See Every GPU Cycle, Catch Every Anomaly, Control Every Cost

Per-Second GPU Visibility

Monitor NVIDIA and Intel GPUs in real-time. Catch thermal throttling, memory leaks, and utilization drops instantly - not minutes later.

Sub-2-Second Alerting

Detect inference latency spikes, training stalls, and resource exhaustion before they cascade. 80% faster MTTR than traditional monitoring.

90% Lower Monitoring Costs

Predictable per-node pricing with no metric cardinality charges. Monitor unlimited GPUs, containers, and custom metrics without surprise bills.

ML-Powered Anomaly Detection

18 unsupervised models per metric train automatically. 99% false positive reduction - surface anomalies that matter.

60-Second Deployment

One-line install, zero configuration. Auto-discover GPUs, containers, and Kubernetes workloads. Dashboards and alerts active immediately.

Kubernetes-Native

Monitor pods, containers, nodes, and autoscaling in real-time. Track ephemeral training jobs without losing visibility.

Optimize GPU Training at Scale

Guarantee Sub-Second Response Times

Unified Kubernetes Observability

Accelerate Experimentation Cycles

Empower Operations Teams

NVIDIA DCGM Collector: Deep GPU Monitoring For AI

Netdata's new DCGM collector provides comprehensive real-time monitoring for NVIDIA data center GPUs with hundreds of metrics across GPU, MIG, NVLink, NVSwitch, and CPU scopes, plus built-in alerts, anomaly detection, and AI troubleshooting.

Introducing the Netdata Cloud MCP Server

Connect AI coding agents like Claude Code, Codex, and Cursor to your entire infrastructure with a single endpoint. The Netdata Cloud MCP Server brings infrastructure-wide observability to any MCP-compatible AI tool.

Netdata Implements MCP Protocol

Revolutionize how you interact with your monitoring data!

Netdata excels at infrastructure monitoring (GPUs, APIs, Kubernetes) but requires manual instrumentation for LLM-specific metrics like token tracking and cost attribution. Send token counts via StatsD or OpenTelemetry, and Netdata will provide dashboards and alerts. For native LLM observability, consider complementing Netdata with tools like LangSmith or Langfuse.

Netdata provides superior infrastructure monitoring (10-60× faster granularity, 90% lower cost) but lacks native LLM observability features like hallucination detection. Best approach: Use Netdata for GPU clusters, inference APIs, and Kubernetes (saving 90% on infrastructure monitoring), then complement with LLM-specific tools for application-layer observability.

Yes. Netdata’s nvidia_smi collector provides per-second monitoring of GPU utilization, memory, temperature, power draw, PCIe bandwidth, and MIG instances. Auto-discovered with zero configuration. Also supports Intel GPUs via intelgpu collector. TPU monitoring requires external exporters.

Yes. Netdata’s Helm chart provides native Kubernetes integration with auto-discovery of pods, containers, nodes, and services. Monitor ephemeral training jobs, autoscaling triggers, and resource utilization in real-time. Unified view across infrastructure, applications, and logs without tool sprawl.

Netdata trains 18 unsupervised k-means models per metric using different time windows. Anomalies are flagged only when ALL 18 models agree, achieving 99% false positive reduction (theoretical rate: 10^-36). Models retrain every 3 hours automatically, adapting to changing baselines without configuration.

60 seconds from install to production-ready monitoring. One-line command installs the agent, auto-discovers GPUs and services, generates dashboards, activates 400+ pre-configured alerts, and begins ML training. No query languages, no manual dashboard building, no threshold tuning required.

Predictable per-node pricing with P90 billing that excludes daily spikes and top 3 days per month. No charges for metric cardinality, log volume, or users. Monitor unlimited GPUs, containers, and custom metrics. 90% lower TCO than volume-based platforms. View detailed pricing.

For infrastructure monitoring: Yes. Netdata provides metrics, logs, alerts, ML, and AI troubleshooting in one platform. For LLM application observability: Partial. Netdata excels at infrastructure (GPUs, APIs, Kubernetes) but requires manual instrumentation for LLM-specific features (token tracking, hallucination detection). Best approach: Netdata for infrastructure + complementary LLM tool for application layer.

Not yet. Distributed tracing is planned for Q2 2026. Currently, Netdata ingests OpenTelemetry metrics and logs (production-ready) but not traces. For APM-style tracing today, use complementary tools like Jaeger or Tempo, then export Netdata metrics to Grafana for unified visualization.

Netdata reduces alert noise through component-level alerts that are more accurate and actionable than generic threshold-based alerts. Additional features include hysteresis protection, configurable notification delays, and role-based routing. Cloud-level deduplication eliminates redundant alerts across multiple agents. Note that ML-based anomaly detection is a separate observability signal and does not filter or influence alert notifications.

Yes. Netdata supports fully on-premises deployment with no internet connectivity required. Metrics and logs stay local, ML trains at the edge, and dashboards are served directly from agents. Enterprise On-Prem option provides full Netdata Cloud features (unified dashboards, RBAC, SSO) within your datacenter. SOC 2 Type 2 certified, GDPR/HIPAA/PCI DSS compliant.

Agents are lightweight collectors installed on every node (GPUs, servers, containers). Parents are centralization points that aggregate data from multiple agents, providing unified dashboards and longer retention. For AI workloads: Install agents on all GPU nodes, set up Parents for centralized view. Agents can offload ML training and storage to Parents, reducing production system load.

Yes. Use Netdata’s httpcheck collector for endpoint monitoring, or instrument your API with StatsD timers. Netdata auto-calculates P50/P95/P99 percentiles and provides real-time alerting on SLO breaches. Correlate API latency with GPU utilization, memory pressure, and network bandwidth for instant root cause analysis.

Netdata benchmark at 4.6M metrics/s shows 36% less CPU, 88% less RAM, 97% less disk I/O, and 16× faster queries than Prometheus. Key differences: (1) Per-second vs per-minute granularity, (2) Zero configuration vs manual setup, (3) Built-in ML vs none, (4) Algorithmic dashboards vs manual building, (5) Predictable per-node pricing vs infrastructure costs. Read detailed comparison.

Yes. Netdata monitors AWS, Azure, GCP, and on-premises infrastructure with unified dashboards. Deploy agents across all environments, set up Parents per region/cloud, and access everything through Netdata Cloud. Track cross-cloud training jobs, inference APIs, and resource utilization without tool sprawl. Data sovereignty maintained - metrics stay in each cloud.