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 ML anomaly detection; Business includes 10 AI credits per month. No per-metric charges. No charges for metrics volume, Logs Monitoring (querying logs in place), users, data retention or containers. 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 available in the Business plan, which includes 10 AI credits per month; alert explanations consume no credits.

For infrastructure monitoring, yes. Netdata provides comprehensive infrastructure observability with ML anomaly detection, AI-powered RCA, and natural language troubleshooting. Netdata also ingests OpenTelemetry traces for application-level distributed tracing, explored in the Traces tab of Netdata Cloud. Many customers reduce costs significantly by moving infrastructure monitoring to Netdata while keeping specialized APM tools for code-level profiling.

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. Logs Monitoring queries systemd-journal, Windows Event Log and macOS unified logs in place (no pipeline), and Logs Management stores logs collected from any source on your own Netdata Agents, never in Netdata Cloud. Only metadata (node names, chart titles, alert configs) travels to Netdata Cloud - never observability data. Result: an architecture that supports GDPR, HIPAA, PCI DSS programs.

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 no limit on nodes and all Business 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 alignment.

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).