Root Cause in Seconds, Not Hours

Organizations typically achieve 80% MTTR reduction with Netdata. Incidents that took 75 minutes to diagnose with traditional tools resolve in 5 minutes because: (1) ML has already detected anomalies in real-time, (2) the correlation engine evaluates thousands of metrics in seconds and surfaces root cause in the top 30-50 results, and (3) AI can generate complete RCA reports in 2 minutes with hypothesis, evidence, and recommendations.

Netdata runs 18 independent machine learning models per metric, each trained on different 6-hour windows with 3-hour offsets. An anomaly is only flagged when ALL 18 models agree, creating a consensus mechanism that achieves 99% false positive reduction in anomaly detection. This is fundamentally different from single-model approaches or static thresholds.

Most operational anomalies last 2-10 seconds - database deadlocks, memory allocation failures, network microbursts, container lifecycle events. With 30-second monitoring, you miss 90% of these incidents because they’re averaged away. Netdata’s per-second collection captures every event, providing 30× more training data for ML and enabling precise timeline reconstruction to determine which component failed first (root cause) versus which failed after (cascade effects).

No configuration required. ML training begins automatically 15 minutes after installation. The unsupervised k-means clustering learns your infrastructure’s unique patterns without any input. Models retrain continuously every 3 hours to stay current with evolving patterns. You don’t need ML expertise, data science teams, or manual threshold tuning - it just works.

Netdata’s AI is grounded in real-time ML-based anomaly detection that runs on every metric, every second, at the edge. When AI analyzes your infrastructure, it’s working with actual anomaly flags from 18 models per metric - not just statistical correlations. This prevents the hallucination problem plaguing other AI tools. Plus, Netdata offers two integration paths: managed AI in Netdata Cloud OR bring your own LLM via Model Context Protocol (MCP) - giving you flexibility and data sovereignty.

Yes. Netdata’s edge-native architecture means each node operates independently, so adding 100 nodes performs exactly like the first 100 nodes - no central bottleneck, no performance degradation. The platform processes 4.5+ billion metrics per second globally, with proven deployments exceeding 100,000 nodes. When you query via Netdata Cloud, requests distribute across all Agents and Parents in parallel, so evaluating thousands of metrics completes in seconds.

Installation completes in approximately 60 seconds with a one-line command. Automated dashboards appear immediately with all auto-discovered metrics. ML training begins 15 minutes after first data collection. By 3 hours, you have meaningful anomaly detection. Within 48 hours, the full 18-model ensemble per metric is established for optimal accuracy. There’s no weeks-long configuration project - you get value in the first hour.

Everything for RCA: unlimited metrics collection (3,000-20,000+ metrics per node with no upper limit), ML anomaly detection (18 models per metric), unlimited logs (systemd-journal/Windows Event Log), 400+ pre-configured alerts, automated dashboards, metric correlations, Anomaly Advisor, unlimited users with RBAC, and high availability options. The only additional costs are AI credits (10 free sessions/month included, additional sessions available) and advanced features on higher-tier plans. See pricing details.

Netdata queries systemd-journal files (Linux) and Windows Event Logs directly where they live - no shipping, no central cluster, no ingestion lag. Because journald already indexes every field in every log entry, Netdata gets instant full-text search and field filtering without maintaining separate infrastructure. This achieves 90% cost reduction versus Elasticsearch/Splunk while providing 200× more accurate queries (analyzes 1M entries before sampling vs 5K for traditional tools) and automatic log-metric timestamp synchronization. Learn more about zero-pipeline logs.

Yes, extensively. Data ingestion: Prometheus, OpenMetrics, StatsD, OpenTelemetry (metrics + logs), custom collectors in Go/Python/Bash. Data export: Prometheus remote write, InfluxDB, Graphite, OpenTSDB, TimescaleDB, and more. Alerts: PagerDuty, Slack, email, webhooks (20+ integrations). Visualization: Native Grafana datasource plugin. AI: Model Context Protocol (MCP) for any LLM. Netdata fits into your ecosystem rather than requiring you to change everything. See all integrations.

All observability data (metrics, logs) stays on your infrastructure - Netdata Cloud only stores metadata (node names, chart titles, alert configurations). This satisfies GDPR, HIPAA, PCI DSS, and regional data residency requirements by design. For complete air-gapped environments, Netdata Cloud On-Prem provides the full control plane within your datacenter. The platform is SOC 2 Type 2 certified with comprehensive audit logging for compliance teams.

Multiple factors combine: (1) Per-node pricing eliminates volume-based anxiety - unlimited metrics, logs, users included. (2) No data egress charges from edge-based processing. (3) Logs without pipelines eliminates Elasticsearch/Splunk infrastructure (90% of logs cost). (4) Tool consolidation - Netdata replaces metrics platform, logs pipeline, APM components, and dashboard tools. (5) Efficient storage (0.6 bytes/sample) versus traditional TSDBs. (6) No centralization overhead - no massive clusters consuming resources. See cost efficiency details.

Edge architecture solves the fundamental tradeoffs of observability: (1) Zero latency for anomaly detection - ML runs during data collection, not in separate batch jobs. (2) 100% data fidelity - no sampling required because each node only processes its own metrics. (3) Linear scalability - adding nodes doesn’t create a centralization bottleneck. (4) Failure isolation - ML continues working during network partitions. (5) Data sovereignty - metrics never leave premises. (6) Cost efficiency - no massive central clusters processing everyone’s data. Learn about distributed architecture.

Yes, Netdata excels with ephemeral infrastructure. Parent nodes aggregate data from short-lived children (pods, containers, auto-scaling VMs), maintaining historical context even after the original node is gone. Per-second collection captures complete pod lifecycle events that minute-based monitoring misses entirely. Automated discovery means new pods are immediately monitored without configuration. The edge architecture naturally fits the distributed, dynamic nature of Kubernetes. See Kubernetes monitoring.

Netdata Cloud AI provides managed, optimized features: AI Troubleshoot (interactive investigations), Ask AI (alert explanations), AI Insights (automated reports), all with Netdata’s pre-built playbooks and 10 free sessions/month included. MCP (Model Context Protocol) runs directly on your Agents/Parents, works with any LLM (Claude, ChatGPT, Gemini, etc.), and is completely free and open-source. Choose Cloud for ease and optimization, or MCP for complete control and bring-your-own-LLM. Learn more about MCP integration.