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Vibe Coding 2026-04-12 12 min read

TL;DR: Most vibe coding projects die before launch — not because the code is bad, but because the infrastructure decisions are wrong. The AI picked your database, you did not read the pricing page, and now you are locked into a platform you cannot afford at scale. This article covers 7 vibe coding mistakes, including the infrastructure ones nobody talks about, and what to do instead.

Table of Contents

The Real Reason Vibe Coding Projects Fail

Vibe coding in 2026 is incredible. You can go from idea to working prototype in an afternoon. Cursor, Lovable, Bolt, v0 — the tools keep getting better, and the barrier to building software has never been lower.

But here is the thing nobody talks about: most vibe coding projects still die before launch.

Not because the code is broken. Not because the AI hallucinated a function that does not exist. Those problems are visible and fixable. The projects that die quietly are the ones where everything works in development, but the infrastructure decisions made in the first 30 minutes create problems that surface weeks later.

Wrong database. Wrong pricing tier. Wrong platform coupling. By the time you notice, rewriting is the only option — and most people quit instead.

This article covers 7 vibe coding mistakes that kill projects. You have probably seen articles about the first two and the last two. The middle three — mistakes 3, 4, and 5 — are the ones that actually matter, and almost nobody writes about them.

If you are building a real product with vibe coding, these are the mistakes to avoid.

1. Asking Your AI for Everything at Once

The mistake: You open Cursor or Lovable and type something like: "Build me a SaaS with user authentication, Stripe payments, team management, role-based permissions, real-time collaboration, and a dashboard with analytics."

Why it happens: It feels efficient. You know what you want the final product to look like, so why not describe the whole thing? The AI seems capable enough.

What actually happens: The AI generates a massive codebase with dozens of files, half-implemented features, and architectural decisions that conflict with each other. The auth system does not work with the payment flow. The team management assumes a data model that the dashboard does not support. You spend more time debugging the generated code than you would have spent building it incrementally.

What to do instead: Break your project into vertical slices. Build one complete feature at a time:

  1. User can sign up and log in
  2. User can create a thing (whatever your core object is)
  3. User can pay for the thing
  4. Add the next feature

Each slice should be working and testable before you start the next one. This is not just vibe coding advice — it is how experienced engineers build software. The AI is a tool, not a project manager.

2. Skipping the Planning Phase

The mistake: You jump straight into prompting without writing down what you are building.

Why it happens: Planning feels slow when you could be shipping. The whole point of vibe coding is speed. Writing a spec document feels like the old way of doing things.

What actually happens: You make contradictory decisions across prompting sessions. On Monday you tell the AI users have "workspaces." On Wednesday you call them "projects." On Friday the AI generates code that references both, and neither works. Your data model has three tables that should be one, and one table that should be three.

What to do instead: Write a one-page spec before you touch any AI tool. It does not need to be formal. Just answer these questions:

  • Who uses this and what do they do?
  • What are the 3-5 core user flows?
  • What is the data model? (even a rough sketch of tables/objects)
  • What is the tech stack?

Thirty minutes of planning saves hours of rework. Give this document to the AI as context in every session. You will get dramatically better output because the AI has a coherent picture of what it is building.

3. Letting Your AI Pick Your Database

The mistake: You ask the AI to build your app and accept whatever database it suggests without questioning it.

Why it happens: LLMs have strong defaults based on their training data. In 2026, that default is almost always Supabase. It shows up in tutorials, it is integrated into tools like Lovable and v0, and AI models have seen thousands of "build with Supabase" examples.

The AI is not malicious. It is optimizing for "get something working fast." Supabase does get something working fast. But the AI is not optimizing for "build something you can afford at scale" or "pick infrastructure you actually control."

What actually happens: Your entire application is built on top of Supabase's client library, Supabase's auth, Supabase's row-level security, and Supabase's real-time subscriptions. You did not choose this stack — the AI chose it for you, and you approved it because it worked.

Three months later, you have users, and you discover that Supabase costs $599/month at the Team tier with no middle ground between that and the $25/month Pro plan. Or you realize you need MySQL for your use case, not Postgres. Or you want to self-host. But every query in your codebase goes through supabase-js, so migrating means rewriting everything.

