How do I convert my v0/Bolt.new prototype to a production app?

Export your code, set up a proper development environment with Git, add a real database (PostgreSQL/MySQL), implement secure authentication (Auth0, Clerk, or custom), add API rate limiting, set up error monitoring, configure CI/CD pipelines, and deploy to production infrastructure (Vercel, AWS, or similar).

How long does it take to convert an AI prototype to production?

Typically 2-4 weeks for a basic MVP, 4-8 weeks for a standard app with user accounts and payments, and 2-4 months for complex applications. The timeline depends on how much of the AI output is usable and how complex your backend requirements are.

Should I keep the AI-generated code or rewrite it?

Keep what works (usually 60-80% of UI components), refactor what's suboptimal (state management, API calls), and rewrite what's missing (authentication, database, business logic). Don't throw everything away—AI-generated UI is usually solid.

What's the first thing I should add to my AI prototype?

Version control (Git) and a real database. Without these, you can't track changes or persist user data. Next priorities: authentication, environment configuration, and error handling. The AI prototype likely has none of these properly implemented.

Can I deploy my AI prototype directly to production?

Only for static landing pages with no user data. For anything with forms, user accounts, or data storage, no. AI prototypes lack security measures, proper error handling, and scalable architecture. Deploying directly risks data breaches and poor user experience.

What database should I use for my AI-to-production app?

For most apps: PostgreSQL (via Supabase or Neon) offers the best balance of features and scalability. For simpler apps: SQLite or Firebase. For high-scale: Consider managed databases like PlanetScale or AWS RDS. Match complexity to your needs.

Do I need different hosting for production vs prototype?

Yes. AI tools often deploy to preview/demo URLs not meant for production traffic. Production needs: custom domain, SSL certificates, CDN, monitoring, backup systems, and potentially auto-scaling. Vercel, Netlify, or AWS are common production choices.

How much does prototype-to-production conversion cost?

Expect $5,000-$15,000 for basic MVPs, $15,000-$35,000 for standard apps with payments, and $35,000+ for complex applications. The AI prototype typically saves 30-50% compared to building from scratch. Get detailed estimates before starting.

How do I convert my v0/Bolt.new prototype to a production app?

Export your code, set up a proper development environment with Git, add a real database (PostgreSQL/MySQL), implement secure authentication (Auth0, Clerk, or custom), add API rate limiting, set up error monitoring, configure CI/CD pipelines, and deploy to production infrastructure (Vercel, AWS, or similar).

How long does it take to convert an AI prototype to production?

Typically 2-4 weeks for a basic MVP, 4-8 weeks for a standard app with user accounts and payments, and 2-4 months for complex applications. The timeline depends on how much of the AI output is usable and how complex your backend requirements are.

Should I keep the AI-generated code or rewrite it?

Keep what works (usually 60-80% of UI components), refactor what's suboptimal (state management, API calls), and rewrite what's missing (authentication, database, business logic). Don't throw everything away—AI-generated UI is usually solid.

What's the first thing I should add to my AI prototype?

Version control (Git) and a real database. Without these, you can't track changes or persist user data. Next priorities: authentication, environment configuration, and error handling. The AI prototype likely has none of these properly implemented.

Can I deploy my AI prototype directly to production?

Only for static landing pages with no user data. For anything with forms, user accounts, or data storage, no. AI prototypes lack security measures, proper error handling, and scalable architecture. Deploying directly risks data breaches and poor user experience.

What database should I use for my AI-to-production app?

For most apps: PostgreSQL (via Supabase or Neon) offers the best balance of features and scalability. For simpler apps: SQLite or Firebase. For high-scale: Consider managed databases like PlanetScale or AWS RDS. Match complexity to your needs.

Do I need different hosting for production vs prototype?

Yes. AI tools often deploy to preview/demo URLs not meant for production traffic. Production needs: custom domain, SSL certificates, CDN, monitoring, backup systems, and potentially auto-scaling. Vercel, Netlify, or AWS are common production choices.

How much does prototype-to-production conversion cost?

Expect $5,000-$15,000 for basic MVPs, $15,000-$35,000 for standard apps with payments, and $35,000+ for complex applications. The AI prototype typically saves 30-50% compared to building from scratch. Get detailed estimates before starting.

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STEP-BY-STEP GUIDE

How to Convert Your AI Prototype to a Real App: Step-by-Step

Overview

Converting a v0/Bolt.new/Lovable prototype to production takes 2-4 weeks for a basic MVP. The process involves: exporting code → adding database → implementing auth → building APIs → security audit → deployment. This guide covers each step with specific tools and timelines.

You've built something cool with an AI tool. Now what? This guide walks through the exact process our teams use to take AI prototypes to production — the same process we've used for 100+ client projects.

