Tokenomics in enterprise AI
Summary
This article explains tokenomics as a practical discipline for controlling enterprise AI costs and usage. It shows how token consumption is driven by prompt design, model choice, context size, output length, retries, and governance. It outlines optimization tactics such as workload segmentation, model routing, caching, response shaping, batch execution, and context pruning. It also maps those practices to AWS, Azure, and Google Cloud and gives a DevTest example that shows how token controls can reduce waste and improve operating discipline.
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