Build a SaaS MVP with AI

"Build a SaaS with AI" is the most over-promised phrase on the internet right now. Here's the honest version: AI can get a solo builder from idea to a deployed, working MVP in a day or two — genuinely, for real — but only if you can read code well enough to review what it writes. This isn't "no code." It's "less code, faster, with a very fast junior developer who needs supervision." Set that expectation and the workflow below delivers; ignore it and you'll ship something you can't debug.

The reason planning comes first is scope creep. Left to its own devices, an AI will happily build you fourteen features and finish none of them well. A tight plan — max five features — is what keeps the project shippable.

What you'll walk away with

• A realistic MVP plan: a feature list, data model, and tech stack for a solo builder
• A working app with the core features actually implemented
• A live, publicly accessible URL
• A codebase you understand well enough to keep building on

Step by step, with the judgment that matters

1. Plan tightly with ChatGPT. Describe the product and ask for an MVP feature list capped at five, a simple data model, and a recommended stack for a solo builder. The cap is the whole point — the difference between a shipped MVP and a permanent side project is ruthless scope. Everything not on the list is version two.

2. Build with Cursor, one feature at a time. The prompt that saves you is "implement the core features one at a time, asking me to confirm each step." Do not let it generate the whole app in one shot — you won't be able to review it, and when something breaks you won't know where. Confirm each feature, run it, then move on. When it needs a database, ask it to wire up a hosted Postgres provider like Supabase or Neon during scaffolding.

3. Deploy with Vercel. This is the easy part. Push to a Git repo, import it into Vercel, set your environment variables, and deploy. Vercel auto-detects most frameworks with zero config. The thing people forget is the environment variables — the app works locally and then 500s in production because a key isn't set.

A real run

For a simple habit-tracker SaaS, ChatGPT scopes it to five features (auth, create habit, daily check-in, streak view, basic settings), a three-table data model, and a Next.js + Supabase stack. In Cursor you build each feature, confirming as you go, catching one bug where streaks reset at the wrong timezone. Supabase handles auth and the database; Vercel deploys it. Two focused days, and it's live at a real URL.

Where it goes wrong

• "Do I need to code?" Basic familiarity is close to required for reviewing and debugging Cursor's output. It lowers the bar enormously versus scratch, but it isn't zero.
• It works locally, breaks in production. Almost always an unset environment variable on Vercel. Check the deploy logs — they'll name the missing key.
• The AI painted itself into a corner. It generated too much at once and now can't fix its own tangle. Revert to the last working commit and rebuild that feature in smaller steps.

The honest cost

$0 to about $20. Cursor, Vercel (Hobby), and Supabase all have real free tiers that cover an MVP. You'd pay for Cursor Pro ($20/mo) if you burn through the free fast-request limit during heavy build sessions, or upgrade Supabase/Vercel only once you have real users.