Days 1-2

Setup & Audit

Days 2-6

Database & Auth

Days 6-14

API & Features

Days 14-21

Test & Deploy

8-Step Production Conversion Process

Export & Set Up Version Control

Day 1

Export code from v0/Bolt.new/Lovable
Initialize Git repository
Set up branch strategy (main, develop, feature)
Create initial commit
Connect to GitHub/GitLab

TOOLS: GitGitHub

Audit & Clean the Code

Days 1-2

Review all AI-generated code
Remove unused components
Fix TypeScript/linting errors
Organize folder structure
Document what exists vs what's needed

TOOLS: ESLintPrettierTypeScript

Set Up Database

Days 2-4

Choose database (PostgreSQL recommended)
Design proper schema with relationships
Add indexes for performance
Set up migrations
Implement data validation

TOOLS: PrismaDrizzleSupabase

Implement Authentication

Days 4-6

Choose auth provider (Auth0, Clerk, custom)
Implement sign up/sign in flows
Add session management
Set up role-based access
Secure all protected routes

TOOLS: NextAuthClerkAuth0

Build API Layer

Days 6-10

Create API routes for all features
Add input validation
Implement rate limiting
Add proper error responses
Document API endpoints

TOOLS: tRPCREST APIZod

Add Business Logic

Days 10-14

Implement core features AI couldn't build
Add payment integration (if needed)
Build admin functionality
Add email notifications
Implement business rules

TOOLS: StripeSendGridcustom code

Testing & Security

Days 14-18

Write critical path tests
Security audit (OWASP basics)
Performance testing
Cross-browser testing
Mobile responsiveness check

TOOLS: VitestPlaywrightLighthouse

Deploy to Production

Days 18-21

Set up production environment
Configure CI/CD pipeline
Add monitoring & logging
Set up backups
Configure custom domain & SSL

TOOLS: VercelGitHub ActionsSentry

Production Readiness Checklist

Verify these before launching:

Security

HTTPS enforced

Input sanitization

SQL injection prevention

XSS protection

CSRF tokens

Rate limiting

Secure headers

Data

Database backups automated

Data validation on all inputs

Proper error handling

Audit logging for sensitive actions

Performance

Images optimized

Code splitting implemented

CDN configured

Database queries optimized

Caching strategy in place

Monitoring

Error tracking (Sentry)

Uptime monitoring

Performance monitoring

Log aggregation

When to DIY vs Hire Help

✓ DIY If...

You're a developer comfortable with full-stack
Simple MVP with basic features
No user payments or sensitive data
Learning experience is part of the goal
Timeline is flexible

✓ Hire Help If...

Handling user data or payments
Need to launch quickly and reliably
Complex backend or integrations needed
Security and compliance requirements
Your time is better spent elsewhere

Our engineering teams in Bangalore and Coimbatore specialize in AI-to-production conversions. We've done this 100+ times.

Need Help Taking Your Prototype to Production?

Share your v0, Bolt.new, or Lovable project. We'll provide a detailed assessment and timeline.

Get Free Production Assessment →

Related Guides

Cost GuideTool ComparisonAI LimitationsOur Services

Frequently Asked Questions

Common questions about AI automation for AI prototype conversion

How do I convert my v0/Bolt.new prototype to a production app?
Export your code, set up a proper development environment with Git, add a real database (PostgreSQL/MySQL), implement secure authentication (Auth0, Clerk, or custom), add API rate limiting, set up error monitoring, configure CI/CD pipelines, and deploy to production infrastructure (Vercel, AWS, or similar).
How long does it take to convert an AI prototype to production?
Typically 2-4 weeks for a basic MVP, 4-8 weeks for a standard app with user accounts and payments, and 2-4 months for complex applications. The timeline depends on how much of the AI output is usable and how complex your backend requirements are.
Should I keep the AI-generated code or rewrite it?
Keep what works (usually 60-80% of UI components), refactor what's suboptimal (state management, API calls), and rewrite what's missing (authentication, database, business logic). Don't throw everything away—AI-generated UI is usually solid.
What's the first thing I should add to my AI prototype?
Version control (Git) and a real database. Without these, you can't track changes or persist user data. Next priorities: authentication, environment configuration, and error handling. The AI prototype likely has none of these properly implemented.
Can I deploy my AI prototype directly to production?
Only for static landing pages with no user data. For anything with forms, user accounts, or data storage, no. AI prototypes lack security measures, proper error handling, and scalable architecture. Deploying directly risks data breaches and poor user experience.
What database should I use for my AI-to-production app?
For most apps: PostgreSQL (via Supabase or Neon) offers the best balance of features and scalability. For simpler apps: SQLite or Firebase. For high-scale: Consider managed databases like PlanetScale or AWS RDS. Match complexity to your needs.
Do I need different hosting for production vs prototype?
Yes. AI tools often deploy to preview/demo URLs not meant for production traffic. Production needs: custom domain, SSL certificates, CDN, monitoring, backup systems, and potentially auto-scaling. Vercel, Netlify, or AWS are common production choices.
How much does prototype-to-production conversion cost?
Expect $5,000-$15,000 for basic MVPs, $15,000-$35,000 for standard apps with payments, and $35,000+ for complex applications. The AI prototype typically saves 30-50% compared to building from scratch. Get detailed estimates before starting.