Why model three tiers instead of one number?

Costs that look trivial at 10K requests a day become a major budget line at 1M. Seeing all three tiers side by side reveals where your margin breaks and forces a caching, routing, or pricing decision before you scale into it.

How is per-request cost calculated?

It multiplies your prompt tokens by the model's input price and completion tokens by its output price, both per million tokens, then sums them. That per-request cost is multiplied across each daily volume tier and extended to monthly and annual figures.

What does the cost-to-revenue ratio tell me?

It is your AI cost divided by revenue per request. Below roughly 30 percent you usually have healthy margin; above 100 percent each request loses money. It is the fastest sanity check that your unit economics survive scale.

Where does pricing come from?

The tool uses representative public per-million-token list prices. Vendors change prices and offer volume or batch discounts, so confirm against the provider's current pricing page before committing budget.

At-Scale AI Cost Planner

At-scale AI cost planner

A model that costs a few dollars a day in a prototype can cost six figures a year in production. This planner takes one request’s token profile and projects it across 10,000, 100,000, and 1,000,000 requests a day — or a custom volume — so you can see daily, monthly, and annual spend at each tier and, crucially, what fraction of your revenue it eats.

How it works

You enter prompt tokens, completion tokens, and a model. The tool computes per-request cost from the model’s input and output prices (per million tokens), then multiplies that across each scale tier and extends it to monthly (×30) and annual (×365) figures. Adding revenue per request layers on a cost-to-revenue ratio and gross margin, so the table tells you not just what AI costs but whether the business math holds at volume. Everything runs in your browser.

Tips and notes

Use realistic token counts — measure a few real requests rather than guessing, since output tokens usually dominate cost and are easy to underestimate. If the 1M tier shows your margin collapsing, that is the signal to introduce a cheaper model for easy requests, prompt caching for repeated input, or a higher price. Batch APIs and volume discounts can cut the at-scale tiers materially, so treat list-price figures as the conservative ceiling. Always confirm live pricing before sizing a large deployment.