Lovable Review: AI App Builder Tested on 5 Real Projects

What Is Lovable?

Lovable (formerly GPT Engineer) rebranded in late 2025 and relaunched as a vibe coding platform focused on building full-stack web applications from natural language prompts. The company raised around $23 million in funding and has attracted roughly 150,000 users since the rebrand, according to their public metrics.

The core workflow: you describe what you want in plain English, Lovable generates a complete Next.js application with React components, TypeScript, Tailwind CSS, and Supabase for the backend. You get a live preview, can iterate with follow-up prompts, and deploy to Vercel or Netlify with a single click. The generated code exports to GitHub, so you're not locked in.

What sets Lovable apart from Bolt.new or Replit Agent is the code quality. Most AI builders produce functional but messy code. Lovable's output is structured enough that a developer could pick up the project and continue working on it without starting over. That's a meaningful difference if you're building something you intend to maintain.

How We Tested

We built five progressively complex projects over ten days, starting with a simple habit tracker and ending with an e-commerce storefront with Stripe integration. Each project was evaluated on time-to-deploy, code quality, number of manual fixes needed, and whether the final result was something you could show to a client or user.

All tests were run on the Starter plan ($20/month). We documented every prompt, every error, and every manual fix. The goal was to answer one question: can a non-developer or solo founder actually ship with this tool?

What We Built: Results by Project

Project 1: Habit Tracker

The habit tracker was the cleanest outcome. One prompt got us a working app with user registration, a list of habits with daily checkboxes, streak counting, and a simple bar chart for weekly progress. Total time from first prompt to deployed app: about 22 minutes.

The code was well-organized—separate components for the habit list, streak display, and chart. Supabase auth worked out of the box with email/password and Google OAuth. The one issue: the streak calculation reset incorrectly if you missed a day and then checked in again. We fixed it with a two-line change to the date comparison logic.

Project 2: Blog CMS

This went well until we asked for the Markdown editor. Lovable generated a basic textarea with no preview, no formatting toolbar, and no image handling. We had to manually prompt three more times to get a usable editor, and even then the image upload only worked after we fixed a Supabase storage bucket permission that Lovable created incorrectly.

The admin panel was functional. Categories, tags, draft/publish toggle, SEO fields—all present and working. The public blog layout was clean but unmistakably shadcn/ui. It looked like every other AI-generated site, which is fine for an MVP but would need custom styling for anything brand-specific.

Project 3: SaaS Dashboard

Here is where things got interesting. The role-based access control was partially correct—Lovable set up three roles (admin, editor, viewer) and created the Supabase RLS policies. But the policies had a gap: editors could see admin-only settings if they navigated directly to the URL. We spent about 40 minutes debugging RLS policies that Lovable generated with subtle logic errors.

The analytics charts used Recharts and looked genuinely good. CSV export worked on the first try. The team invite flow, however, was broken—it generated the invite email template but forgot to create the API route that actually sends the email. This is a pattern we noticed: Lovable handles individual features well but sometimes misses the connections between them.

Projects 4 and 5: Booking System and E-commerce

Both of these pushed Lovable past its comfort zone. The booking system's calendar worked for basic time slot selection, but conflict detection between overlapping bookings failed completely. Recurring bookings generated duplicate entries. We ended up rewriting the booking logic manually, which took longer than building it from scratch would have.

The e-commerce project was the most disappointing. Lovable generated a beautiful product catalog and cart interface, but the Stripe integration was outdated—it used Stripe Checkout in a way that's been deprecated. The order history page showed all orders to all users (an RLS oversight). The inventory management had no stock validation, so you could buy 50 units of a product with 3 in stock.

Pricing Breakdown

Lovable restructured its pricing in January 2026. There are three tiers, and the free option is genuinely usable for evaluation.

Starter at $20/month is the sweet spot for most users. The 100 monthly generations sound limiting, but each generation can produce or modify multiple files. We used about 60 generations across our five test projects. Unless you're iterating heavily on complex features, 100 is enough.

Growth at $50/month makes sense for agencies or founders building multiple products simultaneously. The unlimited generations and team features justify the jump. For solo builders, Starter covers it.

Lovable vs Competitors

We've tested all the major vibe coding tools. Here's how Lovable stacks up against the competition based on our direct experience. For a broader comparison, our vibe coding tools overview covers all five platforms in detail.

Lovable vs Bolt.new: Bolt is faster for throwaway prototypes, but Lovable's code is cleaner and the Supabase integration is significantly more reliable. If you plan to keep developing the project, Lovable saves time in the long run.

Lovable vs Cursor: Different tools for different users. Cursor is a developer's tool that requires coding knowledge. Lovable is designed for founders, designers, and people who want to build without writing code. Cursor produces better code, but you need to understand what it's generating.

Lovable vs Replit Agent: Replit is cheaper and handles deployment automatically, but the code quality is noticeably worse. Lovable's output is something you can hand to a developer later. Replit's output often needs to be rewritten.

What Users and Third-Party Reviews Say

On G2, Lovable holds a 4.4/5 rating from around 80 reviews. Users consistently praise the speed of prototyping and the quality of generated UI. The most common complaints mirror what we found: complex logic breaks, and the shadcn/ui styling gets repetitive.

On Product Hunt, the GPT Engineer relaunch hit around 1,800 upvotes. Community sentiment is broadly positive for prototyping but skeptical about production use. Several reviewers noted the same Supabase RLS issues we encountered.

The Discord community (roughly 15K members) is active and helpful. We saw multiple users sharing workarounds for the same state management bugs we hit. The Lovable team responds to issues within a day or two, which is better than most tools in this space.

Where Lovable Falls Short

These limitations don't make Lovable bad—they define its appropriate use case. It's a prototyping and MVP tool, not a replacement for professional development. The trouble comes when users treat it as the latter.

Who Should Use Lovable?

Frequently Asked Questions

Final Verdict

Lovable is the most polished vibe coding tool we've tested for building new web applications from scratch. The code quality is genuinely above average, the Supabase integration saves real time, and the GitHub export means you're not trapped in the platform.

But it has clear boundaries. Complex state management, payment integrations, and nuanced security requirements all need human developers. The generated code is a starting point, not a finished product. If you go in with that expectation, Lovable delivers genuine value.

For founders validating ideas, designers building interactive prototypes, or anyone who needs a functional web app faster than traditional development allows—Lovable earns its place in the toolkit. Just don't ship what it generates without security review.