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…