Production Readiness Checklist for AI-Built Apps (Lovable, Bolt, Cursor, v0): Security, Auth, and Launch

If you built with Lovable, Bolt.new, v0, or exported code from Cursor / Windsurf, the emotional arc is the same: demo day feels amazing, launch week feels terrifying. That is normal. Production is not “more prompts”—it is risk management. The sections below mirror how we triage rescue work at VibeCheetah: what must be true before you invite customers, what often breaks per platform, and when to pull in a human who has seen the failure mode before.

How to run this checklist in one focused session

You do not need a dedicated “security week.” Block two to three hours, grab a cofounder or friend as a second pair of eyes if you can, and walk top to bottom. Start with auth and data isolation: create two accounts and try to break your own rules. Then open your hosting dashboard and confirm every secret your app needs exists there—database URL, auth provider keys, Stripe keys, email API keys—with no duplicates pointing at old preview projects. Third, hit your production URL over HTTPS and walk the signup, pay, and logout flows on a clean browser profile or incognito window so you are not fooled by cached cookies.

Fourth, trigger at least one intentional error (bad card, invalid form) and confirm you see a sane message on screen and a corresponding line in logs or your error tracker. Fifth, skim your repository for obvious leaks: search for sk_live, BEGIN PRIVATE KEY, and your provider names in committed files. If anything shows up outside env configuration, stop and rotate those credentials before continuing. Finally, write down three sentences: what your backup plan is, where logs live, and who gets paged if checkout is down. Even if “who” is still you, naming it removes denial.

Why the “last 10%” feels like 90% of the project

AI tools excel at generating screens, APIs, and happy-path flows. They are weaker at the invisible glue: environment parity, subtle auth bugs, migration ordering, and security edges. Those issues rarely show up in the preview URL you share with friends—they show up under SSL on a custom domain, under load, or when a user does something unexpected. That is why no-code and AI builder limitations hurt most at the boundary between “demo” and “business.” This checklist is how you shrink that gap without pretending you need a Fortune-500 security program on day one.

Another under-appreciated factor is feedback delay. In preview, you see errors immediately in the same tab where you prompted. In production, users ghost you, analytics look “fine,” and revenue quietly stalls. Instrumentation is not vanity—it is how you shorten the loop again. That is why observability sits alongside auth in our non-negotiables, not as an appendix for later.

Common security gaps in AI-generated apps (and how to spot them)

Models pattern-match to tutorials. Tutorials optimize for clarity, not threat models. The same snippets appear across thousands of repos, which means the same mistakes cluster: client-side-only route guards, “temporary” admin flags, open CORS for debugging that never gets tightened, and SQL assembled from string concatenation when the ORM path looked too verbose. You are not looking for elegance—you are looking for places where trust is assumed instead of enforced.

• Trusting the client: If a React component decides whether to show the billing page, that is UX, not security. The server must reject unauthorized API calls every time.
• Overly permissive API routes: Handlers that accept arbitrary user IDs or role strings from the body without matching them to the authenticated session.
• Webhook endpoints without verification: Payment and integration webhooks must validate signatures from the provider, not just parse JSON.
• Default admin credentials or seed users left enabled in production databases.
• Verbose errors returned to browsers that leak stack traces, table names, or internal IDs.

You do not need to become a penetration tester. You do need a skeptical pass: for each feature that touches money, PII, or deletion, ask “what happens if a logged-out user calls this endpoint with curl?” If the answer is unclear, it is a launch blocker until clarified.

Privacy, compliance, and founder sanity (lightweight)

Full compliance programs come later for many startups, but a few basics prevent self-inflicted crises. If you store emails or names, have a one-page privacy policy that matches reality: what you collect, why, and which subprocessors touch data (hosting, email, analytics). If you take payments, your processor’s requirements (PCI scope, Stripe’s rules) matter more than a generic AI disclaimer. If you serve EU users, familiarize yourself with GDPR basics—lawful basis, data export/delete expectations—not because a blog post replaces legal advice, but because “we didn’t think about it” is an expensive retrospective.

From an SEO and trust perspective, a calm, accurate privacy page plus a working contact channel beats keyword stuffing every time. Users and search engines both reward pages that answer “what happens to my data?” without hand-waving.

Non-negotiables before you invite real users

Read this section first if you are a non-technical founder. Each item is a yes/no gate. If any answer is “I am not sure,” treat it as “no” until verified.

Identity and access (auth)

• Can users only see their own data? Open two test accounts and confirm cross-account access is impossible on every sensitive page and API.
• Are “admin” or internal routes actually protected? Obscure URLs are not security. Someone will find them.
• Password reset and email flows work on production, not only in preview.

For a deeper workflow on finding bugs in generated code, pair this with our debugging AI-generated code guide.

Secrets and configuration

• No API keys in the browser bundle unless they are designed to be public (most are not).
• Production env vars live in your host (Vercel, Railway, etc.), not only in a chat transcript or a local .env file.
• Stripe, email, and database URLs point at live vs test resources intentionally—never by accident.

Transport and domains (HTTPS)

• Custom domain + SSL configured at your hosting provider; mixed HTTP/HTTPS assets do not break auth cookies or embeds.
• Canonical URLs—decide whether www or bare domain is primary and stick to it.

