Committed training capacity is pulling against the buyer’s need to burst inference workloads.
Procurement needs enforceable capacity terms while ML engineers need room to absorb production demand.
Who you’d be doing this for
“Procurement keeps asking what we actually own, and I can’t point to one clean answer.”
Irfan Than · Director of Machine Learning Platform
Leads the enterprise AI team whose production training and inference workloads depend on the proposed Kubernetes capacity.
What is at stake
A $12M annual commitment is stalled because training capacity and inference burst use are bundled together. You must weigh a clear commercial commitment against the flexibility ML engineers need.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- procurement certainty vs. inference flexibility
- annual commitment vs. capacity exposure
- fast signature vs. durable expansion
Why Crusoe
Crusoe Cloud GPU Compute must translate high-performance Kubernetes capacity into terms enterprise AI buyers can approve.
Written with these in mind
Not your kind of problem? 76 more at Crusoe, or browse every organization.
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.