# Build, Buy, Fine-Tune, or Orchestrate?

**Track:** Building the AI-Native Product — AI for Entrepreneurship — complete (29)
**Framework / surface:** venture strategy
**Level:** Intermediate
**Prerequisites:** The AI-Native Stack

**In one line:** The decision ladder from foundation API to custom model — climbed only where ownership creates unreproducible value.

## Theory, aesthetics & inspiration

The stack describes the layers; the entrepreneurial decision is where on the ladder to own. The rungs run from renting a foundation API, to running open weights, to grounding with retrieval and context, to wiring tools, to orchestrating workflows and agents, to fine-tuning on proprietary data, to distilling capability into cheap specialized models, to — rarely, and expensively — training something custom. Each rung upward trades capital and maintenance burden for control, margin, and differentiation, and each is worth climbing only where it passes one test: does this produce customer value that somebody else cannot economically reproduce? Renting the frontier is usually right for raw capability, because the labs' vendor competition does your R&D; owning becomes right where proprietary data makes a fine-tune genuinely yours, or where distillation converts a proven expensive behavior into margin at scale. And the ladder moves: the exponential regularly turns yesterday's fine-tuning project into today's prompt, so date every ownership decision and re-decide it annually.

**Founder question:** What are you building that somebody else cannot economically reproduce?