Azure Monitor

Once services are deployed, the next challenge is knowing whether they are healthy right now and why they fail when they are not. Looking through each service console one by one does not scale, and hearing about an outage from users is already too late.

Monitoring used to focus mostly on long-lived servers. Cloud platforms changed that by introducing short-lived functions, containers, and managed services whose behavior had to be correlated across metrics, logs, and traces. Observability platforms became essential because no single raw signal explained production behavior anymore.

Azure Monitor collects numerical metrics and textual logs from Azure services, then layers queries, visualizations, and alerts on top. Application Insights extends that model down into request and dependency tracing so teams can connect platform signals with application behavior. Alerts feed action groups that notify people or trigger automated responses.

Azure Monitor and Application Insights overlap, but they sit at different layers of observability. Monitor collects platform-level metrics and logs: CPU, memory, disk, network counters, and resource diagnostic logs across Azure services. Application Insights goes deeper into request-level tracing, tracking individual HTTP calls through service dependencies, surfacing slow queries, failed external calls, and exception chains. When investigating infrastructure capacity or availability, Monitor metrics and log queries are the starting point. When tracing a single slow request across service dependencies to find exactly where latency accumulated, Application Insights is where the answer lives.

Monitor is often one of the first services a team configures after deployment because healthy operations depend on metrics, logs, and alerting being available from the start. It helps with capacity trends, incident response, and automatic escalation workflows. Teams still need to manage retention and cost carefully, especially when log volume grows quickly.