Open source, and what comes next
The blast-radius confinement, the approval gates, the audit trail — none of it sits behind a tier. A black box that reboots your GPUs should not exist at any price.
Open source and self-hosted in your cluster. Detection, the remediation ladder, safety controls, approvals, the audit trail, the CLI, the API, the web panel and the Grafana dashboard all live in the open-source repository, and the roadmap is public.
View on GitHubDesign partners — three slots
I am looking for a small number of teams running real GPU fleets to shape KubeNeuron around the failure modes that only show up at scale. You get direct roadmap influence and direct access to the person building it. I get the hardware reality no lab reproduces.
Multi-cluster control, fail-slow detection, reliability-aware placement and checkpoint-aware recovery are the directions under discussion — design partners decide the order.
Become a design partnerWhere this goes — in development
Traditional schedulers see GPU type, count, quota, and price. KubeNeuron also sees real-time health, degradation, failure history, and workload risk — and that data is worth more to the scheduler than to the dashboard.
8 GPUs available6 healthy
1 degraded — short jobs only
1 at risk — quarantinedWe are building toward placing AI jobs where they are most likely to finish successfully — on healthy local infrastructure first, and on cloud capacity when local hardware is full, too slow, or too risky. The metric that matters is cost per successfully completed job, not cost per GPU-hour: the cheapest accelerator is not cheap when the job dies after ten hours.
None of this section ships yet. Health-adjusted capacity and reliability-aware placement are in development. What ships today is everything above this section.
Build the safe path first
Read the source, run the dry-run ladder, and tell us where the policy model breaks.