours and support stateful workflows through managed session storage. Some workloads also benefit from dedicated, larger-capacity environments — for example, when agents need to run continuously for multiple days, access GPUs or the underlying OS, or run multiple collaborating agents on the same host.

Today, I’m happy to announce runtime instances, a new complementary compute option in Amazon Bedrock AgentCore Runtime that gives your agents persistent, managed infrastructure purpose-built for complex agent workloads.

What you get
Runtime instances provides AWS-managed EC2 infrastructure where you deploy multiple agents in a single runtime, each with their own dependencies and artifact types. Your agents can collaborate on the same host within shared sessions that persist for up to 14 days. The service supports GPU acceleration for compute-intensive tasks, session stop/restart to save costs during idle periods, and containerized deployments for teams that want to ship independently. For knowledge that needs to survive beyond a session, runtime instances pairs naturally with Amazon Elastic Block Store (Amazon EBS) and AgentCore Memory, which gives your agents long-term recall across sessions and environments.

Before today, if you wanted to keep your agents running for days or they needed GPU access, or multi-agent coordination, you had to build and manage that infrastructure yourself. You provisioned EC2 instances, configured networking, set up session management, handled scaling, and stitched together monitoring. Runtime instances handles all of that for you while integrating with the same AgentCore APIs, identity controls, and observability you already use with AgentCore Runtime microVMs.

A few things that should make agent developers smile: your agents can call each other as tools within a shared session, iterating autonomously until the job is done. You bring any framework (CrewAI, LangGraph, LlamaIndex, Strands) and any model. Packaging is minimal, a @app.entrypoint decorator and a zip file or container image. And if your workflow spans days, hibernate Monday night and resume Wednesday morning with everything intact.

Runtime microVMs and runtime instances are complementary compute options that you can use independently or together through the same AgentCore runtime APIs. A lightweight orchestrator agent on runtime microVM can coordinate and dispatch work to specialized worker agents running on instances. The orchestrator handles API calls, task routing, and result aggregation using runtime microVM’s fast scaling, while workers on Instances perform compute-intensive tasks like code compilation, security scanning, or GUI automation that require persistent state and direct OS access.

Let me show you how it works
I built two agents for this demo: a code writer agent that generates Python code from natural language descriptions, and a code reviewer agent that analyzes the generated code for bugs, security issues, and style improvements. Both agents share the same file system, so the reviewer can read whatever the writer produces without any data transfer or API calls between them.

Here is the code writer (simplified, no error handling):

writer = Agent(
    model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    system_prompt=(
        "You are a senior Python engineer. "
        "Given a task, return ONLY a single Python code block — no prose."
    ),
)

@app.entrypoint
def handler(event, context):
    task = event.get("task") or event.get("prompt")
    session_id = getattr(context, "session_id", None) or event.get("session_id")
    session_dir = SHARED_DIR / session_id
    session_dir.mkdir(parents=True, exist_ok=True)

    code = str(writer(task))
    (session_dir / "code.py").write_text(code)

    return {"agent": "writer", "wrote": str(session_dir / "code.py"), "code": code}

Here is the code reviewer agent (simplified, no error handling):

reviewer = Agent(
    model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    system_prompt=(
        "You are a strict Python code reviewer. "
        "Given code, return 3 bullet points: bugs, style, suggestions."
    ),
)

@app.entrypoint
def handler(event, context):
    session_id = getattr(context, "session_id", None) or event.get("session_id")
    code_path = SHARED_DIR / session_id / "code.py"
    code = code_path.read_text()
    review = str(reviewer(f"Review this code:\n\n{code}"))

    return {"agent": "reviewer", "read": str(code_path), "review": review}

Each agent is a Python application using Strands Agents with an @app.entrypoint decorator and a model of its choice. I package each one as a zip file. For this demo, I use the AWS Management Console. You can also use the AgentCore CLI, the AWS Command Line Interface (AWS CLI) or infrastructure as code.

Step 1: Create a capacity provider.

A capacity provider defines the EC2 infrastructure your agents run on. In the AgentCore console, I select Runtime in the left navigation, then select the Capacity providers tab and Create capacity provider.

I give it a Name, select Linux (64-bit ARM) as the Operating system, and choose c7g.2xlarge as the Allowed instance types. This gives me 8 vCPUs and 16 GiB of memory, enough for both agents to run comfortably side by side.

Further down, I configure the VPC, subnets, and security groups for network access. Under Storage configuration, I keep the default gp3 volume. Under Service access, I select Create a new service role and let the console create the infrastructure role that manages EC2 instances on my behalf.

I select Create capacity provider and wait a few seconds. The status moves to Active.

Note the capacity provider configuration summary: operating system, instance type, subnets, security group, instance profile, and infrastructure role. Once created, only the description can be edited, so verify your settings before you proceed.

Step 2: Create a runtime and deploy the first agent.

Back on the Runtime page, I select Create runtime. I give it a Name, select Instances as the Compute type, and choose the Capacity provider I created in the previous step.

Under Agent source, I select S3 Source, then Upload to S3. I choose my agent zip file (ACIDemoWriter.zip), set the Language runtime to Python 3.13, and specify agent.py as the Agent entry point. This is the file that contains my @app.entrypoint decorated function. Under Permissions, I select Create default role to let the console provision the IAM role my agent needs.

I select Create runtime and wait for the status to become Ready.

I repeat the same process for my code reviewer agent. I create a second runtime, select the same capacity provider, upload my reviewer agent zip file, and wait for it to become Ready. Both agents now share the same underlying EC2 infrastructure.

The console shows me a View invocation code section with ready-to-use Python, TypeScript, and JavaScript snippets to invoke my agent programmatically. But for this demo, I use the built-in test feature. I select Test on the writer agent’s page.

Step 3: Invoke agents and observe collaboration.

The Runtime playground opens. At the top, I see three fields: Runtime agent, Endpoint, and Session ID. The console generates a session ID automatically. I take note of it because I will reuse it with the reviewer agent.

