1. Metrics Collection and Monitoring: Datadog is a comprehensive monitoring and analytics platform that allows users to collect, visualize, and alert on metrics from various sources. It provides a wide range of integrations and can collect metrics from infrastructure, applications, and logs. In contrast, StatsD is a lightweight daemon for collecting and aggregating application metrics, mainly focused on counting and timing events. It is designed to be simple and easy to deploy and can be used as a complement to Datadog for specific use cases.
2. Flexibility and Customization: Datadog offers a high level of flexibility and customization options for metric collection and monitoring. Users can create custom metrics, define complex queries and alert conditions, and visualize data using various chart types and dashboards. On the other hand, while StatsD allows for some configuration options, it has limited customization capabilities compared to Datadog. It mainly focuses on collecting and aggregating basic metrics like counts and timings.
3. Scalability and Performance: Datadog is designed to handle large-scale metric collection and monitoring for enterprise-grade environments. It scales with the growing needs of the infrastructure and can handle millions of metrics per second. It offers advanced functionalities like high-resolution metrics and distributed tracing. In contrast, StatsD is a lightweight solution and may not be suitable for high-scale deployments. Its performance may start to degrade when handling a high volume of metric data.
4. Data Visualization and Analysis: Datadog provides powerful data visualization and analysis capabilities. It allows users to create custom dashboards with various widgets and chart types to visualize metrics in real-time. It offers built-in anomaly detection algorithms and anomaly graphs to identify and investigate unusual patterns in data. On the other hand, StatsD does not offer built-in data visualization and analysis tools. Users need to integrate it with other tools or platforms to visualize and analyze the collected metric data.
5. Monitoring Infrastructure and Applications: Datadog monitors not only the infrastructure but also the applications running on it. It provides out-of-the-box integrations with popular software and services, enabling users to monitor application performance metrics, logs, and traces seamlessly. On the contrary, StatsD is primarily focused on collecting application metrics and does not have built-in capabilities to monitor infrastructure or integrate with other monitoring tools.
6. Alerting and Notification: Datadog offers flexible alerting and notification capabilities to keep users informed about any anomalies or issues with the monitored metrics. Users can set up alert conditions based on various criteria and configure notifications via emails, Slack, PagerDuty, and other channels. In contrast, StatsD does not provide native alerting and notification features. Users need to rely on other tools or write custom scripts to implement alerts based on the collected metrics.

In Summary, Datadog is a comprehensive monitoring platform offering flexible customization, scalability, visualization, and alerting capabilities, whereas StatsD is a lightweight metric collection daemon with limited customization and visualization functionalities, mainly focused on application metric collection.