Lovable vs Figma Make looks like a fight between two prompt-to-app builders, and on the first afternoon it is one. Both turn a sentence or a design into a working React app, both charge in AI credits, and both give you a public link. The difference shows up the week after, when someone has to keep building on what they made. Figma Make is a design tool that grew a code tab. Lovable is a code tool that happens to start from a chat box.
We checked both against Figma's pricing page and help center, and Lovable's plans, on November 9, 2026.
What you pay
Figma doesn't sell Make on its own. It comes with a Figma seat, and the seat decides your AI credits:
| Figma Make | Lovable | |
|---|---|---|
| Free | Starter: 150 AI credits a day, up to 500 a month | 5 credits a day, up to 30 a month |
| Entry paid | Professional Full seat, $16 a month, 3,000 AI credits | Pro, $25 a month, 100 credits plus 5 a day |
| Cheaper seats | Dev $12 or Collab $3, 500 AI credits each | None, one plan per workspace |
| Bigger plans | Organization Full seat $55, Enterprise $90 (both billed yearly) | Business, $50 a month |
| Publishing | Needs a Full seat on paid plans | Every paid plan |
Don't compare 3,000 with 100. Figma's credits are spread across every AI feature in Figma, not only Make, and its help pages don't publish a fixed cost per Make prompt. Lovable's credits pay for building and, since June 2026, for running the live app on Lovable Cloud too. The honest summary is that a designer who already pays for a Full seat gets Make for nothing extra, and a founder who doesn't use Figma is buying a design seat to get an app builder.
Publishing: figma.site and a custom domain
Figma Make publishes to an address like three-random-words.figma.site. You can attach your own domain, but the number of custom domains depends on your plan and is shared with Figma Sites. There's password protection, which applies to the figma.site version and not the preview. One catch on the free Starter plan: Figma's help page says Starter users must also publish the file to the Figma Community, so your "private prototype" is a public community file too. Read that before you put a client's flows in it.
Lovable publishes to a lovable.app subdomain and takes a custom domain on paid plans. Neither gives you a server you control. Both host the thing for you, which is fine until it isn't.
The backend is where they split
Figma Make offers two backends. Payload, which Figma hosts in the eastern United States, comes with a database, file storage, auth and an admin panel. Or Supabase, where you connect your own Supabase project under Integrations. So far that sounds close to Lovable, which runs on Supabase too.
Then there's this line in Figma's own help page: "Figma Make currently uses key-value stores rather than setting up a full SQL database." In practice that means a single table of keys and JSON values, read and written by one generated server function. No orders table with a foreign key to customers. No row level security policy per table, because there's one table.
A founder sent us a Figma Make link a few weeks ago. Lovely booking flow, Supabase connected, published on a figma.site address, about 300 real bookings in it. Every booking was a JSON blob under a key like booking: plus a timestamp. Finding "all bookings for this customer next week" meant reading every row and filtering in JavaScript. It worked at 300. It would not have worked at 30,000, and adding a second staff login with different permissions was simply not possible without redesigning the data.
Lovable builds real Postgres tables on Lovable Cloud or your own Supabase project, with migrations and RLS policies it writes as it goes. They're not always good policies (we've written about what the Lovable security scan misses), but they are tables you can query, index and fix.
GitHub: one way vs both ways
This is the part I'd weigh most. Figma Make pushes to GitHub, and Figma's help page is blunt about it: "It's a one-way push from Figma Make → GitHub." Changes made in GitHub don't come back into Make, and the next push overwrites them. Make only pushes to repositories it created, only to the default branch, and only one GitHub account or organization can be connected per Figma team. Payload-backed apps don't get the GitHub integration at all yet.
So the day a developer fixes something in the repo, the Make file is a fork you can no longer use. You either stop prompting in Make or you lose the developer's work.
Lovable's GitHub sync goes both ways. A developer can push a fix, and the next Lovable prompt builds on top of it. That's the single reason we tell founders who plan to hire help later to start in Lovable rather than Make. It's not about which AI writes nicer components. Make's components are often nicer, honestly, because it starts from a real design file.
Which one to use
Use Figma Make when the output is a prototype: a clickable demo for investors, a usability test, a flow your designer wants to feel in the hand before anyone writes real code. If your team lives in Figma, it is the fastest way from a frame to something you can tap. You can even bring Figma designs into Lovable later when it's time to build the real thing.
Use Lovable when the app is the product, it holds data with relationships, and a developer will touch the code within a year. And if you've already built something real in Figma Make, plan the move early. Rebuilding the data as tables is a few days of work at 300 records and a much worse week at 30,000.
To see what a Lovable app will cost once it has users, run the free Lovable cost check on the repo. If the prototype works and you want it ready for real users, that's Lovable to production, from $500, and if it's on Lovable Cloud and you want the database in your own name, Cloud to Supabase starts at $150.
Originally published on lovable2live.com. Aakash Verma, software developer and founder of Lovable 2 Live.
Top comments (1)
The "who keeps the code" question is the right one, and I'd add "who keeps the data and the access rules". Self-promotion warning: we built MyJackfruit.com for the step after the prototype. You and our Kai-Zen agents quantify what stakeholders need first, then build on a backend designed for data ownership and fine-grained access control, while you keep working in your own GitHub.