In the Input field, I type a JSON payload asking the writer agent to generate code:

{"prompt": "write a fibonacci suite"}

I select Run. After a few seconds, the Output panel shows the agent’s response. The writer agent generated a Python module with two implementations of a Fibonacci sequence (a list-based function and a generator) and wrote it to /tmp/agentcore-session/ca5ec24d-07f5-4eeb-add1-5ba416bf9eb2/code.py. Notice the session ID in the file path. That directory is the shared file system for this session.

Step 4: Invoke the reviewer agent in the same session.

Now I switch the Runtime agent dropdown to ACIDemoReviewer. The important part: I paste the same session ID (ca5ec24d-07f5-4eeb-add1-5ba416bf9eb2) in the Session ID field. This is what connects the two agents.

I type a simple prompt:

{"prompt": "review the code"}

I select Run. The reviewer agent reads the file the writer produced from the shared session directory and returns a detailed code review. It finds no critical bugs but suggests adding type hints, input validation, and simplifying the edge case handling.

The two agents never exchanged messages or called each other’s APIs. They collaborated through the shared file system that runtime instances provide within a session. You can extend this pattern to any number of agents: a test agent that runs the code, a documentation agent that generates README files, a security agent that scans for vulnerabilities, all sharing the same working directory.

Key details
Here are a few things to know as you get started:

• Supported OS: Linux (ARM64 and x86_64) at launch.
• Session persistence: Sessions persist for up to 14 days.
• Runtimes: Python 3.11-14 with native code support. Container images also supported.
• GPU: Support for GPU-accelerated instance types.
• Integration: Uses the same AgentCore APIs, identity, observability, and policy controls as AgentCore Runtime.
• Pricing: Standard EC2 pricing plus a management fee for AgentCore orchestration.
• Regions: US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), and Europe (Frankfurt, Ireland)

To get started, visit the runtime instance in Amazon Bedrock AgentCore documentation and create your first capacity provider.

— seb

Today, we’re announcing the general availability of vector search in Amazon DynamoDB. You can now store vector embeddings alongside your operational data in DynamoDB and run similarity searches directly against that data, without replicating it to a separate vector store.

DynamoDB supports native vector search with single-digit millisecond latency at 99%+ recall, and is designed for any scale, even trillions of vectors. There are no servers to provision, patch, or manage, and no software to install, maintain, or operate. The service has no versions, no maintenance windows, and zero downtime maintenance.

Vector indexes have no storage limits and scale horizontally as your data grows. You can now build applications that require semantic retrieval on agentic memory, retrieval augmented generation, recommendation engines, personalized experiences, anomaly detection, and more using DynamoDB and its native vector search.

If your application already uses DynamoDB, adding vector search previously required copying data into a dedicated vector database while maintaining a synchronization pipeline between the two services. This added operational overhead, data movement costs, licensing costs, and the challenge of maintaining predictable low latency at scale. With vector search built into DynamoDB, your vectors and operational data share the same serverless infrastructure and the same pay-per-request pricing model.

Vector search in DynamoDB introduces a new index type that you create on an attribute storing vector embeddings. You generate embeddings using a model of your choice, such as Amazon Bedrock Titan Text Embeddings, Cohere Embed, or OpenAI text embedding models, and store them as a list of floats in your table using a standard PutItem call. You then create a vector index on that attribute and specify the number of dimensions, the distance function, and any non-vector attributes you want to use as filters to narrow search results at query time. The SearchVectors API accepts a query vector, the number of results to return (up to 100), and optional filter conditions. It returns results ranked by similarity.

Use vector search in DynamoDB when your operational data already lives in DynamoDB and you want to add similarity search without provisioning a separate database or managing a synchronization pipeline. DynamoDB is fully serverless, so vector search scales automatically with no infrastructure to manage. It supports up to 4096 dimensions, Euclidean, Cosine, and Dot product distance functions, and inline filtering.

Getting started with vector search in DynamoDB
This walkthrough shows how to add vector search to an existing DynamoDB table using the DynamoDB console. The scenario contains an online sporting goods store with a product catalog table. Each item has standard operational attributes such as productId, category, description, marketplace, name, and price. The goal is to add semantic search so shoppers can find products using natural language queries rather than exact keyword matches.

1. Prepare DynamoDB table
To enable semantic search, I first generate vector embeddings for the product descriptions already in my table. Embeddings are numerical representations of text generated by a machine learning model that capture the meaning of the content. Two items with similar descriptions will have embeddings that are close to each other in vector space, which is what makes similarity search possible.

I can generate embeddings using Amazon Bedrock Titan Text Embeddings or another embedding model, then add them to my table using the AWS Management Console, AWS Command Line Interface (AWS CLI), AWS SDKs, AWS CloudFormation, or other infrastructure-as-code (IaC) tools.

For an existing table like ProductCatalog, I add the embeddings to each item as a new attribute named descriptionEmbedding using an UpdateItem call. DynamoDB stores vector embeddings using its existing List data type. Each element in the list is a Number that represents a single float value of the embedding vector. This means I do not need a new data type or schema change to start storing vectors alongside my existing operational attributes.

2. Create vector index
In the DynamoDB console, open the ProductCatalog table and choose the Indexes tab. I choose Create vector index. On the Create vector index page, I fill in the index details as follows. I enter ProductDescriptionIndex as the Index name and descriptionEmbedding as the Vector attribute.

I enter the number of Dimensions that matches my embedding model’s output and select Cosine as the Distance function. Cosine measures the angle between vectors rather than their magnitude, which makes it effective for comparing semantic similarity of text embeddings. Vector search in DynamoDB also supports Euclidean and Dot product distance functions.

• Euclidean: Use when the magnitude of the vectors is meaningful, such as clustering items by a numeric value like purchase count.
• Dot product: Use when both direction and magnitude matter, such as in recommendation systems that weight interest alignment and frequency together. As a general rule, match the distance function to the one used to train your embedding model for the best accuracy.

