Netdata is an open-source, real-time infrastructure monitoring platform designed for DevOps and SRE teams who need instant, per-second visibility into their entire stack — from servers and containers to applications and hardware sensors — without complex configuration. It eliminates the delays and complexity of traditional monitoring tools by collecting and visualizing every metric in real time, powered by edge-based ML and distributed architecture.
Built in C for efficiency, Netdata runs as a lightweight agent on over 100 platforms including Linux, macOS, Windows, and Kubernetes. It uses a parent-child distributed model to scale from single nodes to millions of metrics per second, with optional cloud or on-premises centralized management. The ecosystem includes a web UI, mobile apps, and integrations with Prometheus, InfluxDB, and Grafana — all while using only 5% CPU and 150MB RAM per node.
What You Get
- Real-Time Per-Second Metrics - Collects and visualizes every metric at 1-second intervals with no sampling lag, enabling live troubleshooting of performance issues as they happen.
- Zero-Configuration Auto-Discovery - Automatically detects and monitors system resources, services, containers, VMs, and applications without manual plugin configuration or agent tuning.
- ML-Powered Anomaly Detection - Runs unsupervised machine learning models on every metric at the edge to detect anomalies, predict failures, and surface root causes without predefined thresholds.
- High-Performance Long-Term Storage - Uses tiered storage with ~0.5 bytes per sample to retain metrics for over a year on minimal disk space (3GB), enabling historical analysis without bloated databases.
- AI Co-Engineer & Root Cause Analysis - Automatically correlates anomalies across metrics, identifies blast radius, and generates AI-powered reports to accelerate incident resolution.
- Distributed Parent-Child Architecture - Scales horizontally by streaming metrics from edge agents to centralized Parent nodes without centralizing raw data, enabling monitoring across multi-cloud and air-gapped environments.
- 800+ Built-In Integrations - Monitors nginx, Apache, MySQL, PostgreSQL, MongoDB, Docker, Kubernetes, Proxmox, Windows Event Log, GPUs, and more — all with zero configuration.
- Mobile Monitoring Apps - Native iOS and Android apps provide real-time alerts, dashboards, and biometric authentication for on-the-go infrastructure oversight.
- Air-Gapped & On-Premises Cloud - Deploy the full Netdata Cloud platform (with RBAC, SSO, and compliance) inside your private network for data sovereignty and regulatory compliance.
- Algorithmic Dashboards - Dynamically generated visualizations that adapt to system behavior, allowing deep exploration with infinite zoom, pan, and filtering — no query language required.
Common Use Cases
- Running a high-traffic SaaS platform - An engineering team uses Netdata to monitor 500+ Kubernetes pods and 200+ microservices in real time, detecting memory leaks and API latency spikes before users are impacted.
- Managing hybrid cloud infrastructure - A DevOps team deploys Netdata agents across AWS, Azure, and on-premises data centers to unify monitoring without centralizing sensitive data, using Parent nodes to aggregate metrics securely.
- Compliance-heavy government environment - A public sector agency runs Netdata Cloud On-Premises in an air-gapped network to meet strict data sovereignty laws while still gaining AI-powered anomaly detection and audit-ready dashboards.
- Scaling containerized workloads - A DevOps engineer uses Netdata’s auto-discovery to monitor 10,000+ Docker containers across 500 hosts, identifying resource contention and container crashes without writing custom exporters.
Under The Hood
Architecture
- Modular plugin architecture with distinct collector layers (Go, Python, Windows) decoupled from core logic via standardized interfaces
- Event-driven alerting system with pluggable notification backends configured through declarative metadata
- Agent-Cloud Link enforces data sovereignty via secure, outbound-only WebSocket communication with zero local metric storage
- Configuration-driven collectors using YAML metadata to define auto-detection, metrics, and dependencies
- High-performance C-based core with custom dictionary and RRD structures for real-time time-series handling
- Clear service layer separation: data collection, alerting, and cloud sync operate as independent, low-coupling components
Tech Stack
- C-based daemon with embedded HTTP server and SQLite for time-series storage
- Python collectors leveraging pyyaml3 and urllib3 for dynamic, plugin-based data ingestion
- Web UI built with D3.js, Chart.js, and jQuery, served via embedded server for real-time visualizations
- Autotools-based build system with custom Makefiles for cross-platform compatibility
- MQTT and WebSocket protocols for secure remote communication
- Configuration validation via yamllint and structured JSON schemas for API and plugin definitions
Code Quality
- Extensive Go test suites with unit and integration tests using testify, covering configuration parsing and edge cases
- Strong type safety and structured error handling in Go, with robust mock-based testing for external dependencies
- Consistent, domain-aligned naming conventions for metrics, collectors, and configuration structures
- Modular design enabling independent testing and deployment of plugins, collectors, and core components
- Comprehensive crash reporting via sentry-native with signal-safe breadcrumbing and contextual tagging
- Limited test coverage in C components and absence of custom error types in low-level code reduce overall robustness
What Makes It Unique
- Zero-metric storage architecture ensures all data remains local while enabling secure cloud coordination
- Dynamic chart generation with auto-discovery of dimensions and labels eliminates need for pre-defined schemas
- Integrated machine learning-powered anomaly detection within the monitoring pipeline, no external services required
- Unified plugin system supporting Go, C, and Python collectors with consistent metadata-driven configuration
- Self-healing alerting system that correlates metrics, logs, and health checks into actionable troubleshooting workflows
- Lightweight, embeddable crash reporting with async-signal-safe hooks for low-level C daemon diagnostics