How to Calculate AI Application Costs

Estimating what an AI application will cost to run comes down to four questions: how many people will use it, how much will each interaction cost, does it need retrieval, and what infrastructure does it need beyond the model calls?

1. Start with usage, not pricing

Cost is driven by volume before it's driven by rate. Estimate monthly active users and requests per user per month first — a rough number is fine, since you can adjust it later.

2. Estimate tokens per request

Input tokens include the system prompt, the user's message, conversation history, and any retrieved context. Output tokens are whatever the model generates. See what LLM tokens are if you need to estimate these from example text.

3. Apply model pricing

Multiply tokens by the selected model's per-million-token rate — see how LLM pricing works. This is usually the largest cost driver for high-volume applications.

4. Add retrieval cost, if applicable

If the application uses RAG, add embedding cost (a function of your document set size and re-indexing frequency) and the extra input tokens retrieved context adds to every request. See what RAG is for the full picture.

5. Add infrastructure

Vector database, storage, caching, and hosting costs vary too much by vendor to estimate generically — use your own quote or a representative low/expected/high range rather than a single guessed number.

Put it together

The AI Budget Planner walks through exactly these steps and computes the total, with a cost breakdown and a low–high range rather than one number presented as exact. The AI Project Cost Calculator runs the same calculation in a single form if you don't need the guided flow.