Anomaly Detection With Mathematical Precision

Netdata uses k-means clustering - one of the most proven and transparent unsupervised ML algorithms. We train 18 independent k-means models per metric (each on different time windows) and require unanimous consensus before flagging an anomaly. This ensemble approach delivers 99% false positive reduction in anomaly detection while maintaining complete algorithmic transparency. Unlike proprietary black-box models, k-means is mathematically simple, computationally efficient, and has decades of research validation.

First anomaly detection results appear within 15 minutes of installation. Models begin training immediately on live data and start providing value fast. Full confidence builds over 2 days as all 18 models complete their initial training cycles. Unlike competitors requiring 1-3 weeks of historical data, Netdata delivers actionable ML within minutes.

No. Netdata begins training immediately on live data as it’s collected. Each of the 18 models trains on 6-hour windows with 3-hour staggered intervals, creating rolling coverage. This approach adapts faster to infrastructure changes than systems requiring weeks of historical data. You get ML working from day one, not after lengthy baseline establishment periods.

Each metric is monitored by 18 independent k-means models, each trained on different time windows (6-hour windows, staggered by 3-hour intervals). An anomaly is flagged ONLY when ALL 18 models unanimously agree the value is anomalous. This consensus requirement dramatically reduces false positives in anomaly detection - if even one model thinks the value is normal, no anomaly is flagged. The theoretical false positive rate is (0.01)^18 = 10^-36.

No. Models retrain automatically every 3 hours, adapting continuously to infrastructure changes. Whether you’re scaling up, migrating services, or changing configurations, the ML adapts without manual intervention. This is a key advantage over static thresholds that break during changes. The continuous retraining ensures models stay current with your infrastructure’s evolving behavior.

ML runs on ALL metrics automatically - every single one discovered by Netdata. Unlike platforms where ML is a premium add-on requiring metric selection, Netdata applies 18 models to every metric at no additional cost. This comprehensive coverage is crucial because the ‘unimportant’ metric you didn’t select often reveals the root cause during incidents.

Minimal. ML training runs as a low-priority background task consuming ~2-4% CPU for typical workloads (10,000 metrics). The University of Amsterdam validated Netdata as the most energy-efficient monitoring solution - even with ML enabled. ML automatically yields resources to data collection, ensuring monitoring never impacts production workloads.

Netdata delivers 90% cost reduction compared to enterprise platforms. While traditional solutions charge for ML as a premium add-on with volume-based pricing, Netdata includes sophisticated 18-model consensus ML on unlimited metrics with predictable per-node pricing. Our edge architecture eliminates cloud ML infrastructure costs, enabling flat pricing instead of volume-based charges. See pricing details.

Yes. Netdata is designed for compliance: ML trains at the edge so data stays on-premises (GDPR/HIPAA data sovereignty), uses transparent k-means algorithm auditable by regulators (EU AI Act explainability), provides historical anomaly storage for forensic analysis, and is SOC 2 Type 2 certified. ML processing happens where data lives - satisfying the strictest data residency requirements. Learn more about data sovereignty.

Yes. Netdata provides transparency into the consensus process. You can see the anomaly bit for each sample and investigate which models detected anomalies. This transparency is crucial for understanding edge cases and building trust in the system. The open-source algorithm is visible on GitHub for complete auditability.

Netdata’s k-means approach detects: point anomalies (unusual high/low values), contextual anomalies (pattern deviations), collective anomalies (multivariate issues), concept drifts (gradual baseline shifts), and change points (sudden state changes). The 18-model ensemble with different time windows captures both short-term spikes and longer-term pattern changes, providing comprehensive anomaly coverage.

Netdata includes ML built-in (18 models per metric) while Prometheus/Grafana require external ML tools. Netdata needs zero configuration vs extensive PromQL queries and dashboard building. Netdata provides automatic anomaly detection vs manual rules, and built-in root cause analysis vs manual investigation. Many organizations use Netdata for infrastructure ML while keeping Prometheus for specialized needs.

Yes, comprehensively. Netdata monitors node-level metrics (CPU, memory, disk, network), pod-level resource usage, container cgroups metrics, Kubernetes API metrics, and service endpoints - all with ML on every metric automatically. Our per-second granularity captures short-lived container events that minute-level monitoring misses. Deploy via Helm chart for easy Kubernetes integration.

Yes. Netdata is cloud-agnostic and works on AWS, Azure, GCP, on-premises, and edge environments. It monitors hybrid cloud architectures with no vendor lock-in, provides the same ML everywhere, and offers unified dashboards across all clouds via Netdata Cloud. Each node monitors itself independently - multi-cloud is just multiple independent nodes unified through a single control plane.

Multiple tiers: Community (GitHub discussions, Discord, public forums - free), Business (email/ticket support during business hours with SLA), Enterprise (24/7 support, dedicated team, phone support, custom SLA). All plans include comprehensive documentation, video tutorials, and community resources. Professional services available for migration assistance and custom onboarding.