Private AI Is Repeating Private Cloud's Mistake

During an unrelated Buyer Room session, one of the delegates described what their platform team had built: a representative private cloud with the ability to burst to the public cloud. That’s the reference architecture we’ve seen for the past ten years. IT provides a cloud-like experience to developers through a platform team that normalizes the public and private experience behind one API.

The project failed. Developers didn’t adopt the private API.

We’re about to see the same motion with AI adoption and development. And like most things, I’ll start with my own experience.

I built the private option. I still call OpenAI.

I’m now a developer shipping an entire suite of client-facing products and tools. I’ve invested in local AI. I have 256 GB of unified memory on my desk and can run models up to 120 billion parameters, which can technically service any product I provide to my customers. We’ve built lab after lab showing that my NVIDIA CUDA-based systems can replicate every product, service, and application I run in the public cloud with local AI. Other than the bandwidth bill, I could have a zero-dollar cloud bill for AI.

Yet I, an infrastructure expert, still choose the simplicity of an API call to OpenAI or Google Cloud over local AI. Why?

The first reason is personal. I don’t want to carry a duty pager. I am my own platform team, and that means running the Postgres vector database, the model runtime, the security scans, and the firmware updates. All the platform team activities. I simply don’t want to run them. My time is worth more than the API bill.

However, at scale, the argument flips. Or at least that’s the argument. A dedicated platform team services a set of internal clients who have my same need, centralizes the capability, and makes it invisible to the developer. That is the argument for private cloud. It’s the argument VMware is making with VMware Cloud Foundation (VCF) 9.1. More importantly, it’s the private AI argument Dell Technologies is making, HPE is making, and every major enterprise OEM is making. It’s the same argument they made for private cloud.

We’ve yet to see private cloud win

And private cloud has yet to succeed at scale. That’s the second, harder reason, and I’m not sure I have a satisfying answer to it beyond stating the question plainly.

Why should the developer select the private solution, which innovates slower than the public one? If I’m getting new services that scale reliably to cloud scale, why would I take the risk of going with a small service provider? Because that’s what my internal IT team is. As a developer, I don’t get the argument.

Yes, I take on security concerns I’d rather not own. I have to manage the vendor relationship. I carry burdens IT would normally carry for me. But the trade-off is exceptionally compelling. I get Jev, TypeSafe AI’s classifier. I get OpenAI’s Decisions API, announced yesterday. I get new model capabilities far faster than my IT team can iterate.

That’s the learning from private cloud. Once the private cloud is deployed, the line in the sand is drawn. What I now have looks very much like the requirements I had six months ago, before the project started. My requirements have moved. IT can’t keep up with my feature requests, because that’s not how IT is designed. It isn’t a product that can be staffed and innovated as fast as the public cloud. Private AI is walking into the same wall.

Is it all lost?

No. There’s a strong argument for both private cloud and private AI. It just isn’t at the cutting edge. For applications that aren’t regulated or security sensitive, private AI isn’t going to be where innovation happens. It’s going to be where steady state operations happen. Same as private cloud.

So design for that. Innovation happens in the public cloud. Steady state moves to the private cloud. As we build our private cloud and private AI platform groups, that principle has to be in the design, not bolted on after the developers have already walked past the private API.

Stop forcing an unnatural fit for new development on the private platform. Build a software development lifecycle (SDLC) where innovation happens on the public side and proven workloads get moved to the private side. That’s the job of the platform team. Not to out-innovate OpenAI, but to be the place the work lands once it stops changing.