From Incident to Insight in Seconds

Netdata combines four factors: (1) Per-second granularity catches issues 10-60× faster than minute-based tools, (2) ML-based anomaly detection helps identify unusual patterns across thousands of metrics, (3) Automatic correlation identifies root cause in top 30-50 results without manual investigation, (4) AI-powered troubleshooting provides instant explanations and recommended actions. Together, these reduce typical MTTR from 2-3 hours to 15-20 minutes.

Traditional monitoring uses static thresholds (CPU > 80%) that require manual tuning. Netdata trains 18 unsupervised ML models per metric using k-means clustering with different time windows. Anomalies are only flagged when ALL 18 models agree unanimously, achieving a theoretical false positive rate of 10^-36. Models retrain every 3 hours automatically, adapting to infrastructure changes without manual tuning. Note: Anomaly detection is a separate signal for investigation - it does not automatically suppress or filter alerts.

Yes. Netdata Functions provide browser-based access to processes, network connections, systemd services, and IPMI sensors with the same per-second precision as console tools like top, iostat, and netstat. You get current state plus historical data with ML analysis. This enables operations centers to troubleshoot without infrastructure access while maintaining security compliance.

Most operational anomalies last 2-10 seconds. Minute-based monitoring averages these away, making them invisible. A 5-second disk saturation spike appears as harmless 8% average utilization in minute-based tools. Netdata’s per-second collection reveals the true 100% saturation, enabling accurate diagnosis. This catches microbursts, transient issues, and cascading failures before they become outages.

Netdata offers two AI integration paths: (1) Netdata Cloud AI Features provide managed AI with AI Troubleshoot, Ask AI, and AI Insights reports using Netdata’s optimized playbooks. Includes 10 free sessions per month with additional sessions via credits. (2) Model Context Protocol (MCP) lets you bring your own LLM (Claude, ChatGPT, Gemini) running directly on agents/parents. Your data never leaves your infrastructure. Choose managed convenience or BYOLLM control based on your requirements.

When you select a time window on any chart, Anomaly Advisor compares it to a baseline (4× window length) and evaluates ALL available metrics. It ranks thousands of metrics by anomaly severity using ML-based scoring. Root cause typically surfaces in the top 30-50 results. The system also reveals cascading failure sequences and blast radius, showing which metrics changed first and how problems propagated.

Yes. Netdata’s distributed architecture is designed for distributed systems. Each agent monitors its node independently while Parents aggregate data for infrastructure-wide views. Anomaly Advisor correlates across all nodes to identify distributed issues. Per-second granularity reveals timing relationships between services. Logs and metrics from the same source enable instant correlation without timestamp matching challenges.

60 seconds from installation to first insight. Netdata auto-discovers infrastructure, generates dashboards algorithmically, and starts ML training immediately. After 15 minutes, anomaly detection is operational. Pre-configured alerts work from day one. No manual configuration, no dashboard building, no query language learning required. Engineers are productive immediately.

Many organizations replace traditional monitoring entirely with Netdata for infrastructure observability. Netdata provides comprehensive coverage: metrics, logs, processes, network connections, and services. However, Netdata does not yet support distributed tracing (planned Q2 2026). For APM-heavy use cases, Netdata complements tracing tools by providing infrastructure context. Most customers achieve 90% cost reduction while gaining better visibility.

Netdata is explicitly designed for regulated industries. All metrics and logs stay on-premises (only metadata syncs to Cloud). SOC 2 Type 2 certified with GDPR, HIPAA, and PCI DSS alignment. Edge-based ML requires no cloud dependency. systemd-journal supports Forward Secure Sealing (FSS) for tamper detection. On-premises deployment option available for complete air-gapped operation. Bring-your-own-LLM via MCP keeps AI analysis under your control.

Netdata provides 400+ pre-configured component-level alerts that adapt automatically to infrastructure changes. These use intelligent thresholds with rolling windows rather than fixed values. Alerts are grouped by category and class in dashboards for easier management. ML-based anomaly detection provides an additional signal for investigation, helping you identify unusual patterns across your infrastructure. However, anomaly detection is separate from alerting - it’s another view of your data, not a filter for alerts.

Zero formal training required. Dashboards are algorithmic and adapt automatically. Point-and-click analysis replaces query languages. AI provides natural language explanations. Junior engineers get senior-level visibility from day one. Optional 2-hour workshop covers advanced features (ML, AI, Functions) but is not required for productivity.

All troubleshooting features are included in base per-node pricing: ML anomaly detection, Anomaly Advisor, algorithmic dashboards, Netdata Functions, and pre-configured alerts. AI features include 10 free sessions per month; additional sessions via credits. MCP (bring-your-own-LLM) is free in open-source version. No per-metric, per-GB, or per-feature charges. See detailed pricing.

Yes. Netdata is designed for ephemeral infrastructure. Agents monitor pods with per-second granularity. Parents aggregate data for pods that disappear. ML models adapt to pod lifecycle automatically. Kubernetes-specific dashboards show cluster, namespace, pod, and container metrics. Auto-discovery works for new pods instantly. Historical data retained even after pods terminate. Learn about Kubernetes monitoring.