LLM API Budget Forecaster
12-month LLM cost forecast with best, expected, and worst-case growth scenarios. Built for engineering leaders and finance planning.
| Month | Expected users | Expected cost | Best case | Worst case |
|---|---|---|---|---|
| Month 1 | 1,000 | $4.25 | $4.25 | $4.25 |
| Month 2 | 1,150 | $4.89 | $5.53 | $4.46 |
| Month 3 | 1,322 | $5.62 | $7.18 | $4.69 |
| Month 4 | 1,521 | $6.46 | $9.34 | $4.92 |
| Month 5 | 1,749 | $7.43 | $12.14 | $5.17 |
| Month 6 | 2,011 | $8.55 | $15.78 | $5.42 |
| Month 7 | 2,313 | $9.83 | $20.51 | $5.70 |
| Month 8 | 2,660 | $11.31 | $26.67 | $5.98 |
| Month 9 | 3,059 | $13.00 | $34.67 | $6.28 |
| Month 10 | 3,518 | $14.95 | $45.07 | $6.59 |
| Month 11 | 4,046 | $17.20 | $58.59 | $6.92 |
| Month 12 | 4,652 | $19.77 | $76.17 | $7.27 |
Prices verified 2026-08-20 · Reduce costs with model routing · Caching savings
How to Use
Set your current baseline
Enter current users, average tokens used per user per month (input and output separately).
Choose your model
Select the LLM you're using or planning to use. Prices are live from the weekly-verified dataset.
Set growth scenarios
Enter expected, best-case, and worst-case monthly growth rates. All three are forecasted for 12 months.
Export for finance
The 12-month table with three scenarios is what a finance team needs for budget planning. Share the URL to preserve your inputs.
Why AI budget forecasting is hard
LLM costs scale with users AND usage depth. A 2× user increase doesn't mean 2× cost if users engage more (more tokens per session) or you add new AI features. This forecaster models linear user growth — adjust for known product changes. The three-scenario format (best/expected/worst) is the standard finance teams expect for budget approval.
How to Cut This Cost
Lock in annual commit discounts with your provider (OpenAI, Anthropic, Google) for large spend — typically 15–25% discount on top of base pricing.
Model-route routine tasks to cheaper models. If 70% of your queries are simple, routing them to GPT-5 mini cuts that 70% by 85–90%.
Implement prompt caching from day 1. At scale, the static prefix (system prompt + docs) is often 40–60% of total input tokens — caching cuts that to 4–6%.
FAQ
Have more questions? Contact us
Last verified: 2026-08-20 · methodology · data sources