Every business wants AI that works like ChatGPT. But most won't send their sensitive data to Google’s, OpenAI's or Anthropics’ servers. Nor should they.
There is an opportunity in that tension.
Companies are caught between two realities. Their employees expect AI tools that actually help; smart assistants, automated workflows, intelligent data analysis. But their legal teams won't let corporate secrets flow through third-party APIs.
The solution isn't to give up on AI. It's to bring AI in-house.
Three paths to private AI
The cloud-hosted approach. Rent private GPU clusters from providers like Hetzner or Fly.io. You get dedicated hardware in a data center, but it's still managed infrastructure. Pay monthly, scale as needed, upgrade without buying new boxes. This feels like the sweet spot for most businesses right now—private enough for lawyers, flexible enough for rapid AI development.
The on-premise service model. Buy or rent actual hardware that sits in your office. Think a stack of Mac Studios wired together, but with a maintenance contract. It's like the old IBM model from the 1950s—you get the hardware and the expertise to keep it running. Complete control, zero data leaving your building.
The prebuilt cluster approach. Sell businesses a complete package—computers, GPUs, software, all pre-configured. Plug it in and start training models. No IT department headaches, no linking machines together, no wondering if your setup actually works.
What makes this work
The real opportunity isn't just selling hardware. It's making private AI actually usable.
Most businesses can't hire AI engineers. But they might have one or two developers who could fine-tune models if the platform made it simple enough. That's the key; build developer tools so good that tweaking AI models becomes easy, a competitive advantage, not a technical nightmare.
And businesses need proof their data stays safe. Complete telemetry. Zero data loss guarantees. Show them exactly what's happening to their information.
Why now
Smaller AI models keep getting better. What required massive server farms last year now runs on expensive-but-manageable local hardware. The gap between cloud AI and on-premise AI is shrinking fast.
Meanwhile, the business case gets stronger every month. Companies see AI boosting productivity but hate the privacy trade-offs. They want both benefits—and they're willing to pay for infrastructure that delivers them.
The businesses that figure out private AI first will have a serious edge. And someone needs to build the infrastructure to make that possible.