LLM API Budget Forecasting — 12-Month Planning Guide
LLM costs compound: more users, longer conversations, new features. This guide covers how to build a 12-month cost forecast, what growth assumptions to use, and what to watch for as you scale.
The base model
Monthly cost = users × requests_per_user × tokens_per_request × price_per_token. Each variable grows independently. A product with 20% monthly user growth, 10% growth in usage per user, and stable token counts compounds to 2.4× cost in 6 months — without any pricing changes.
Growth assumptions for forecasting
Be conservative: assume 15–25% monthly growth in users for successful products. Assume 5–10% growth in tokens/request as features expand. Build best/expected/worst scenarios at +50% and -50% of your growth assumption. For enterprise sales: usage is lumpy (pilot → rollout spikes).
Cost cliffs to plan for
1. Context growth: more features = longer system prompts = higher cost per request. 2. Agentic loops: each step compounds. 3. Rate limits at growth: hitting rate limits forces architecture changes. 4. Deprecation: models get deprecated, requiring migration work and potential quality changes. Track deprecation dates in advance.
When to renegotiate pricing
At $1,000+/month with any provider: request a committed use discount. At $5,000+/month: dedicated account rep and custom pricing are available from Anthropic, OpenAI, and Google. Enterprise agreements unlock: lower per-token prices, SLAs, BAAs, dedicated capacity, higher rate limits.
Related calculators
Related guides
Frequently Asked Questions
What is a typical LLM API cost at different scale stages?
Side project (1K users): $20–100/month. Early startup (10K users): $200–1,000/month. Growth stage (100K users): $2,000–10,000/month. Scale stage (1M users): $20,000–100,000/month. Optimize aggressively before scale — 80% of production cost comes from 20% of request types.
Guide updated 2026-08-20. Pricing data verified weekly. See methodology · sources.