Work out mlops cost per model instantly with clear inputs, formula shown and shareable results.
Fully loaded cost per model is per-model training and serving plus an allocated share of the shared platform: feature store, registry, orchestration, monitoring and the team maintaining them. The platform share is what makes the first few models look absurdly expensive and the twentieth look cheap, and it is the honest number to compare against each model's business value.
Cost allocation
total = models x (training + serving) + platform; per model = total / models
For decisions about retiring or adding models, marginal cost is the right figure. For portfolio-level reporting, fully loaded cost prevents the platform from looking free.
Serving, because it runs continuously, and idle endpoints in particular. Consolidating low-traffic models onto shared multi-model endpoints is normally the biggest saving available.