Azure Application Monitoring with OpenTelemetry

Azure application monitoring should be treated as a production decision rather than a feature comparison. Start with critical user journeys, dependencies, current load, failure cost, and operational ownership. Define a measurable result and a safe rollback for every proposed change. This evidence separates the actual constraint from assumptions and prevents the team from adding complexity before it has proved the need.

Azure Application Monitoring: Logs metrics and traces

Evaluate logs metrics and traces through a representative production scenario instead of a general best-practice list. Record trace propagation, resource attributes, and service-level indicators. Capture the baseline before making a change; otherwise, the team cannot show whether the decision improved reliability, delivery speed, or operating cost.

Compare at least two viable options and document the limit of each one. Review sampling, retention, alert thresholds, and operator runbooks. The decision must account for peak load, permissions, dependent services, and the engineers who will operate it. It should also explain how it affects the next concern: opentelemetry design.

Before rollout, verify correlation across browser, API, queue, and database spans. Set a stopping threshold, name the person who can pause the release, and describe the state restored by rollback. Monitoring must expose the cause of failure rather than only reporting that an error occurred.

OpenTelemetry design

Evaluate opentelemetry design through a representative production scenario instead of a general best-practice list. Record trace propagation, resource attributes, and service-level indicators. Capture the baseline before making a change; otherwise, the team cannot show whether the decision improved reliability, delivery speed, or operating cost.

Compare at least two viable options and document the limit of each one. Review sampling, retention, alert thresholds, and operator runbooks. The decision must account for peak load, permissions, dependent services, and the engineers who will operate it. It should also explain how it affects the next concern: application insights.

Before rollout, verify correlation across browser, API, queue, and database spans. Set a stopping threshold, name the person who can pause the release, and describe the state restored by rollback. Monitoring must expose the cause of failure rather than only reporting that an error occurred.

If implementation needs additional capacity, Azure development expertise can turn the assessment into owned work packages, acceptance criteria, and a knowledge-transfer plan.

Application Insights

Evaluate application insights through a representative production scenario instead of a general best-practice list. Record trace propagation, resource attributes, and service-level indicators. Capture the baseline before making a change; otherwise, the team cannot show whether the decision improved reliability, delivery speed, or operating cost.

Compare at least two viable options and document the limit of each one. Review sampling, retention, alert thresholds, and operator runbooks. The decision must account for peak load, permissions, dependent services, and the engineers who will operate it. It should also explain how it affects the next concern: log analytics.

Before rollout, verify correlation across browser, API, queue, and database spans. Set a stopping threshold, name the person who can pause the release, and describe the state restored by rollback. Monitoring must expose the cause of failure rather than only reporting that an error occurred.

Log Analytics

Evaluate log analytics through a representative production scenario instead of a general best-practice list. Record trace propagation, resource attributes, and service-level indicators. Capture the baseline before making a change; otherwise, the team cannot show whether the decision improved reliability, delivery speed, or operating cost.

Compare at least two viable options and document the limit of each one. Review sampling, retention, alert thresholds, and operator runbooks. The decision must account for peak load, permissions, dependent services, and the engineers who will operate it. It should also explain how it affects the next concern: correlation and tracing.

Before rollout, verify correlation across browser, API, queue, and database spans. Set a stopping threshold, name the person who can pause the release, and describe the state restored by rollback. Monitoring must expose the cause of failure rather than only reporting that an error occurred.

The related architecture guide provides more context for testing adjacent assumptions and release dependencies.

Correlation and tracing

Evaluate correlation and tracing through a representative production scenario instead of a general best-practice list. Record decision boundaries, non-functional requirements, and named owners. Capture the baseline before making a change; otherwise, the team cannot show whether the decision improved reliability, delivery speed, or operating cost.

Compare at least two viable options and document the limit of each one. Review an architecture record that includes rejected alternatives. The decision must account for peak load, permissions, dependent services, and the engineers who will operate it. It should also explain how it affects the next concern: alerts and dashboards.

Before rollout, verify a thin end-to-end slice for the largest assumption. Set a stopping threshold, name the person who can pause the release, and describe the state restored by rollback. Monitoring must expose the cause of failure rather than only reporting that an error occurred.

The related architecture guide provides more context for testing adjacent assumptions and release dependencies.

Alerts and dashboards

Evaluate alerts and dashboards through a representative production scenario instead of a general best-practice list. Record trace propagation, resource attributes, and service-level indicators. Capture the baseline before making a change; otherwise, the team cannot show whether the decision improved reliability, delivery speed, or operating cost.

Compare at least two viable options and document the limit of each one. Review sampling, retention, alert thresholds, and operator runbooks. The decision must account for peak load, permissions, dependent services, and the engineers who will operate it. It should also explain how it affects the next concern: telemetry cost and incident workflow.

Before rollout, verify correlation across browser, API, queue, and database spans. Set a stopping threshold, name the person who can pause the release, and describe the state restored by rollback. Monitoring must expose the cause of failure rather than only reporting that an error occurred.

The related architecture guide provides more context for testing adjacent assumptions and release dependencies.

Telemetry cost and incident workflow

Evaluate telemetry cost and incident workflow through a representative production scenario instead of a general best-practice list. Record cost exports, tagging coverage, amortized charges, and unit economics. Capture the baseline before making a change; otherwise, the team cannot show whether the decision improved reliability, delivery speed, or operating cost.

Compare at least two viable options and document the limit of each one. Review commitment discounts against variable demand. The decision must account for peak load, permissions, dependent services, and the engineers who will operate it. It should also explain how it affects the next concern: logs metrics and traces.

Before rollout, verify idle capacity, data transfer, and telemetry ingestion. Set a stopping threshold, name the person who can pause the release, and describe the state restored by rollback. Monitoring must expose the cause of failure rather than only reporting that an error occurred.

When You May Need External Development Expertise

An independent review of Azure application monitoring is useful when a change crosses application code, data, and cloud infrastructure, or when the team lacks recent experience with a similar workload. A useful assessment should return prioritized risks, viable options, an implementation sequence, acceptance criteria, and a clear knowledge-transfer plan.

• Decision 1: For logs metrics and traces, record the baseline, target, owner, failure scenario, and rollback action.
• Decision 2: For opentelemetry design, record the baseline, target, owner, failure scenario, and rollback action.
• Decision 3: For application insights, record the baseline, target, owner, failure scenario, and rollback action.
• Decision 4: For log analytics, record the baseline, target, owner, failure scenario, and rollback action.
• Decision 5: For correlation and tracing, record the baseline, target, owner, failure scenario, and rollback action.
• Decision 6: For alerts and dashboards, record the baseline, target, owner, failure scenario, and rollback action.