What to do instead: Choose your database deliberately. Ask yourself:

  • Do I need Postgres-specific features, or will any relational database work?
  • What does this database cost at 10x my current usage?
  • Can I migrate away from this provider without rewriting my application layer?
  • Am I paying for a database, or am I paying for a platform I only use as a database?

If the answer is "standard MySQL or Postgres on a $6/month server," that is probably the right answer. You can always add services later. You cannot easily subtract a platform once your code depends on it.

4. Ignoring the Pricing Cliff Until It Is Too Late

The mistake: You build on a platform's free tier without reading the pricing page.

Why it happens: Free tiers are designed to be frictionless. You get started without a credit card, everything works, and pricing feels like a problem for later. The AI never mentions cost. It does not know or care what things cost.

What actually happens: Every major developer platform in 2026 has a pricing cliff — a point where costs jump dramatically with no middle ground:

  • Supabase: Free to $25/month (Pro) to $599/month (Team). That is a 24x jump with nothing in between.
  • Vercel: Free to $20/month (Pro) to $400+/month (Enterprise). The Pro plan has spend limits, but once you need features like firewall rules or SLA guarantees, you are in enterprise territory.
  • Firebase: Free to pay-per-read. Sounds reasonable until your app does 50 million reads in a month because a poorly optimized query runs on every page load, and your bill is $300.
  • PlanetScale: Had a generous free tier, then removed it entirely and shut down. Your database just... disappeared.

The free tier trap is the same pattern every time: get developers hooked on free, then charge enterprise prices when they are too locked in to leave.

What to do instead: Before you use any service, find the pricing page and answer one question: what does this cost when my project succeeds?

Not the free tier cost. Not the hobby plan cost. The cost at 10,000 users, 100,000 API calls, 50GB of data. Write that number down.

Then compare it to the alternative: a $6-15/month VPS running MySQL or Postgres directly. For most vibe coding projects, the answer is obvious.

Rule of thumb: never use usage-based pricing for core infrastructure. Fixed billing only. You should know what your database costs next month before the month starts.

5. Coupling Your Codebase to a Platform

The mistake: You use the platform's proprietary client library for everything — supabase-js, Firebase SDK, PlanetScale's serverless driver — instead of standard database drivers.

Why it happens: The AI generates code using these libraries because they are well-documented and the AI has seen thousands of examples. The platform's quickstart guide uses them. They provide nice abstractions. It feels like the right way to use the service.

What actually happens: Every database query in your application is written in a proprietary format:

// This is not SQL. This is supabase-js.
const { data, error } = await supabase
  .from('users')
  .select('*')
  .eq('email', userEmail)
  .single()

That query works great on Supabase. It works on literally nothing else. If you decide to move to self-managed Postgres, or MySQL, or any other provider, you are rewriting every single query in your application.

Compare that to standard SQL through a standard driver:

const [rows] = await db.query(
  'SELECT * FROM users WHERE email = ? LIMIT 1',
  [userEmail]
)

That query works on MySQL, Postgres (with minor syntax changes), SQLite, and any provider that speaks SQL. Migrating means changing a connection string, not rewriting your data layer.

What to do instead: Use standard database drivers. In Python, that is mysql-connector-python or psycopg2. In Node, that is mysql2 or pg. Write actual SQL.

Yes, the AI might generate code that uses supabase-js by default. Override it. Tell the AI: "Use the pg driver with raw SQL queries. Do not use supabase-js." You will get working code that is portable.

This is not about avoiding Supabase specifically. The same logic applies to Firebase, Convex, or any proprietary data layer. Standard interfaces protect you. Proprietary ones lock you in.

6. Not Understanding the Generated Code

The mistake: The AI writes code that works, so you ship it without reading it.

Why it happens: The whole promise of vibe coding is that you do not need to understand every line. And for prototypes, that is true. But if you are building something you intend to run in production, there is a difference between "do not write every line" and "do not understand any line."

What actually happens: The AI generates a React component with 200 lines of state management. It works. You ship it. A week later, users report that the page freezes when they have more than 50 items. You open the component and have no idea what it does. You cannot debug it, so you ask the AI to fix it. The AI rewrites the component, introduces a new bug, and you are in a loop.

Or worse: the AI generates an API endpoint that concatenates user input directly into a SQL query. It works perfectly in development. In production, it is a SQL injection vulnerability waiting to happen.

