Estimate Token and Infrastructure Cost for One Active App User

How to Use This Tool

Translate sessions and tokens into per-user variable cost before choosing a plan price. Calculate AI app feature cost per active user from sessions, tokens, editable token price and per-user infrastructure.

The failure AI Feature Cost/User is designed to catch

A low cost per generation can still consume subscription margin when engaged users trigger the feature many times each month. The boundary is the job stated in Estimate Token and Infrastructure Cost for One Active App User; AI Feature Cost/User is not intended to score or transform a different workflow.

The AI Feature Cost/User input contract

The fields used for this specific operation are AI sessions per active user, Tokens per session, Price per million tokens, Other infrastructure per user. Keep the source values beside the AI Feature Cost/User result, because replacing the original would remove the evidence needed to reproduce or reverse the operation.

• For AI Feature Cost/User, AI sessions per active user starts at 20 in the worked case; replace that example with the matching source value.
• For AI Feature Cost/User, Tokens per session starts at 2500 in the worked case; replace that example with the matching source value.
• For AI Feature Cost/User, Price per million tokens starts at 5 in the worked case; replace that example with the matching source value.
• For AI Feature Cost/User, Other infrastructure per user starts at 0.05 in the worked case; replace that example with the matching source value.

Worked result for AI Feature Cost/User

The executable case called Default decision scenario expects out: $0.30. Verify that observation before entering real material, and then change one AI Feature Cost/User field at a time so an unexpected direction or formatting change can be traced to a specific input.

Reading the AI Feature Cost/User output

It combines ai sessions per active user, tokens per session, price per million tokens and other infrastructure per user into one decision result using the formula explained on the page. Apply that answer only when AI sessions per active user, Tokens per session, Price per million tokens, Other infrastructure per user describe the same scope and format as the worked operation. If the source uses different units, quoting, nesting, timing or account rules, a plausible-looking AI Feature Cost/User output is not sufficient validation.

Assumptions attached to AI Feature Cost/User

• AI Feature Cost/User assumes that all inputs describe the same unit or reporting period unless the field explicitly says otherwise.
• AI Feature Cost/User assumes that the model includes only the four visible inputs and does not infer hidden platform charges.
• AI Feature Cost/User assumes that the page never calls an AI model; token ratios, prices, limits and observed rates are user-supplied planning assumptions.

If one of these AI Feature Cost/User assumptions is false, keep the result as a diagnostic rather than production or decision data, and choose an implementation that explicitly supports the missing rule.

Evidence maintained for AI Feature Cost/User

The recorded reference is NIST — AI Risk Management Framework. Reopen that source when the definition, format, fee or policy behind AI Feature Cost/User changes; private configuration and downstream acceptance still have to be checked in the user's own system.

Where AI Feature Cost/User runs

The named operation executes in browser JavaScript without an ecech calculation API. For AI Feature Cost/User, local execution reduces transmission but does not control browser extensions, device security or the destination where the result is pasted, so sensitive inputs still require the user's normal handling rules.