AI assistants and search decision

Lovable

A capable engineer can build a usable chat-to-scaffold prototype (LLM integration, git ops, deploy hooks) in about a week, but reproducing Lovable's integrated credits, hosting grants, connector ecosystem, and enterprise polish at scale is nontrivial—keep paying if you need the full hosted experience.

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You pay

$25/mo

$300/yr

Not verified against a pricing page.

You’d pay instead

$50one-off30 h to build

$75/mo8 h/mo upkeep

On cash alone, building overtakes the subscription at 4 seats.

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Lovable alternatives, with the arithmetic →

What a replacement has to do

  • User chats with an LLM -> LLM returns or edits React/Tailwind code -> system applies changes, runs tests, and deploys or exports the project

What it still won’t have

  • Credits/consumption economy and built-in grants
  • Integrated first-class hosting, scaling, and security scanning
  • Large template gallery and polished UX around iteration/history
  • Built-in connectors to many third-party tools and enterprise features
  • Team/workspace credit management and enterprise support

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 4 seats.

Paid seatsseats

Money you would actually spend

Keep paying
—

Subscription price × seats × 12

Build it
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AI build —APIs + hosting —

Time you would spend

—

—

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a minimal chat-driven AI web-app generator using Node + Next.js (TypeScript), React, Tailwind, a small Postgres DB (Supabase), and OpenAI-compatible LLM API. Scope: chat UI, server-side LLM prompt handler that returns code edits, a git-backed project workspace per user, apply/commit edits to a Next.js scaffold, export/sync to GitHub, and trigger a Vercel deployment; include a simple hosting fallback to serve built apps from Supabase storage. Out of scope: multi-workspace credit accounting, enterprise billing, marketplace, and advanced template gallery. Deliver: runnable repo, Dockerfile, CI workflow, basic e2e tests for chat->code->deploy flow, error handling for LLM failures and git conflicts, and docs to run locally and on a $20/month VPS.
How we checked5 sources · 3/3 runs agreed · evidence score 64

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 5 cited sources+3
  • 3/3 assessment runs agreed+4
  • Evidence score64

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.

How scoring works →

Cited sources · 5

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

! Price not confirmed on the page - this pricing page renders its price in the browser✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 1 moat recorded