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Aspiring founders and early-stage Startup CEOs planning AI SaaS products cannot accurately model how LLM API call costs scale proportionally with user growth. After launch, when MAU hits 10,000, they face unexpected monthly API costs of several thousand dollars, frequently forcing pivots or shutdowns. The opportunity cost of investing 3–6 months of development without understanding the cost structure is enormous.
Input expected MAU, per-feature API call frequency, token consumption, and infrastructure specs to simulate monthly cost curves and break-even points. Visualizes cost comparisons across models (OpenAI/Anthropic/local LLMs) and savings from caching and batching strategies, helping founders make Go/No-Go decisions before committing to development.
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |