Built an MVP with Replit, Cursor, or Loveable? What's the Hardest Part of Getting It to Production?

The last year has changed how quickly software gets built.

A Replit app gets hundreds of upvotes. A Loveable prototype impresses investors. A Cursor-built MVP attracts its first 50 or even 500 users. Getting from idea to working product has never been faster.

The real challenge begins after that initial success.

Once real users start depending on your application, the questions become very different:

• Can your infrastructure scale without breaking?
• Is your application secure enough for production?
• Do you have automated testing to prevent regressions?
• How do you monitor performance and detect failures?
• Can you deploy updates confidently without downtime?
• Is your architecture ready for thousands of users instead of dozens?

AI coding tools are exceptional at helping teams build quickly, but production readiness requires another layer of engineering.

This is where experienced product engineering teams make the difference. Companies like GeekyAnts specialize in taking promising MVPs and transforming them into reliable, production-ready products with scalable infrastructure, robust security, automated testing, CI/CD pipelines, observability, and long-term maintainability.

Shipping an MVP is an achievement.

Building a product that can support real customers for years is a different challenge altogether.

I'm curious to hear from founders and engineers:

• What was the biggest obstacle you faced after launching your MVP?
• Was it scaling, security, deployment, performance, technical debt, or something else?
• Did AI tools help beyond the MVP stage, or did you need experienced engineers to take it further?

Let's discuss your experiences.