Why Your Vibe-Coded App Breaks in Production — FixMyVibe.codes

You built something. Cursor, Bolt, Lovable — whichever AI tool you used, the app runs. It looks great in the browser, demo went well, you showed it to friends. Then you launch it. Real users arrive. The errors start.

This is the vibe-coded production gap — and it’s more common than you’d think.

What Is “Vibe Coding”?

Vibe coding is the practice of building software primarily through AI prompts rather than traditional hand-written code. You describe what you want, the AI generates it, you test it in your browser, iterate, repeat. It’s genuinely powerful. You can ship an MVP in days that would have taken a senior developer weeks.

The problem isn’t the approach. The problem is what AI tools optimise for: making it work in the happy path. The demo scenario. The “logged-in user with a complete profile who does exactly what you expect.”

Production is everything else.

The Happy Path Illusion

When you test your own app, you use it correctly. You fill in all the form fields. You have a profile picture. You don’t hammer the button five times in rapid succession. You don’t paste a SQL injection string into the search box.

Your real users will do all of these things — not maliciously, just because people are unpredictable.

AI-generated code handles the happy path beautifully. It struggles with the edges.

The Five Ways Vibe-Coded Apps Break

1. Missing Error Handling

Consider this pattern, common in AI-generated API routes:

// What AI generates
async function getUserProfile(userId) {
  const user = await db.users.findOne({ id: userId });
  return user.profile; // Crashes if user is null
}

If user is null — because the user deleted their account, because the database blipped, because someone navigated directly to /users/deleted-user — this throws an unhandled exception. In development you never hit this case. In production, someone will hit it within 24 hours.

The fix is a single null check, but AI assistants skip it because it doesn’t make the demo more impressive.

2. No Input Validation

AI tools are great at generating forms. They’re less reliable at making those forms secure. Common patterns we see:

Text fields with no maximum length (someone submits 50,000 characters and crashes your database write)
File uploads with no type checking (someone uploads an executable instead of a profile image)
Number fields that don’t validate range (order quantity of -1 gives someone a refund they didn’t earn)
Email fields that only check for @ presence, not actual format

Validation isn’t glamorous. It doesn’t appear in the demo. The AI skips it.

3. Hardcoded Values That Only Work on Your Machine

API keys embedded in frontend code. Database connection strings in source files. Environment-specific URLs hardcoded rather than read from environment variables. These are invisible problems until they hit a different environment.

The specific danger: API keys in frontend code are public. Anyone who opens DevTools can read them. We regularly find OpenAI keys, Stripe test keys, and AWS credentials embedded directly in JavaScript bundles.

4. Security Gaps

AI-generated authentication code often skips the details that matter:

JWT tokens that never expire (valid for years by default)
No rate limiting on login endpoints (brute force attacks work instantly)
Missing authorisation checks (the /api/users/:id endpoint returns any user’s data to any authenticated user)
Password reset links that don’t expire

None of these cause visible bugs in development. They become incidents in production.

5. No Database Indexing

This is the sleeper issue. AI generates queries that work perfectly on a database with 10 rows of test data. At 100,000 rows, without indexes, the same query scans every row. Your app doesn’t crash — it just slows down to an unusable crawl as you grow.

We’ve seen apps go from 200ms load times to 15-second load times after crossing the 50,000 user mark. The code hadn’t changed. The data had grown.

Why This Happens

AI coding tools are trained on code examples that demonstrate functionality. The examples that make it into training data are the ones that clearly illustrate a concept — they’re not production battle-hardened. Edge cases, error handling, and security considerations are underrepresented because they make the “interesting” code harder to read.

The tools also optimise for getting you to a working demo quickly. A demo doesn’t need rate limiting. A demo doesn’t need auth token expiry. Adding these would make the generated code longer, harder to understand on first read, and no better for the demo.

What Production-Ready Actually Means

When developers talk about “production-ready” code, they mean code that works:

When the network is slow or unavailable
When database calls fail
When users do unexpected things
When 10,000 people use it simultaneously
When someone actively tries to break it
When you’re asleep and can’t fix things manually

Vibe-coded apps typically handle the first demo scenario well. Production-ready handles all the scenarios you didn’t demo.

The Gap Is Fixable

Here’s what’s important to understand: this isn’t a reason to not vibe code. AI tools have genuinely changed what’s possible for non-technical founders. You can build real software. The gap between “working demo” and “production-ready” is just narrower than you might think — and it’s systematically addressable.

Most apps we assess have 15–40 issues across error handling, security, validation, and performance. The vast majority are small, targeted fixes — not rewrites. A focused 1–2 week engagement typically closes the gap.

The best time to close it is before you have production users. The second best time is right now.

Wondering if your vibe-coded app has hidden issues? Get a free code assessment — we’ll identify the gaps before your users do.