ai-cost-check

To estimate AI feature costs before building, calculate per-request and per-user monthly expenses by projecting token sizes and API usage across different models. This upfront economics modeling computes marginal costs at scale to determine financial viability before development.

AI cost as a percentage of revenue is a financial projection comparing your total AI serving expenses against expected income. You need this metric during pre-launch product planning to confirm user willingness to pay can sustainably cover marginal scaling costs.

Calculate per-user AI costs at scale by simulating expenses for 10k or more users, comparing model selections like GPT-4 Turbo versus GPT-3.5. This cost modeling projects monthly spend variations based on different token sizes and usage frequencies.

Yes, you can challenge the premise of proposed AI features during feasibility reviews by asking tough validation questions. This process confirms actual user need and willingness to pay before you invest in development or attempt expensive financial projections.

The best ways to optimize AI feature costs include implementing caching strategies, selecting cheaper models, and applying prompt optimization. These adjustments classify unsustainable features as viable by reducing per-request token usage and overall scaling expenses.

You should avoid building an AI feature when cost modeling classifies it as unsustainable, meaning optimizations like caching and cheaper models cannot reduce marginal costs below your revenue threshold. Premise validation also helps reject features lacking user willingness to pay.