Token Budget Planning: Allocate, Cap, and Alert on AI Spend
Plan token budgets across teams and projects: set allocation pools, enforce ceilings, alert on anomalies, and attribute cost so AI spend stays predictable.
AI spend feels uncontrollable until you budget it like any other cloud resource. The trick is that a dollar is not the natural unit — tokens are. Plan in tokens, then convert to dollars, and you can forecast, allocate, and cap usage with the same rigor you apply to CPU or storage.
This guide lays out a token budget framework: how to forecast demand, split a shared budget across teams, enforce ceilings before the invoice arrives, and alert on the drift that quietly doubles your bill.
Forecast: know your burn rate
- Inventory every call site: chat features, summaries, embeddings, evaluations, background jobs.
- Estimate tokens per request at the 50th and 95th percentiles — averages hide the long prompts that dominate spend.
- Compute a monthly burn rate from call volume times per-request tokens, then add headroom for growth and new features.
Allocate: split the budget by team and feature
A single shared budget guarantees nobody knows who is driving cost. Allocate pools per team, per feature, or per environment — usually a mix. Production chat gets one pool, batch enrichment another, experimentation a third. Allocation gives you a place to ask the question 'why did support's pool double this month?' instead of staring at one undifferentiated bill.
Enforce ceilings before the invoice
- Set a hard monthly token ceiling per pool at the gateway, not in the app code.
- Configure soft warnings at 50, 75, and 90 percent with automatic emails to the pool owner.
- Define what happens at the ceiling: block new requests, fall back to a cheaper model, or require an owner override with a written justification.
Alert on anomalies, not just limits
Ceilings stop overspend; alerts catch the pattern before you hit the ceiling. A new prompt that accidentally includes the full database doubles tokens overnight. A retry loop that fires on a failing model multiplies calls. Alert on day-over-day growth, requests per minute spikes, and cost per request trending up — each of these is a leak that a static ceiling will not catch until it is too late.
Attribute cost back to decisions
- Tag every request with model, feature, team, and environment; the gateway should stamp these automatically.
- Report cost per request per feature monthly, and treat per-request cost as a product metric.
- When a feature exceeds its per-request target, the fix is usually obvious: shorter context, caching, or a cheaper model.
FAQ
How do I estimate tokens per request?+
Measure with your provider's tokenizer or the tokenizer library, not by word count (roughly one token per 0.75 words in English). Sample real requests from logs and measure prompt plus completion tokens at p50 and p95.
What happens when a pool hits its ceiling?+
That depends on the policy you set: block, degrade to a cheaper model, or require an owner override. Blocking with a clear error is the safest default for anything with a hard cost constraint.
Should budgets be per user or per feature?+
Per feature first, per user second. Cost is driven by what the code does — a prompt bug multiplies cost for every user at once. Per-user limits only help when individual power users dominate.
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