Application Signals

Monitor application health in real time

Track performance against business goals

View relationships between services and dependencies

Quickly identify and resolve performance issues

Enable Application Signals to automatically collect metrics and traces from your applications, and display key metrics such as call volume, availability, latency, faults, and errors. Quickly see and triage current operational health, and whether your applications are meeting their longer-term performance goals, without writing custom code or creating dashboards.

Create and monitor service-level objectives (SLOs) with Application Signals. Easily create and track status of SLOs related to CloudWatch metrics, including the new standard application metrics that Application Signals collects. See and track the service level indicator (SLI) status of your application services within a services list and topology map. Create alarms to track your SLOs, and track the new standard application metrics that Application Signals collects.

See a map of your application topology that Application Signals automatically discovers, that gives you a visual representation of your applications, dependencies, and their connectivity.

Application Signals works with CloudWatch RUM, CloudWatch Synthetics canaries, AWS Service Catalog AppRegistry, and Amazon EC2 Auto Scaling to display your client pages, Synthetics canaries, and application names within dashboards and maps.

Use Application Signals for daily application monitoring – Use Application Signals within the CloudWatch console, as part of daily application monitoring:

Using Application Signals incurs charges. For information about CloudWatch pricing, see Amazon CloudWatch Pricing.

Application Signals cross-account – With Application Signals cross-account observability, you can monitor and troubleshoot your applications that span multiple AWS accounts within a single Region.

You can use Amazon CloudWatch Observability Access Manager to set up one or more of your AWS accounts as a monitoring account. You’ll provide the monitoring account with the ability to view data in your source account by creating a sink in your monitoring account. You use the sink to create a link from your source account to your monitoring account. For more information, see CloudWatch cross-account observability.

For proper functionality of Application Signals cross-account observability, ensure that the following telemetry types are shared through the CloudWatch Observability Access Manager.

Dynamic service grouping and filtering – Group and filter services with Application Signals' dynamic grouping capabilities. Automatically aggregate metrics and SLIs of services within groups, allowing you to start from a group view and dive deep into specific problematic areas. Application Signals automatically discovers and groups services based on their configuration and relationship. You can also create custom groups that align with your business needs. For example, you can group services by business units, teams, or critical tiers. View consolidated performance metrics, track group-level SLIs, and quickly identify non-performing services. Using grouping, you can organize your large-scale distributed services into logical groups that align with your operational needs and simplifies monitoring them especially during incidents.

The system analyzes the service dependency graph and creates groups where the root node (a service with no upstream dependencies) becomes the group name. All services that depend on this root service, either directly or indirectly, are automatically included in the group. For example, if Service A calls Service B, which in turn calls Service C, all three services will be grouped together with Service A as the group name since it's the root of the dependency chain. This automatic grouping mechanism provides a natural way to visualize and manage related services based on their actual runtime interactions and dependencies.

Create custom groupings using AWS tags or OpenTelemetry attributes that align with your team structure, business domains, or operational requirements. Custom groupings enable you to organize services according to your specific monitoring and troubleshooting workflows. For more information, see Configuring custom groups.

Last deployment tracking – You can track latest deployment tracking for each service and its dependencies, which provides crucial context for troubleshooting without any manual configuration or setup. Identify and monitor the last deployment time for each service and dependencies, providing crucial context for operational analysis and troubleshooting without additional configuration or setup requirements. Correlate deployment times with performance changes, detect deployment-related issues, and maintain a comprehensive deployment history across your application landscape. This feature helps teams quickly determine if recent deployments contributed to service degradation and supports faster incident resolution.

Application Signals automatically correlates deployment events with performance metrics, helping you quickly identify whether recent deployments are contributing to service issues. The system tracks deployment timing and provides visual indicators when performance changes align with deployment activities.

Maintain a comprehensive view of deployment activities across your application landscape. View deployment frequency, timing patterns, and success rates to optimize your deployment strategies and identify potential risk factors.

Automated audit findings – Discover critical insights through Application Signals' automated audit findings such as EMF logs of Application Signals RED metrics, traces, and application logs. The service analyzes your applications to report observations, including finding top outliers who contributed to the latency, faults, or errors, finding issues started from dependency services, and finding the detailed error information from application logs to help better understand the root cause. The audit system employs advanced analytics to detect patterns, highlight resource inefficiencies, and suggest root causes. Findings are prioritized based on severity and potential business impact, enabling teams to focus on the most critical issues first. Get actionable recommendations for improving service reliability, and performance without manual analysis.