Where the real movement is happening
None of this means repatriation is fake. It means it's concentrated in one category of workload: predictable, steady-state compute that doesn't benefit from elastic pricing, and increasingly, AI inference running at a high enough volume that per-instance cloud pricing stops making sense.
The clearest public numbers still come from 37signals, the company behind Basecamp and HEY, because most companies making this move won't put a dollar figure on it. 37signals spent $600,000 on servers and expects to save $7 million over five years compared to staying on AWS, with annual infrastructure costs dropping from $2.3 million to $840,000. Dropbox's move is older but larger in scale — the company shifted infrastructure off AWS and saved close to $75 million over two years, while keeping some workloads in the cloud rather than exiting completely. Neither company treated this as an ideological rejection of cloud computing. Both treated it as a spreadsheet problem: workloads running at consistent, high utilization stop benefiting from pay-as-you-go pricing, and the premium you're paying for elasticity you're not using becomes visible.
AI workloads are pushing the same math further. Continuous GPU inference at scale can cost well into six figures a year on a single reserved cloud instance, and vendors selling on-prem GPU hardware now advertise payback periods measured in months for customers running inference constantly rather than in bursts. The threshold isn't universal. Bursty, unpredictable AI workloads still belong in the cloud, where you only pay for what you use — it's the always-on, high-volume workloads where the on-prem math starts to win.