appinsights-instrumentation

To instrument Azure applications with OpenTelemetry, you install the instrumentation libraries, initialize Application Insights, and begin collecting traces and metrics across your services. This provides end-to-end observability for proactive troubleshooting.

Application Insights instrumentation supports multiple languages including Node.js, Python, .NET, and Java, alongside various Azure deployment targets such as App Service, AKS, and Azure Functions for comprehensive telemetry collection.

The best way to set up distributed tracing for microservices is integrating OpenTelemetry with Application Insights. This enables correlation and centralized metrics, granting end-to-end trace visibility across services with minimal code changes.

Configuring SLO-driven dashboards and alerts requires defining your SLO targets and deployment targets as inputs. The instrumentation process then outputs customized dashboards and alerting rules to detect and resolve issues faster.

Yes, OpenTelemetry sampling is supported to manage telemetry data volume. The instrumentation covers sampling configuration alongside correlation and dashboards, helping you balance trace visibility and monitoring costs in production.

Using OpenTelemetry instead of native SDKs provides standardized telemetry across different languages and deployment targets. This approach ensures vendor-neutral instrumentation while maintaining Application Insights capabilities for end-to-end observability.