What inputs does the AI Product Margin Calculator need?

It takes 8 inputs: subscription_price, avg_api_calls_per_day, avg_input_tokens, avg_output_tokens, input_cost_per_million, output_cost_per_million, hosting_cost_per_user (default 0), other_per_user_costs (default 0). Outputs returned: apiCostPerUser, totalCostPerUser, grossMarginPerUser / grossMarginPercent, apiSharePercent / dominantCostDriver / scaleTiers.

Where does the AI Product Margin Calculator get its benchmark data?

Reference data is sourced from: Anthropic pricing (as of 2026-04-24); OpenAI pricing (as of 2026-04-24); Google Gemini pricing (as of 2026-04-24).

What can the AI Product Margin Calculator not tell me?

Known limitations: Prompt caching and context-window reuse can cut effective cost by 30–80%. The tool does not infer this — users must reduce the avgInputTokens figure to reflect it. Does not model failure-mode costs: retries, long-running tool calls, or regenerations inflate token counts in production. Pricing is a snapshot; if the as-of date is stale, consult the live vendor page before making a pricing decision.

Methodology: AI Product Margin Calculator

1. Scope

Calculates per-user contribution margin for an AI-powered product by netting subscription revenue against token costs, hosting, and other per-user expenses. It does not model enterprise negotiated rates or prompt-caching discounts.

2. Inputs and outputs

Inputs

subscription_price number (currency/mo)
avg_api_calls_per_day number

Per user. Multiplied by 30 for a monthly call count.
avg_input_tokens number (tokens/call)
avg_output_tokens number (tokens/call)
input_cost_per_million number (currency)

List price per 1M input tokens.
output_cost_per_million number (currency)

List price per 1M output tokens.
hosting_cost_per_user number (currency/mo) default: 0
other_per_user_costs number (currency/mo) default: 0

Outputs

apiCostPerUser

Per-call token cost × calls per day × 30.
totalCostPerUser

apiCostPerUser + hosting_cost_per_user + other_per_user_costs.
grossMarginPerUser / grossMarginPercent

subscription_price − total cost, and that over price.
apiSharePercent / dominantCostDriver / scaleTiers

API share of cost, the biggest driver, and revenue/cost/margin at 100 / 1K / 10K users.

Engine source: src/lib/ai-product-margin-calculator/engine.ts

3. Formula / scoring logic

cost_per_call    = (input_tokens * input_cost_per_m + output_tokens * output_cost_per_m) / 1_000_000
api_cost_per_user = cost_per_call * avg_api_calls_per_day * 30
total_cost       = api_cost_per_user + hosting_cost_per_user + other_per_user_costs
gross_margin     = (subscription_price - total_cost) / subscription_price

4. Assumptions

Tokens are per CALL, and the monthly cost multiplies by avg_api_calls_per_day × 30 — not a flat monthly token total.
Users are priced uniformly. Tiered or usage-metered pricing requires running the tool once per tier.
Token costs are list prices; negotiated commitments or prompt-caching discounts are applied manually on top.

5. Data sources

Anthropic pricing as of 2026-04-24
OpenAI pricing as of 2026-04-24
Google Gemini pricing as of 2026-04-24

6. Known limitations

Prompt caching and context-window reuse can cut effective cost by 30–80%. The tool does not infer this — users must reduce the avgInputTokens figure to reflect it.
Does not model failure-mode costs: retries, long-running tool calls, or regenerations inflate token counts in production.
Pricing is a snapshot; if the as-of date is stale, consult the live vendor page before making a pricing decision.

7. Reproducibility

Input
subscription_price = $29/mo, avg_api_calls_per_day = 10, avg_input_tokens = 400, avg_output_tokens = 200, input_cost_per_million = $3, output_cost_per_million = $15, hosting_cost_per_user = $0.50, other = $0.

Expected output
cost per call = $0.0042, apiCostPerUser = $1.26 (× 300 calls/mo), totalCostPerUser = $1.76, grossMarginPercent = 93.9.

