Amazon CloudWatch Omni is the next evolution of CloudWatch — unified observability that brings your applications and AI agents into one reimagined experience, with auto-discovered topology, natural language queries, and AI-guided investigation powered by AWS DevOps Agent.

Learn how Amazon CloudWatch Omni delivers AI-powered observability purpose-built for generative AI and agentic workloads. Trace, evaluate, and experiment with AI agents across any framework—directly from your IDE or a standalone web experience—using open standards and built-in evaluators for quality, correctness, and coherence.

AWS announces the general availability of Amazon CloudWatch Omni, an evolution of Amazon CloudWatch. Omni is an AI-powered observability experience organized around your teams and the applications they run, so that you can observe and troubleshoot your applications and agents in one place. It combines the interoperability of OpenTelemetry with the scale and reliability of CloudWatch. And it meets you wherever you work: a standalone web experience with single sign-on (SSO) for your team, or a local IDE extension for getting hands-on with your agents. With CloudWatch Omni, you create spaces in your central accounts to see telemetry across your AWS accounts and Regions, as well as other clouds, including Azure workloads. Omni automatically discovers services, maps dependencies, and surfaces golden metrics to help streamline your operational workflows. Using Omni, you can interact with telemetry however you prefer: via chat, through a guided point-and-click path in the console, or directly from a tool of your choice leveraging Agent Toolkit for AWS. Ask a question in natural language and Omni finds the relevant telemetry, builds dynamic views of the signals you care about, and helps you get to root cause powered by AWS DevOps Agent. Prefer to drive yourself? Point and click through the signals that matter most, whether you're investigating a degrading application or diving deep into a trace or evaluation. Omni also features a dedicated agent observability experience with an evaluation-driven development workflow for AI workloads across frameworks including LangGraph, CrewAI, OpenAI Agents SDK, Vercel AI SDK, and Strands. For every prompt, model call, and tool invocation, Omni helps you evaluate quality and run experiments to validate fixes before you ship. To get started, create your Omni space from the CloudWatch console, configure SSO, and sign in to the standalone web experience. Agent developers can install the free CloudWatch Omni extension for VS Code, Cursor, and Kiro to instrument, debug, and evaluate agents locally (no AWS account required). CloudWatch Omni is generally available in US East (N. Virginia), US West (Oregon) and Europe (Ireland). To learn more, see the Amazon CloudWatch Omni product page and documentation. For pricing, see the CloudWatch Omni pricing page.

AWS announces the general availability of Amazon CloudWatch Omni, an evolution of Amazon CloudWatch. Omni is an AI-powered observability experience organized around your teams and the applications they run, so that you can observe and troubleshoot your applications and agents in one place. It combines the interoperability of OpenTelemetry with the scale and reliability of CloudWatch. And it meets you wherever you work: a standalone web experience with single sign-on (SSO) for your team, or a local IDE extension for getting hands-on with your agents. With CloudWatch Omni, you create spaces in your central accounts to see telemetry across your AWS accounts and Regions, as well as other clouds, including Azure workloads. Omni automatically discovers services, maps dependencies, and surfaces golden metrics to help streamline your operational workflows. Using Omni, you can interact with telemetry however you prefer: via chat, through a guided point-and-click path in the console, or directly from a tool of your choice leveraging Agent Toolkit for AWS. Ask a question in natural language and Omni finds the relevant telemetry, builds dynamic views of the signals you care about, and helps you get to root cause powered by AWS DevOps Agent. Prefer to drive yourself? Point and click through the signals that matter most, whether you're investigating a degrading application or diving deep into a trace or evaluation. Omni also features a dedicated agent observability experience with an evaluation-driven development workflow for AI workloads across frameworks including LangGraph, CrewAI, OpenAI Agents SDK, Vercel AI SDK, and Strands. For every prompt, model call, and tool invocation, Omni helps you evaluate quality and run experiments to validate fixes before you ship. To get started, create your Omni space from the CloudWatch console, configure SSO, and sign in to the standalone web experience. Agent developers can install the free https://marketplace.visualstudio.com/items?itemName=AmazonWebServices.amazon-cloudwatch-omni for VS Code, Cursor, and Kiro to instrument, debug, and evaluate agents locally (no AWS account required). CloudWatch Omni is generally available in US East (N. Virginia), US West (Oregon) and Europe (Ireland). To learn more, see the https://aws.amazon.com/cloudwatch/omni/ and https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/cloudwatch-omni.html. For pricing, see the https://aws.amazon.com/cloudwatch/omni/pricing/.

