Operational Intelligence That Works From Day One

Traditional AIOps platforms centralize data processing and charge based on volume, often requiring lengthy deployment projects. Netdata distributes intelligence to the edge - processing ML where data is generated - enabling 60-second installation to full visibility, transparent per-node pricing, and complete data sovereignty. We deliver the same AIOps capabilities (anomaly detection, root cause analysis, AI investigations) without the complexity, costs, or cloud dependencies.

Enterprise deployment completes in days, not months: Day 1 - Install Agents across infrastructure (hours via automation). Day 2 - Deploy Parents for centralization (optional). Week 1 - Add credentials for protected endpoints, review pre-configured alerts, invite teams. Result: Production-ready AIOps operational within one week. ML training begins automatically at installation, with first anomaly detection available in 15 minutes.

Each Netdata Agent runs unsupervised k-means clustering with 18 independent models per metric. Models train on 6-hour windows, staggered every 3 hours, covering 54 hours of behavioral patterns. Anomalies are flagged only when ALL 18 models agree (consensus mechanism), achieving theoretical false positive rate of 10⁻³⁶ per metric. Training happens locally using spare CPU cycles (~2-4% overhead) with complete data sovereignty - no data egress required.

Sub-2-second total latency from event to insight: (1) 1-second data collection, (2) <1-second ML evaluation during collection, (3) anomaly bit stored with sample immediately, (4) 1-second visualization latency. This real-time performance enables catching 2-10 second anomalies that are invisible to minute-based monitoring used by traditional AIOps platforms.

18-model consensus mechanism: Each model has ~1% false positive rate at 99th percentile threshold. Consensus requirement (ALL 18 must agree) reduces FP rate to (0.01)¹⁸ = 10⁻³⁶ per metric. Adaptive memory forgets recent anomalies within 3-54 hours to prevent repeated flagging of the same conditions. Detects point anomalies, contextual patterns, collective issues, change points, and concept drifts.

Proven scale: 4.5+ billion metrics per second processed globally; 100,000+ node deployments; 1.5M downloads daily. Linear scalability means each Agent operates independently - adding nodes doesn’t degrade existing performance. No centralized bottlenecks. Performance: <5% CPU, 150-200 MB RAM per agent; 0.6 bytes/sample storage. Validated by University of Amsterdam ICSOC 2023 study as most energy-efficient monitoring solution.

Transparent per-node model: Business plan pricing with volume discounts. Includes unlimited metrics, logs, users, dashboards, ML, and AI features. No per-metric charges. No data-volume fees. P90 billing pays for sustained usage, not spikes. Result: 90% cost reduction vs traditional platforms.

ML Anomaly Detection: 18-model ensemble per metric, automatic training. Anomaly Advisor: Automated root cause analysis ranking thousands of metrics. AI Insights: Four report types (Infrastructure Summary, Performance Optimization, Capacity Planning, Anomaly Analysis). AI Chat: Natural language investigations via Model Context Protocol. Alert AI: One-click troubleshooting for every alert. All included in Business plan with 10 free AI sessions monthly.

For infrastructure monitoring, yes. Netdata provides comprehensive infrastructure observability with ML anomaly detection, AI-powered RCA, and natural language troubleshooting. For application-level distributed tracing, we complement APM solutions today (traces planned Q2 2026). Many customers reduce costs significantly by moving infrastructure monitoring to Netdata while keeping specialized APM tools for deep application analysis.

800+ integrations auto-discovered: Operating systems (Linux, Windows, FreeBSD, macOS), containers (Docker, Kubernetes, Podman), databases (PostgreSQL, MySQL, MongoDB, Redis), web servers (NGINX, Apache), message brokers (Kafka, RabbitMQ), cloud platforms (AWS, Azure, GCP), hardware (IPMI, SNMP), custom metrics (StatsD, OpenMetrics, Prometheus, OpenTelemetry). Scripts.d supports 4000+ Nagios plugins. No manual configuration required for most integrations.

Complete data sovereignty by design: All ML computation happens on Agents where data is collected. Time-series database stores data locally on each Agent/Parent. Queries execute distributed to Agents/Parents. Log management uses direct systemd-journal access (no pipeline). Only metadata (node names, chart titles, alert configs) travels to Netdata Cloud - never observability data. Result: GDPR, HIPAA, PCI DSS compliant by architecture.

Zero specialized skills required. Installation: One-line command or container deployment. Configuration: Minimal (only credentials for protected endpoints). Query language: None (point-and-click NIDL framework). Dashboard building: Zero (algorithmic generation). ML tuning: Zero (automatic baseline learning). AI usage: Natural language (no technical queries). Result: Junior engineers get senior-level insights automatically.

Yes. 14-day free trial (Business plan) with unlimited nodes and full features. Open-source agent available free forever (GPLv3+) with local dashboards. Homelab plan available for personal non-commercial use. No credit card required for trial. Professional services available for migration assistance. See our pricing page for details. Start your free trial.

Netdata uses transparent, well-understood algorithms (k-means clustering, not black-box neural networks). Anomaly Advisor provides evidence-based rankings showing why metrics are flagged. AI Insights include supporting data for recommendations. Alert Troubleshooting explains root cause with correlated signals. All ML decisions auditable via anomaly bits stored with samples. SOC 2 Type 2 certified with GDPR, HIPAA, PCI DSS compliance.

Industry-leading efficiency independently validated by University of Amsterdam ICSOC 2023 study: CPU: <5% of single core (typical with ML for 5,000 metrics). Memory: 150-200 MB RAM (including ML, depends on metrics discovered). ML Overhead: Only 2-4% CPU and ~18KB RAM per metric. Storage: 0.6 bytes/sample (industry-leading compression). Disk I/O: Zero additional overhead for ML (bit reuse in existing format).