Infrastructure Monitoring for AI WorkloadsMonitor the infrastructure powering your LLM applications with per-second visibility into GPUs, containers, databases, and system resources. Detect bottlenecks before they impact inference latency, optimize resource utilization, and reduce cloud costs - all with zero configuration.
Comprehensive monitoring for the systems running your LLM applications
Track NVIDIA and AMD GPU utilization, memory, temperature, and PCIe bandwidth per-second to optimize inference performance and prevent thermal throttling.
Monitor CPU, memory, network, and disk usage for every Kubernetes pod and container running LLM inference servers, vector databases, and supporting services.
Track PostgreSQL, MongoDB, and vector database query performance, connection pools, and storage I/O to prevent retrieval bottlenecks in RAG applications.
ML-powered anomaly detection on every metric identifies resource saturation, memory leaks, and performance degradation before they cause LLM failures.
Identify overprovisioned GPU nodes and right-size container resources with per-second utilization data - reduce cloud costs by 30-50% without impacting performance.
Auto-discovers Kubernetes clusters, GPU nodes, databases, and caches in 60 seconds. No manual configuration, no query languages, no dashboard building required.
Trusted by organizations running AI workloads at scale
80% faster MTTR
Learn about real-time monitoring
90% cost reduction vs commercial APMs
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Sub-2-second latency from event to insight
Explore AI troubleshooting
60 seconds to first dashboard
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Infrastructure Monitoring Comparison
See how Netdata’s edge-native architecture delivers superior infrastructure visibility at a fraction of the cost
Capability
Netdata
Traditional Monitoring
Data Granularity
✅ Per-Second
Catch transient GPU spikes and bottlenecks
⚠️ Per-Minute or Worse
Miss critical infrastructure events
GPU Monitoring
✅ Advanced
NVIDIA/AMD utilization, memory, temperature, PCIe bandwidth
⚠️ Basic
Limited GPU metrics, manual configuration
Container Visibility
✅ Comprehensive
Per-pod CPU, memory, network, disk with auto-discovery
⚠️ Limited
Requires manual instrumentation and labels
Anomaly Detection
✅ Automated
ML on every metric, 99% false positive reduction
⚠️ Manual
Static thresholds, high false positive rate
Setup Time
✅ 60 Seconds
One-line install, auto-discovery, instant dashboards
❌ Days to Weeks
Complex configuration, manual dashboard building
Query Language
✅ None Required
Point-and-click analysis, no PromQL needed
❌ Required
PromQL, SQL, or custom query languages
Pricing Model
✅ Predictable
Per-node pricing, unlimited metrics and logs
❌ Unpredictable
Per-metric/log charges, surprise bills
Data Sovereignty
✅ On-Premises
All data stays local, SOC 2 Type 2 certified
⚠️ Cloud-Only
Data egress required, compliance challenges
Monitor NVIDIA and AMD GPU utilization, memory usage, temperature, fan speed, power consumption, and PCIe bandwidth per-second. Detect thermal throttling, memory saturation, and performance degradation before they impact inference latency.
Per-second GPU metrics with ML anomaly detection
Learn more about GPU monitoringKey capabilities that keep your LLM infrastructure running smoothly
Per-second metrics reveal transient issues and microbursts that minute-level monitoring misses, enabling faster troubleshooting and optimization.
Unsupervised machine learning on every metric identifies resource saturation and performance degradation automatically with 99% false positive reduction.
Zero-configuration deployment auto-discovers Kubernetes clusters, GPU nodes, databases, and supporting services in 60 seconds.
Anomaly Advisor correlates infrastructure issues across components, surfacing root causes in the top 30-50 metrics automatically.
Per-node pricing with unlimited metrics and logs eliminates surprise bills and enables accurate budget forecasting.
All observability data stays on-premises by default, ensuring compliance with GDPR, HIPAA, and data residency requirements.
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