Amazon CloudWatch Omni: AI-first observability for agents and applications 0 views Eyal Estrin unread, 7:47 AM (4 minutes ago)    to https://aws.amazon.com/about-aws/whats-new/2026/09/amazon-cloudwatch-omni-ai/ https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/cloudwatch-omni.html https://aws.amazon.com/blogs/aws/introducing-amazon-cloudwatch-omni-collaborative-ai-powered-observability-for-your-applications/ https://aws.amazon.com/blogs/aws/introducing-amazon-cloudwatch-omni-ai-powered-observability-for-generative-ai-and-agentic-workloads/ https://aws.amazon.com/blogs/mt/introducing-amazon-cloudwatch-omni-observability-for-the-ai-era/ https://marketplace.visualstudio.com/items?itemName=AmazonWebServices.amazon-cloudwatch-omni Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/CloudWatch_Synthetics_Canaries.html introduces multi-check blueprints, enabling customers to create comprehensive synthetic tests using simple JSON configuration files. This new feature addresses the challenge many customers face when developing custom scripts for basic endpoint monitoring, which often lack the depth needed for thorough synthetic testing across various check types like HTTP endpoints with different authentication methods, DNS record validation, SSL certificate monitoring, and TCP port checks. With multi-check blueprints, customers can now bundle up to 10 different monitoring steps, one step per endpoint, in a single canary, making API monitoring more cost-effective and easier to implement. The solution provides built-in support for complex assertions on response codes, latency, headers, and body content, along with seamless integration with AWS Secrets Manager for secure credential handling. Customers benefit from detailed step-by-step results and debugging capabilities through the existing CloudWatch Synthetics console, significantly simplifying the process of implementing comprehensive API monitoring compared to writing individual custom canaries for each check. This feature streamlines monitoring workflows, reduces costs, and enhances the overall efficiency of synthetic monitoring setups. Multi-check blueprints are available in all commercial AWS regions where Amazon CloudWatch Synthetics is offered. For pricing details, see https://aws.amazon.com/cloudwatch/pricing/. To learn more about multi-check blueprints and how to get started, see the https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/CloudWatch_Synthetics_Canaries_Blueprints.html.

https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/GenAI-observability.html

Lists the metrics that AWS recommends that you set alarms on to monitor your AWS infrastructure.

https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/ContainerInsights.html now supports collection of GPU metrics at sub-minute frequencies for AI and ML workloads running on Amazon EKS. Customers can configure the metric sample rate in seconds, enabling more granular monitoring of GPU resource utilization. This enhancement enables customers to effectively monitor GPU-intensive workloads that run for less than 60 seconds, such as ML inference jobs that consume GPU resources for short durations. By increasing the sampling frequency, customers can maintain detailed visibility into short-lived GPU workloads. Sub-minute GPU metrics are sent to CloudWatch once per minute. This granular monitoring helps customers optimize their GPU resource utilization, troubleshoot performance issues, and ensure efficient operation of their containerized GPU applications. Sub-Minute GPU metrics in Container Insights is available in all AWS Commercial Regions and the AWS GovCloud (US) Regions. To learn more about Sub-Minute GPU metrics in Container Insights, visit the https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Container-Insights-metrics-enhanced-EKS.html#Container-Insights-metrics-EKS-GPU page in the Amazon CloudWatch User Guide. Sub-Minute GPU metrics in Container Insights are available for no addition cost. For Container Insights pricing, see the https://aws.amazon.com/cloudwatch/pricing/.

