Saturday, January 17th 2026 · 7 min read
Migrate from Lovable, v0 or Replit to a Next.js Codebase
Outgrown your AI-generated prototype? Learn how to transition from Lovable, v0, Bolt, or Replit to a scalable Next.js codebase. We have helped dozens of teams make this move.
You built something amazing with Lovable, v0, Bolt, or Replit. The prototype is working, users are signing up, and suddenly you realize: this thing might actually become a real business.
But now you're hitting walls. The monthly bills are adding up. The generated code is becoming harder to modify. You need features these platforms don't support well. And you're starting to wonder if you actually own what you've built.
You're not alone. We've helped dozens of teams make this exact transition, and we've done it ourselves. Here's everything you need to know about moving to a production-ready codebase.
Why Teams Outgrow AI Code Generators
AI code generation platforms are incredible for getting started. They lower the barrier to building software and let you validate ideas in hours instead of weeks. But they come with trade-offs that become painful as you scale:
The Recurring Cost Problem
Most AI code generators charge monthly fees that scale with usage:
| Platform | Typical Monthly Cost | Annual Cost |
|---|---|---|
| Lovable | $20-100/month | $240-1,200/year |
| v0 | $20-50/month | $240-600/year |
| Replit | $25-100/month | $300-1,200/year |
| Bolt.new | $20-100/month | $240-1,200/year |
Over 2-3 years, you could spend $1,000-3,000+ just on the platform - and you still don't fully own the code.
Compare this to a one-time starter kit purchase that gives you complete ownership forever.
Code Quality Concerns
AI-generated code often has issues that don't surface until you scale:
- Inconsistent patterns - Different parts of the app follow different conventions
- Missing error handling - Happy path works, edge cases crash
- Security gaps - Authentication and authorization that looks right but isn't
- Performance problems - No caching, redundant queries, unnecessary re-renders
- Technical debt - Quick fixes that become permanent problems
We've seen teams spend weeks debugging issues that stem from generated code making assumptions that don't hold in production.
Limited Customization
These platforms excel at common patterns but struggle with:
- Complex multi-tenant architectures
- Custom billing logic (usage-based, per-seat, hybrid)
- Advanced authentication (SSO, SAML, organization-level permissions)
- Integration with specific third-party services
- Performance optimization for your specific use case
When you need something the platform doesn't support well, you're stuck.
Vendor Lock-in
What happens if:
- The platform raises prices significantly?
- They discontinue features you depend on?
- They get acquired and change direction?
- You need to bring on developers who don't know the platform?
With generated code locked into a platform, these scenarios become existential risks.
The Migration Path
Moving from an AI code generator to a production codebase isn't as daunting as it might seem. Here's how we approach it:
Step 1: Audit What You Have
Before migrating, understand what you're working with:
- Features inventory - List every feature and user flow
- Data model - Document your database schema and relationships
- Integrations - Note all third-party services (Stripe, auth providers, etc.)
- Custom logic - Identify business rules that are specific to your product
Step 2: Choose the Right Foundation
Don't start from scratch. Use a production-ready starter kit that includes:
- Authentication (email, OAuth, magic links, MFA)
- Billing integration (Stripe subscriptions, one-time payments, metered billing)
- Multi-tenancy (organizations, teams, role-based access)
- Database setup (migrations, seeding, type-safe queries)
- UI components (50+ production-ready components)
- Deployment configuration (Vercel, Railway, Docker)
This gives you a solid foundation that would take months to build from scratch.
Step 3: Migrate Incrementally
Don't try to rebuild everything at once:
- Set up the new codebase with your starter kit
- Migrate authentication first - this is foundational
- Move your data model and seed with production data
- Rebuild features one at a time, starting with the most critical
- Test thoroughly before switching users over
- Run in parallel until you're confident in the new system
Step 4: Improve as You Go
Migration is an opportunity to fix issues in the original code:
- Add proper error handling and loading states
- Implement caching for frequently accessed data
- Set up monitoring and error tracking
- Add comprehensive input validation
- Write tests for critical paths
Why Achromatic for Your Migration
We built Achromatic specifically for teams making this transition. Here's what you get:
One-Time Cost, Lifetime Ownership
Pay once, own forever. No monthly fees, no usage limits, no surprises. The code is yours to modify, deploy, and scale however you want.