I enter marketplace as the Partition key. The vector index partition key controls how DynamoDB distributes vectors across partitions, allowing the index to scale out while maintaining predictable latencies. Each search is scoped to a single partition key value, so a product catalog serving multiple marketplaces can search within one marketplace’s inventory without scanning the entire index. The partition key is optional, but recommended for large datasets with high query throughput.

I expand Inline filter attributes and add category as a filter attribute. This helps me narrow search results to a specific product category at query time. Filter conditions support exact-match values only; range conditions such as BETWEEN or BEGINS_WITH are not supported. I leave Attribute projections set to All so that all table attributes are returned with my search results. Choose Create vector index and wait for the index status to change to Active.

3. Run vector search
I generate a query vector from a natural language search term such as “lightweight running shoes for summer” using the same embedding model I used for the product descriptions. In the DynamoDB console, I choose Explore items in the left navigation pane and select the ProductCatalog table.

Choose Search to switch to vector search mode. I select ProductDescriptionIndex from the Select a vector index dropdown, paste the query vector into the Search vector field, and set Number of results (Top K) to 5. I enter US as the Partition key value to scope the search to the US marketplace. I expand Inline filter attributes and set category equal to footwear to narrow the search to footwear products only. Now, choose Run.

DynamoDB returns the five most semantically similar products in the footwear category, ranked by similarity score, alongside the standard operational attributes such as name and price in the same response. The similarity score’s meaning depends on the distance function selected for the index. For Cosine and Euclidean distance functions, lower similarity score values indicate higher similarity, with a score of 0 indicating identical vectors. For the dot product distance function, higher similarity score values indicate higher similarity.

To interact with vector search programmatically, including calling APIs and searching documentation, try the AWS MCP Server and plugins with your preferred AI coding tool. To learn more, visit the Amazon DynamoDB Developer Guide.

Get started today
Vector search in Amazon DynamoDB is generally available in all commercial AWS Regions, including the AWS GovCloud (US) Regions. For Regional availability and a future roadmap, visit the AWS Capabilities by Region. For pricing details, visit the Amazon DynamoDB pricing page.

Start exploring vector search in DynamoDB today and send feedback to AWS re:Post for Amazon DynamoDB or through your usual AWS Support contacts.

— Esra

On July 13, 2006, we launched Amazon Simple Queue Service (Amazon SQS) as one of the first three services available to customers, alongside Amazon EC2 and Amazon S3. We had learned firsthand that distributed systems need a reliable way to pass messages between components without creating tight dependencies. If one service called another directly and that service was slow or unavailable, failures cascaded through the entire system. Message queuing solved this by letting services communicate asynchronously: a producer could drop a message into a queue and move on, while a consumer picked it up when ready. This approach kept individual service failures from affecting the rest of the system.

When Amazon SQS launched publicly in July 2006, it made this pattern available to every AWS customer. Twenty years later, that core function, decoupling producers from consumers, remains the reason customers use SQS. The scale, performance, and operational controls around it look very different now though.

Jeff Barr covered the first 15 years of SQS milestones in his 15th anniversary post, from the original 8 KB message limit in 2006 through FIFO queues, server-side encryption, and Lambda integration. Over the last five years, we have continued to scale SQS, added stronger security defaults, and introduced new capabilities that address increasingly complex workload patterns.

Key milestones between 2021 and 2026
High throughput mode for FIFO queues (2021): In May 2021, we launched general availability of high throughput mode for FIFO queues, supporting up to 3,000 transactions per second (TPS) per API action, a tenfold increase over the previous limit. We continued raising this ceiling over the following two years: to 6,000 TPS in October 2022, to 9,000 TPS in August 2023, and to 18,000 TPS in October 2023, before reaching 70,000 TPS per API action in select Regions by November 2023.

Server-side encryption with SSE-SQS (2021): In November 2021, we introduced server-side encryption with Amazon SQS-managed encryption keys (SSE-SQS), giving customers an encryption option that required no key management. In October 2022, we made SSE-SQS the default for all newly created queues, so customers no longer needed to explicitly enable it.

Dead-letter queue redrive enhancements (2021): We progressively expanded how customers recover unconsumed messages from dead-letter queues. In December 2021, we added DLQ redrive to source queue directly in the SQS console. In June 2023, we extended this capability to the AWS SDK and CLI through new APIs, including StartMessageMoveTask, CancelMessageMoveTask, and ListMessageMoveTasks. In November 2023, we added redrive support for FIFO queues.

Attribute-based access control, ABAC (2022): In November 2022, we introduced ABAC, giving customers the ability to configure access permissions based on queue tags rather than maintaining static policies as resources scaled.

JSON protocol support (2023): In November 2023, we added support for the JSON protocol in the AWS SDK, reducing end-to-end message processing latency by up to 23% for a 5 KB payload and lowering client-side CPU and memory usage.

Amazon EventBridge Pipes console integration (2023): We added the ability to connect a queue directly to EventBridge Pipes from the SQS console, routing messages to a broad range of AWS service targets without writing custom integration code.

Extended Client Library for Python (2024): We brought the Extended Client Library, previously available for Java, to Python developers, allowing messages up to 2 GB to be sent through SQS by storing the payload in Amazon S3 and passing a reference through the queue.

FIFO in-flight message limit increase (2024): We increased the in-flight message limit for FIFO queues from 20,000 to 120,000 messages, so consumers can process significantly more messages concurrently without being constrained by the previous ceiling.

Fair queues for multi-tenant workloads (2025): We introduced fair queues to mitigate the noisy neighbor problem in multi-tenant standard queues. By including a message group ID when sending messages, customers can prevent a single tenant from delaying message delivery for others, without any changes required on the consumer side.

1 MiB maximum message payload size (2025): We increased the maximum message payload from 256 KiB to 1 MiB for both standard and FIFO queues, helping customers send larger messages without offloading data to external storage. AWS Lambda event source mapping for SQS was updated in parallel to support the new payload size.

