The model inference cost gets all the attention, but it's often the smallest line item. The real costs hide in the supporting infrastructure: data pipelines to feed the model, vector databases for retrieval, monitoring and observability for production AI, human review loops for quality assurance, and the organizational overhead of coordinating across data, ML, and product teams. We call this the 'inference tax' — for every dollar spent on model inference, enterprises typically spend three to five dollars on everything around it.
CFOs who budget only for API calls or GPU hours consistently underestimate total AI cost of ownership by 60–80%.
This question reflects common advisory themes. It is editorially curated, not sourced from individual conversations.
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