Data durability and backups

• You know how to restore the database or at least export critical data on a schedule that matches how painful total loss would be.
• Migrations have been applied in an order that matches production reality (empty DB → current schema), not only your laptop.

Observability (know when things break)

• Server and client errors surface somewhere you will actually read (hosted logs, Sentry, LogRocket, or equivalent).
• Background jobs and webhooks log failures; payment and signup flows do not fail silently.

Abuse, cost, and scale (minimum viable hardening)

• Rate limits on login, signup, and expensive AI or payment endpoints where feasible.
• File uploads (if any) have size/type limits and do not execute as code on the server.
• Third-party quotas (email, AI, maps) won’t bankrupt you if a bot hits a public endpoint—see also when AI debugging and API spend spiral.

Together, these items define minimum viable production discipline. You can always add formal threat modeling, dependency pinning policies, and multi-region failover later; you cannot easily undo a launch where accounts were wide open or keys were pasted into a public repo. If you want velocity and sleep, treat this block as the bar for your first hundred real users—not perfection for your first million.

Platform-specific gotchas

The checklist above is universal. Below is what we see most often when projects come from specific AI builders. Use this with our deploy AI-generated code and budget hosting stack guides for implementation detail.

Lovable (and similar full-stack builders)

Preview success does not guarantee production parity. Custom domains, database connection strings, and auth redirect URLs must be updated in the deployment environment, not re-prompted ad nauseam. When something works locally or in preview but fails on the live domain, suspect env vars and callback URLs first. Double-check OAuth allowlists: providers reject redirects that differ by a single trailing slash or http vs https. If your app uses serverless functions, cold starts can expose race conditions that never appeared in a warm dev session—watch timeouts on database pools and external APIs. If you are already live and seeing odd crashes, read why vibe coded apps crash in production next—it overlaps heavily with misconfigured production environments.

Bolt.new

Treat Bolt as a fast prototype engine: you still own the export path, dependency choices, and hosting boundaries. Before go-live, run a full production build locally or in CI, confirm lockfiles are committed, and verify you are not relying on ephemeral sandbox-only behavior.

v0 (and UI-first generators)

v0 often hands you front-end excellence with integration assumptions. Validate every data fetch against real APIs, CORS rules, and auth headers in production. UI routes that “feel logged in” must match server session reality.

Cursor / Windsurf / Claude Code (exported repos)

You have maximum flexibility and maximum foot-guns. Run npm run build on a clean machine, audit package.json for unused or risky dependencies, and ensure CI (even a simple GitHub Action) catches regressions. Pay attention to generated config sprawl: multiple ESLint or TS configs, experimental flags left on, and “just for dev” middleware that shipped because it sat in the wrong folder. Document your deploy branch and required env vars in the README so future-you (or a rescuer) does not reverse-engineer the setup from Discord screenshots. If you are stuck in a loop, how to fix stuck AI coding projects outlines a sane escalation path before you burn more time.

For developers: deeper technical notes

This section complements the founder checklist with implementation-level reminders. Skip if you are not touching the repo—but if you are, these items prevent the “works on my machine” class of production incidents.

• Environment matrix: document required vars in .env.example with comments; forbid copy-paste from chat into production without review.
• Build vs runtime: ensure anything that must be server-only never ships to the client bundle.
• Database: forward-only migrations, backups automated by the provider where possible, and a tested restore drill for anything storing payments or PII.
• Dependencies: run audit tooling appropriate to your stack; AI-generated projects often accumulate overlapping libraries.
• Headers: set sensible security headers at the edge or framework layer where your host allows it.

If you are shipping on a modern JavaScript stack, treat type errors and build warnings as debt that compounds—especially when AI keeps adding files. A green local dev server is weaker evidence than a reproducible CI build artifact. Pair that discipline with staging: even a rough second environment catches “works in preview” illusions before customers do.

When to stop DIY: a simple decision table

You do not hire help because you are “bad at coding.” You hire because opportunity cost and risk compound. Use this table honestly.

Remember that launch anxiety is information. If you are afraid to tweet your link, convert that fear into a single missing checklist item rather than into another generic “polish” week. Shipped with known, documented gaps beats hidden unknowns every time—as long as those gaps are not auth, billing, or data exposure.

Bookmark this page, run the checklist once before your first real traffic, and again after any major auth or payment change—two passes beat one heroic all-nighter.

VibeCheetah exists for exactly this handoff: you keep momentum, we carry production risk across auth, env, deploy, and the ugly edge cases AI glosses over. View pricing and tiers →

Frequently asked questions

After the checklist: ship, then tighten

Passing this checklist does not mean you stop improving. It means you have earned the right to learn from real users instead of from preventable disasters. Schedule a quick weekly review of errors and slow queries for the first month after launch. Revisit rate limits after traffic spikes. Rotate any credential that ever touched a screen share or screen recording. Most importantly, keep a single source of truth for environment configuration so your next feature does not quietly reintroduce preview URLs into production.

If you want a faster shipping rhythm without skipping safety, combine this article with our ship your vibe coded project faster playbook—cadence and checklist together beat heroics.

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