It depends on your use case and internal infrastructure support. A lot of start-ups start on "cloud" when they have unpredictable needs and little immediate cash for kit & sys-admins (to manage more than the bare servers: backups and monitoring and other tasks that a cloud arrangement will offer the basics of at least, will need to be managed by you or a paid 3rd party on your kit). Later when things have settled they can move to more static kit and make a saving in cost at the expense of the flexibility (that they no longer need). Or they go hybrid if their product & architecture allows it: own kit for the static work, spreading load out to the cloud if a temporary boost of CPU/GPU/similar power is needed (this works best for loosely-coupled compute-intensive workloads, which may be the case here depending on exactly what they are trying to get out of ML and what methods & datasets are involved).
Go for some colocation facility where costs are predictable.