AI & GPU metrics
GPU and LLM performance metrics from the on-prem AI workspace.
- GPU and cluster metrics on one screen
- Click to zoom in
- Works alongside the rest of Cloud Admin
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AI & ML
GPU ops, LLMs, and predictive signals on your hardware
Run AI workloads, deploy on-prem LLM models, and leverage predictive maintenance signals — all on your own infrastructure.
Sovereign AI
Run AI Workloads
AI inference and training are capacity programs, not side projects. FusioNative ties GPU utilization, LLM deployment signals, and pod-level GPU usage into the same console as the rest of your estate.
Product walkthrough
See it in Cloud Admin
Screens from the live product, with a short note on when you would open each view.
Outcome
Ship models faster without surrendering control
Give ML platforms the telemetry they expect while keeping security and infrastructure teams in one operational loop.
AI & Machine Learning · Inference
On-prem LLM Models
Manage every on-prem model from one cockpit: flip between Overview and Deployed Models, watch GPU signals from DCGM, correlate CPU and memory with cluster pods, and track inference throughput, latency, tokens, and network paths serving traffic. When you are ready to ship, choose Deploy Existing (reuse an attached cluster) or Deploy New (stand up fresh resources), without losing Prometheus or optional Ollama exporter telemetry.
Product walkthrough
On-prem LLM models, step by step
Screens from the live product, with a short note on when you would open each view.
LLM Overview
Your starting point for on-prem LLM models: KPI cards and charts summarize fleet health, including on-prem llm models overview with gpu and inference metrics. Spot drift early, then drill into the tab that explains the root cause.
- GPU and cluster metrics on one screen
- Charts link utilization to time so you spot spikes quickly
- One click into deeper tabs when something looks off
Click the screenshot to open full size, zoom, and pan.
Deploy · Cluster
The deploy wizard’s Cluster step is where operators confirm llm deploy wizard cluster step Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
- Wizard validates each step before you continue
- Settings stay consistent with cluster quotas and GPU pools
- Review the full stack before anything reaches production
Click the screenshot to open full size, zoom, and pan.
Deploy · Model
The deploy wizard’s Model step is where operators confirm llm deploy wizard model selection Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
- Wizard validates each step before you continue
- Settings stay consistent with cluster quotas and GPU pools
- Review the full stack before anything reaches production
Click the screenshot to open full size, zoom, and pan.
Deploy · Deployment
The deploy wizard’s Deployment step is where operators confirm llm deployment configuration step Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
- Wizard validates each step before you continue
- Settings stay consistent with cluster quotas and GPU pools
- Review the full stack before anything reaches production
Click the screenshot to open full size, zoom, and pan.
Deploy · Resources
The deploy wizard’s Resources step is where operators confirm llm resource and gpu allocation Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
- Wizard validates each step before you continue
- Settings stay consistent with cluster quotas and GPU pools
- Review the full stack before anything reaches production
Click the screenshot to open full size, zoom, and pan.
Deploy · Storage
The deploy wizard’s Storage step is where operators confirm persistent storage for llm runtime Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
- Wizard validates each step before you continue
- Settings stay consistent with cluster quotas and GPU pools
- Review the full stack before anything reaches production
Click the screenshot to open full size, zoom, and pan.
Deploy · OpenWebUI
The deploy wizard’s OpenWebUI step is where operators confirm openwebui configuration step Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
- Wizard validates each step before you continue
- Settings stay consistent with cluster quotas and GPU pools
- Review the full stack before anything reaches production
Click the screenshot to open full size, zoom, and pan.
Deploy · Service
The deploy wizard’s Service step is where operators confirm llm service exposure step Each screen validates inputs before you advance, so GPU, storage, and networking stay aligned with cluster quotas and your on-prem policy.
- Wizard validates each step before you continue
- Settings stay consistent with cluster quotas and GPU pools
- Review the full stack before anything reaches production
Click the screenshot to open full size, zoom, and pan.
Model operations
Inference is production, treat telemetry like any other SLO
When GPUs, tokens, and latency share one Overview, AI platform teams rehearse capacity instead of guessing during spikes.
Truthful gaps
Missing exporters surface as explicit signals, no synthetic token rates.
Deploy with intent
Existing vs net-new paths keep brownfield and greenfield teams on rails.
See the whole stack
Pods, GPUs, and network tubes tie back to model serving, not disconnected dashboards.
Put on-prem LLMs next to the clusters that host them
Operate LLM Models from Cloud Admin alongside Kubernetes, observability, and delivery workflows.
Get a demoAI & Machine Learning · Reliability
Predictive Maintenance
See where your cluster is headed before users do: Overview ties a risk score and incident probability to compact recommendations, trend charts, and forecast rows with horizons and confidence. Fleet Risk rolls assessments across clusters by provider, region, and environment. Audit streams policies, planned actions, reinforcement-learning feedback, and alerts in one filterable feed. Under the hood, signal pipelines ingest Kubernetes events, GPU and hardware posture, anomaly features, topology RCA, and predictive HPA signals, so blast radius and drivers stay explainable.
Product walkthrough
See it in Cloud Admin
Screens from the live product, with a short note on when you would open each view.
Predictive Overview
Your starting point for predictive maintenance: KPI cards and charts summarize fleet health, including predictive maintenance risk score and recommendations. Spot drift early, then drill into the tab that explains the root cause.
- KPI strip shows the numbers leadership cares about first
- Charts link utilization to time so you spot spikes quickly
- One click into deeper tabs when something looks off
Click the screenshot to open full size, zoom, and pan.
Fleet Risk
Actionable signals instead of raw logs: fleet risk breakdown by provider and region. Each item ties back to predictive maintenance so owners know what to fix now versus what can wait.
- Problems ranked so the noisiest failures surface first
- Enough context to assign an owner without opening five tools
- Clear next step: scale, restart, patch quota, or escalate
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Audit Feed
Actionable signals instead of raw logs, predictive maintenance audit alerts. Each item ties back to predictive maintenance so owners know what to fix now versus what can wait.
- Same layout your operators see in production
- Click to zoom in
- Works alongside the rest of Cloud Admin
Click the screenshot to open full size, zoom, and pan.
Proactive ops
Prediction without theater, signals tied to drivers you can inspect
When risk, RCA, and audit share one narrative, incident reviews start with evidence instead of anecdotes.
Score plus story
Gauge and classifier probability sit next to recommendation copy tied to rationale fields.
Fleet-wide prioritization
Rollups show where to spend the next hour, not every cluster equally.
Auditable automation
Policy and RL events land beside alerts so automation stays reviewable.
Put predictive maintenance next to the clusters you protect
Run the risk engine from Cloud Admin alongside AI workloads, metrics, and fleet operations.
Get a demo