What to do instead: Read the generated code before committing it. You do not need to understand every line of CSS or every utility function. But you need to understand:

  • How data flows through your application
  • What your API endpoints accept and return
  • How database queries are constructed
  • Where user input is processed

Treat AI-generated code as a pull request from a junior developer. It is probably fine, but review it before it goes to production.

7. Deploying Without a Security Review

The mistake: You push to production without auditing the code for security issues.

Why it happens: Vibe coding moves fast. You built the thing, it works locally, and you want to ship. Security review feels like a speed bump.

What actually happens: The three most common security issues in AI-generated code:

  1. API keys in client-side code. The AI puts your Stripe secret key or database password in a .env file that gets bundled into the frontend, or hardcodes it directly in a client-side file.

  2. No input validation. The AI generates endpoints that accept whatever the client sends without checking types, lengths, or formats. A malformed request crashes the server or worse.

  3. SQL injection. The AI builds queries by concatenating strings instead of using parameterized queries. This is less common in 2026 than it used to be, but it still happens, especially in less common frameworks.

What to do instead: Before you deploy, do a 30-minute security pass:

  • Search your codebase for hardcoded secrets (API keys, passwords, tokens)
  • Verify that every database query uses parameterized inputs, not string concatenation
  • Check that your .env file is in .gitignore
  • Confirm that API endpoints validate input before processing it
  • Test what happens when you send unexpected data to your endpoints

This is not a full penetration test. It is the minimum viable security review. If you are handling payments or sensitive data, do more.

The Pattern Behind Mistakes 3-5

Look at mistakes 3, 4, and 5 together. They are all the same root problem: defaulting to whatever the AI suggests instead of making deliberate infrastructure choices.

The AI picks Supabase. You accept it. (Mistake 3.) You do not check what Supabase costs at scale. (Mistake 4.) Your codebase is now coupled to supabase-js. (Mistake 5.)

Three months later, you have a working product with users, and you discover you are on a path to $599/month in database costs for what is essentially Postgres. Migrating means rewriting your data layer. So you either pay it, or you abandon the project.

This pattern is not unique to Supabase. Swap in Firebase, Convex, Neon, or any platform with a generous free tier and a proprietary client library. The mechanics are identical.

The fix is also identical: make infrastructure decisions deliberately. Choose your database, your hosting, and your billing model before you write the first prompt. Give the AI constraints. "Use MySQL with the mysql2 driver. Deploy on a VPS. No proprietary SDKs." You will get output that is boring, portable, and cheap to run.

Boring is the goal. Your product should be interesting. Your infrastructure should not be.

FAQ

What is the single biggest vibe coding mistake?

Letting the AI make infrastructure decisions for you (mistake 3). Code quality issues are fixable. Choosing the wrong database or platform creates structural problems that require a rewrite to fix. Always pick your database, hosting, and billing model before you start prompting.

Is vibe coding viable for production applications?

Yes, if you treat it as a development methodology, not a magic wand. Plan before you prompt, review what the AI generates, and make deliberate infrastructure choices. The speed advantage is real — you just need to apply it to a sound foundation instead of building fast on sand.

How do I avoid the free tier pricing trap?

Check the pricing page before you start building. Find the cost at 10x your expected usage and decide whether you can afford it. Prefer services with fixed monthly billing over usage-based pricing. A $12/month VPS with MySQL has the same cost at 100 users and 100,000 users.

Should I use Supabase / Firebase / PlanetScale for vibe coding?

These are good products with real use cases. The mistake is not using them — it is using them by default because the AI suggested it, without understanding the pricing and lock-in implications. If you evaluate the pricing, understand the coupling, and decide it is the right fit, go for it. The problem is when the AI makes that decision and you never question it.

Conclusion

Vibe coding is the fastest way to build software in 2026. But speed without direction just means you hit the wall faster.

The seven mistakes in this article follow a pattern. The first two (prompting and planning) are about working with the AI effectively. The last two (code review and security) are about shipping responsibly. Those four get covered in every vibe coding article.

The middle three — database choice, pricing awareness, and platform coupling — are the ones that actually kill projects. They are invisible during development and catastrophic in production. They are also the easiest to prevent: spend 30 minutes choosing your tech stack deliberately before you write your first prompt.

Your AI is an incredible code generator. It is a terrible infrastructure architect. Keep those roles separate, and your vibe coding project has a much better chance of surviving past launch.

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