Observability, Real-Time Monitoring & Health Checks
Clear visibility into system behavior across your AI infrastructure.
AI workloads depend on infrastructure that behaves consistently under load. Standardized performance metrics for clear operational visibility.
Fleet Management
Observability
Get clear visibility into what’s affecting performance, including job scheduling and workload activity, without managing the underlying infrastructure.
GPU Health & Usage
See how your GPUs are performing at a glance. Quickly understand availability, usage levels, and overall health.
Network Performance
Stay on top of how data moves across each host. Monitor speed, stability, and connectivity to ensure everything runs smoothly.
Storage Performance
Keep track of how your data is stored and accessed. Get visibility into performance and capacity.
Real-Time Monitoring
Monitoring is focused on maintaining infrastructure stability in GPU-dense environments. Internal TensorWave teams receive alerts and operational signals for platform-level anomalies and conduct immediate remediation.
Infrastructure service availability
Node-level signals
GPU utilization anomalies
Hardware conditions (power, thermal, error states)
Storage latency and network health indicators
Slurm Insights
Get clear visibility into how workloads are scheduled and executed across your environment. Slurm insights help you track job activity, understand resource usage, and keep performance consistent as you scale.
Preconfigured prolog and epilog scripts
Cluster utilization
Node health and status
Health Checks
Infrastructure health is continuously evaluated across critical paths. Health signals are reviewed by TensorWave operations teams and tied to internal remediation workflows.
Node readiness and service availability
Fabric connectivity
Storage responsiveness
Control service uptime
GPU state validation