Is Your Cursor App Production Ready? The Complete Checklist
When “It Works” Isn't Enough
You built something with Cursor, Lovable, Bolt, v0, or Claude Code and it works. The demo is impressive. Users can sign up, click around, and see real data. But “it works on my machine” is a fundamentally different statement than “it works for 10,000 users at 3am when the database connection drops.” The gap between a working prototype and production-ready software is where most AI-built apps silently fail.
Production readiness is not a single feature you add at the end. It is a collection of concerns that span every layer of your application: how errors are caught and reported, how the system behaves under load, whether sensitive data is properly protected, and what happens when things inevitably go wrong. AI coding tools are exceptional at building the happy path. They generate clean UI code, wire up API routes, and scaffold database schemas in minutes. But they rarely build the safety nets that keep your app running when conditions are less than ideal.
This guide walks through every category of production readiness that AI coding tools typically skip. Whether you built your entire app with Cursor or used AI assistants for specific features, this checklist will help you identify what needs attention before real users depend on your software.
What Cursor and AI Coding Tools Get Right
AI coding tools deserve credit for what they do well. Cursor and similar assistants produce clean, idiomatic code that follows modern framework conventions. They scaffold Next.js apps with proper file structure, generate React components with sensible prop interfaces, and wire up database queries using ORMs correctly. The initial architecture is often solid — proper separation of concerns, typed interfaces, and reasonable project organization. For getting from zero to a working prototype, nothing else comes close to the speed these tools provide.
The problem is not the code quality on the happy path. The problem is everything that surrounds it. AI tools optimize for the scenario where every request succeeds, every input is valid, and every external service responds instantly. They skip edge cases not because they cannot handle them, but because you did not ask for them. Error states, retry logic, security hardening, monitoring, graceful degradation — these are the concerns that separate a demo from a product. And they are precisely the concerns that AI-generated codebases consistently lack.
The Production Readiness Gap
Security
Security is the highest-stakes gap in AI-generated code. Authentication endpoints often lack rate limiting, which means an attacker can brute-force passwords with no resistance. Session tokens may never expire. API keys get hardcoded into source files and committed to git history. Input validation is frequently absent, leaving your app vulnerable to SQL injection, cross-site scripting, and command injection attacks.
These are not theoretical risks. Automated scanners probe every publicly deployed application for exactly these weaknesses, and AI-generated code has predictable patterns that make it easier to exploit. Review the authentication checklist to audit your auth implementation and close the most critical gaps first.
Monitoring and Error Tracking
A production app without monitoring is flying blind. When your AI tool generated your backend, it almost certainly did not set up error tracking, alerting, health check endpoints, or structured logging. This means when something breaks in production — and it will — you will learn about it from an angry user rather than an automated alert. By then, the problem may have been affecting users for hours.
At minimum, production apps need an error tracking service like Sentry that captures unhandled exceptions with full context, a health check endpoint that external monitors can ping, and structured logging that makes it possible to trace a request through your system. The monitoring checklist covers the full set of observability requirements.
Payments
Payment handling is where production readiness gaps become directly expensive. AI-generated payment code frequently processes price calculations on the client side, where users can manipulate the values. Webhook endpoints accept payloads without verifying signatures from the payment provider. Idempotency keys are missing, so network retries can charge customers multiple times for the same transaction.
Subscription management is another common gap — AI tools often check subscription status by reading local state rather than verifying with Stripe or your payment provider, which means users can bypass paywalls by manipulating client-side data. See the payments checklist for a complete audit of AI-generated payment code.
Data and Reliability
AI coding tools generate database schemas and queries that work, but they rarely address what happens when things go wrong. Backup strategies are absent. Migration plans do not exist. Database-level validation constraints are missing, which means your application code is the only thing preventing invalid data from entering the system. If a bug bypasses your app logic, the database accepts whatever it receives.
Retry logic for external service calls is almost never present in AI-generated code. When a third-party API times out or returns a transient error, your app simply fails instead of retrying with exponential backoff. Connection pooling, graceful shutdown handling, and circuit breakers are all absent by default. The data management checklist covers backup, export, and reliability requirements.
Legal and Compliance
Legal compliance is the category that AI coding tools ignore most completely. No AI assistant will generate a privacy policy, implement cookie consent, or build a data deletion workflow unless you explicitly ask for it. Yet these are legal requirements in most jurisdictions. GDPR requires that European users can request deletion of their personal data. CCPA gives California residents similar rights. Without these capabilities, your app is non-compliant from day one.
Beyond privacy regulations, there are practical concerns: terms of service that limit your liability, cookie consent banners that satisfy ePrivacy requirements, clear data retention policies, and transparent disclosure of how user data is processed. These are not optional extras for a production app — they are baseline requirements that protect both your users and your business.
The Production Readiness Checklist
How Vibe Check Automates This
Manually auditing every domain in this guide takes hours, and the results are only as good as your knowledge of each area. Vibe Check automates the entire process. The CLI plugin runs inside Claude Code and scans your actual codebase across all production readiness domains — security, monitoring, payments, reliability, legal compliance, and more. It identifies specific gaps and generates actionable prompts to fix them. Install it with npx vibe-check-cc and run a full scan with /vibe-check:check in Claude Code.
If you want to understand your production readiness risks before writing any code, the web app at vibe-check.cloud provides guided assessments without requiring code access. Describe what you are building and Vibe Check identifies the domains that need attention based on your app's specific features and architecture. Either way, you get a clear picture of what stands between your working prototype and a production-ready application.
Get Your App Production Ready
Find out what your AI coding tool missed before your users do.