How Much Does It Cost to Audit and Repair a Broken AI MVP? (2026 Pricing)

Short answer: auditing a broken AI MVP costs $2,500 for a 3-day audit with a written fix plan. Repairs start at $800 for a single production blocker. A full stabilization (observability, guardrails, evals, and handover) is a 14-day reliability sprint scoped from the audit. Rebuilding is the most expensive option and is rarely the right first move.

Those are my prices. I publish them because "it depends" is not useful when you are deciding whether your MVP is worth saving.

AI MVP audit and repair pricing at a glance

You don't have to start with the audit. If you already know the single thing that is broken (say, the app falls over when two users upload at once), a targeted fix is the cheaper door.

What the $2,500 audit includes

The audit exists so you stop paying people to guess. In three days I go through:

• Code and architecture: how the app is structured, where the AI calls live, and what an AI coding tool generated that nobody has read.
• Errors and reliability: what actually fails in production and how often, from logs if they exist, from reproducing it if they don't.
• Security: authentication, authorization, secrets handling, input validation, and prompt injection paths.
• LLM cost: cost per request, where tokens are wasted, and what will happen to the bill at 10× traffic.
• Observability: whether you could tell what went wrong the next time it breaks.

You get a written report with a prioritized fix list. It is written so that your own team, a freelancer, or I can execute it. Plenty of founders take the report and fix it themselves; that is a fine outcome.

What drives repair cost up or down

The price of fixing an AI MVP is mostly set by how hard it is to change safely, not by how many bugs there are.

If your app sits mostly in the right-hand column, budget for the reliability sprint rather than a single fix.

Fix or rebuild?

Most broken AI MVPs should be fixed, not rebuilt. The parts that took the longest to get right (screens, flows, the data model, the onboarding) usually work. The failures cluster in a handful of places:

1. Authentication and authorization
2. Secrets and API keys
3. Error handling and retries around LLM calls
4. Retrieval and prompt quality
5. Missing logging, tracing, and alerts
6. Runaway token costs

All six can be fixed in place. A rebuild makes sense only when the audit shows the core can't be made safe: for example, a data model that can't separate one customer's data from another's.

How to keep the cost down before you call anyone

• Write down the three failures that hurt most, with steps to reproduce.
• Turn on logging for every LLM call (input, output, latency, cost), even crudely.
• Move secrets out of the code and rotate any that were committed.
• Freeze new features until the blockers are fixed. Adding features to an unstable AI app multiplies the repair bill.

Need a second opinion on your AI MVP?

I'm Rajesh Dhiman, an AI systems engineer based in India, and I rescue AI-built and vibe-coded apps for founders worldwide. See how the AI Code Rescue service works, or read what AI code rescue involves first.