Lovable review 2026: what $25 buys, and what you take when you leave

Pro starts at $25 per month and top-ups cost $0.30 per credit. What remains is to establish what a credit buys, what is consumed while you do nothing, and what you retrieve when you decide to leave with your application.

Lovable review 2026, in one billing line: Pro starts at $25 per month according to vendor documentation consulted September 2, 2026; top-ups cost $15 for 50 credits, or $0.30 each; and you leave with the code, a Vite and React project hostable elsewhere.

What Lovable does better than Maestro

Publishing. A Lovable project exists at an HTTPS address with a working database without you configuring anything. Maestro produces code on your Mac that someone must then deploy and operate. The day-one difference is clear and explains the success: $400 million raised on August 12, 2026, a $13.3 billion valuation and 60 million projects created since November 2024 (Lovable's Series C announcement). Since August 5, 2026, each published application also receives a security page at its own address showing which controls are active.

What a credit buys

Official documentation gives numerical examples: a simple update costs 0.50 credits, removing a component 0.90, adding authentication 1.20 and a homepage with images 1.70. In Default mode, the verbatim formulation is ‘credits vary based on task complexity’; in Plan mode, which prepares without modifying the project, each message costs 1 credit. The free offer provides 5 building credits daily, capped at 30 monthly. Monthly credits expire two months after issuance, top-ups last twelve months and daily allowances do not roll over. The exact credits included in the first Pro tier vary between the sources collected: we do not publish that figure.

Consumption nobody anticipates

A user on Reddit (r/lovable, September 2026) describes measuring their own bill: twenty messages analysed, 281.4 credits consumed, or 14.07 per exchange, ranging from 8.9 to 21.1 by message, and 70.4% of a 400-credit monthly allocation for twenty interactions. They criticise a consumption increase arriving ‘without the slightest transparency’ about what consumes credits. Another reports (r/lovable, June 2026) that unpublished projects burn 2.67 credits daily without being opened, around 80 monthly, and support said they could accept the consumption or delete the projects.

What you can take out, and what you cannot bring in

On exit, Lovable is clear and says explicitly: ‘you are never locked in’; the project synchronises both ways with a repository, can be cloned, changed elsewhere and hosted wherever you want. That is more generous than classic no-code. Entry, however, is closed: the official FAQ answers ‘No, currently there is no way to start a Lovable project from already existing code’. You cannot entrust Lovable with an existing application, and the editing platform itself remains a managed service you cannot install yourself.

The April incident, as Lovable tells it

On April 20, 2026, a security researcher publicly reported that public-project data was accessible to any logged-in user. Lovable published its analysis two days later: between February 3 and April 20, 2026, conversation history and source code for public projects could be viewed by any user with the link because of a server regression; a February 22 report had been closed without escalation to the security team. A fix came within two hours, then all public projects were made private. The post-mortem is honest and deserves credit to the vendor; the habit it should leave you with applies to every generator.

Decide before paying

Take the change you will make most often, changing a field, adding a filter, and estimate it at 1 credit, or $0.30 as a top-up. Multiply by the number of revisions in an ordinary week, then by four. Added to the subscription, this amount is your real cost. If it fits and you want an application online this week, Lovable is a good purchase. If your project already exists elsewhere, or you intend to maintain it for years, first look at what each tool leaves you when you leave and the documented traps of AI development.