You set a $2,000 monthly budget for OpenAI. You're confident it's enough. Then you launch a new feature that uses GPT-4 for code generation. Three days later, you've spent $1,800. You're over budget, and the month just started.
This isn't a failure of budgeting. It's a fundamental mismatch between how AI APIs are priced and how budgets work.
Why AI Costs Are Unpredictable
AI API costs are usage-based. You pay per token, per request, or per minute. There's no fixed capacity like cloud compute. If usage doubles, costs double. If usage triples, costs triple.
This makes AI costs volatile. A single feature launch can spike costs overnight. A bug that causes retries can multiply costs. A viral product that drives more usage can explode costs.
Cloud costs are more predictable. You provision capacity, and costs scale with capacity. If you need more, you scale up. If you need less, you scale down. Capacity changes are gradual.
AI costs change instantly. One request can cost $0.01 or $10 depending on model, context length, and tokens generated. There's no gradual scaling.
Why Budgets Fail
Budgets assume predictability. They assume you can set a limit and stay within it. But AI usage is unpredictable. You can't set a $2,000 budget and expect to stay within it if usage can triple overnight.
Budgets also assume you can control spending. But AI costs are driven by product usage. If your product is successful, usage grows, and costs grow. You can't cap costs without capping success.
This is why AI budgets fail. They're static limits in a dynamic system. They tell you when you're over budget, but they don't tell you why or what to do about it.
What Works Instead
Instead of budgets, use forecasts and daily monitoring.
Forecasts tell you where you're heading. "Based on current pace, you'll spend $3,500 this month." This is more useful than a budget because it accounts for trends and current usage.
Daily monitoring tells you when something changes. "Today's spend is 40% above normal. Here's why." This catches problems early, before they become expensive.
Together, forecasts and daily monitoring give you:
- Awareness: Where are costs heading?
- Early warning: Is something wrong right now?
- Context: Why did costs change?
Budgets give you none of these. They just tell you when you're over a limit you probably can't control anyway.
The Token Problem
AI API costs are also hard to predict because of tokens. Tokens aren't words. They're sub-word units that vary by model and language. A 100-word prompt might be 120 tokens or 150 tokens depending on the model.
This makes cost estimation difficult. You can't estimate costs from user requests. You have to measure actual token usage, which you only know after the fact.
This is why daily monitoring matters. You can't predict token usage accurately, but you can detect when it changes unexpectedly.
The Model Problem
Different models cost different amounts. GPT-4 is expensive. GPT-3.5-turbo is cheap. Claude Opus is expensive. Claude Haiku is cheap.
If you switch models, costs change. If you use multiple models, costs are unpredictable. A single feature might use GPT-4 for complex tasks and GPT-3.5 for simple tasks, making costs hard to forecast.
This is another reason budgets fail. Model choices are product decisions, not cost decisions. You can't optimize costs without optimizing product quality.
What to Do
Don't rely on budgets for AI costs. Instead:
- Forecast monthly spend based on current pace and trends
- Monitor daily for unexpected changes
- Set alerts when daily spend exceeds normal by a threshold (e.g., 30%)
- Investigate anomalies when alerts fire
This gives you control without limiting growth. You catch problems early, understand why costs changed, and make informed decisions.
Budgets are for predictable costs. AI costs aren't predictable. Use forecasts and monitoring instead.