AI economics
AI Feature Attribution
How much of your ARR is actually driven by your AI features? Net of infra cost, with cohort gross margin.
Result
1,500 AI-feature users pay $465.12 a year against a base ARPU of $372.09 for the other 3,500. Annual AI infra cost: $96,000.
ARR attribution split
Gross AI-attributable ARR vs net (after infra cost).
How to use it
- Enter your total ARR, the fraction of users who use the AI features, the ARPU uplift those users show, your monthly AI infra cost, the churn difference for AI users, and your total user count. The tool separates your user base into AI and non-AI cohorts and back-solves the base ARPU, so it isolates how much of your revenue the AI features actually earn rather than letting AI take credit for revenue it did not move.
- Read the ARR attributable to AI (the ARPU premium times the AI users), the net figure after annual AI infra cost, the AI cohort gross margin, and the ARR kept by lower churn; the line under the tiles shows the AI and non-AI user counts, both ARPUs and the annual infra cost. The net AI-attributable ARR is the honest number, because it credits AI only for the incremental revenue and then subtracts what the AI infrastructure costs to deliver it.
- Use the net figure to answer whether the AI features pay for themselves. It is common for an AI feature to show an impressive ARPU uplift that mostly evaporates once the infra cost is netted out, and this tool surfaces that gap directly instead of letting a gross uplift number justify the spend on its own.
- Read the ARR kept by lower churn as a second source of value, reported separately so it is not double-counted. If AI users churn meaningfully less, the retained revenue can rival the direct ARPU uplift, which reframes the AI feature from a revenue add-on into a retention investment. Test a conservative uplift and a higher infra cost together to see how robust the attribution is before you defend the feature's budget.
- Re-run as AI adoption spreads through your base, as infra costs change, or as you measure real cohort churn rather than estimate it. Pair this with the agent cost per validated customer tool for the acquisition-side economics, so you see both what AI features cost to run and what they earn across the full customer lifecycle.
Related calculators
- Run the numbers Agent Cost Per Validated Customer
AI and infra spend per activated retained user, with gross margin at any subscription price.
- Run the numbers Runway With AI Cost Shock
Stress-test runway against an LLM vendor price hike with break-even revenue trajectory.
- Run the numbers Free Trial to Paid Target
Reverse-solve trial→paid conversion rate from revenue target, signups, and ARPU.
- Run the numbers AI vs Human Support Cost
Compare AI-first and human-only support cost with token spend and escalation overhead.
Articles
- 14 min read Build vs Buy in 2026: When the LLM API Kills Your Moat
Build vs buy in 2026: which AI product features survive when Anthropic and OpenAI ship the same thing, and why distribution moats outlast a model wrapper.
- 13 min read Pricing AI Features: Cost-Plus vs Value-Based
Pricing AI features: cost-plus breaks because token cost per customer can vary 100-fold; a per-seat base plus a usage cap holds margin and room for value.
- 11 min read SaaS Churn: Cohort vs Aggregate vs Net Dollar
SaaS churn three ways: aggregate churn, cohort churn and net dollar retention give different answers from one dataset. The math, and which to use when.
More in AI product economics
Understand the costs, margins, and pricing of building AI-powered products.
- Startup Cost Estimator
- Profit Margin Calculator
- Profit Margin / Markup / Discount Calculator
- SaaS Pricing Strategy Calculator
Use it from code
The same calculation as a JavaScript module you can import. It runs where you import it, with no request, key or rate limit.
import { compute } from "https://aibizhub.io/engines/ai-feature-attribution.js"; Input and output contract, TypeScript types and the guide for agents.