Should I fix or rebuild my broken AI-built app?

Most of the time, fix. The working 80% an AI tool produced (UI, routes, data flow, happy path) is usually salvageable, and adding the missing production 20% — auth, error handling, data integrity, security — is far cheaper than starting over. Rebuild only when the data model or auth foundation is fundamentally unsound, because then everything you'd add sits on a broken base.

How do I know if my app needs a rebuild?

Ask whether what's missing is an addition or a correction. Additions — missing constraints, loose RLS, absent error handling, no input validation — mean a fix. Corrections to the foundation — no clear record ownership, tenant data intermixed at the schema level, no real auth model — may justify a rebuild. You usually can't tell from the outside, so the first step should be an audit, not a quote.

Is rebuilding always more expensive than fixing?

Usually, but not always. A rebuild throws away the correct 80% and resets your timeline to zero, so for most apps a targeted fix is much cheaper and faster. The exception is a very small app or one on a fundamentally broken foundation, where rebuilding on solid ground is genuinely faster than untangling the existing code.

Why would an agency recommend a fix instead of a rebuild?

Because a fix is usually the honest answer — and it's the smaller quote, so recommending it means earning less. If an agency jumps straight to 'rebuild everything' without auditing first, get a second opinion. A good engineer audits, tells you whether what's missing is an addition or a foundation problem, and only recommends a rebuild when it's genuinely cheaper.

Process

Rebuild vs Fix: Should You Repair or Restart Your Vibe-Coded App?

Most broken AI-built apps should be fixed, not rebuilt — the 80% is salvageable. Here's the honest test for the rare cases where a rebuild is actually cheaper.

July 27, 20263 min readProcess

Short answer: most vibe-coded apps that broke in production should be fixed, not rebuilt — the working 80% an AI tool produced is usually salvageable, and a fix is far cheaper. A rebuild is only the right call when the data model or auth foundation is fundamentally unsound. The trap is that a rebuild *feels* cleaner, so people pay for one when a targeted fix would have shipped in a fraction of the time. Here's how to tell which situation you're actually in.

Why "Just Rebuild It" Is Usually Wrong

When an app breaks, the instinct is to start over. But the AI tool already solved the boring, correct 80% — the UI, the routes, the basic data flow, the happy path. What's missing is the production 20%: real auth, error handling, data integrity, security. Throwing away the 80% to re-do it is paying twice for work that was fine. A rebuild also resets your timeline to zero, when a fix could unblock the actual problem in weeks. See what that 20% is in Lovable, Bolt & Cursor Got You 80% There.

When a Fix Is the Right Call (Most of the Time)

Fix — don't rebuild — when:

The app works for the happy path and breaks on edge cases, load, or specific users.
The data model is basically sound — tables and relationships make sense, even if constraints are missing.
Auth exists but is misconfigured (e.g. Supabase RLS off or too loose) rather than absent.
The failures are identifiable — specific bugs, leaks, or crashes you can point to.

This is the common case. Missing constraints, loose RLS, absent error handling, and unvalidated inputs are all *additions* to a sound structure.

When a Rebuild Is Genuinely Cheaper

Rebuild when the foundation is wrong — because everything you'd add sits on sand:

The data model is fundamentally broken — no clear ownership of records, tenant data intermixed at the schema level, relationships that can't represent the real domain.
There is no real auth concept and user/permission boundaries were never modeled, so isolation can't be retrofitted safely.
The app is tiny — a rebuild-on-solid-foundations is genuinely faster than untangling it.
It was built on a dead-end the product has already outgrown.

The tell: if fixing means first *undoing* core decisions, a rebuild may win. If fixing means *adding* the missing production layer, a fix wins.

The Honest Test

Ask: is what's missing an addition, or a correction? Additions (constraints, error handling, RLS policies, validation, guardrails) → fix. Corrections to the foundation (data ownership, auth model, tenant isolation) → maybe rebuild. You usually can't tell from the outside, which is why the first step is always an audit, not a quote.

Two more honest notes: a good engineer earns *less* recommending a fix (it's the smaller quote), so if someone jumps straight to "rebuild everything," get a second opinion. And a "fix" isn't always cheaper if the app is throwaway-tiny. Veracode found only 55% of AI-generated code is secure (Veracode, 2026) — so the audit isn't optional either way; you need to know what's under the hood before deciding.

We start with a scoped audit and give you the fix-or-rebuild verdict in writing — including when the honest answer is "rebuild." Get a free AI App Health Check, see how we fix vibe-coded apps, or book a call.