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KubeOn GPU pricing ยท Early access

Editions for how far the optimizer goes

Start with Insights to measure every GPU-hour and see what the optimizer would do. Move to Optimize to let it act in Approve or Automatic mode, with burst to other regions of the same cloud, and to Enterprise for burst across clouds and into your data centers.

Insights

Every GPU-hour measured and owned, with proposals and no write access.

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Quoted on monitored GPUs, annually

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  • GPU cost by team, idle GPU time and DCGM utilization
  • Sharing, sizing and scale-to-zero proposals
  • GPU type fit recommendations
  • Budgets and alerts on GPU spend
  • GPU policies in Recommend mode
  • KubeOn Cloud hosting
  • Standard support
Adds the optimizer

Optimize

For platform and ML teams that want the optimizer to act.

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Quoted on monitored GPUs, annually

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  • Everything in Insights
  • GPU optimizer in Approve and Automatic modes
  • MIG and time-slicing applied in your change windows
  • GPU autoscaling with spot and on-demand fallback
  • Packing and scale to zero
  • GPU burst to other regions of the same cloud
  • Audit log with one-click revert
  • SSO with SAML or OIDC, and a self-hosted hub option
  • Priority support

Enterprise

For multi-cloud GPU fleets, owned data centers and regulated programs.

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Quoted on monitored GPUs, annually

Request early access
  • Everything in Optimize
  • GPU burst across clouds and into your data centers
  • Owned GPU rate cards and hardware amortization
  • On-premises and air-gapped hubs
  • Custom roles and audit export
  • Security review and custom terms
  • Named technical account manager
  • 24/7 support with an SLA

KubeOn GPU is in early access for selected enterprise customers, and quotes include the rollout plan. GPU compute stays on your own cloud bills and in your own data centers.

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What each KubeOn GPU edition includes

Features included in each KubeOn edition
FeatureInsightsOptimizeEnterprise
Measure
GPU node cost split by GPU requestsIncludedIncludedIncluded
Idle GPU time, pricedIncludedIncludedIncluded
DCGM utilization and GPU memory per podIncludedIncludedIncluded
MIG slices allocated by shareIncludedIncludedIncluded
Owned GPU rate cardsNot includedRate cardsRate cards and hardware
Optimize
Proposals with patches (Recommend mode)IncludedIncludedIncluded
Approve and Automatic modesNot includedIncludedIncluded
MIG partitions and time-slicingProposalsIncludedIncluded
GPU autoscaling, spot firstProposalsIncludedIncluded
Packing and node removalProposalsIncludedIncluded
Scale to zeroProposalsIncludedIncluded
GPU burst to other regions of the same cloudNot includedIncludedIncluded
GPU burst across clouds and into your data centersNot includedNot includedIncluded
Control
Policy limits, windows and scopesIncludedIncludedIncluded
Audit logIncludedIncludedIncluded
One-click revertNot includedIncludedIncluded
Custom roles and audit exportNot includedNot includedIncluded
Deployment and support
KubeOn Cloud (region of your choice)IncludedIncludedIncluded
Self-hosted hub in your cloud accountNot includedIncludedIncluded
On-premises and air-gappedNot includedNot includedIncluded
SSO with SAML or OIDCNot includedIncludedIncluded
Support hoursBusiness hoursExtended24/7
Technical account managerNot includedNot includedIncluded

How we count

The terms in a KubeOn GPU quote

Plain definitions, so the quote and the invoice say the same thing.

Monitored GPU

A physical GPU seen by an agent during the billing period, measured as the average number present at once. A GPU split into MIG slices or time-sliced still counts once.

Burst GPU

A GPU attached by GPU burst counts as a monitored GPU only for the hours it is attached. The cloud charges for it stay on your own bill.

Optimized cluster

A cluster running the GPU optimizer. There is no separate charge per cluster: GPUs are counted the same way with or without it.

User

A person who signs in to KubeOn. People who only receive reports, alerts or approval requests by email are not counted.

Your quote

What sets the price

We share prices with each client, based on their requirements. Every KubeOn GPU quote is built from the same four inputs.

Monitored GPUs
The average number of physical GPUs present at once over the billing period, across every cluster with an agent. CPU nodes are not counted.
Edition
How far the optimizer may go: Insights for measurement and proposals, Optimize for Approve and Automatic modes with burst inside one cloud, Enterprise for burst across clouds and into owned data centers.
Deployment
KubeOn Cloud and self-hosted hubs use the same editions. A self-hosted hub needs Optimize; on-premises and air-gapped hubs need Enterprise.
Term
Quoted annually, with multi-year terms and volume pricing available. KubeOn, KubeOn AI and KubeOn GPU can share one agreement.

Worked example: monitored GPUs for a 30-day month

GPUs are averaged over time, so autoscaling peaks and burst count only for the hours they run.

Example GPU fleet with each pool's peak and average GPU count
PoolPeakAverage
ml-inference8 L40S all month, autoscaling to 12 for 10 hours a day129.7
ml-notebooks2 A10G on weekdays from 07:00 to 20:00, scaled to zero otherwise20.8
dc1-gpu8 A100 all month, one split into three MIG slices88.0
burst4 L40S in eu-central-1 for 3 days of the month40.4
Total2618.8

This counts as about 19 monitored GPUs, not its 26-GPU peak.

  • Autoscaling and scale to zero count for the hours GPUs run: GPUs the optimizer removes stop counting when they leave.
  • MIG slices and time-sliced replicas count as the one physical GPU they share.
  • Burst GPUs count only while they are attached to your cluster.
  • Owned GPUs in your data center count the same way as cloud GPUs.
  • Growth during the year is handled the way your quote sets out, with no surprise charges month to month.

FAQ

KubeOn GPU pricing questions

Billing

Your cloud providers do, at your own rates, and your data center costs stay yours. KubeOn GPU is licensed on monitored GPUs; it never resells compute, and burst nodes are created in your own accounts.

Editions

Yes. Insights runs every policy in Recommend mode, so the proposals are already there. Moving to Optimize lets the same policies run in Approve or Automatic mode.

Tell us about your GPU fleet. We will plan early access with you.

GPU types, how many run at once, which clouds and data centers, and how far you want the optimizer to go.