Outgrowing v0: How to Transition Your AI MVP to a Scalable Production Stack

You had an idea. You built something fast. Maybe you used v0 to turn your vision into a working MVP in days instead of weeks. That was the right move. Tools like v0 make it easy to prototype and test assumptions without hiring a full engineering team. But now you’re hitting limits, and you’re not alone.

We’ve worked with startups, enterprise teams, and even a quirky Private Equity firm that built on v0, proved value quickly, and then hit a wall. If your AI product is gaining traction, crashing under real usage, or raising security and scalability concerns, it’s probably time to move beyond v0 and into something more robust.

v0 is great for bootstrapping, but once you’ve validated your idea, it starts to become a drag on growth. The most common issues we see include:

• • Performance and Scaling: You don’t get control over how your app is deployed. So when you need to scale, you’re at the mercy of limitations baked into the platform.
  • Code Maintainability: The code generated by v0 can be difficult to manage—often verbose, auto-generated, and hard to test. It works, but it’s brittle.
  • Data & API Security: Most MVPs aren’t built with authentication layers, secure data storage, or proper input validation in mind. For context, here’s how OWASP ranks the most common security flaws found in production systems.
  • DevOps Blind Spots: You may find yourself working without version control, deployment automation, logging, or proper rollback mechanisms.
  • Cloud Lock-In: If your app is deeply coupled to the v0 infrastructure, migration becomes more painful the longer you wait.

Not everyone needs to abandon v0 right away. But it’s worth evaluating if:

• • You have real users and uptime/reliability is starting to matter.
  • You’re duct-taping fixes to keep the app alive. What seems like a small fix creates a thousand lines of code and “breaking changes” in other areas of the application. 
  • You’re planning to onboard engineers but no one wants to inherit the current state.
  • You’re getting serious about compliance, security, or enterprise readiness.
  • You’re spending more time fighting the tool than building features.

If you’ve hit one of those inflection points, here’s what tends to help—and how we typically approach it:

The first step is mapping out what’s actually happening in your app. This isn’t just about reading code—it’s about understanding what business logic v0 encoded, how the data flows work, and what dependencies are in play. Often, the app functions well enough that people haven’t noticed how fragile it is until they try to change something. Doing a full, professional audit is a good idea.

In many cases, it’s not necessary to throw everything out. It’s better to peel off high-risk components—like brittle integrations or untested logic—and start rebuilding those in a modular way. It’s important to stabilize what you have before you go adding features.

We usually see people migrate to AWS, Azure, or GCP—depending on what other systems they already use. Most teams benefit from containerizing the application (Docker) and setting up serverless functions or managed services where appropriate. This gives you better reliability, cost control, and observability.

For insights on scaling AI applications effectively, consider reading this TechRadar article on scaling at the speed of success, which discusses the importance of designing systems with horizontal scalability and leveraging cloud-native architectures.

If you’re curious about how we approach this, our custom software development services go deep into cloud-native architectures, infrastructure modernization, and replatforming.

Even lightweight CI/CD pipelines can make a huge difference in confidence and speed. Logging, error tracking, feature flagging, and rollbacks aren’t luxuries—they’re survival tools once your app is in production. If you’re not already familiar, 12factor.net is still a solid guide for building maintainable, cloud-native applications.

Additionally, integrating DevOps and MLOps into a unified software supply chain can improve efficiency and deployment in enterprise AI applications. This TechRadar article emphasizes the necessity of such integration to enhance collaboration and streamline operations.

Most teams that come off v0 have a lot of uncertainty. What we’ve found helps is short development cycles, user feedback in the loop, and not over-engineering the first version of the rewrite. It’s okay to grow into complexity.

It’s worth saying out loud: the AI tooling landscape is shifting fast. What looks like the right long-term move today might feel outdated next year. More tools are coming out that promise to bridge the gap between prototyping and production. Some will succeed. Others won’t.

We expect the line between no-code and production-ready will blur over the next 6–12 months. Platforms like v0 may evolve to support better deployment, observability, and testability—or they may not. In the meantime, teams with real users can’t afford to wait. When the tooling isn’t ready, having access to engineers who can stabilize, extend, and maintain the system becomes a necessity.

For a comprehensive understanding of transitioning AI projects from pilot to production, this LinkedIn article provides valuable insights into the necessary steps and considerations.

If you’re reading this because you’re feeling stuck, you’re not alone. We’ve seen all flavors of this transition. What matters most is having a plan—something that gets you from where you are to a version of your app that’s maintainable, secure, and ready to grow.

We’ve also seen this play out in franchise technology transformations and health tech systems, where teams needed to scale quickly and couldn’t afford to start from scratch.

Whether that means auditing your existing setup, rewriting critical paths, or moving to a new cloud-native architecture, the right time to start thinking about it is before something breaks.

Q: Is it possible to keep parts of my v0 app and only migrate the backend or APIs?
A: Yes. We’ve found that gradual migration works well—especially if you want to retain the UI or use v0-generated components as scaffolding while rebuilding backend systems on cloud infrastructure.

Q: How long does a typical migration from v0 to AWS or Azure take?
A: It depends on complexity, but for most early-stage products, expect 3–8 weeks for a clean transition with infrastructure setup, code stabilization, and CI/CD in place.

Q: Will I lose any features by moving off v0?
A: Not if the transition is scoped carefully. In fact, you’ll usually gain flexibility and control. It’s also a good moment to drop unused features or harden fragile logic.

Q: Can I still iterate quickly after moving to cloud infrastructure?
A: Definitely, especially if you add automated deployment and testing pipelines. You’ll trade speed built on abstraction for speed built on visibility and tooling.

Q: Is v0 bad for startups?
A: Not at all. It’s a great tool for prototyping. The challenge is knowing when to move on before limitations create tech debt or missed opportunities.