The production cutover protects capacity access but exposes an unresolved latency pattern.
The customer needs the new GPU node group to meet its launch date, while its application team needs evidence that sustained traffic will remain stable.
Who you’d be doing this for
“My launch review is Friday, and I still can’t tell whether that latency spike is a test artifact or our first outage.”
Thin Hoang · ML Engineering Manager
Leads the enterprise AI team preparing a customer-facing inference service for production.
What is at stake
Long-running Kubernetes tests still produce intermittent latency spikes as the customer’s current capacity window closes. You must weigh launch capacity against the cost of committing before the customer can trust sustained performance.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- launch capacity vs sustained reliability
- customer deadline vs evidence quality
- production commitment vs reversibility
- renewal confidence vs technical uncertainty
Why Crusoe
Kubernetes-based production inference depends on reliable GPU capacity and clear guidance when node delivery evidence does not match sustained workload behavior.
Written with these in mind
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