Scale Infrastructure Fearlessly - Cardinality Can’t Hold You Back

Protection activates automatically when any metric context (containers, disks, network interfaces) exceeds 1,000 instances AND >50% are ephemeral (no longer actively collected). The system clears long-term retention for inactive time series while preserving all real-time data. Thresholds are configurable via netdata.conf for specialized environments.

Active data is never lost. Protection only affects long-term retention (tier 1 and tier 2) for instances that have zero retention in tier 0 (per-second data) - meaning they’re no longer actively collected. Real-time data for active metrics, recent historical data, and any data for currently monitored resources remain completely intact. You only lose long-term historical trends for resources that no longer exist (e.g., deleted containers from weeks ago).

Datadog requires manual configuration of tag allowlists and blocklists - you must predict which labels you’ll need for future troubleshooting and tune the cardinality estimator. Netdata’s approach is fundamentally different: zero configuration, automatic protection based on activity, no cost impact from cardinality, and edge processing handles high cardinality locally before streaming. With Netdata, you don’t manage cardinality - you simply don’t think about it.

Yes - this is exactly what Extreme Cardinality Protection was designed for. Netdata automatically monitors every pod from the first second of life (per-second granularity captures short lifecycles), detects when pods stop running, clears long-term storage for inactive pods, and preserves all active data. Real-world proof: Nodecraft achieved 90% reduction in downtime with Netdata monitoring their rapidly scaling container infrastructure.

At 4.6 million metrics per second (equivalent to ~1,000 monitored nodes): Netdata uses ~47 GB RAM vs Prometheus ~383 GB RAM - 88% less memory. This comes from edge processing (each agent handles its own cardinality locally with 150-200MB per agent), parent aggregation (receive pre-processed streams), intelligent compression (Gorilla + ZSTD achieve ~0.6 bytes per sample), and per-CPU aggregation (eBPF metrics aggregated in-kernel before userspace).

No manual configuration required for 99% of deployments. Netdata’s defaults are production-ready with protection enabled, 1,000 instance threshold, and 50% ephemerality minimum. You only need to adjust if you expect >1,000 instances per context per node (rare) or want more/less aggressive protection. For eBPF collectors, default hash table sizes are production-safe and handle thousands of processes.

Netdata collects every metric every second (tier 0) with no sampling or averaging, automatically creates per-minute aggregates (tier 1) and per-hour aggregates (tier 2) in parallel with no post-processing, and monitors tier 0 as source of truth for protection. When instances drop from tier 0 (no longer collected), they’re marked as ephemeral. After threshold reached, tiers 1 & 2 are cleared for ephemeral instances only. Result: real-time per-second data for troubleshooting, efficient long-term trends for capacity planning, automatic cleanup of unused historical data.

Netdata uses flat per-node pricing with unlimited metrics, labels, cardinality, users, dashboards, ML anomaly detection, and retention. Traditional platforms charge based on unique time series (cardinality), making high-cardinality infrastructure prohibitively expensive. With predictable per-node pricing and no data volume charges, you achieve 90% cost reduction compared to traditional monitoring platforms. No egress fees, no cardinality multipliers, no per-metric charges, no surprise bills.

For infrastructure monitoring: yes, completely. Netdata provides superior capabilities for hosts, VMs, containers, Kubernetes, databases, message queues, web servers, cloud platforms, network devices, and hardware sensors. Migration strategy: deploy Netdata in parallel (week 1), teams use Netdata for troubleshooting (weeks 2-4), disable redundant collectors in existing tools (month 2), decommission expensive platforms (month 3). For APM/traces: Netdata currently focuses on infrastructure - distributed tracing planned for 2026. Most customers eliminate infrastructure monitoring costs (largest component) while keeping APM for now.

Time to first dashboard: 60 seconds with one-line install and automatic discovery. Time to first insights: 60 seconds with all auto-discovered metrics visualized immediately. Time to ML anomaly detection: 15 minutes for baseline, then 18 models per metric detect anomalies automatically. Complete enterprise deployment: Day 1 (design topology, deploy agents/parents), Day 2 (add credentials, review alerts, invite team). Proof: KAUST achieved 50% improvement in troubleshooting efficiency, Nodecraft detects problems within 30 seconds vs 2-3 minutes before.