Token Budget
A per-task, per-user, or per-month cap on how many tokens a feature is allowed to consume. Without one, costs drift upward invisibly. With one, every design decision has a known constraint.
A token budget is the discipline of treating tokens like any other scarce resource. You set a ceiling for what a single user interaction, a single agent run, or a single monthly customer is allowed to consume. You enforce it in code. You monitor drift. It is the simplest, cheapest form of FinOps for AI workloads, and most teams skip it entirely until they see a bill.
The Simple Version
Pick a number per task. For a support chatbot: maybe 5,000 tokens per turn, 50,000 per conversation. For an agent: maybe 100,000 per run. Code the limit. When a request would exceed it, either truncate, fall back to a cheaper model, or refuse. Budgets force the design choices that keep costs predictable.
Why It Matters
AI costs are per-call, not per-month. A feature that looks cheap in testing can become a runaway bill in production the first time a power user discovers it. Token budgets prevent this. They also surface design questions early. “This task actually needs 200,000 tokens” is useful signal that the design is wrong, not that you need a bigger budget.
How It’s Used on This Site
Every TWO agent has a documented token budget. Research agent: 40,000 tokens. Wisdom agent: 25,000. Writer agent: 50,000. When an agent exceeds budget, we investigate. If the digest runs 80,000 tokens on a given day, something about the research shape changed and we look before we pay twice for the same problem tomorrow.