Deploying AI-Generated Code: The Hidden Risks Nobody Mentions

Building the code is only half the story. Getting it to production — and keeping it running there — is where most AI-generated applications fall apart.

Here is the typical deployment process for a vibe-coded app: push code to GitHub, connect the repository to Railway or Vercel or Render, and let the platform auto-deploy every time you push a change. It works. It is simple. And it is missing virtually every practice that professional teams use to deploy safely.

This article covers what those practices are, why they matter, and what specific risks you are taking when you skip them.

Risk 1: No CI/CD Pipeline

CI/CD — Continuous Integration and Continuous Deployment — is the process of automatically testing, building, and deploying code through a series of checks before it reaches production. It is the single most important piece of deployment infrastructure, and AI-generated projects almost never have it.

Without CI/CD, here is what your deployment process looks like: you make a change, push it, and it goes live. Immediately. No checks. No tests. No validation. If the change has a bug, that bug is now in production. If the change breaks a feature you did not test, that feature is now broken for all your users.

With CI/CD, pushing code triggers a pipeline. The pipeline runs automated tests. It checks for code quality issues. It builds the application. If any step fails, the deployment is blocked. The broken code never reaches production. You find out about the problem in a notification, not in a customer email.

Risk 2: No Staging Environment

A staging environment is a copy of your production environment where you test changes before they go live. It uses the same server configuration, the same database type (with test data), and the same external service connections. It is production, minus the real users.

AI-generated projects do not have staging environments. They have development (your laptop) and production (the live site). Nothing in between.

This means you are testing changes in an environment that is fundamentally different from where they will run. Your laptop has different memory, different software versions, different network conditions, and different configuration from your production server. A change that works on your laptop can break in production for reasons that have nothing to do with your code.

A staging environment catches these discrepancies before your users do. Without one, your users are your staging environment. They are the first people to encounter every deployment, including the broken ones.

Risk 3: No Rollback Plan

When a deployment goes wrong in a professional setup, the team rolls back — reverts to the previous version within minutes. The broken change is undone, the application is restored, and the team has time to diagnose and fix the problem without production being down.

When a deployment goes wrong in a vibe-coded setup, there is usually no rollback capability. The previous version is not preserved. There is no one-click revert. The only option is to find the bug, fix it, and push another deployment — while the broken version is live and your users are experiencing the problem in real time.

How long does it take to find and fix a bug under pressure, with users complaining and revenue potentially being lost? Sometimes minutes. Sometimes hours. Sometimes days. During all of that time, your application is broken.

Risk 4: No Monitoring or Alerting

When your application breaks in production, how do you find out? If the answer is “when a customer tells me,” you have no monitoring.

Professional applications have monitoring that tracks response times, error rates, server resource usage, and application health. When error rates spike, when response times degrade, or when the server runs out of memory, the team gets an alert — often before users notice anything is wrong.

AI-generated applications have none of this. They run in a black box. You know they are working because nobody is complaining. You find out they are broken because someone is complaining. Between the break and the complaint is a gap — minutes, hours, sometimes days — during which your application is failing and nobody knows.

Risk 5: No Environment Management

Production applications need different configurations for different environments. Development uses test API keys, a local database, and debug logging. Production uses live API keys, a production database, and minimal logging. Staging sits somewhere in between.

AI-generated code frequently has no environment management. Configuration is hardcoded. API keys are embedded in the source code. There is no distinction between development and production settings. This creates several problems.

First, you might deploy with development settings active. Your production app talks to a test database, uses sandbox payment keys, or sends emails from a test account. The app “works” but it is not doing what it should.

Second, changing configuration requires changing code. Want to switch to a new API key? Edit the source file, push a new deployment. This is both risky (you might break something while editing) and insecure (your API keys are in your code repository).

Third, you cannot debug production issues in a development environment because the environments are not separated. Every investigation risks accidentally affecting production data.

Risk 6: No Secrets Management

API keys, database passwords, third-party service credentials — these need to be stored in environment variables or a dedicated secrets manager. They should never appear in source code.

In AI-generated projects, secrets are regularly hardcoded directly in source files. This means they are committed to your Git repository, where they live forever in the commit history. Even if you remove them later, anyone with access to your repository history can find them. Bots actively scan GitHub for exposed credentials. An exposed Stripe key can be exploited within minutes of being pushed.

Risk 7: No Logging Strategy

When something goes wrong in production, you need to know what happened. Professional applications have structured logging — a record of every significant action, error, and state change, with enough context to diagnose problems after the fact.

AI-generated code has almost no logging. When a user reports a problem, you cannot see what request they made, what data was involved, or where things went wrong. You are debugging blind. Even basic request logging transforms your ability to diagnose production issues. Without it, every bug report is a mystery. With it, most issues can be diagnosed in minutes.

The Deployment Checklist

If you have an AI-generated application in production, here is the minimum deployment infrastructure you should have in place.

1. A CI/CD pipeline that runs automated checks before deploying. Even if you have no tests, you can at least verify that the application builds successfully.

2. A staging environment where changes are verified before reaching production. This does not need to be expensive — most hosting platforms support this natively.

3. Rollback capability. Know how to revert to the previous version within minutes. Most platforms support this, but you need to verify and practice it before you need it.

4. Basic monitoring. At minimum, uptime monitoring that pings your application every few minutes and alerts you when it is down. Services like UptimeRobot offer free tiers that cover this.

5. Environment variables for all configuration that differs between environments. No hardcoded API keys, database URLs, or service credentials in source code.

6. Structured logging that records errors, failed requests, and significant application events.

The Real Cost of Skipping This

Every item on that list is cheap to implement. A CI/CD pipeline takes an hour to set up. A staging environment costs a few dollars per month. Monitoring has free tiers. Environment variables are free. Logging takes an afternoon.

The cost of not having them is measured in downtime, data loss, security incidents, and the hours you spend firefighting problems that proper infrastructure would have prevented or caught early.

The code is only half the product. The deployment infrastructure is the other half. AI tools build the first half. Nobody builds the second. That is the gap where production failures live, and closing it is one of the highest-value investments you can make in your AI-built application.