On-prem servers are back.

On-prem servers are back. It's the server wild west all over again. Because running AI models in the cloud? The costs add up faster than anyone budgeted for.

Token costs stack up. Data leaves your walls. And you're renting control you should own.

So what's happening? - People are racking Mac Minis. - Building bare metal homelabs. - Spinning up local models with projects like OpenClaw. - Standing up their own data center capacity to run models on hardware they control.

Cost. Security. Control. The trifecta that always wins long-term.

Here's the part that makes me laugh.

Jake Warner + my good friends at Cycle.io have been grinding on bare metal orchestration for over a decade. Back when saying "on-prem" in a meeting got you laughed out of the room.

Now, its rack diagrams. Folks debating GPU density like it's 2003 all over again.

The infrastructure pendulum always swings. Cloud was the right move for a lot of workloads. But AI inference at scale, especially when your data is sensitive and your margins matter, is pulling teams back to hardware they can touch.

Happy to compare notes if you're evaluating on-prem for AI workloads. It's a real conversation worth having.

Post Builder Notes: - Illustration + Image Created Nano Banana Pro. - Wispr Flow text to voice - Claude