Lovable: our review

Lovable describes an application in a conversation, in French or English, and gets back a complete codebase in return: interface, business logic, database, authentication. The result isn't a fixed mock-up but a running application, with a working backend from the very first generation. That's what sets the tool apart from a plain interface generator: it raises the question of what happens to that code once the prototype is approved.

What it actually does

You describe a need — a sign-up form, an order-tracking page, an internal dashboard — and Lovable writes the corresponding code, runs it in a live preview, then offers to publish the app on a lovable.app subdomain. Each message modifies the existing project rather than starting over, which allows iterating in small steps: change a button's colour, add a page, wire up authentication. Plan mode, separate from the default mode, lets you discuss an approach before starting generation, which avoids spending a full credit on a wrong direction.

Getting the code out, and what that changes

This is the question that decides whether Lovable stays a prototyping tool or becomes a genuine production starting point. The answer here is clear: every project can be synced with a GitHub repository, in both directions, from the free tier onward — there's no need to move to a paid plan to get your code out. Once the link is set up, you work in your own editor, open pull requests, and deploy elsewhere than on Lovable's infrastructure if you want. On the Enterprise plan, a company can even keep a copy of the code in its own GitHub Enterprise, which addresses a common constraint at a firm or a public body. Another point worth noting for an SME in the legal sector or accounting: an automatic security scan runs on every publish and checks the database configuration, in particular row-level access rules (RLS) — one of the most common and costly mistakes in AI-generated applications, often invisible without a dedicated audit.

What an iteration costs

Lovable charges in credits, and the default mode varies their cost according to the complexity of the task: changing a button's style costs about 0.50 credit, adding full authentication around 1.20. Plan mode, meanwhile, charges 1 credit per message regardless of the outcome. This detail matters: a poorly worded instruction that doesn't achieve the expected result still uses up its credit, exactly like a successful iteration. The Pro plan, at $25 a month, includes 100 credits shared across the whole workspace — no per-user billing, which benefits a team working together on the same project. These credits expire two months after being issued, and one-off top-ups remain possible beyond the plan's allowance.

Where it falls short

Two caveats to know before rolling the tool out widely. First, the promise shown on the security page — "your data is never used to train our models" — is nuanced in the privacy policy, the binding contractual document: anonymised or aggregated data may be used to improve the product, unless you explicitly opt out or move to the Business plan. Second, the automatic security scan remains a basic check, not an audit: it catches known misconfigurations, not a business-logic flaw specific to the application. Publishing quickly without a human review of the generated code remains a real risk, particularly for an application handling customer data.

Our recommendation

Lovable suits a team that wants to validate an idea quickly and is willing to sync the code to GitHub before any serious production launch. See also our selection of application generation tools. Avoid it if no one in the organisation can review the generated security configuration, or if the expected volume of iterations far exceeds the Pro plan's 100 monthly credits.