Common Mistakes in AI-Built SaaS Apps

AI-built SaaS apps tend to fail in remarkably consistent ways. The mistakes are not random one-offs; they cluster in the same few places, because they all share a root cause. AI-assisted building optimizes for what is visible, and these are precisely the things that are invisible until a real user hits them. The upside of that consistency is that once you know the pattern, the mistakes are easy to look for.

Here are the ones that come up again and again.

The most dangerous mistake is missing per-user data isolation. With a single test user, every query looks correct, so the gap never surfaces in development. In production, a forgotten filter returns someone else's rows. This is why row-level security matters so much, covered in Supabase RLS for SaaS, and why it should be the default rather than a later addition.

A close second is billing that flips access optimistically instead of reconciling against the payment provider. The result is users who paid but cannot access what they bought, or users who cancelled but still can. It works in the demo because the demo never exercises the unhappy paths, which is the same dynamic behind why vibe-coded SaaS apps break at launch.

The rest of the usual suspects all share the "looked fine in the demo" trait:

• No handling for failed requests, so users hit blank screens
• Inputs trusted without validation, so bad data slips in
• Security defaults assumed rather than verified
• Auth checks in the UI but not on the server

Each is invisible with one cooperative test user and very visible with real, uncooperative traffic. The prototype vs production app checklist is a good way to audit for them.

All of these mistakes pass the demo and fail the launch, because the demo tests the happy path and launch tests everything else. The most effective prevention is to give the agent a sound foundation and explicit rules, so it is not responsible for reinventing these systems. That is the role of AGENTS.md guardrails: the correct patterns already exist, and the agent stays on them.

aSaaSin removes the most common AI-built mistakes by making the right thing the default: data isolated per user, billing reconciled correctly, auth enforced on the server, and validation at the boundary. The agent builds on top of correctness instead of around it.

If you build with AI tools, see pricing or explore the docs.