The constant underneath the change
Despite two decades of feature additions, the fundamental use case for SQS has not shifted. Customers use it to decouple services, buffer bursts of traffic, and build systems that stay resilient when individual components fail. That same pattern now extends to AI workloads. Customers use SQS queues to buffer requests to large language models, manage inference throughput, and coordinate communication between autonomous AI agents operating as independent services. For an example of this architecture in practice, read Creating asynchronous AI agents with Amazon Bedrock.

To learn more about Amazon SQS, visit the Amazon SQS product page, review the developer guide, or explore recent updates on the AWS Blogs.

— Esra

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

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.

Today, we’re announcing AWS CloudFormation Express mode, a new deployment mode that accelerates deployments for developers and AI tools iterating on infrastructure. Express mode accelerates deployments by completing when CloudFormation confirms resource configuration is applied, rather than waiting for extended stabilization checks. This reduces deployment time by up to 4 times for iterative development workflows and production scenarios.

How it works
Every CloudFormation deployment performs stabilization checks after resource configuration is applied. These checks serve an important purpose when you need to confirm resources can serve traffic before shifting load.

However, many workflows do not require full stabilization to proceed. Express mode benefits two primary use cases: iterative development workflows and production scenarios where you are comfortable with eventual stabilization. These use cases include iterating on infrastructure configurations during development, testing individual components of your application, and AI-assisted infrastructure development that benefits from sub-minute feedback loops.

With Express mode, CloudFormation completes deployments when resource configuration is applied, without waiting for stabilization checks. Resources continue becoming operational in the background. CloudFormation automatically retries dependent resources that encounter transient failures during provisioning within the same stack, without requiring any customer intervention. This built-in resilience handles timing issues between resources as they stabilize. Express mode changes when the deployment completes, not how resources are provisioned.

For example, when I create an Amazon Simple Queue Service (SQS) queue with a dead letter queue (DLQ), Standard mode takes 64 seconds, but Express mode completes in up to 10 seconds. In the case of deleting an AWS Lambda function with network interface attachment, Standard mode takes 20–30 minutes, but Express mode completes in up to 10 seconds based on my benchmarking test.

Get started with CloudFormation Express mode
When you create a CloudFormation stack in the AWS Management Console, choose Enable in the Express mode under Stack deployment options.

You can also use AWS Command Line Interface (AWS CLI), AWS SDKs, or IaC tools like AWS Cloud Development Kit (CDK), and AI tools such as Kiro.

Activate Express mode by setting the --deployment-config parameter to EXPRESS when creating, updating, or deleting stacks. No template changes are required. Express mode disables rollback by default for the fastest iteration experience. To re-enable rollback, set disableRollback to false in the deployment-config for production environments, or implement monitoring/cleanup mechanisms for failed deployments.

aws cloudformation create-stack \ 
   --stack-name my-app \ 
   --template-body file://template.yaml \ 
   --deployment-config '{"mode": "EXPRESS", "disableRollback": true}' \

For example, use the Express mode when you build infrastructure incrementally, adding resources one at a time. Ensure your IAM role templates follow the principle of least privilege.

# Iteration 1: Deploy IAM role
aws cloudformation create-stack \
--stack-name my-microservice \
--template-body file://iteration1-iam.yaml \
--deployment-config '{"mode": "EXPRESS"}' \
--capabilities CAPABILITY_IAM
--role-arn arn:aws:iam::123456789012:role/CloudFormationDeployRole

# Iteration 2: Add Lambda function
aws cloudformation update-stack \
--stack-name my-microservice \
--template-body file://iteration2-lambda.yaml \
--deployment-config '{"mode": "EXPRESS"}' \
--capabilities CAPABILITY_IAM
--role-arn arn:aws:iam::123456789012:role/CloudFormationDeployRole

# Iteration 3: Add SQS queue and event source mapping
aws cloudformation update-stack \
--stack-name my-microservice \
--template-body file://iteration3-sqs.yaml \
--deployment-config '{"mode": "EXPRESS"}' \
--capabilities CAPABILITY_IAM
--role-arn arn:aws:iam::123456789012:role/CloudFormationDeployRole

For AWS CDK, activate Express mode with the cdk deploy --express command when you deploy your CDK stack. This command retrieves your generated CloudFormation template and deploys it through the CloudFormation Express mode, which provisions your resources as part of a CloudFormation stack.

Express mode works with all existing CloudFormation templates and supports all CloudFormation features including change sets and nested stacks. When you enable Express mode on a parent stack, all nested stacks also use Express mode. If you need resources to be fully operational before proceeding with traffic or testing, continue using the default deployment behavior, which performs stabilization checks before completing.

Now available
AWS CloudFormation Express mode is available today in all AWS commercial Regions at no additional cost. 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 check troubleshooting about this new feature, try using the AWS MCP Server and plugins with your preferred AI tool. To learn more, visit the CloudFormation documentation.

Start accelerating your deployments today, and send feedback to AWS re:Post for AWS CloudFormation or through your usual AWS Support contacts.

— Channy

When you run compute-intensive workloads like real-time analytics, batch processing, video encoding, scientific modeling, or CPU-based machine learning inference, every percentage point of performance matters. You need instances that deliver higher throughput per vCPU, faster memory access, and more network bandwidth, all while keeping your costs in check.

Today I am happy to announce the general availability of Amazon Elastic Compute Cloud (Amazon EC2) C9g and C9gd instances, powered by AWS Graviton5 processors. C9g instances are compute-optimized and deliver up to 25% higher performance per vCPU compared to previous-generation C8g instances. They feature the fastest memory of any processor instance in the cloud, with DDR5 8800MT/s DIMMs, 5x more L3 cache, and up to 3x higher packet-processing performance compared to Graviton4-based instances. The faster memory and larger caches mean your workloads spend less time waiting on data, translating into higher throughput for in-memory analytics, faster agentic loops, and more responsive real-time applications.