Production-Grade Architecture
Our starter kits include patterns that AI generators often miss:
// Type-safe API routes with proper error handling
export const createProject = authAction
.schema(createProjectSchema)
.action(async ({ parsedInput, ctx }) => {
// Permission check
if (!hasPermission(ctx.user, 'projects:create')) {
throw new ActionError('Insufficient permissions');
}
// Create with proper transaction handling
const project = await db.transaction(async (tx) => {
const project = await tx.insert(projects).values({
...parsedInput,
organizationId: ctx.organization.id,
createdBy: ctx.user.id
});
await tx.insert(activityLog).values({
type: 'project.created',
projectId: project.id,
userId: ctx.user.id
});
return project;
});
// Invalidate relevant caches
revalidateTag(`org-${ctx.organization.id}-projects`);
return { success: true, project };
});AI-Friendly Codebase
Ironically, our starter kits work better with AI coding assistants than AI-generated code does. We include:
CLAUDE.mdandAGENTS.mdfiles that help AI understand the codebase- Cursor rules for consistent code generation
- Clean, documented patterns that AI can extend
- TypeScript throughout for better AI autocomplete
Features That Would Cost Thousands in Tokens
Here's the reality: building production-ready features with AI code generators is expensive. Not just in platform fees, but in the sheer volume of tokens needed to generate, debug, and iterate on complex functionality.
Consider what's included out of the box with Achromatic:
| Feature | Estimated Tokens to Generate | What You'd Spend |
|---|---|---|
| Credits/usage system | 50,000-100,000 tokens | $5-15 in API costs + hours of debugging |
| Admin panel with impersonation | 80,000-150,000 tokens | $10-25 + security review |
| Profile management + settings | 40,000-80,000 tokens | $5-12 |
| Multi-tenant organizations | 100,000-200,000 tokens | $15-35 + architecture headaches |
| Role-based access control | 60,000-120,000 tokens | $8-18 |
| Stripe billing integration | 80,000-150,000 tokens | $10-25 + webhook debugging |
That's potentially $50-130+ in tokens alone just to generate these features - and that assumes they work correctly the first time (they won't). Factor in iterations, bug fixes, and the inevitable "actually, can you change how this works" prompts, and you're looking at 3-5x that cost.
With Achromatic, all of this is built, tested, and working on day one.
Battle-Tested by 750+ Teams
Our starter kits power everything from side projects to YC-backed startups to enterprise applications. The patterns have been refined through real-world usage and feedback.
Real Migration Stories
From Lovable to Launch
A productivity app team came to us after 6 months on Lovable. They had paying users but were hitting platform limitations:
- Couldn't implement the custom workspace sharing they needed
- Monthly costs were eating into their runway
- Generated code was becoming unmaintainable
Migration time: 3 weeks
Result: 60% reduction in monthly infrastructure costs, full feature flexibility, and a codebase their new hire could actually understand.
From v0 Prototype to Series A
A B2B SaaS started with v0 for their MVP. After raising seed funding, they needed:
- SOC 2 compliant architecture
- SSO for enterprise customers
- Usage-based billing with Stripe
v0 couldn't handle these requirements. They migrated to Achromatic Pro and were enterprise-ready in 6 weeks.
From Replit to Revenue
A solo founder built their entire product on Replit. When they hit $10k MRR, they realized:
- They were paying more in platform fees than hosting would cost
- Performance was suffering on shared infrastructure
- They needed better monitoring and error tracking
After migrating, their hosting costs dropped 80% and page load times improved by 3x.
We're Here to Help
If you're sitting on a Lovable, v0, Bolt, or Replit project that's ready to graduate to a real codebase, reach out. We've done this migration many times and can help you avoid the common pitfalls.
Getting Started
Ready to make the move? Here's what to do:
- Check out our starter kits - Find the one that matches your stack
- See the live demo - Experience what you're getting
- Contact us - For complex migrations or custom requirements
The sooner you migrate, the less technical debt you'll carry with you. Your future self will thank you.
Building on a solid foundation isn't just about the code - it's about having the confidence to scale without fear. That's what a production-ready starter kit gives you.
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