How much does a vibe code audit cost? What you get at each level (2026)

You built an app with Lovable, Bolt, Replit, v0, Cursor or Claude, and it works. Before real users and real payments arrive, you want someone to check it. That check goes by many names: a vibe code audit, a code review, a production-readiness review. The cost question has an honest answer that is less satisfying than a number: it depends on how deep the audit goes and what happens after it.

This guide explains what a good audit of an AI-built app should cover, the three levels you will usually be offered, what changes the price at each level, and the questions to ask before you pay for one.

What a production-readiness audit should cover

AI tools are good at making an app work for the person building it. The gaps show up when other people use it. A useful audit checks the areas that break first with real users:

• Security: exposed API keys, code that runs in the browser when it should run on a server, and outdated or risky dependencies.
• Database access rules: whether every table that holds user data has rules that limit who can read and change it. On Supabase, that means Row Level Security policies that check the signed-in user.
• Logins and roles: whether permissions are enforced on the data, not just by hiding buttons. A simple test: signed in as two different users, can either see the other's records?
• Payments: whether payment results are confirmed on the server (for example through verified webhooks), and what happens when a payment fails, is retried or arrives twice.
• Secrets: where keys live, whether any were pasted into prompts, chats or public repositories, and whether they have been rotated.
• AI cost limits: if the app calls AI models, whether usage limits stop a runaway loop or a heavy user, and whether each task uses a suitable model rather than the largest one.
• Deploy: whether the code lives in a repository you own, whether there is a staging environment, how a bad release is rolled back, and whether errors and downtime raise an alert.

An audit that skips logins, database rules and payments is checking the easy parts. Those three are where AI-built apps most often need work.

The three usual levels

1. Automated scan

A tool runs over your code, dependencies or live site and reports known patterns: leaked keys, vulnerable packages, missing security headers, common misconfigurations. It is fast and repeatable, and worth running regularly.

What it can't tell you is whether your app's own logic is right. A scanner doesn't know that a customer should only see their own invoices, or that a booking must not be taken twice. Those are the problems that hurt AI-built apps, and they need a person who understands what the app is for.

2. Written review by an engineer

An engineer reads your code and setup against a checklist like the one above, tries the important flows, and writes up what they find. A good report rates every finding by severity and turns the findings into a plan: what to fix now, what to fix next, and what to leave alone.

This is the level most founders actually need before launch. You get a clear picture of where the app stands, and you can decide who fixes it: you, your team, your AI tools or the reviewer.

3. Review plus fixes

The same review, followed by the agreed fixes: permissions enforced on the data, secrets moved and rotated, payment paths handled, a staging environment, tests for the flows that matter, and a handover. This costs the most, because it includes the work itself, and it varies most from app to app.

What changes the price at each level

• Size and moving parts: how many user flows, roles, integrations and data sources there are.
• Depth: a tool's output, a person's written review, or a review followed by fixes.
• Access: a review of the live app alone sees less than one that can also read the code and the database setup.
• How much needs to change: for review plus fixes, the findings decide the scope. The fair way to price that work is a fixed scope agreed in writing after the review, not an open-ended hourly estimate before anyone has looked.

What to ask before you pay for an audit

• Who does the review: a tool, a person, or both? Who signs off the findings?
• Will I get a written report that I keep, whatever I decide afterwards?
• Is every finding rated by severity, with a recommended order of fixes?
• Do you check permissions on the data itself, not just what the screens show?
• Do you check payments, secrets, AI usage limits and the deploy setup?
• What do you need from me? (A good reviewer won't ask you to send passwords, API keys or customer data in a form.)
• Is this a penetration test or a certification? Most production-readiness reviews are neither, and that's fine, as long as everyone knows which it is.
• If fixes are needed, is the price fixed and agreed in writing before work starts?
• Can I fix the findings myself, or with my own team, if I prefer?

Where the TechTose Scan fits

The TechTose Scan is a free production audit that combines the first two levels: an agent built for this job does the groundwork, and a person signs off the result. You send your app, code or idea. Scout, the TechTose agent behind the Scan, maps the codebase or idea and drafts the report, checking security, database, logins and roles, payments, secrets, AI setup and deploy. A TechTose architect then reviews every finding and its severity before anything reaches you. Agents do the work; a person is accountable for it.

You get a written report in 48 hours. Every finding is rated by severity, and the report ends with a prioritised fix plan: fix now, fix next, leave alone. A 30-minute walkthrough of the findings is included, and you keep the report either way.

If your app is in good shape, we'll tell you. If a small fix is enough, we'll show you how to do it yourself. If it needs real work, you'll get a fixed-scope plan, with no pressure to buy. That work is Harden, which makes an AI-built app production-ready without a rewrite; its starting price is on the pricing page, and the exact quote follows a short call.

Built with a specific tool? Start with its production checklist: Lovable, Bolt, Replit, v0, Cursor, or Claude and ChatGPT.