C9g instances are ideal for batch jobs, video encoding pipelines, or distributed analytics that can utilize Amazon Elastic Block Store (Amazon EBS) for storage. It is also a natural fit for agentic AI workloads, where concurrent environments and CPU-bound reasoning steps benefit from Graviton5’s higher core count and larger caches. As AI shifts from answering questions to taking actions, running code, and orchestrating multi-step tasks, the demand for CPU compute is growing, and C9g instances are built for this shift.

Some workloads also need fast local storage alongside that compute power. Choose C9gd when your application benefits from high-speed, low-latency local NVMe SSD storage, for example scratch space during HPC simulations, temporary caches for ML inference, or local buffers for ad-serving engines.

Graviton5-based instances with NVMe instance store volumes also support detailed performance statistics, providing high-resolution I/O metrics, including latency histograms broken down by I/O size, up to 1-second granularity and accessible via Amazon CloudWatch or nvme-cli at no additional cost.

C9g and C9gd instances at a glance
C9g and C9gd instances are available in 11 sizes ranging from medium to 48xlarge, plus a bare metal option. They offer up to 15% higher network bandwidth and 20% higher EBS bandwidth on average across sizes compared to the previous generation, with the largest 48xlarge size delivering up to 100 Gbps of network bandwidth and up to 72 Gbps of EBS bandwidth, a 2x increase.

C9g	vCPUs	Memory (GiB)	Network Bandwidth (Gbps)	EBS Bandwidth (Gbps)
medium	1	2	Up to 15	Up to 12
large	2	4	Up to 15	Up to 12
xlarge	4	8	Up to 15	Up to 12
2xlarge	8	16	Up to 17	Up to 12
4xlarge	16	32	Up to 17	Up to 12
8xlarge	32	64	17	12
12xlarge	48	96	25	18
16xlarge	64	128	34	24
24xlarge	96	192	50	36
48xlarge	192	384	100	72
metal-48xl	192	384	100	72

C9gd instances add local NVMe SSD storage with up to 30% higher storage performance compared to previous-generation local storage instances.

C9gd	vCPUs	Memory (GiB)	Instance Storage (GB)	Network Bandwidth (Gbps)	EBS Bandwidth (Gbps)
medium	1	2	1 x 59	Up to 15	Up to 12
large	2	4	1 x 118	Up to 15	Up to 12
xlarge	4	8	1 x 237	Up to 15	Up to 12
2xlarge	8	16	1 x 474	Up to 17	Up to 12
4xlarge	16	32	1 x 950	Up to 17	Up to 12
8xlarge	32	64	1 x 1900	17	12
12xlarge	48	96	3 x 950	25	18
16xlarge	64	128	1 x 3800	34	24
24xlarge	96	192	3 x 1900	50	36
48xlarge	192	384	3 x 3800	100	72
metal-48xl	192	384	3 x 3800	100	72

Both families are well-suited for high-performance computing (HPC), batch processing, gaming, video encoding, scientific modeling, distributed analytics, CPU-based machine learning inference, and ad serving.

Here are some additional capabilities:

• Instance Bandwidth Configuration (IBC) lets you adjust the allocation of bandwidth between Amazon EBS and Amazon VPC networking by up to 25%, helping you optimize performance for workloads with specific bandwidth requirements such as databases and caching.
• ENA Express support for enhanced networking.
• Up to 128 EBS volumes can be attached to virtual instances.
• Support for Savings Plans, On-Demand, Spot Instances, Dedicated Instances, and Dedicated Hosts.

Nitro Isolation Engine
Security and isolation are foundational requirements for running workloads in the cloud. Within the Nitro System, the AWS Nitro Hypervisor is designed to isolate instances from each other as well as AWS operators. With C9g and C9gd instances we are raising the bar even further with the Nitro Isolation Engine, an enhancement to the Nitro System, which enforces isolation of instances and harnesses formal verification to provide assurances of isolation with mathematical precision. C9g and C9gd instances are the first set of compute-optimized instance types to feature Nitro Isolation Engine, a purpose built component that is responsible for enforcing isolation between virtual machines, including mediation of all access to virtual machine memory, CPU register state, and I/O devices through a minimal set of APIs.

To learn more about the Nitro Isolation Engine, visit the blog post. For details on the formal verification results, including scope and assumptions, see our technical white paper.

Now available
Amazon EC2 C9g and C9gd instances are now available in US East (Ohio, N. Virginia), US West (Oregon), and Europe (Frankfurt). Additional regions will follow.

You can launch C9g and C9gd instances today using the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDKs. For pricing information, visit the Amazon EC2 Pricing page.

To learn more, visit the Amazon EC2 C9g and C9gd instances page and send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

— seb

Editor’s Note: Updated 7/1/2026- Paragraph about Nitro Isolation Engine rewritten for clarity.

If you manage TLS certificates for your applications, you know the challenge: certificates expire, and when they do, your customers see errors or your service goes down. As certificate validity periods get shorter (the Certification Authority (CA)/Browser Forum mandates reduced maximum validity to 100 days starting March 2027, and to 47 days by 2029), manual renewal processes become untenable. You need automation.

Automatic Certificate Management Environment (ACME) is an open protocol for requesting, renewing, and revoking TLS certificates without human intervention. It’s the same protocol behind Let’s Encrypt, and it’s supported by dozens of clients across every platform.

Today we’re announcing ACME support for public certificates in AWS Certificate Manager (ACM). ACM now provides a fully managed ACME server endpoint that works with any ACMEv2-compatible client, such as Certbot, cert-manager for Kubernetes, acme.sh, or any other client you already use. You can issue public TLS certificates from Amazon Trust Services through the standard ACME protocol.

Before today, if you wanted automated certificate management using the ACME protocol, you relied on external certificate authorities alongside ACM, leading to a fragmented visibility experience. Some certificates lived in ACM, others were managed externally with no central dashboard. PKI administrators had limited ability to control who could request certificates or which domains were allowed.

With ACME support in ACM, you can now set up one or more managed ACME endpoint that allows you to centrally manage and monitor ACME certificate usage across your organization.

