Most AI app ideas look cheap in a demo and become expensive at scale. A prototype with 100 users may cost almost nothing. The same product with 10,000 active users, long chat history, large prompts and expensive models can become a serious monthly bill. This calculator estimates the real LLM cost before you build — users, calls, tokens, model choice and simple optimisations like caching or batching.
Cheapest models for this workload
Same usage, every model ranked by monthly cost — bar length shows relative cost.
| Model | Cost / month | Per user |
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Build your AI app fast with v0 →
Referral link — v0 by Vercel turns a prompt into a working app UI. Then estimate its running cost above.
How to read this
Your AI bill is mostly users × calls × tokens × price. The biggest levers, in order: which model (small vs frontier is often 20–50× cheaper), output length (output costs 3–5× input — cap it), and input length (trim system prompts and retrieved context). Caching repeated context and batching non-urgent jobs cut more.
Picking a model? Compare them head-to-head on the AI API cost calculator, or read GPT vs Claude vs Gemini. Building RAG or a chatbot? Those have their own cost drivers (embeddings, vector DB, retrieval size) — guides coming. If your app is an agent loop rather than independent calls, read how agent context accumulation works — resending growing history each step changes the math this estimator doesn't model. For the pricing mechanics behind the numbers, see how LLM API pricing works.
Frequently asked questions
Why does my AI app cost estimate change so much with output length?
Output tokens are billed 3-5x higher than input tokens on almost every model, and the model has to generate every output token one at a time, so a chatty model with long replies can cost more than a terse one even with identical input. Capping max output length is one of the cheapest ways to cut an AI app bill.
Is the model my app uses in testing the one I should budget for at scale?
Not necessarily. Many teams prototype on a frontier model for quality, then swap to a cheaper model (or route easy requests to a small model and hard ones to a large one) once usage grows, because the cost gap between frontier and budget models is often 20-50x for similar quality on routine tasks.
What is the single biggest cost lever for an AI app?
Model choice, in most cases — switching from a frontier model to a right-sized smaller model typically cuts cost far more than any prompt optimisation. After that, output length and trimming the context you resend on every call (system prompt, retrieved documents, chat history) matter most.
Does this estimator account for context that grows over a conversation?
No — it uses a fixed average input/output per call, which is a reasonable approximation for independent requests (like a classifier or one-shot assistant) but understates cost for multi-turn chat or agent loops, where each step resends the growing history. For that pattern, see how agent context accumulation works.