Detect anomalies before they become incidents with Datadog AIOps

As IT environments scale, proactive anomaly detection becomes crucial for preventing unplanned downtime and minimizing user impact. Datadog applies AIOps principles to its monitoring solutions, enabling users to detect issues across their entire technology stack, reduce time spent on investigating and resolving issues, consolidate related alerts, build automated troubleshooting workflows, and more. The company's integrated AIOps approach includes built-in machine learning algorithms that allow for early anomaly detection, outlier detection, and forecasting capabilities. Datadog offers three ML algorithms—Basic, Agile, and Robust—to identify anomalies early so users can take action to prevent incidents. Additionally, Watchdog is an AI-powered engine that uses these ML algorithms to automatically flag anomalies and outliers, forecast potential bottlenecks, conduct automated business impact analysis and root cause analysis (RCA), and detect faulty code deployments. Datadog also enables users to configure custom alerts based on their chosen metrics, allowing for greater control over the monitoring process.