There's a specific kind of organizational panic that happens around quarter-end when a cloud computing invoice arrives and nobody in the room β not the CTO, not the FinOps lead, not the platform team β can explain why it's 40% higher than last month. The tools are all "approved." The pilots are all
The cloud bill arrived last quarter, and nobody in the room could explain it. Not the engineering lead. Not the FinOps analyst. Not the VP of infrastructure. The numbers were real, the line items were there β but the story connecting AI tools to actual spend had gone completely dark. This is not an
Most engineering leaders I talk to share a specific, uncomfortable admission: they can open their cloud dashboard, point at a number, and not be able to tell you β within 30% accuracy β why that number is what it is. Not because they're bad at their jobs. Because the structure of modern AI stacks ma
Most engineering teams discover their AI infrastructure problem the same way: a Slack notification at an inconvenient hour, a finance team asking pointed questions about a line item that doubled without a corresponding feature launch, or a quarterly cloud review where the numbers simply don't match
Most engineering teams I talk to have solved the visible AI cost problem. They've negotiated better committed-use discounts, right-sized their training clusters, and put guardrails on GPU provisioning. The bill looks manageable. Then, three quarters later, the CFO is asking why cloud spend climbed 3