As a PKI administrator, you get centralized controls that go beyond basic certificate issuance. You can bind IAM roles to ACME accounts for fine-grained access control over which domains each client can request. You can define domain scopes at the endpoint level to enforce organization-wide policies. And you get centralized monitoring and visibility in the same place: AWS CloudTrail logs every certificate request for auditability, Amazon CloudWatch tracks operational metrics, and ACM sends expiry notifications when certificates are approaching renewal. Using ACM, your PKI team can search all certificates, whether issued through the ACM console, an API call, or ACME.

How it works
To get started, you first set up a dedicated ACME endpoint, configure authorization controls using External Account Binding (EAB), validate which domains the endpoint can issue certificates for, and point your existing ACME clients to the new endpoint.

The domain validation step is important: it separates who can set up certificate issuance from who can request certificates. The PKI administrator validates domains once at the endpoint level, using DNS credentials that stay with the admin. Application owners who need certificates never touch DNS. They register with an EAB credential, and the endpoint enforces which domains and scopes they’re allowed to request. This means you can distribute certificate automation broadly across your organization without distributing DNS keys along with it.

I start this demo from the ACME certificates page in the AWS Certificate Manager console.

I already have a few endpoints and certificates in this account, I walk you through creating a new one from scratch. First, I select Create ACME endpoint.

I give my endpoint a name. The Endpoint type is Public. ACME clients will connect over the public internet. The Certificate type is Public. The certificate will be issued by Amazon Trust Services and trusted by browsers and operating systems by default. For the certificate key type, I keep the default ECDSA P-256. RSA 2048 and ECDSA P-384 are also available if your clients require them.

Scrolling down, I configure the domain. I enter my domain name and select the domain scope. The scope controls exactly what certificate patterns your ACME clients are allowed to request for this domain. If I check only Exact domain, clients can only request certificates for that specific domain name. Adding Subdomains allows certificates for any subdomain (for example, api.example.com or dev.example.com). Adding Wildcards allows wildcard certificates (*.example.com). By leaving a scope unchecked, you prevent any client using this endpoint from requesting that type of certificate, even if their ACME request is otherwise valid. For a production endpoint, you might enable only Exact domain and Subdomains while leaving Wildcards unchecked to enforce a stricter security posture.

I also select my Amazon Route 53 hosted zone from the drop down menu. ACM then automatically creates the DNS CNAME records needed for domain validation, so I don’t have to do it manually. When my domain is hosted outside of Route 53, I manually create the provided CNAME record at my DNS provider instead. This is a meaningful difference from typical ACME setups where each client handles its own domain verification independently.

These centralized controls give PKI administrators a single place to authenticate domains, restrict which certificate types (ECDSA or RSA) clients can request, and further limit wildcard issuance. Having these governance capabilities built in means you don’t need to purchase a separate certificate lifecycle management product or invest in building a custom policy layer yourself, both of which come at significant cost and operational overhead.

I select Create ACME endpoint

After a few seconds, the endpoint is created. The console shows a Setup progress tracker with the next steps. My domain shows a “Validating” status. The validation method is DNS validation, where ACM verifies that you control the domain by checking for a specific CNAME record. Because I selected my Route 53 hosted zone during creation, I select Create records in Route 53 to let ACM handle the DNS validation automatically.

The validation completes in a few seconds and the status changes to Success.

Now I need to create External Account Binding (EAB) credentials. EAB credentials are a key identifier and a symmetriceiifcbnurtbikvdfjkuhfnitrbvuihigbgfegvhnucdv

HMAC key that lets your ACME client register an account with the ACME server. Once registered, the client generates its own asymmetric key pair, which is then used to authenticate all subsequent certificate requests. On the endpoint details page, I select the External account binding tab, then select Create EAB. I give the credential a name and optionally set an expiration time, ideally no longer than needed to complete client registration.

After I select Create EAB credential, the console shows the Key ID and HMAC Key. I note these values because I need them to configure my ACME client. The setup progress now shows four green checkmarks.

I’m ready to request a certificate. On the endpoint details page, I expand the CLI reference section. The console provides ready-to-use command examples for both Certbot and acme.sh. I copy the Certbot command and run it inside a container using the certbot/certbot image.

certbot certonly --standalone --non-interactive --agree-tos \
    --email <EMAIL> \
    --server https://acm-acme-enroll.us-east-1.api.aws/<ENDPOINT_ID>/directory \
    --eab-kid <EAB_KID> \
    --eab-hmac-key <EAB_HMAC_KEY> \
    --issuance-timeout <ISSUANCE_TIMEOUT> \
    -d <DOMAIN>

I replace the placeholders with my endpoint URL, EAB credentials, and domain name. The --eab-kid and --eab-hmac-key arguments are how Certbot registers with your ACME endpoint using the External Account Binding credentials I generated earlier. Each ACME client has its own syntax for this step, so check your client’s documentation for the exact flags.

Certbot contacts the ACME endpoint and returns a valid certificate signed by Amazon Trust Services.

I use openssl to view the certificate before installing it.

The certificate is now visible in the ACM console under the ACME certificates tab, alongside any certificates issued through the console or API.

Availability and pricing
ACME support in AWS Certificate Manager is available today in all commercial AWS Regions and will be available in AWS GovCloud (US), the China Regions, and the AWS European Sovereign Cloud partitions at a later date.

Pricing is per domain included in each certificate at the time of issuance, with a different price for fully qualified domain names and wildcards. Volume tiers are calculated based on total domain occurrences across all certificates issued per month in your AWS account. For details, see the ACM pricing page.

To get started, visit the ACM section on the AWS console or read the documentation.

— seb

Update 8/11/2026: EAB uses a HMAC symmetric key, not a HMAC key pair as I originally wrote.

Today, we are announcing AWS Lambda MicroVMs, a new serverless compute primitive within AWS Lambda that lets you run code generated by users or AI in isolated, stateful execution environments. You get virtual machine level isolation, near-instant launch and resume, and direct control over environment lifecycle and state, all without managing infrastructure or building expertise in complex virtualization technologies. Lambda MicroVMs are powered by Firecracker, the same lightweight virtualization technology that has powered over 15 trillions of monthly Lambda function invocations.

