How to Enforce LLM Spend Limits Per Team Without Slowing Down Your Engineers

Every AI platform team eventually hits the same moment: finance sends a spreadsheet, engineering doesn't know where the tokens went, and someone on the data science team just ran a 400,000-token context window against GPT-4o to test a hypothesis on a Friday afternoon. LLM costs don't creep up on you. They sprint. According to Andreessen Horowitz, AI infrastructure spending — primarily on LLM API calls — is consuming 20–40% of revenue at many early-stage AI companies. For enterprises, uncontrolled LLM usage across teams can turn a predictable cloud cost line into a surprise at the end of every billing cycle. The instinct is to lock things down: centralize API keys, require approvals, add manual budgeting steps. But that instinct is wrong. The moment you make it hard for engineers to access LLMs, they route around the controls — using personal API keys, shadow accounts, or skipping experimentation altogether. You trade cost visibility for velocity, and you lose both. The right approach i