8. Change log

2026-04-24 methodology page first published. Pricing snapshot 2026-04-24.

Worked example

Run live against the same engine this site ships (/engines/ai-product-margin-calculator.js). The inputs and outputs below are recomputed on every build and independently re-verified in CI — they are never hand-authored.

Input

tool: ai_product_margin_calculator
subscription_price: 29
avg_api_calls_per_day: 20
avg_input_tokens: 500
avg_output_tokens: 1000
input_cost_per_million: 2.5
output_cost_per_million: 10
hosting_cost_per_user: 0.5
other_per_user_costs: 0.25

Output

apiCostPerUser: 6.75
totalCostPerUser: 7.5
grossMarginPerUser: 21.5
grossMarginPercent: 74.1
apiSharePercent: 90
dominantCostDriver: AI API
scaleTiers[0].users: 100
scaleTiers[0].totalRevenue: 2900
scaleTiers[0].totalCost: 750
scaleTiers[0].totalProfit: 2150
scaleTiers[0].marginPercent: 74.1
scaleTiers[1].users: 1000
scaleTiers[1].totalRevenue: 29000
scaleTiers[1].totalCost: 7500
scaleTiers[1].totalProfit: 21500
scaleTiers[1].marginPercent: 74.1
scaleTiers[2].users: 10000
scaleTiers[2].totalRevenue: 290000
scaleTiers[2].totalCost: 75000
scaleTiers[2].totalProfit: 215000
scaleTiers[2].marginPercent: 74.1
insight: AI API is 90% of your per-user cost ($6.75/mo). At 10K users, that is $67500/month on API alone. Caching responses or using a tiered model approach could significantly improve margins.

Frequently asked questions

What does the AI Product Margin Calculator calculate?: Calculates per-user contribution margin for an AI-powered product by netting subscription revenue against token costs, hosting, and other per-user expenses. It does not model enterprise negotiated rates or prompt-caching discounts.
What inputs does the AI Product Margin Calculator need?: It takes 8 inputs: subscription_price, avg_api_calls_per_day, avg_input_tokens, avg_output_tokens, input_cost_per_million, output_cost_per_million, hosting_cost_per_user (default 0), other_per_user_costs (default 0). Outputs returned: apiCostPerUser, totalCostPerUser, grossMarginPerUser / grossMarginPercent, apiSharePercent / dominantCostDriver / scaleTiers.
What formula does the AI Product Margin Calculator use?: The exact computation is: cost_per_call = (input_tokens * input_cost_per_m + output_tokens * output_cost_per_m) / 1_000_000; api_cost_per_user = cost_per_call * avg_api_calls_per_day * 30; total_cost = api_cost_per_user + hosting_cost_per_user + other_per_user_costs; gross_margin = (subscription_price - total_cost) / subscription_price
Can I verify the AI Product Margin Calculator with a worked example?: Yes. With subscription_price = $29/mo, avg_api_calls_per_day = 10, avg_input_tokens = 400, avg_output_tokens = 200, input_cost_per_million = $3, output_cost_per_million = $15, hosting_cost_per_user = $0.50, other = $0. the tool returns cost per call = $0.0042, apiCostPerUser = $1.26 (× 300 calls/mo), totalCostPerUser = $1.76, grossMarginPercent = 93.9.
Where does the AI Product Margin Calculator get its benchmark data?: Reference data is sourced from: Anthropic pricing (as of 2026-04-24); OpenAI pricing (as of 2026-04-24); Google Gemini pricing (as of 2026-04-24).
What can the AI Product Margin Calculator not tell me?: Known limitations: Prompt caching and context-window reuse can cut effective cost by 30–80%. The tool does not infer this — users must reduce the avgInputTokens figure to reflect it. Does not model failure-mode costs: retries, long-running tool calls, or regenerations inflate token counts in production. Pricing is a snapshot; if the as-of date is stale, consult the live vendor page before making a pricing decision.