AI Agents for
GPU Infrastructure
AI agents that detect, diagnose, and optimize your GPU clusters 24x7, from facility operations to white space GPU errors, managed in a single pane.
Integrates with your existing infrastructure
Kubernetes
NVIDIA
Docker
Prometheus
Grafana
Linux
TerraformThe Problem
Running GPU clusters is brutally hard
These failure modes silently cost GPU operators millions in wasted compute every year.
Token economics are left on the table
Token throughput is shaped end-to-end, from GPU kernel efficiency to batch scheduling. Without full-stack visibility, cost-per-token drifts and no one knows why.
GPU failures are silent killers
ECC errors, thermal throttling, and driver crashes happen silently. By the time your team notices, training jobs have wasted hours of expensive compute.
Incident response is slow and manual
Engineers scramble through dashboards, SSH into nodes, cross-reference logs. Mean-time-to-resolution stretches from minutes to hours while GPUs sit idle.
Utilization never reaches its potential
GPU clusters run at 40–60% utilization on average. Scheduling inefficiencies and thermal constraints leave performance on the table, costing thousands per month.
See It In Action
Watch Factryze at work
Built for AI/ML Labs and Neo Clouds
For AI/ML Labs
Catch the failures your dashboards miss.
Continuous detection, cross-layer root-cause, and runbooks that compound. For teams running training and inference on private GPU clusters.
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For Neo Clouds
Hit your SLAs without doubling the SRE team.
Fleet-wide health, multi-tenant isolation, customer-facing transparency. For GPU-as-a-Service providers operating at neo cloud scale.
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Frequently asked questions
Ready to automate your GPU ops?
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