AWS Lambda now supports https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/CloudWatch-Application-Monitoring-Sections.html, an application performance monitoring (APM) solution, enabling developers and operators to easily monitor the health and performance of their serverless applications built using Lambda. Customers want an easy way to quickly identify and troubleshoot performance issues to minimize the mean time to recovery (MTTR) and operational costs of running serverless applications. Now, Application Signals provides pre-built, standardized dashboards for critical application metrics (such as throughput, availability, latency, faults, and errors), correlated traces, and interactions between the Lambda function and its dependencies (such as other AWS services), without requiring any manual instrumentation or code changes from developers. This gives operators a single-pane-of-glass view of the health of the application and enables them to drill down to establish the root cause of performance anomalies. You can also create Service Level Objectives (SLOs) in Application Signals to closely track the performance KPIs of critical operations in your application, enabling you to easily identify and triage operations that do not meet your business KPIs. Application Signals auto-instruments your Lambda function using enhanced https://aws-otel.github.io/ libraries, delivering better performance (cold start latency and memory consumption) than before. To get started, visit the Configuration tab in Lambda console and enable Application Signals for your function with just one click in the “Monitoring and operational tools” section. To learn more, visit the https://aws.amazon.com/blogs/aws/track-performance-of-serverless-applications-built-using-aws-lambda-with-application-signals/, https://docs.aws.amazon.com/lambda/latest/dg/monitoring-application-signals.html, and https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/CloudWatch-Application-Signals-Enable-Lambda.html. Application Signals for Lambda is available in all commercial https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ where Lambda and CloudWatch Application Signals are available.  

https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Database-Insights.html announces support of fleet monitoring for https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/limitless.html. Database Insights is a database observability solution that provides a curated experience designed for DevOps engineers, application developers, and database administrators (DBAs) to expedite database troubleshooting and gain a holistic view into their database fleet health. Database Insights consolidates logs and metrics from your applications, your databases, and the operating systems on which they run into a unified view in the console. Using its pre-built dashboards, and automated telemetry collection, you can monitor fleet health across all your database types in one place, and drill down seamlessly from fleet overview to individual instance analysis. Database Insights offers two curated monitoring views: a fleet health dashboard for estate-wide visibility and an instance dashboard for detailed performance analysis. Aurora Limitless PostgreSQL databases were previously supported through instance-level monitoring — enabling you to track load distribution across shard groups. We're now extending this capability to include fleet-level monitoring, which allows you to view the overall health of your entire database fleets, including Aurora clusters, RDS instances, and Aurora Limitless PostgreSQL databases, all from a single unified dashboard. You can get started with Database Insights for Aurora Limitless by enabling it on your Limitless databases using the Aurora service console, AWS APIs, and SDKs. Database Insights for Aurora Limitless is available in all regions where Aurora Limitless is available and applies a new ACU-based pricing – see https://aws.amazon.com/cloudwatch/pricing/ for details. For further information, visit the https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Database-Insights.html.

Amazon EC2 Capacity Manager is a new centralized solution that consolidates capacity monitoring and management across all AWS accounts and regions, eliminating operational overhead and providing optimization opportunities for EC2 infrastructure at scale.

AWS インフラストラクチャをモニタリングするためにアラームを設定することを AWS がお勧めするメトリクスを一覧表示します。

Amazon CloudWatch now offers centralized visibility into critical AWS service telemetry configurations, such as Amazon VPC Flow Logs, Amazon EC2 Detailed Metrics, and AWS Lambda Traces. This enhanced visibility enables central DevOps teams, system administrators, and service teams to identify potential gaps in their infrastructure monitoring setup. The telemetry configuration auditing experience seamlessly integrates with AWS Config to discover AWS resources, and can be turned on for the entire organization using the new AWS Organizations integration with Amazon CloudWatch. With visibility into telemetry configurations, you can identify monitoring gaps that might have been missed in your current setup. For example, this helps you identify gaps in your EC2 detailed metrics so that you can address them and easily detect short-lived performance spikes and build responsive auto-scaling policies. You can audit telemetry configuration coverage at both resource type and individual resource levels, refining the view by filtering across specific accounts, resource types, or resource tags to focus on critical resources. The telemetry configurations auditing experience is available in US East (N. Virginia), US West (Oregon), US East (Ohio), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm) regions. There is no additional cost to turn on the new experience, including for AWS Config. You can get started with auditing your telemetry configurations using the https://us-east-1.console.aws.amazon.com/cloudwatch/home?region=us-east-1#, by clicking on Telemetry config in the navigation panel, or programmatically using the API/CLI. To learn more, https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/telemetry-config-cloudwatch.html.

