Netdata
by netdata
Real-Time Infrastructure Monitoring for Modern DevOps Teams
Open-source observability platform delivering per-second metrics, intelligent alerts, and ML-powered anomaly detection with zero configuration.
- 77,453+ GitHub stars
- Built with C
- Per-second metrics collection with automatic application discovery and zero configuration
- GNU General Public License v3.0 license
About This Project
Netdata transforms infrastructure monitoring by providing real-time, per-second metrics collection and visualization without the complexity of traditional observability stacks. Built in C for maximum performance, it auto-detects thousands of applications and services the moment they start, requiring zero configuration to begin monitoring your entire infrastructure.
Unlike heavyweight monitoring solutions that demand extensive setup and maintenance, Netdata delivers immediate value with its distributed architecture. Each node runs an autonomous agent that collects, stores, and analyzes metrics locally, eliminating the need for centralized data lakes while providing sub-second granularity. The built-in machine learning engine automatically learns normal behavior patterns and alerts you to genuine anomalies, dramatically reducing alert fatigue.
The platform seamlessly integrates with existing tools like Prometheus, Grafana, and InfluxDB, while offering its own intuitive web interface for instant troubleshooting. With support for Docker, Kubernetes, databases, and over 800 integrations, Netdata scales from single servers to massive cloud-native deployments. Its efficient design means minimal resource overhead—typically under 1% CPU and a few MB of RAM per monitored system.
As a CNCF sandbox project with enterprise-grade features available in both open-source and commercial editions, Netdata empowers lean teams to achieve full-stack observability without the traditional cost and complexity barriers.
Key Features
- Per-second metrics collection with automatic application discovery and zero configuration
- Built-in machine learning for intelligent anomaly detection and adaptive alerting
- Distributed architecture with autonomous agents eliminating centralized bottlenecks
- 800+ pre-configured integrations covering databases, containers, cloud services, and applications
- Low resource footprint optimized for high-performance monitoring at scale
How You Can Use It
Real-time performance monitoring for containerized microservices in Kubernetes clusters
Proactive anomaly detection and intelligent alerting for production infrastructure
Troubleshooting performance bottlenecks with per-second metric granularity
Distributed monitoring across hybrid cloud and on-premises environments without centralized data collection
Who Is This For?
DevOps engineers, SREs, and platform teams managing distributed systems who need comprehensive observability without operational overhead