Why customers need this
Over the past few years a new class of multi-tenant applications has emerged that all share the need to hand each end user their own dedicated execution environment in which to safely run code that the application developer did not write. AI coding assistants, interactive code environments, data analytics platforms, vulnerability scanners, and game servers that run user-supplied scripts all fit this pattern. Building that capability today means making a difficult choice. Virtual machines deliver strong isolation but take minutes to start. Containers launch in seconds, yet their shared-kernel architecture requires significant custom hardening to safely contain untrusted code. Functions as a service are optimized for event-driven, request-response workloads, but are not designed for long-running interactive sessions that need to retain environment state across user interactions. That leaves developers either accepting tradeoffs between performance and isolation, or investing significant engineering resources to build and operate custom virtualization infrastructure to achieve isolated execution while delivering low-latency experiences to end-users. This presents an effort that demands deep expertise and pulls engineering time away from the product they are actually trying to build.

Lambda MicroVMs is purpose-built for exactly this gap. Each MicroVM gives a single end user or session its own isolated environment that launches rapidly, retains memory and disk state for the length of the session, and pauses to a low idle cost when the user steps away. Because the same Firecracker technology already underpins AWS Lambda Functions, you inherit the operational maturity of a service that has been running this stack at scale.

Let’s try it out
To get started, I navigated to the AWS Lambda console, where Lambda MicroVMs now appears in the left-hand navigation menu. I first need to create a MicroVM Image.

I packaged a Flask web app and its Dockerfile into a zip file, uploaded it to an Amazon Simple Storage Service (Amazon S3) bucket.

My Flask API – app.py

import logging

from flask import Flask, jsonify

app = Flask(__name__)
logging.basicConfig(level=logging.INFO)


@app.route("/")
def hello():
    app.logger.info("Received request to hello world endpoint")
    return jsonify(message="Hello, World!")


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

My Dockerfile

FROM public.ecr.aws/lambda/microvms:al2023-minimal
RUN dnf install -y python3 python3-pip && dnf clean all

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY app.py .

EXPOSE 5000

CMD ["gunicorn", "--bind", "0.0.0.0:5000", "app:app"]

I used the following command to create my MicroVM Image.

aws lambda-microvms create-microvm-image \
--code-artifact uri=<path/to/s3/artifact.zip> --name <VM_image_name> \
--base-image-arn arn:aws:lambda:us-east-1:aws:microvm-image:al2023-1 \
--build-role-arn <IAM role ARN>

You can also create the MicroVM Image in the AWS Console as in the image above. Once I ran the command, Lambda retrieved the zip, ran the Dockerfile, initialized the application, and took a Firecracker snapshot of the running disk and memory state. Build logs streamed in real time to Amazon CloudWatch under /aws/lambda/microvms/<image-name>, and when the image was ready it appeared in the console with its Amazon Resource Name (ARN) and version number.

aws lambda-microvms run-microvm \
--image-identifier arn:aws:lambda:<region>:<acct>:microvm-image:my-image \
--execution-role-arn arn:aws:iam::<acct>:role/MicroVMExecutionRole \
--idle-policy '{"maxIdleDurationSeconds":900,"suspendedDurationSeconds":300,"autoResumeEnabled":true}'

Launching can also be done via the AWS Console or the CLI. I passed the image ARN and an idle policy configured to auto-suspend after 15 minutes of inactivity and auto-resume on the next incoming request. No networking setup was required. Lambda assigned the MicroVM a unique ID, returned a dedicated endpoint URL, and started a new MicroVM with my Flask app already running, since it was resumed from a snapshot. My Flask app was already running the moment the launch completed. One API call to get a fully initialized, bootstrapped compute environment.

To send traffic, I generated a short-lived auth token with the CLI and attached it to a plain HTTPS request using the X-aws-proxy-auth header. The request landed on my Flask app immediately. I then let the MicroVM sit idle past the suspend threshold, at which point the MicroVM was suspended, with its memory and disk state snapshotted and stored. I then sent another request, and it resumed with the application state fully intact. From the client side, the pause never happened.

How it works
Under the covers, Lambda MicroVMs delivers three capabilities that, until today, no single AWS compute service offered together. The first is virtual machine level isolation, which comes from Firecracker. Each session runs in its own dedicated MicroVM with no shared kernel and no shared resources between users, so untrusted code supplied by one user is contained to their execution environment, without access to other environments or the underlying system. The second is rapid launch and resume. The model is image-then-launch: you create a MicroVM Image by supplying a Dockerfile and code packaged as a zip artifact in Amazon S3, and Lambda runs your Dockerfile, initializes your application, and takes a Firecracker snapshot of the running environment’s memory and disk state. Every subsequent MicroVM launched from that image resumes from the pre-initialized snapshot rather than booting cold, which means launches and idle resumes both achieve near-instant startup latency. Even a multi-gigabyte interactive session comes back online quickly enough to feel responsive to the end user. The third is stateful execution. A running MicroVM retains memory, disk, and running processes across the user’s session. During idle periods, a MicroVM can be suspended – with memory and disk state intact – and resumed when traffic arrives. Installed packages, loaded models, and working filesets are readily available when the user resumes their session. MicroVMs support up to 8 hours of total runtime and can be suspended automatically after a configurable idle window, which makes it straightforward to build products as varied as software vulnerability scans that complete in minutes, data analytics applications that run for hours, and interactive coding sessions with extended idle periods. As Lambda MicroVMs are started from pre-initialized snapshots, applications generating unique content, establishing network connections, or loading ephemeral data during initialization may need to integrate with service-provided hooks for compatibility.

Lambda MicroVMs is a new resource within AWS Lambda, with a distinct API surface. Lambda Functions remain the right choice for event-driven, request-response workloads, and Lambda MicroVMs is purpose-built for multi-tenant applications that need to hand each end user or session their own isolated environment to execute user- or AI-generated code. The two complement each other. An application using Lambda Functions for its event-driven backbone can call into Lambda MicroVMs for the steps that need to run untrusted code in isolation. You bring the application, and the service delivers the execution environment.

