Generative AI has moved from a few experiments in data scientists' notebooks to a major line item on most organizations' cloud bills. As a data engineer who has built the systems behind that spend, I have observed this shift happen firsthand. The costs for GPU hours, model inference, vector storage, and agent orchestration add up very quickly. Yet AI spend is often unpredictable and hard to attribute to teams, and even harder to tie to the value those dollars create. Most organizations are using GenAI this way, yet few can answer a simple question: what do all these dollars buy, and is the spend even worth it?
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