Monitoring is the reason teams buy Datadog, and it is also where most Datadog estates quietly go wrong. The platform will happily collect everything, alert on anything, and page whoever you tell it to. Whether that adds up to a system your team trusts at 2am depends entirely on how it is set up.
Start with the foundations: what Datadog monitoring actually covers and how to set it up, and the fuller walk-through of setting up cloud monitoring with Datadog from agent install to first dashboards. From there, the work is choosing what deserves an alert. Datadog ships more alerting features than most teams ever use, and the ones that matter most, composite monitors, downtimes, SLO alerts, exist to cut noise rather than add coverage. Setting up Datadog alerts properly is a one-day job that pays back every week.
Noise is the failure mode. Every alert that fires and means nothing trains your team to ignore the one that does. Alert fatigue has specific causes and specific fixes, and anomaly detection, tuned properly, replaces a wall of static thresholds with monitors that understand what normal looks like.
Beyond the basics, Datadog reaches into the places incidents actually start: the Service Map shows what depends on what when something breaks, network monitoring answers the "is it the network?" question with data, and connection pool monitoring catches database exhaustion before requests start queueing. CI and code-level signals belong in the same platform: GitHub Actions monitoring and Python error tracking both feed the same correlated view. When an incident does start, the Slack integration puts the alert where your team already works.