Netdata’s distributed architecture processes metrics at the edge - each Agent stores data locally using industry-leading compression (0.6 bytes/sample). This eliminates the centralized index explosions that drive costs in traditional monitoring. You pay per node, not per metric or by metrics volume, making costs predictable regardless of cardinality.

Most operational anomalies last 2-10 seconds. Minute-based monitoring averages away these critical signals, missing 90% of incidents. Netdata’s per-second collection captures microbursts, transients, and cascading failures that traditional tools never see - enabling faster detection and accurate root cause analysis.

Each metric trains 18 unsupervised k-means models locally on staggered time windows. Anomalies are flagged only when all 18 models agree, achieving a theoretical false positive rate of 10^-36. Models retrain every 3 hours to adapt to baseline shifts. No thresholds to tune, no training data required, no maintenance needed.

Yes. Netdata’s distributed architecture means cardinality doesn’t affect central infrastructure - metrics never centralize. Each node handles its own cardinality locally with automatic cleanup of ephemeral metrics. Proven at millions of unique time series per deployment without performance degradation. Resources scale linearly per metric—not exponentially—and contained impact means issues stay within the streaming hierarchy rather than cascading globally.

Netdata achieves 0.6 bytes per sample through Gorilla compression plus ZSTD - significantly more efficient than traditional TSDBs that use 2-16 bytes per sample. Multi-tier architecture provides per-second, per-minute, and per-hour resolution in parallel without post-processing. In Netdata benchmark testing at 4.6M metrics/second, Netdata provides 15× longer retention than Prometheus on the same disk.

Agents buffer metrics locally on disk and automatically replay when connectivity restores - ensuring zero data loss. Each Agent operates autonomously with local storage, ML, and alerting. Parents provide centralization and high availability but aren’t required for monitoring to continue.

Netdata ingests OpenTelemetry, Prometheus, OpenMetrics, and StatsD natively. It exports to Prometheus, InfluxDB, Graphite, OpenTSDB, and TimescaleDB. Grafana plugin available for existing workflows. You can run Netdata alongside current tools or gradually migrate - no rip-and-replace required.

Agents collect metrics locally with full autonomy - storage, ML, alerting, dashboards. Parents aggregate streams from multiple Agents for centralization, longer retention, and unified dashboards. Parents are optional but recommended for ephemeral infrastructure (Kubernetes, auto-scaling) and high availability.

Netdata’s distributed architecture means queries execute in parallel across Agents/Parents - adding nodes adds query capacity. Memory-mapped indices and extent grouping optimize local queries. In Netdata benchmark testing at 4.6M metrics/second, Netdata queries are 16× faster than Prometheus while using 88% less memory.

Yes. Configure retention per tier - time-based, size-based, or hybrid policies. Default is 14 days per-second, 3 months per-minute, 2 years per-hour. Adjust based on your needs. Storage is local and disk-bound only - no vendor charges for retention duration.

Agents automatically detect container lifecycle events and clean up metadata for stopped containers while preserving historical data. Parents provide persistent storage for ephemeral Agents. Metrics from short-lived containers remain queryable with full context even after containers terminate.

Netdata distributes intelligence to the edge instead of centralizing data. This eliminates cardinality cost traps (90% cost savings), provides true per-second granularity (vs their 15-60 second intervals), and keeps data on-premises for sovereignty. ML runs on every metric automatically, not just selected ones as paid add-ons.

Install to dashboard in 60 seconds. One-line command deploys Agent, auto-discovers integrations, generates dashboards, and starts ML training. No configuration files, no manual setup, no query languages to learn. Add credentials only for protected services (databases, APIs). Production-ready immediately.

Yes. Ingest via OpenTelemetry, Prometheus exporters, OpenMetrics, StatsD, or custom collectors (Go, Python, Bash, Node.js). All custom metrics get the same treatment - per-second collection, ML anomaly detection, algorithmic dashboards, NIDL analytics. No additional configuration or cost.