AI Infrastructure
Unified Observability Across the AI Data Center
VIGIA MasterSuite delivers a single operational view across GPU fleets, server health, network fabric, cluster workloads and AI services — enabling teams to move from reactive troubleshooting toward proactive infrastructure management across the full AI data center stack.
The challenge
AI data centers introduce a new level of operational complexity — performance issues can originate anywhere across a multi-layer stack of GPUs, servers, networks, clusters and AI services. Isolated monitoring tools, manual correlation, and reactive troubleshooting are no longer sufficient. A 5.5% GPU idle waste rate across a large fleet is not a configuration problem. It is a visibility problem.
Capabilities
- Full-stack GPU monitoring — utilization, VRAM, power, temperature and workload behavior correlated in one view, across every node in the fleet simultaneously
- Cluster and AI service observability — dependency mapping, intelligent health scoring and cross-workload correlation that reveals how services compete for resources
- Event correlation and predictive alerting — Detects developing infrastructure issues before they interrupt model training runs, inference workloads or serving capacity
- Capacity analytics and energy/cooling telemetry — historical and real-time data for data-driven infrastructure planning, energy optimization and cooling strategy
Platform
- GPU Fleet Monitor — Real-time utilization, VRAM, power draw, thermal state and workload behavior — correlated across every GPU node in the cluster. Not just metrics: operational intelligence that drives allocation decisions.
- Cluster Intelligence Engine — AI service observability with dependency mapping and intelligent health scoring. Understands how workloads interact, where contention arises, and how failures cascade — before they reach production impact.
- Infrastructure Capacity Analytics — Historical telemetry and real-time signal processing for data-driven capacity planning, energy optimization and thermal management — turning infrastructure data into strategic decisions.
Results
- 1% GPU Idle Waste — Down from 5.5%
- 100% Stack Visibility: Infra to AI Services
- <5m Root-Cause Diagnosis via Unified Correlation
Use cases
- GPU Fleet Utilization & Waste Reduction
- Workload & Cluster Health Monitoring
- AI Data Center Capacity Planning
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