Cursor App Works Locally But Not in Production? Here's the Fix

You've just spent a few evenings with Cursor, crafting what feels like a genuinely innovative application. It runs beautifully on localhost:3000, the UI is slick, and the backend hums along. You're ready to share it with the world. Then, the inevitable happens: you deploy, and your Cursor app works locally but not in production. This isn't a unique problem; it's a rite of passage for many AI-generated apps that move from local sandbox to live server. The gap between 'working on my machine' and 'accessible via a URL' is often wider than expected, leading to frustration and wasted time. At Convergex AI, we regularly see these issues with 'vibe coded' applications. The good news is that most of these deployment pitfalls are predictable and fixable.

Why Your Cursor App Breaks in Production

When your app runs locally, your laptop handles everything: the server runtime, the file system, the database, and the environment variables. This creates a convenient, self-contained bubble. Production, however, is a different beast entirely. It's a remote server, often a containerized environment, with its own specific configurations, network access, and security protocols. Cursor and other AI coding assistants excel at generating functional code, but they don't solve the underlying infrastructure problem. They focus on the what to build, not the how to deploy it reliably at scale.

This fundamental difference is why a Cursor app works locally but not in production. The key is understanding these discrepancies and proactively addressing them before deployment.

Common Pitfalls and How to Fix Them

1. Hardcoded localhost URLs

One of the most frequent culprits when a Cursor app works locally but not in production is the presence of hardcoded localhost URLs. While perfectly functional in your development environment, these absolute URLs will cause your deployed application to send requests back to your local machine, or simply fail when trying to resolve a non-existent localhost on the server.

The Problem: Your AI assistant, by default, often generates code that assumes a local development setup. This leads to API endpoints, callback URLs for authentication, or image assets being hardcoded like http://localhost:3000/api/data.

The Fix: Replace all instances of localhost with dynamic environment variables. This allows your application to adapt to different environments (development, staging, production) without code changes.

Actionable Steps:

1. Search Your Codebase: Perform a global search for localhost:, 127.0.0.1:, and 0.0.0.0:.

2. Define Environment Variables: For each hardcoded URL, create a corresponding environment variable. For frontend-facing URLs, remember to prefix them with NEXT_PUBLIC_ if you're using Next.js, or similar conventions for other frameworks, to expose them to the client-side bundle.

   // Before (bad)
   const API_BASE_URL = "http://localhost:3000/api";
   
   // After (good)
   const API_BASE_URL = process.env.NEXT_PUBLIC_API_BASE_URL || "/api";
   

3. Configure Production Environment: Set the API_BASE_URL (or whatever you named it) in your deployment platform's environment variable settings to your actual production URL (e.g., https://yourdomain.com/api).

2. Missing Environment Variable Configuration

Your .env file is a development convenience. Production servers don't read it. This is a critical distinction that often trips up Cursor-built apps.

The Problem: Your app runs fine in Cursor because all secrets, API keys, database credentials, and callback URLs are loaded from your local .env file. When deployed, these variables are simply missing, leading to silent failures, crashes, or incorrect behavior. RepoAssistant highlights that production needs a clean environment variable map, not just local files.

The Fix: Audit every variable your app needs and configure them directly on your production server or deployment platform.

Actionable Steps:

1. Audit All Variables: Search your entire codebase for process.env references. List every single variable you find.
2. Categorize Variables:
   • Public Frontend Variables: (e.g., NEXT_PUBLIC_STRIPE_KEY) – available at build time and runtime for client-side code.
   • Server-Only Secrets: (e.g., DATABASE_URL, STRIPE_SECRET_KEY) – must only be available on the server, usually at runtime.
3. Update Local/Test Values: Identify any URLs or keys that still point to local or test services and ensure you have production-ready replacements.
4. Set Variables on Production: Load each identified variable into your deployment platform's environment store. This is typically done through a dedicated section in your hosting provider's dashboard (e.g., Vercel, Railway, Supabase, Jetpacked). Ensure sensitive secrets are rotated where needed.

3. SQLite-to-Postgres Migration Woes

SQLite is a fantastic database for local development. It's file-based, requires no setup, and just works. However, it's almost never suitable for a production application, especially one with concurrent users or complex data needs.

The Problem: SQLite databases are file-based, which means they don't scale well, aren't designed for concurrent writes across multiple instances, and often get lost or reset in ephemeral production environments (like serverless functions or container restarts).

The Fix: Migrate to a robust, production-grade relational database like PostgreSQL. Many Cursor-built apps, especially those using Prisma, are designed to work with PostgreSQL in production, as demonstrated by complex apps like VibeSplit, which leverages Next.js 15 with Prisma and PostgreSQL.

Actionable Steps:

1. Choose a Provider: Select a managed PostgreSQL service (e.g., Supabase, Railway Postgres, Render, AWS RDS, Azure Database for PostgreSQL).

2. Update Database URL: Obtain the DATABASE_URL from your chosen provider and set it as an environment variable in your production environment.

3. Migrate Your Schema: If you're using an ORM like Prisma, you'll need to apply your schema to the new PostgreSQL database.

   npx prisma migrate deploy
   

   Ensure your schema.prisma file is correctly configured for PostgreSQL:

   datasource db {
     provider = "postgresql"
     url      = env("DATABASE_URL")
   }
   

4. Data Migration (If Applicable): If you have existing data in your local SQLite database that you need to preserve, you'll need to export it and import it into your new PostgreSQL database. This often involves using tools like pgloader or custom scripts.

Verifying Your Fixes in a Real Environment

Before you declare victory, you need to verify that your fixes actually work in a real production-like environment. Skipping this step is why most first deployments fail.

1. Local Build Check: Run your build command locally (npm run build or yarn build). If it fails here, it will definitely fail in production. Fix any errors before proceeding.
2. Deployment Platform Logs: After deploying, immediately check your deployment platform's build and runtime logs. These logs are invaluable for identifying missing environment variables, database connection issues, or other runtime errors.
3. Manual Testing: Thoroughly test every feature of your deployed application. Pay close attention to:
   • API Calls: Ensure all frontend API requests are hitting the correct production endpoints.
   • Authentication: Verify login, logout, and any third-party auth flows (e.g., Supabase Auth magic links).
   • Database Interactions: Create, read, update, and delete data to confirm your PostgreSQL connection is solid.
   • External Integrations: Test any third-party APIs (e.g., payment gateways, email services) that rely on specific callback URLs or API keys.
4. Monitoring and Alerting: Set up basic monitoring and alerting for your production application. Tools like Sentry, LogRocket, or even simple uptime monitors can alert you to issues before your users do.

Successfully deploying an AI-generated application often takes more than just a few prompts. It requires a detailed understanding of the deployment lifecycle and common pitfalls. If you're struggling to get your Cursor app from localhost to live, you're not alone. Many developers find themselves in this situation, and it can feel like a maze of configuration and infrastructure challenges.

If you find yourself stuck, remember that Convergex AI specializes in taking 'vibe coded' applications and turning them into production-ready products. We can help fix Cursor apps that work locally but break in production, refactor messy codebases, and set up reliable deployments so you can ship with confidence.