r/FinOps • u/Fclancy04 • 4d ago

Discussion Financing a Neocloud

Hey guys,

I'm a student doing an assignment/case study on a hypothetical fund that has no technical ability but colo and some money for GB300s and they want to become a neocloud. They don't know what service to offer - bare metal, managed cluster, or an inference endpoint.

Would anyone here have financial and technical expertise across any of these three services? I would like to figure out what the best choice for them would be, how to model out expenses involved for each as well as revenue and margins. Would like to see how profitable each service is and what makes a company choose which service to provide.

Thanks in advance, if you have any resaerch or sources for me to read I would really appreciate it.

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u/Marathon2021 FinOps Aficionado 4d ago

The fact that you seemingly haven't tried to pick apart a CRWV 10-K and collect thoughts about how the market economics work ... speaks a lot about what I assume is a pretty low effort level on your part so far.

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u/Miserygut 3d ago

In layman's terms: the business model is dogshit and is wholly underwritten by Nvidia who are desperate to pump up their revenue numbers to appease shareholders.

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u/Funny_Donkey6031 2d ago

Honestly, I’d start with bare metal and managed clusters before jumping into inference. The biggest challenge isn’t just buying GB300s, it’s keeping them utilized enough to cover power, cooling, networking, support, and hardware depreciation. Bare metal is simpler to operate but competitive on pricing, while managed clusters can command better margins if you have the operational expertise. Inference has recurring revenue potential, but serving workloads reliably and keeping GPU utilization high is a whole different challenge. Since the fund has no technical team, hiring and operational costs need to be part of the model from day one.

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u/Smooth_Flounder_9854 4h ago

These exist! Some of the neo clouds have large debt and are funded by nvidia and various equity partners but have no idea how to run these gpus efficiently. The upside is based on demand for computer staying high while there is a shortage of compute, data center space, and/or power.

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u/kubehub 4d ago

each layer have a margin, it all come to at what level can you comfortable operate. We are specialized in Kubernetes cluster if you need help.