<p><a href="https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/ContainerInsights.html" contenteditable="false" style="cursor: pointer;">Amazon CloudWatch Container Insights</a> now supports collection of GPU metrics at sub-minute frequencies for AI and ML workloads running on Amazon EKS. Customers can configure the metric sample rate in seconds, enabling more granular monitoring of GPU resource utilization.<br> <br> This enhancement enables customers to effectively monitor GPU-intensive workloads that run for less than 60 seconds, such as ML inference jobs that consume GPU resources for short durations. By increasing the sampling frequency, customers can maintain detailed visibility into short-lived GPU workloads. Sub-minute GPU metrics are sent to CloudWatch once per minute. This granular monitoring helps customers optimize their GPU resource utilization, troubleshoot performance issues, and ensure efficient operation of their containerized GPU applications.<br> <br> Sub-Minute GPU metrics in Container Insights is available in all AWS Commercial Regions and the AWS GovCloud (US) Regions.<br> <br> To learn more about Sub-Minute GPU metrics in Container Insights, visit the <a href="https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Container-Insights-metrics-enhanced-EKS.html#Container-Insights-metrics-EKS-GPU" contenteditable="false" style="cursor: pointer;">NVIDIA GPU metrics</a> page in the Amazon CloudWatch User Guide. Sub-Minute GPU metrics in Container Insights are available for no addition cost. For Container Insights pricing, see the <a href="https://aws.amazon.com/cloudwatch/pricing/" contenteditable="false" style="cursor: pointer;">Amazon CloudWatch Pricing Page</a>.</p>

Today, AWS launches simplified enablement of AWS CloudTrail events in Amazon CloudWatch, a monitoring and logging service that helps you collect, monitor, and analyze log data from your AWS resources and applications. With this launch, you can now centrally configure collection of CloudTrail events in https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/telemetry-config-cloudwatch.html alongside other popular AWS log sources such as Amazon VPC flow logs and Amazon EKS Control Plane Logs. CloudWatch's ingestion experience provides a consolidated view that simplifies collecting telemetry from different sources for accounts in your AWS Organization thus ensuring comprehensive monitoring and data collection across your AWS environment. This new integration leverages https://docs.aws.amazon.com/awscloudtrail/latest/userguide/cloudtrail-service-linked-channels.html (SLCs) to receive events from CloudTrail without requiring trails, and also provides additional benefits such as safety-checks and termination protection. You incur both https://aws.amazon.com/cloudtrail/pricing/ and https://aws.amazon.com/cloudwatch/pricing/ based on custom logs pricing. To learn more about enablement of CloudTrail events in CloudWatch and supported AWS regions, visit the https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/telemetry-config-cloudwatch.html

Summer is well and truly here in the UK! I’m a bit of a summer grinch though so, unlike most people, I’m not crazy about “the glorious sun” scorching me when I’m out and about. On the upside, this provides the perfect excuse to retreat to the comfort of a well-ventilated room where I can […]

New capabilities help optimize application performance, analyze unlimited prefixes, and simplify metrics analysis through S3 Tables integration.

Amazon CloudWatch now allows customers to automatically configure telemetry across their AWS Organization for six critical AWS services: AWS CloudTrail Management Events, AWS CloudTrail Data Events, Amazon Route 53 Resource Query Logs, Amazon EKS Control Plane logs, Network Load Balancer access logs, and AWS WAF WebACL Logs. This enhancement to CloudWatch enablement rules helps organizations maintain consistent monitoring and security practices at scale. With today's launch, customers can create organization-wide enablement rules that automatically configure logging for both existing and new resources across these services. For example, security teams can create rules to automatically configure CloudTrail Management Events and CloudTrail Data Events for all accounts in their organization, ensuring comprehensive audit trails. Operations teams can enforce consistent logging practices by automatically enabling EKS Control Plane logs across clusters with specific resource tags. Enablement rules leverage AWS Config Service-Linked recorders to discover resources that meet the rule criteria and automatically enable log ingestion to CloudWatch. CloudWatch's telemetry auto-enablement capability for these additional services is available in the following AWS commercial regions: US East (Ohio), US East (N. Virginia), US West (N. California), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Osaka), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), Europe (Stockholm), and South America (São Paulo). Paulo). Customers incur charges for configuration items of resource types that are setup for enablement rules, according to https://aws.amazon.com/config/pricing/. Log ingestion is billed as per https://aws.amazon.com/cloudwatch/pricing/. To learn more about telemetry auto-enablement for these services, visit thehttps://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/telemetry-config-rules.html.

