Introduction I'm going to be direct: your ML platform probably costs more than you think. Not because the technology is bad. But because nobody measured the total cost—infrastructure AND the engineers keeping it running. Last quarter, I worked with an enterprise ML team that discovered their platform cost $49,600/hour. Not for compute. For everything: servers, storage, pipelines, monitoring, AND the engineering overhead. $122K per month. $1.78M per year. They thought it was $1.35M. Here's where the gap came from—and how they fixed it. The Hidden Cost Breakdown Visible Costs (What They Knew): ├─ Compute (training + serving): $120/hour ├─ Storage: $20/hour ├─ Data pipelines: $10/hour └─ Monitoring: $4/hour = $154/hour = $1.35M/year ✓ Hidden Costs (What They Didn't Know): ├─ Infrastructure maintenance: 0.5 FTE ($50K/year) ├─ Pipeline management: 0.8 FTE ($80K/year) ├─ Model deployment: 0.7 FTE ($70K/year) ├─ Debugging/incidents: 0.5 FTE ($50K/year) └─ Governance: 0.5 FTE ($50K/year) = 3 FTE = $300K/year ✗ Real…