Use cases
When you should optimize?
Cloud and AI/ML costs have grown faster than revenue, and no one “owns” optimization.
Leadership is asking for a cost-reduction plan, but your team lacks the time or a structured approach.
You run multiple AI/ML workloads (training, inference, data pipelines) and struggle to see which ones drive spend.
You’ve already done “obvious” optimizations, but bills are still higher than expected.
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