The cost of keeping a generative or predictive model useful is not the compute you pay to train it, but the endless loop of data‑pipeline grooming, drift monitoring, and model‑version rollback that follows. Each new transaction the system sees subtly shifts the statistical landscape, and without a dedicated guard‑rail, the model’s confidence erodes, prompting false alerts or missed fraud.

Teams often celebrate the moment the model goes live, then assume the job is done, ignoring the fact that every change in regulation, product feature, or customer behavior forces a retraining sprint that consumes engineer time and budget. At a fintech that launched a neural‑based risk scorer, the data science lead spent most of the quarter after launch wrestling with mislabeled edge cases, rebuilding feature stores, and negotiating with compliance to certify each new version – work that the original business case never accounted for.

The hidden upkeep creates a “maintenance tax” that silently drags down ROI, and because it is invisible on the balance sheet, executives frequently over‑invest in new models while the real bottleneck is the invisible staff effort keeping the old ones alive.