Production Readiness Checklist for Vibe Coded Apps

Localhost:3000 looks like a masterpiece.

The onboarding works. The dashboard loads. The buttons do what they are supposed to do. The database has real-looking data in it. For the first time, the thing that used to exist only in your head is sitting in front of you as an actual app.

You almost want to kiss the metaphorical forehead of Lovable, Bolt, Replit, Cursor, Claude Code, Copilot, or whichever vibe coding tool got you here.

But then comes the awkward question.

Now what?

How do you share this app with the world? Is it safe to deploy? Will the app survive more than five people clicking around at the same time? Do you need better error handling, a proper database setup, logging, tests, or a cleaner architecture before you launch?

In this post, we’ll walk through what to check after your vibe-coded app works locally, but before you trust it with real users.

Not from the perspective of killing momentum or rewriting everything from scratch, but from the perspective of turning a promising AI-built prototype into something you can actually ship.

The Vibe Code Pre-Deployment Checklist

Vibe-coded apps tend to share a predictable set of weaknesses. The following checklist will help you remove them before launch.

Remove Hardcoded Secrets From the Repo

AI-assisted coding tools are very comfortable placing API keys, tokens, database URLs, and service credentials directly into files if it helps them get the feature working faster. That may be fine for a throwaway demo, but it is one of the first things you need to fix before your app goes anywhere near production.

Use an open-source secret scanning tool like gitleaks to find secrets across your codebase.
Follow that with a manual check for anything that should be kept protected. Check common files where vibe coding tools often leave secrets behind, especially .env, .env.local, config files, client setup files, Supabase/Firebase initializers, and deployment instructions generated in markdown.
Make sure secrets are not being used inside frontend code. In Next.js, React, Vite, and similar stacks, anything exposed to the browser should be treated as public.
Add .env, .env.local, .env.production, and other local environment files to .gitignore if they are not already there.
Rotate any exposed keys before deployment. If an API key, database password, or service token was ever committed to the repo or pasted into an AI chat, assume it is compromised.
Ask your coding tool to explain where each environment variable is used, but do not let it decide what is safe to expose without checking the framework’s rules yourself.

Enable Authentication and Authorization on Every Endpoint

AI coding tools often protect the visible parts of the app while leaving the underlying endpoints too trusting. The UI may hide a button or redirect logged-out users, but if the API route, server action, or backend function does not enforce access itself, the feature is not actually protected.

Review every API route, server action, backend function, webhook handler, and database operation that reads, creates, updates, or deletes user data. Make sure all of these check whether the user is authenticated before doing anything sensitive.
Add authorization checks after authentication. Knowing who the user is is not enough; you also need to verify what they are allowed to access or change.
Verify that users can only access records that belong to them, their team, their organization, or whatever ownership model your app uses.
Test direct requests to protected endpoints while logged out. If the request still works, the app is not ready.
Test direct requests as one user trying to access another user’s data by changing an ID in the URL, request body, or query string.
Ask your coding tool to list every endpoint and explain how each one enforces authentication and authorization. Then verify the answer manually, because this is exactly the kind of thing AI tools tend to be overconfident about.

Check the Codebase for Vulnerabilities

Vibe coding tools are good at installing whatever package gets the feature working, but they are not always careful about whether that package is outdated, vulnerable or simply unnecessary. Before deployment, you should scan both your dependencies and your own source code, because risk can come from a bad package or from how the app uses otherwise normal code.

Run a dependency vulnerability scan with the package manager your project uses.
For npm projects, start with:

npm audit
For Yarn, use:

yarn audit
For Python projects, consider tools like:

pip-audit
Do not blindly run every automatic fix command without reviewing the result. Tools like npm audit fix --force can upgrade major versions and break your app in ways that are hard to trace.
Pay special attention to packages your AI coding tool installed for one small task and then never cleaned up. Vibe-coded projects often accumulate dependencies that are no longer used.
Use an open-source security scanner like Semgrep to scan both your source code and dependencies for any vulnerabilities.
Treat scan results as a review queue, not a panic button. Some findings will be irrelevant to your app, but each serious or high-confidence issue should be understood before you deploy.

Add Rate Limiting and Abuse Protection

A single malicious actor can send thousands of requests to your application in seconds, turning an unprotected endpoint into a direct cost and performance problem. If that endpoint calls an OpenAI API, for example, your credits could get drained within minutes. Here’s how to avoid that:

Identify every endpoint that triggers a costly or sensitive action, e.g., AI generation, email sending, SMS sending, file uploads, payment flows, exports, scraping, or heavy database queries.
Add rate limits to public and authenticated endpoints. Public endpoints usually need stricter limits, but logged-in users should not get unlimited access either.
Rate limit by user ID when the user is logged in, and by IP address or device/session fingerprint when they are not.
Do not rely on frontend button states like “disabled while loading” as your only protection. A user can bypass the UI and call the endpoint directly.
Add abuse protection to authentication routes, especially login, signup, password reset, magic links, and OTP verification.
Log suspicious behavior, such as repeated failed login attempts, unusually high AI usage, repeated uploads, or many requests from the same IP.

Enforce Database-Level Access Controls

The next thing you need to protect is your database.

Review every table or collection that stores user data, team data, private content, payment information, files, messages, settings, or admin-only records.
Make sure users can only read, create, update, and delete records they are actually allowed to access.
If you use Supabase, enable and review Row Level Security policies instead of relying only on frontend filters like where user_id = currentUser.id.
If you use Firebase, check your Firestore or Realtime Database security rules. Do not leave broad rules like allow read, write: if true in place after the prototype phase.
Test the obvious attack: log in as one user, copy the ID of another user’s record, and see whether the app can fetch or modify it by changing the request manually.
Ask your coding tool to explain your access model table by table, then verify it directly in your database provider’s dashboard or emulator. This is not an area where you want to trust a confident summary.