Now available
AWS Lambda MicroVMs is available today in the US East (N. Virginia, Ohio), US West (Oregon), Europe (Ireland) and Asia Pacific (Tokyo) Regions, on the ARM64 architecture, with up to 16 vCPUs, 32 GB of memory, and 32 GB of disk per MicroVM. Idle MicroVMs can be suspended explicitly through an API call or automatically through a lifecycle policy, which reduces the running cost while preserving full state for fast resume. Pricing details can be found on the AWS Lambda pricing page.

To get started, visit the AWS Lambda console, or learn more on the Lambda MicroVMs product page. For documentation, see the Lambda MicroVMs Developer Guide.

Last week AWS Summit New York City brought together thousands of customers, partners, and builders for a free, one-day event showcasing the latest in cloud and AI innovation. Dr. Swami Sivasubramanian, VP of Agentic AI at AWS unveiled a stack of AI launches in his keynote, all built around one thesis: agents that compound value over time.

• Agents for working – You can launch autonomous agents and access a smarter activity feed with new Amazon Quick features, which now let you create and run multi-step agents directly in the desktop app and consolidates email, Slack, calendar, and tasks into a single prioritized view with personalized rules.
• Agents for securing – You can shift from reactive to proactive security with AWS Continuum, a new AI-native security service that reasons, validates, and acts at machine speed across the full code vulnerability lifecycle. AWS Security Agent (now part of AWS Continuum) adds new features: threat modeling; pull request code scanning with remediation across major Git platforms; and IDE integrations via Kiro power, Claude Code plugin, and MCP.
• Agents for building – You can write, ship, and modernize code in one continuous loop with Kiro, AWS DevOps Agent, and AWS Transform. Kiro introduces a native iOS app; AWS DevOps Agent adds release management capabilities to assess code changes before production; and AWS Transform continuous modernization reduces tech debt autonomously.
• Agents customers create – You can go from agent idea to production in minutes with Amazon Bedrock AgentCore, which now includes a GA harness for infrastructure and orchestration, Web Search, Managed Knowledge Base, policy integrations with Guardrails, and the new AWS Context service for mapping organizational data relationships.

To learn more, visit the Summit recap from our top announcements blog post and Amazon News post.

Last week’s launches
Here are last week’s launches that caught my attention:

• AWS Local Zone in Hanoi, Vietnam – This new Local Zone is one of the first AWS Local Zones in the Asia Pacific with support for Amazon S3 and Amazon EBS Local Snapshots, enabling customers to meet data residency requirements by storing and backing up data locally. To get started, enable the Hanoi Local Zone (ap-southeast-1-han-1a) from the Regions and Zones tab in the AWS Global View or by using the ModifyAvailabilityZoneGroup API.
• AWS Blocks, an open-source TypeScript framework for application developers (preview) – AWS Blocks runs a fully functional local environment with Postgres, authentication, and real-time messaging, no AWS account required. When you’re ready to deploy, the same application code runs on production AWS services with zero changes, and you can drop into AWS CDK at any point for direct resource configuration.
• Grok 4.3 from xAI in Amazon Bedrock – You can use the Grok 4.3 model on Amazon Bedrock, giving you even more choice as you build generative AI applications across reasoning, agentic, and enterprise workflows. Grok 4.3 runs on a new inference engine in Bedrock designed for price performance, with support for tool calling, structured output, and response streaming.
• Amazon S3 annotations: attach rich, queryable context directly to your objects – Amazon S3 now lets you attach up to 1 GB of rich, mutable, and queryable context directly to your objects using annotations, purpose-built for AI agents and autonomous workflows that need to discover, understand, and act on data at scale without maintaining separate metadata systems.
• Amazon ECS announces faster service auto scaling – Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster).
• Amazon EC2 G7 instances accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs – 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.
• Strands Agents introduces new capabilities – Strands is an open source toolkit for building production agents. You can now use better context management in Harness SDK, a new isolated execution environment with Strands Shell, and chaos testing and red teaming in Strands Evals.
• AWS Management Console Private Access – You can access the AWS Console from VPCs without internet connectivity, allowing enterprises to manage their AWS infrastructure through the console while maintaining strict network security controls in air-gapped environments.
• AWS Marketplace Storefront is now generally available – AWS Partners can create and deploy their own branded catalog of solutions and services on their website or application in hours. Channel Partners and Independent Software Vendors can now simplify how they manage their cloud marketplace business and make it easier for customers to discover and purchase their solutions from AWS Marketplace.
• Palo Alto Networks (PANW) Advanced DNS Security on Amazon Route 53 Resolver DNS Firewall (preview) – You can now enforce DNS threat protections from Palo Alto Networks directly on Route 53 DNS Firewall rules, without deploying separate firewalls or modifying VPC configurations — by subscribing to PANW from the DNS Firewall console through the embedded AWS Marketplace widget.

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

Price reductions 
AWS continues to look for ways to increase performance and lower prices for our customers. I noticed a few such efforts last week, so I’d like to share them:

• Amazon S3 Vectors reduces query charges by up to 80% for large vector indexes – This reduction lowers costs for customers running similarity search across large-scale AI, RAG, and semantic search workloads. The new pricing applies automatically with no application changes required.
• Amazon GameLift Servers introduces free network bandwidth – Amazon GameLift Servers provides network bandwidth in and out of AWS at no additional charge for all instance types from generation 6 and later, including On-Demand and Spot, with no commitment required. You now pay only for your Amazon GameLift Servers instance hours; all network bandwidth is free.
• AWS Marketplace reduces listing fee for professional services to 0.5% from 2.5% – This reduction makes it more cost-effective for consulting partners, systems integrators, managed services providers and independent software vendors to transact their services through AWS Marketplace, while retaining the procurement and billing benefits that come with it.

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!

— Channy