Vibe Coding Database Disasters Waiting to Happen

Your application’s code can be messy and still technically work. Your database cannot. Code is replaceable. Data is not.

This is what makes database problems in AI-generated applications uniquely dangerous. A UI bug annoys users. A data loss event can end a business. And the way AI tools set up databases is, to put it gently, optimised for speed of development rather than safety of your information.

Here are the specific database disasters that are sitting inside most vibe-coded applications, ticking away quietly until they are not.

Disaster 1: SQLite in Production

This is the most common one, and it is almost universal in AI-generated web applications. You ask an AI to build you an app with a database. It reaches for SQLite — a file-based database that requires zero configuration, no separate server, and works immediately.

For local development and prototyping, SQLite is excellent. For a production application with multiple users, it is a ticking time bomb.

SQLite handles one write operation at a time. If two users submit a form at the same moment, one of them waits. If ten users submit simultaneously, nine of them wait. If a hundred users are active at once, the app effectively locks up.

Beyond concurrency, SQLite stores your entire database as a single file on disk. If that disk fails, your data is gone. If your hosting provider migrates your container (which platforms like Railway, Render, and Fly.io do routinely), that file may not come with it. Your entire database, vanished, because it was treated as a temporary file on an ephemeral filesystem.

Disaster 2: No Database Migrations

Database migrations are version control for your database structure. They track every change — adding a column, renaming a table, changing a data type — in a sequence of files that can be applied, rolled back, and shared across environments.

AI-generated code almost never includes a migration system. Instead, it creates the database schema directly in the application code or, worse, through raw SQL statements scattered across multiple files. When you need to change the database structure (and you will), there is no controlled way to do it.

This means adding a new field to your customer table requires manually altering the database, hoping you do not break existing data, and praying that your development environment, staging environment, and production environment all have the same structure. They will not.

Without migrations, every database change is a one-way door. There is no rollback. There is no audit trail. There is no way to reproduce the database structure from scratch if you need to set up a new environment.

Disaster 3: No Backups

This one is so obvious it should not need saying, but it does: most AI-generated applications have zero backup strategy.

The AI builds the app. The app stores data. Nobody sets up automated backups. The founder assumes the hosting provider handles it (they usually do not, or only with a paid add-on that was never activated). One bad deployment, one accidental deletion, one database corruption event, and everything is gone.

How much is your data worth? Every customer record. Every transaction. Every configuration. Every piece of history. Now imagine it disappearing overnight with no recovery option.

Disaster 4: No Indexing

When your database has 100 rows, every query is fast. The database scans all 100 rows in milliseconds. You will never notice.

When your database has 100,000 rows, the same query scans all 100,000 rows. Without proper indexes, a query that took 5 milliseconds now takes 5 seconds. A page that loaded instantly now takes 15 seconds because it runs three unindexed queries.

AI-generated code creates tables but rarely creates indexes. The AI does not know how your data will grow or which queries will be run most frequently. It creates a schema that works for the demo dataset of 20 records and calls it done.

The insidious part is that this problem sneaks up on you. The app is fast for months. Then one day it is slow. Then it gets slower. Then it is unusable. The database did not break — it just grew past the point where brute-force scanning every row is viable.

Disaster 5: No Data Validation at the Database Level

AI-generated code might validate data in the application layer — checking that an email looks like an email before saving it. But the database itself has no constraints. No unique constraints, no foreign keys, no check constraints, no not-null requirements.

This means if the application validation has a bug (and it will), bad data flows straight into the database. Duplicate customer records with the same email. Orders referencing products that have been deleted. Negative quantities. Dates in the year 0001. Null values in fields that should always have data.

Application-level validation is your first line of defence. Database constraints are your last. Without both, data corruption is inevitable.

Disaster 6: No Connection Pooling

Every time your app needs to talk to the database, it opens a connection. AI-generated code typically opens a new connection for every single request and may or may not close it properly when done.

At low traffic, this works. At production traffic, you exhaust the database’s connection limit. New requests cannot get a connection. The app returns errors or hangs. Meanwhile, abandoned connections from previous requests are still sitting open, consuming resources.

Connection pooling solves this by maintaining a set of reusable connections. Instead of opening and closing connections constantly, the app borrows one from the pool, uses it, and returns it. It is standard practice in every production application and almost never present in AI-generated code.

The Compound Problem

None of these issues exist in isolation. They stack. An app with SQLite in production, no backups, no migrations, and no indexes is not five separate problems. It is a single fragile system where any one failure can cascade into total data loss.

And unlike code bugs, data problems are often irreversible. You can fix a bug and redeploy in an hour. You cannot un-lose data that was never backed up. You cannot un-corrupt records that were saved without validation. You cannot reconstruct a database schema that was never documented.

What a Proper Data Layer Looks Like

A production-ready database setup includes:

1. A managed database service (PostgreSQL on Supabase, Neon, or AWS RDS) with automatic failover and point-in-time recovery.
2. A migration system that tracks every schema change in version-controlled files.
3. Automated backups running daily at minimum, with retention policies and tested restore procedures.
4. Proper indexing on columns used in WHERE clauses, JOIN conditions, and ORDER BY statements.
5. Database-level constraints — foreign keys, unique constraints, not-null requirements, and check constraints.
6. Connection pooling configured for your expected concurrent user count.

None of this is exotic. It is the baseline. Every production application running real business data needs every item on this list. The prototype taught you what data your business needs to track. The next step is making sure that data is safe.