How much does it cost to fix a vibe-coded app?

The short answer

A single, well-defined fix to an app built with an AI coding tool usually lands near the starting packages: about $100 to $250 on the AI-built app job page, and often under $100 for something small. Making the whole app hold up with real users is a different job, quoted per project, and in the reviews I read it usually ran past $1,000.

There is not much in between. In 197 recent reviews of developers who fix AI-built apps, a quarter of orders were under $100 and half were over $1,000. The two middle bands together were only a quarter. So the useful question is not “what does a fix cost” but “which of the two jobs is mine”, and you can answer most of it before you ask for a quote.

What buyers actually paid

Order price (Fiverr band)	Share of 197 reviewed orders
Under $100	25%
$100–$400	14%
$400–$1,000	11%
Over $1,000	50%

Duration followed a similar shape. Of the orders that showed one, a third were finished within three days and three in five within a week, while about one in five took more than two weeks.

Two cautions when reading these numbers. They are Fiverr’s price bands under each review, not invoices. And three in four of the reviews came from buyers who ordered more than once, so many of the large orders are second or third pieces of work on an app the developer already knew. A first order on an unfamiliar codebase includes the time it takes to read it.

The six things that move the price

1. How many problems, and whether you can reproduce them. “It’s buggy” gets a vague quote with a margin for the unknown. Three problems, each with the steps that make it happen, get a fixed one. The job page brief asks for exactly that.

2. Whether it touches money or personal data. Logins, payments, user uploads and anything one user should not see of another’s are where a careful developer slows down, and they should. A fix in those parts includes checking that the fix did not open something else. Security before launch covers what that review looks like.

3. Where the code lives. A repository on your account, with a history of changes, is quick to read and quick to hand back. A zip file, or an app that only exists inside a builder, adds set-up time to every quote. If the word “repository” is new, what version history is explains it in a few minutes.

4. Whether one change breaks another. If every fix you or the AI tool make breaks something elsewhere, the developer is not fixing one bug but untangling where the same decision is made twice. That is the pattern in an AI-built app that breaks after every change, and it moves a job from the small band to the large one.

5. Whether you want to keep editing in the same tool. Some fixes are easiest outside what a no-code AI builder can edit. If you need to keep working in Lovable, Bolt or Replit afterwards, say so; it narrows how the fix can be done and can change the price.

6. What you want handed back. Code in your repository with a note per fix, a README you can deploy from, and a written list of what was checked for security. Each line is real work. A quote without them is cheaper because it is a smaller job.

Most of what you pay for is finding the problem

I built most of my own scheduled automations with AI coding tools, and the expensive part of fixing them has almost never been the change itself. It has been finding where to make it.

One example from my records. In September 2026 I changed the format of the posts a publishing pipeline of mine produces, and the scheduled publisher kept expecting the old format. Before switching the schedule back on, I ran it in a mode that goes through every step without publishing. That dry run found three separate places where the publisher disagreed with the new format. Nothing would have crashed. It would have discarded valid videos as outdated, skipped some items entirely, and tried to re-render with a tool I had already retired. Together with a fourth issue, these gaps had already produced one post, published by hand, whose picture and video came from two different versions. Each of the three fixes was a few lines. Finding all three took the dry run and a careful read of what the publisher was doing.

A second one taught me how diagnosis itself goes wrong. When a bot of mine seemed to be ignoring some comments, my first check compared the list a platform’s API returned with a count from the same API. They matched, so I called it healthy. They matched because both came through the same filter. The missing comments were only visible outside the API, in the app itself. The fix was small; by then, the comments it could not see had gone unanswered for three days.

That is what a good developer is pricing when their quote is higher than “fix the button”: time to reproduce the problem, find every place it lives, and check the fix against something independent. A quote that skips those steps is cheaper and often ends in a second order.

Make two quotes comparable

Ask each developer to quote against the same lines, and lay the answers side by side:

Line	Ask for
Problems	Each of my listed problems fixed, checked with my reproduction steps on the live app
Cause	A short note per fix: what caused it and where else it could appear
Sensitive parts	Logins, payments and personal data checked, with a list of what was and was not checked
Code	Every change in my repository; nothing only on the developer’s machine
Deploy	A README I can follow to deploy one small change myself
Tool	Whether I can keep editing in my AI builder afterwards
After delivery	Rate and response time for bugs found in the first 30 days

If two quotes differ by a factor of five, the gap usually closes to a few lines in this table that the cheaper one leaves out. Then the decision is whether you need those lines, and for anything with real users, you do.

Make the first order small

A cheap first review is the point of the up-and-coming list on the job page; Amish Sohail’s gig is built around a review before bigger fixes. For a scoped bug list, Teamseven is there for exactly that, and Corpsoft for making an app production-ready once real users arrive.

How you pay for the larger work matters too. I have paid freelancers both ways: a fixed price released on agreed dates, and milestones released after I had checked what each stage produced. For an app repair where the scope will only be clear after the first look, milestones tied to something you can test are the safer shape. Fixed price or milestones goes through the choice.

Where it goes wrong anyway

• The fix reveals the next problem. Common in AI-built apps: the login was masking a database issue. Agree in advance that new problems found during the work are quoted separately, not silently added.
• The AI tool undoes the fix. If you keep prompting the builder after delivery, it can rewrite the fixed part. Ask the developer which files to leave alone.
• The quote was for the symptom. If the same bug comes back within a week, go back to the “cause” line. A fix without a cause note is the one most likely to return.

Wrap-up

Fixing a vibe-coded app costs either a little or a lot, and rarely something in between. Which one depends on how many problems you can reproduce, whether they touch money or personal data, where the code lives, whether changes break each other, which tool you keep using and what you want handed back. Write those down, ask every developer to quote against the same lines, and start with one fix or a paid review. The larger quote you get afterwards will be based on your code, not on a guess.