Measuring What Matters: From $/hr to $/Outcome

Hours are not outcomes. The executive conversation changes when engineering reports $ per delivered unit:

• Training: $/epoch, $/quality-improving epoch (where eval improves), $/final benchmark point.

• Inference: $/1k tokens, $/image, $/call within p95 target.

• ETL/Rendering: $/TB processed, $/frame, $/job.

How to implement

1. Define the unit with product/data science—tie it to customer value.

2. Instrument end-to-end: collect micro-unit usage (vCPU-min, GPU-core-hr, GB-hr) plus storage/egress.

3. Normalize by success: failed attempts don’t count; resubmits roll into the same unit.

4. Attribute fairly: split shared costs (feature store, control plane) by usage or time.

5. Create guardrails: budget alerts at $ per outcome thresholds, not just spend per day.

What changes

• You’ll favor packing and checkpointing over “bigger machines.”

• Teams will choose models and batch sizes that minimize $ per outcome rather than maximizing theoretical FLOPs.

• Procurement becomes dynamic: you can mix cloud, on-prem, and decentralized micro-compute while keeping one comparable KPI.

When the metric aligns with customer value, engineering trade-offs become obvious—and the business speeds up.