AI cost control is the discipline of budgeting, measuring, and enforcing per-workflow spend on language-model APIs. Without it, costs grow with usage in a way that surprises everyone. With it, AI cost behaves like a regular line item.
The control layers.
- Per-workflow token budget enforced at the gateway. Hard cap, fails the call before it hits the model.
- Model router so the right query reaches the right model — small-and-fast for easy work, large-and-expensive only when needed.
- Semantic cache so near-duplicate queries skip the model call entirely.
- Provider prompt caching (Anthropic, OpenAI, Gemini) so identical prompt prefixes don't re-process the same context.
- Per-tenant rate limits so no single customer or integration runs away with the budget.
What to measure.
Cost per request, broken down by workflow. p95 cost per request — the tail matters more than the average. Cost-per- successful-business-outcome (per draft accepted, per ticket resolved, per deal advanced). Without the third metric, AI cost looks high in isolation; with it, the conversation becomes ROI rather than budget defense.
Common anti-patterns.
- No budget at all. Cost grows with usage and surprises the finance team quarterly.
- Budget without observability. When a regression blows the budget, nobody knows which prompt or retrieval change caused it.
- Optimizing the wrong layer. A 10% reduction in token count is worth less than a 10% reduction in unnecessary model calls. Routing and caching beat prompt-trimming.
Frequently asked.
- What is AI cost control?
- AI cost control is the discipline of budgeting, measuring, and enforcing per-workflow spend on language-model APIs. It combines per-workflow token budgets, model routing, semantic caching, provider prompt caching, and per-tenant rate limits into a predictable cost ceiling.
- What's the single highest-leverage thing for cutting AI cost?
- A model router. On workloads with mixed query difficulty, routing easy queries to small fast models and reserving large models for hard queries cuts overall cost 60–80%. Far higher leverage than trimming prompts or shortening responses.
- How do I plan an AI budget?
- Measure the current per-workflow cost distribution over at least a week. Take the p95 and add a 20% margin. Enforce that as a token-budget cap at the gateway. Track cost-per-successful-business-outcome alongside raw cost — the ratio is what tells you whether the workflow is worth running at all.
- Should I move to cheaper models to control cost?
- Sometimes, but route-don't-replace. Moving everything to a smaller model usually hurts quality on a meaningful fraction of queries. Routing easy queries to a smaller model and keeping the large model for hard queries captures the cost win without the quality cost.