Datadog
By Datadog, Inc.
Datadog is a cloud-based observability and security platform that unifies infrastructure monitoring, application performance monitoring (APM), log management, and real-user monitoring in a single product.
Definition
Datadog is a cloud-based observability and security platform that unifies infrastructure monitoring, application performance monitoring (APM), log management, and real-user monitoring in a single product.
Overview
Founded in 2010, Datadog set out to give operations and development teams one place to see metrics, traces, and logs together, instead of stitching together separate tools. It runs as a SaaS platform: lightweight agents installed on hosts, containers, or Kubernetes clusters collect metrics and traces and forward them to Datadog's cloud backend, where they're correlated and visualized on dashboards. Datadog's core strength is correlation across telemetry types. A single dashboard can show infrastructure CPU and memory, application traces from distributed tracing, and the exact log lines tied to a slow request, all filtered by the same tags (service, environment, host). This tag-based model lets teams pivot from a high-level alert down to a specific container or code path without switching tools, which is central to modern observability practice. The platform has grown well beyond monitoring: it now includes APM, log management, real-user monitoring (RUM), synthetic testing, security monitoring (Cloud SIEM and cloud security posture management), and CI/CD visibility. Datadog integrates with hundreds of technologies out of the box — AWS, Docker, Kubernetes, Nginx, and more — making it a common choice for teams running cloud-native infrastructure, a topic covered in SkillVeris's DevSecOps course. It competes with tools like Splunk, New Relic, and open source stacks built on Prometheus and Grafana.
Key Features
- Unified platform combining infrastructure metrics, APM traces, and logs with shared tagging
- Agent-based collection with hundreds of built-in integrations for cloud services and frameworks
- Distributed tracing and flame graphs for pinpointing latency in microservices
- Customizable dashboards and out-of-the-box service-level dashboards
- Anomaly detection, forecasting, and machine learning-based alerting
- Log management with indexing and rehydration for cost-controlled log retention
- Real-user monitoring and synthetic monitoring for front-end and uptime visibility
- Cloud security posture management and Cloud SIEM for security telemetry