Why we started
We spent years running ML platforms inside product companies, and the pattern was always the same: the model was the easy part. The hard part — and the part that ate every weekend and on-call rotation — was serving it. GPU nodes, load balancers, a pile of hand-rolled YAML, and a new pile of it for every new model.
The tools available fell into two camps. There were managed endpoints for someone else's models, with the reseller markup that comes with them. And there were raw cloud primitives that left every hard problem to you. There was no middle path: a platform that runs your model, on GPUs you choose, with scaling and telemetry already handled.
So we built one. The incentives are simple — we make money only when your model runs well on our GPUs, and we lose you the moment we make it hard to leave. That's why there's no proprietary model format, no per-token platform markup, and no walled garden. Your weights live in your storage. Your traffic can run in your VPC. You can export everything and pull your model out with a single command.
"Models got easy and serving didn't. We built ModelCloud because we were tired of re-solving the serving problem every time — and we figured a lot of other teams were too."