From Vibe Code to Production: What Actually Breaks When AI Writes Your SaaS

You built your MVP with Cursor, Lovable, Bolt, or Claude. It works on your machine. The demo looks great. Maybe you even have a few early users poking around. Then someone tries to do something slightly unexpected and the whole thing falls apart in a way you cannot reproduce.

This is not a hypothetical. Roughly 25% of recent Y Combinator startups used AI-generated code in their initial build. The pattern that follows is consistent enough to be predictable: the product ships fast, gets traction, and then hits a wall that has nothing to do with features.

The 9 things that break first

1. Authentication logic that Claude hallucinated

AI models are confident about auth patterns and frequently wrong about edge cases. The most common issue: session handling that works for one user at a time but fails when two users hit the same endpoint within a few seconds. JWT refresh logic is another frequent problem - the AI generates a flow that looks correct but silently drops the user's session under specific timing conditions.

2. No row-level security

This is the one that kills companies. Vibe-coded apps almost never implement proper data isolation between users or tenants. User A can see User B's data by changing an ID in the URL. It is not a bug the AI introduced - it is a guard the AI never thought to add. If you are on Supabase or PostgreSQL, row-level security policies are the fix. If you are on a different stack, you need middleware that enforces tenant boundaries on every query.

3. No soft deletes

AI-generated code uses hard deletes by default. A user clicks "delete" and the record is gone from the database permanently. No recovery, no audit trail, no undo. This becomes a real problem the first time a paying customer accidentally deletes something important and you have to tell them it is gone forever.

4. Error handling that swallows failures

The most dangerous pattern in vibe-coded apps: try { ... } catch (e) { } with an empty catch block. The AI generates these constantly. Your app looks like it is working, but errors are being silently ignored. Payments fail without notification. Webhooks drop without retry. Data writes silently fail and nobody knows until a customer reports missing information three weeks later.

5. No database migrations

AI-generated schemas work for v1. Then you need to add a column, change a relationship, or rename a field. Without a migration system, you are making raw SQL changes to production and hoping nothing breaks. The first time you need to roll back a schema change at 2 AM, you will understand why every mature project uses migrations.

6. Webhook endpoints with no idempotency

Stripe, Clerk, and most third-party services retry webhooks when they do not get a 200 response within a few seconds. If your webhook handler is not idempotent - if processing the same event twice creates a duplicate charge, a duplicate user, or a duplicate notification - you will discover this in production when a customer gets billed twice.

7. Environment secrets in the codebase

AI assistants frequently hardcode API keys, database URLs, and secrets directly in source files or commit them to git. Even if you catch it later and rotate the key, the old key is still in your git history. Anyone with read access to the repo has your production credentials.

8. No monitoring or alerting

If your production app has no error tracking (Sentry, LogSnag, or even basic logging to a file), you are finding out about problems from your users. By the time a user reports a bug, they have already experienced it multiple times and decided whether your product is reliable. First impressions in SaaS are permanent.

9. Async operations that assume instant completion

AI-generated code regularly treats async operations as synchronous. The code sends an email, then immediately checks if it was delivered. It writes to a database, then reads the value back before the write has been committed. It calls an external API and assumes the response will arrive in under 100ms. These work in development and fail under any real load.

The pattern behind the pattern

All nine of these share a common root: AI generates code that works for the demo but does not account for what happens at the boundaries. The happy path is almost always correct. What is missing is the thinking about what happens when things go wrong, when two things happen at the same time, or when a user does something the AI did not anticipate.

The fix is rarely a rewrite. Most vibe-coded MVPs have a solid core - the AI got the main logic right. What they need is a systematic review of the boundary conditions: auth edge cases, data isolation, error handling, and the three or four load-bearing assumptions that are wrong.

How to audit your own code

If three or more of these checks fail, your product is carrying technical debt that will surface as a customer-facing incident. The question is whether you find it first or your users do.

I run a fixed-scope, one-week stabilization audit for solo SaaS founders. I go through your codebase, find every boundary condition that is going to break, and deliver a prioritized document with the exact fixes. You pay only after the audit document is in your inbox.

If this matches where you are, email me at kirill@launchsoloai.com with what you built, what stack you are on, and what is worrying you. I respond within 24 hours with a fixed price or a straight no.