Neurox is a self‑hosted, Kubernetes‑native platform that aggregates real‑time GPU utilization, health, and power metrics from multi‑cloud and on‑prem clusters into unified dashboards and FinOps reports. It auto‑discovers GPU hardware, normalizes data, and provides APIs for workload scheduling, alerting, and cross‑cloud resource orchestration, with Helm‑based zero‑configuration deployment.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises running AI workloads across multiple cloud providers often lack a unified view of GPU utilization, leading to high idle rates and difficulty attributing compute costs to specific projects or teams. Existing monitoring tools are either cloud‑specific or require extensive manual integration, which hampers real‑time resource optimization and accurate FinOps reporting.
Solution
Neurox delivers a self‑hosted, Kubernetes‑native monitoring platform that aggregates GPU metrics from heterogeneous cloud environments into a single pane of glass. The solution auto‑discovers GPU hardware across AWS, GCP, Azure, and on‑prem clusters, normalizes utilization, health, and power data, and presents them through purpose‑built dashboards. Integrated FinOps modules break down spend by business unit, project, team, or GPU type, enabling precise cost allocation and ROI analysis. All components are packaged as Helm charts for rapid, zero‑configuration deployment, and data is stored locally to meet security and compliance requirements. The platform also exposes APIs for downstream automation, such as workload scheduling and alerting based on temperature or power thresholds.
Target Audience
The primary customers are AI/ML engineering and MLOps teams in enterprises that operate distributed GPU clusters across public clouds and on‑premise data centers, as well as cloud service providers offering managed AI infrastructure.
Features
- Helm‑based installer that provisions a combined control and workload agent on any Kubernetes distribution (EKS, GKE, AKS, on‑prem)
- Multi‑cloud auto‑discovery agent that identifies GPU models (A100, H100, V100, etc.) and streams real‑time utilization, temperature, power draw, and error metrics
- Consolidated dashboards with drill‑down views per GPU, cluster, project, and owner, supporting custom widgets and export to CSV/JSON
- FinOps reporting engine that aggregates cost data by GPU type, project, team, and priority, with built‑in ROI calculations and scheduled report generation
- Configurable health alarms that trigger alerts on temperature spikes, power anomalies, or hardware failures via webhook, Slack, or Prometheus Alertmanager
- Scheduler integration layer that can prioritize AI workloads on Kubernetes, reducing idle GPU time and improving cluster scalability
- Federated resource orchestration API for pooling GPU capacity across clouds, enabling cross‑cloud workload placement without manual transfers
- On‑prem data storage with role‑based access control and upcoming SOC 2/ISO 27001 compliance features