Building an AI Side Project That Actually Ships — Lessons from Shipping 3 MVPs

Description and use

The author shares how to ship three AI MVPs by simplifying code, using paid APIs, and setting strict budgets, turning ambitious ideas into functional, low‑cost products.

Problem

Most AI side projects fail due to overambitious scope, costly infrastructure, and poor product focus, leading to silent death before user adoption.

Outcome

Three MVPs were shipped in under a week each, with real users and modest revenue, by focusing on product value, simplifying infrastructure, and using paid APIs over self‑hosted models.

Workflow

• 1. Define a narrow, user‑centric problem (e.g., auto‑tag RSS articles).
• 2. Prototype with minimal code (single Python file).
• 3. Replace heavy local LLMs with paid APIs (e.g., GPT‑4o‑mini).
• 4. Remove unnecessary infrastructure (queues, Docker, GPU).
• 5. Iterate on prompts and schemas to reduce hallucinations.
• 6. Set hard budgets for build and run costs.
• 7. Deploy via simple cron or serverless functions.
• 8. Monitor usage and adjust vendor if pricing shifts.

Evidence

I shipped three actual MVPs—all with real users, all with real (if modest) revenue.

Source

The shipped version took 2 hours to write and costs about $0.03 per run.

Source

I chose the API. The API path costs $100 for the actual usage, and zero maintenance.

Source