Racklion
Straight answers to the questions teams ask before leaving rented cloud.
For steady, high-utilization AI workloads, owning or colocating GPUs usually beats on-demand cloud within 12–24 months once you account for egress, reserved-instance lock-in, and premium managed-service margins. Racklion models your specific utilization before you commit.
Supply is allocation-constrained and moves through OEMs, integrators, and colocation partners rather than a public price list. Racklion sources allocation, negotiates terms, and lines up the power and space to run it.
Cloud repatriation is moving workloads from rented public cloud back to owned or colocated infrastructure. It makes sense when spend grows faster than workload value, when data gravity and egress dominate the bill, or when latency, sovereignty, or vendor concentration become risks.
No. Most teams start with colocation — you own or lease the servers and GPUs and rent space, power, and cooling in an existing data center. Racklion sources the colo, the hardware, and the power so you get ownership economics without building a facility.
Dense AI racks now draw 40–130 kW each, well beyond legacy 5–10 kW designs, which is why power and cooling — not chips — is often the real constraint. Racklion sources data-center space with the power envelope and liquid-cooling readiness your hardware requires.