Add Real Error Handling

AI coding tools often handle errors just enough to make the demo stop crashing, but not enough to make the app understandable when something goes wrong in production. Before launch, you need predictable error handling so users get a clear response, and you get enough information for debugging.

Review every place where the app calls an external service, database, payment provider, AI API, email provider, file storage system, or authentication service.
Replace vague catch blocks that only say console.error(error) or alert("Something went wrong") with intentional handling for the most likely failure states.
Do not expose raw error messages to users. Database errors, stack traces, API responses, and internal IDs can reveal implementation details that should stay private. Return safe, useful messages to the user, such as “We couldn’t save your changes. Please try again.”
Log the actual technical error on the server side, where you can inspect it without showing it to the user.
Add fallback UI for failed loading states, empty states, unavailable data, and failed submissions. Vibe-coded apps often handle the success path beautifully but leave the failure path awkward or broken.
Use a production error tracking tool, such as Sentry or Highlight, so you can see what actually breaks after launch.

Fix the Boring Privacy and Legal Stuff

Next, you need to consider data privacy and the legal side of things. This part feels boring, so most people ignore it, but the moment you collect emails, store user content, process payments, or send data to AI APIs, you need to be clear about what data you collect and how it is handled.

List every type of data your app collects, including names, emails, uploaded files, messages, prompts, payment metadata, analytics events, device information, and anything users type into forms.Also identify which third-party services receive that data.
Check whether your AI features send user-submitted content to external model providers. If they do, your privacy policy should say that clearly.
Do not copy a generic privacy policy from another website and assume it fits. Vibe-coded apps often combine auth, analytics, payments, AI APIs, file uploads, and email tools in ways that a generic template will not accurately cover.
Create the basic legal pages before launch: Privacy Policy, Terms of Service, and, if relevant, Cookie Policy.
Add consent checkboxes where needed, especially for marketing emails, cookies, newsletters, waitlists, and terms acceptance during signup.
Make sure your forms do not collect data you do not actually need. The easiest private data to protect is the data you never collect in the first place.
If your app serves users in regulated industries, handles children’s data, health data, financial data, legal documents, or sensitive workplace information, do not treat this as a template exercise. Get proper legal advice before launch.

Test for Concurrency and Race Conditions

Race conditions are the bugs that do not exist until your app has more than one user, and then suddenly cost you money. Here’s how you avoid them.

Identify flows where two things might happen at once, especially signups, bookings, purchases, credit usage, inventory updates, subscription changes, file uploads, form submissions, and AI generation requests.
Look for code that reads a value, changes it in memory, then writes it back. This pattern is risky when multiple requests can touch the same record at the same time.
Use database transactions for operations that must succeed or fail as one unit, such as creating an order, deducting credits, assigning a booking slot, or updating multiple related records.
Add unique constraints at the database level for things that must never be duplicated, such as usernames, email addresses, invite codes, booking slots, payment references, and team memberships.
Avoid global in-memory state for request-specific data. AI-generated backend code sometimes stores temporary values in module-level variables, which can break badly when multiple users are active.
Run a simple load test against critical endpoints before launch. This will allow you to check whether the app behaves correctly when requests overlap.
Ask your coding tool to identify flows that are vulnerable to duplicate submissions or race conditions, but verify the fixes yourself.

Add Automated Tests (Yes, You Need Them)

AI tools make it very easy to change the app quickly, which also means it is very easy to break something that was working ten prompts ago. Automated tests are a must-have because they give you a repeatable way to check whether the app still behaves correctly after every new prompt, refactor, or deployment.

Start with the core user flows. Test the things that would seriously hurt if they failed. E.g., you should cover signup, login, checkout, onboarding, saving data, deleting data, permission checks, and any flow that calls an external service.
Add tests for expensive actions, such as AI generation, email sending, payment creation, credit deduction, and file uploads.
Mock external services where appropriate. Your tests should not create real Stripe charges, send real emails, or burn AI credits every time they run.
Use end-to-end tests for the most important flows if your app is mostly driven by UI behavior. Tools like Playwright and Cypress are useful here.
Use unit or integration tests for business logic that should stay stable, such as pricing rules, usage limits, permissions, calculations, and data transformations.
Run tests before every deployment, even if the suite is small. A small suite that actually runs is more useful than a perfect testing plan nobody uses.
Ask your AI coding tool to generate the first version of your tests, but review them carefully. Models often write tests that confirm the current implementation instead of testing the behavior the app actually needs.

Choose a Deployment Setup You Can Actually Maintain

AI coding tools can generate deployment instructions for almost anything, but that does not mean every deployment setup is a good fit for you. Before launch, choose an option that matches your current skill level.

For simple frontend or full-stack JavaScript apps, platforms like Vercel, Netlify, Fly.io, or Replit Deployments may be enough.
Avoid jumping straight to complicated setups like Kubernetes, custom VPS, queues, workers, load balancers, or multi-service architectures unless you know why you need them.
Make sure you understand where environment variables are stored, how builds run, where logs appear, and how rollbacks work.
Keep staging and production separate, especially if users or payments are involved.
Document the exact deployment flow in plain language: how to deploy, how to check logs, how to roll back, and what to do if the build fails.
Ask your AI coding tool to explain the deployment architecture back to you. If you cannot understand the explanation well enough to fix a broken build, simplify the setup before launch.

Conclusion

This wraps up our production readiness checklist for your vibe-coded application. We hope you find it useful.

If at any point this starts to feel overwhelming, or if you simply want peace of mind before launch, we can help.

We work with non-technical founders to take vibe-coded prototypes through a proper pre-deployment review, so that you can launch with fewer unknowns and a lot more confidence.

Book yourself a free consultation today.