Enterprise teams are racing to deploy GPT, Claude, and other generative AI tools — but most are doing it without any financial guardrails. AI spend doesn’t trigger audits... it just silently compounds. And with token-based pricing models, even minor inefficiencies like excessive prompt length or unnecessary context can scale into unexpected six or seven-figure bills.
This playbook cuts through the confusion. We unpack how OpenAI and Claude actually price their APIs, how token burn happens in the real world, and how enterprise buyers can forecast usage, prevent overages, and negotiate smarter deals.
You’ll see how subtle choices like using a large context window, generating verbose outputs, or building agents that run multiple token-consuming steps can quietly drain your budget. We break down the hidden enforcement mechanisms vendors use, from rate limits and fair-use clauses to throttling and forced upgrades.
You’ll also learn where real enterprises lost control of token spend and how they got it back. We show how to compare models by risk exposure, not just token cost, and what to push for in enterprise negotiations if you’re scaling AI across internal apps, retrieval-augmented generation, or agent workflows.
This isn’t another AI hype piece. This is how you take back control.