We Tested Vibe Coding Tools to Build an MVP — Here's What Actually Shipped

Why We Ran This Test

Vibe coding stopped being a novelty a while ago. Developer adoption of AI coding tools is now close to universal, with daily usage reported by the vast majority of surveyed developers, even as trust in the accuracy of the output has declined over the same period. Trust in the accuracy of AI-generated code dropped from 43% to 29% over 18 months, even as adoption climbed to 84% among developers.

That's an unusual combination: more people relying on a tool while trusting it less. For a software agency, that gap is exactly where the useful conversation lives, because founders are already using these tools whether or not anyone tells them to. The question worth answering isn't whether vibe coding works. It's what it actually hands you at the end, and what still needs a developer's eyes before real users and real money touch it.

What We Built and How We Tested It

We scoped a small but realistic product: a web app where a real estate agent signs up, adds property leads manually or through a form embed, sees them on a dashboard sorted by status, and gets notified automatically when a lead goes cold for more than three days. That's four moving parts that matter: authentication, a database with real relationships between agents and leads, a UI that a non-technical agent could actually use, and one piece of business logic that runs on a schedule rather than on a click. We judged each tool on four things.

How fast does it produce a first working version? How the generated code held up when we asked for a second and third round of changes, including one deliberately awkward request that doesn't map cleanly to a template. Whether the authentication and database layer had any protection against one user seeing another user's leads. And whether a developer looking at the underlying code would sign off on it without a rewrite.

Tool-by-Tool Results

Lovable

Lovable produced a genuinely presentable app in under an hour. The login screen, the lead form, and the dashboard all looked client-ready without us touching a design tool, and the built-in Supabase connection meant the database was live from the first session. Adding basic fields to the lead form was fast and reliable through the visual “select and edit” workflow.

The trouble started with the automated follow-up notification. Because that logic depends on a scheduled check rather than a user action, Lovable's prompt-driven flow struggled to reason about it consistently, and two attempts produced a notification rule that fired for every lead regardless of status.

We also checked whether one agent's leads were isolated from another's by default, and they were not. Row-level security had to be manually enabled and configured; it did not come on by itself.

Replit

Replit got the closest to feeling like a real development environment, since we could read and edit the generated code directly inside the same browser tab where it was hosted. The agent scaffolded the database schema sensibly and handled the authentication flow without much back and forth.

Where it slipped was consistency across sessions: a change we made to the lead status field in one prompt occasionally got quietly overwritten when we asked for an unrelated dashboard tweak later, which is the kind of regression that's easy to miss if you're not reading the diff.

For a solo builder who wants to learn from the code as they go, this tradeoff is reasonable. For anyone shipping without reviewing every change, it's a real risk.

Cursor + Claude Code

This pairing behaved the most like working with a competent junior developer than like a magic app generator, which is both its strength and its limit. Cursor handled the frontend and the visual polish well, while Claude Code was noticeably better at reasoning through the scheduled notification logic and the relationships between agents and their leads.

The code it produced was cleaner and closer to something a human engineer would have written from scratch, with sensible file structure and reasonable error handling on the parts we tested. The catch is that neither tool builds anything for you if you can't direct it.

Getting a working first version took longer than Lovable, and every step required us to already understand what “correct” looked like. This combination is the strongest of the three for anyone with development experience on the team. It is not a fit for a founder with no technical background who wants to skip hiring entirely.

What “MVP-Ready” Doesn't Mean “Production-Ready”

Every tool we tested got us to something that demoed well. None of them got us to something safe by default. That distinction matters more than most vibe coding coverage admits, and the research backs up what we saw firsthand. Independent studies consistently find that 40% to 62% of AI-generated code contains security vulnerabilities, with AI-written code producing flaws at roughly 2.74 times the rate of human-written code.

The specific failure we hit with Lovable, missing row-level security by default, is not an isolated case. A researcher who audited 50 vibe-coded apps across major platforms found that 88% had database row-level security entirely disabled, not misconfigured but switched off, meaning any query could return any record with no enforcement at the database level.

That same audit found real consequences beyond the lab: of 1,645 publicly listed apps built on Lovable, 170 had critical security failures visible from the outside, on products that already had real users, not unfinished demos.

It's not only a security story, either. Organizations adopting AI coding tools have measured a 41% increase in bug rates after adoption, a pattern researchers attribute to teams moving faster than their review processes can keep up with. None of this means the tools are broken. It means the output is a draft, not a deliverable, and the review step is not optional once real users are involved.

The Real Cost Comparison

Vibe coding tool subscriptions are cheap on paper. Individual plans across the tools we tested run roughly $10 to $50 a month, with usage-based options like Claude Code landing anywhere from about $5 to $50 depending on volume. That's the number most comparisons stop at. It's the wrong number to plan around if the product is meant to go live with real users.

The more relevant figure shows up later. By mid-2026, more than 8,000 of the roughly 10,000 startups that had used AI coding tools to build production apps by the end of 2025 needed either a partial rebuild or dedicated rescue engineering to keep operating, at an average cost of between $50,000 and $500,000 depending on how far the app had grown on its original foundation. That cost doesn't appear all at once either.

Every additional month spent building new features on top of a flawed foundation adds roughly 20% to 30% to the eventual rebuild cost, because each new feature creates dependencies on the parts that were never solid to begin with. A $30-a-month tool subscription that turns into a $150,000 rescue project eight months later is not actually the cheap option. It's a deferred cost with interest attached.

When Vibe Coding Is Genuinely Enough, and When It Isn't

None of this is an argument against vibe coding. For internal tools, throwaway prototypes, and early validation where the goal is simply to find out if anyone wants the product at all, these tools are close to ideal. Speed matters more than architecture at that stage, and a Lovable or Replit build can get a founder in front of real users in days instead of weeks.

The calculation changes the moment the product touches things that matter if they go wrong: real customer data, payment processing, anything an investor's technical due diligence will look at, or anything expected to still be running and growing a year from now. At that point, the question isn't whether the AI wrote working code.

It's whether anyone can explain why it works, which matters the first time something breaks in production and needs a fix rather than another prompt. A prototype that validated the idea is genuinely valuable. It just isn't the same artifact as a product ready to carry a business.

Final Word

The honest position here is not “don't use vibe coding.” It's that vibe coding and custom development solve different problems, and the strongest path for most founders is to use both in sequence. Build the first version fast to prove the idea has legs, then bring in engineers before real users and real transactions depend on the result.

This is exactly the gap TechBinariesworks in: taking a validated, vibe-coded MVP and hardening it into something built to last, whether that means a security and architecture review, a targeted rebuild of the parts that won't scale, or a fresh custom build once the requirements are clear enough to justify it.

This kind of engagement has become common enough that “vibe code rescue” is now a recognized category of development work rather than an edge case, which says more about where the industry actually is than any tool comparison does.

Vibe coding earned its place in how software gets built. It did not eliminate the need for engineering judgment, and every tool we tested proved that within a single afternoon. If you've validated an idea this way and you're ready to make it something real users can depend on, that's the point where a conversation with an engineering team pays for itself. TechBinaries builds exactly that bridge with software development, from a tested idea to a production-ready product.