Datadog's algorithmic monitoring capabilities use machine learning functionality to automatically identify abnormal values in metrics, based on analyses of group behavior or past performance. This allows for the detection of issues such as gradual baseline shifts or recurring fluctuations, which are difficult to catch with traditional threshold-based alerts. Algorithmic monitoring can be used to uncover abnormalities in user traffic, periodic fluctuations over changing baselines, and abnormal loads in distributed databases. By combining anomaly detection and outlier detection, users can gain more fine-grained insights into their infrastructure and applications. This enables the delivery of smarter alerts for issues such as dips in user traffic during peak business hours, or imbalances in load distribution across web servers.