Manual capacity changes protect the weekly peak while delaying the second workload.
The customer needs stable response times, while its platform lead needs a change that does not put the live endpoint at risk.
Who you’d be doing this for
“Every Thursday I’m watching traffic and changing capacity instead of working on the next model release.”
Bianca Moreno · ML Engineer
Owns deployment and reliability for the customer’s production LLM applications.
What is at stake
The customer manually raises capacity before every weekly spike and is holding back its second workload. You must weigh faster adoption against the platform lead’s need to protect the live endpoint.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- peak reliability vs. autoscaling adoption
- manual control vs. managed operations
- live endpoint safety vs. second-workload growth
- customer-specific guidance vs. reusable enablement
Why Crusoe
Managed Inference adoption depends on customers trusting Kubernetes autoscaling for production LLM workloads, not treating managed capacity as a manual reservation.
Written with these in mind
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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.