AWS X-Ray, a service that helps developers analyze and debug distributed applications by providing request tracing capabilities, now offers adaptive sampling to solve a common challenge for DevOps teams, Site Reliability Engineers (SREs), and application developers. These customers often face a difficult trade-off: setting sampling rates too low risks missing critical traces during incidents, while setting them too high unnecessarily increases observability costs during normal operations.             Today, with adaptive sampling, you can automatically adjust sampling rates within user-defined limits to ensure you capture the most important traces precisely when you need them. This helps development teams reduce mean time to resolution (MTTR) during incidents by providing comprehensive trace data for root cause analysis, while maintaining cost-efficient sampling rates during normal operations. Adaptive sampling supports two approaches, Sampling Boost and Anomaly Span Capture. These can be applied independently or can be combined together. Customers can use Sampling Boost to temporarily increase sampling rates when anomalies are detected to capture complete traces and Anomaly Span Capture to ensures anomaly-related spans are always captured, even when the full trace isn't sampled. Adaptive sampling is currently available in all commercial regions where AWS X-Ray is offered. For more information, see the X-Ray documentation. and CloudWatch pricing page for X-ray pricing details.

Amazon CloudWatch now helps you monitor large-scale distributed applications by automatically discovering and organizing services into groups based on configurations and their relationships. SREs and DevOps teams can identify critical dependencies and blast radius impacts to remediate issues faster. You get an always-on, out-of-the-box catalog and map that visualizes services and dependencies across AWS accounts and regions, organizing them into logical groups that align with how customers think about their systems—without manual configurations. You can also apply dynamic grouping based on how you organize applications—by teams, business units, criticality tiers, or other attributes. With this new application performance monitoring (APM) capability, customers can quickly visualize which applications and dependencies to focus on while troubleshooting their distributed applications. For example, SRE and DevOps teams can now accelerate root cause analysis and reduce mean-time-to-resolution (MTTR) through high-level operational signals such as SLOs, health indicators, changes, and top observations. The application map integrates with a contextual troubleshooting drawer that surfaces relevant metrics and actionable insights to accelerate triage. When deeper investigation is needed, teams can pivot to an application-specific dashboard tailored for troubleshooting. The map, drawer, and dashboard dynamically update as new services are discovered or as customers adjust how their environments are grouped—ensuring the view is always accurate and aligned with how teams operate. This new capability is now available in all AWS commercial regions where Application Signals have launched , at no additional cost. To learn more, please visit https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/ServiceMap.html

https://aws.amazon.com/xray/, a service that helps developers analyze and debug distributed applications by providing request tracing capabilities, now offers adaptive sampling to solve a common challenge for DevOps teams, Site Reliability Engineers (SREs), and application developers. These customers often face a difficult trade-off: setting sampling rates too low risks missing critical traces during incidents, while setting them too high unnecessarily increases observability costs during normal operations.             Today, with adaptive sampling, you can automatically adjust sampling rates within user-defined limits to ensure you capture the most important traces precisely when you need them. This helps development teams reduce mean time to resolution (MTTR) during incidents by providing comprehensive trace data for root cause analysis, while maintaining cost-efficient sampling rates during normal operations. Adaptive sampling supports two approaches, Sampling Boost and Anomaly Span Capture. These can be applied independently or can be combined together. Customers can use Sampling Boost to temporarily increase sampling rates when anomalies are detected to capture complete traces and Anomaly Span Capture to ensures anomaly-related spans are always captured, even when the full trace isn't sampled. Adaptive sampling is currently available in all commercial regions where AWS X-Ray is offered. For more information, see the https://docs.aws.amazon.com/xray/latest/devguide/xray-adaptive-sampling.html and CloudWatch https://aws.amazon.com/cloudwatch/pricing/ for X-ray pricing details.