I Built My App With AI. Now What?

You prompted Cursor or Bolt.new, watched it generate a working app in minutes, and felt like a god. Then you tried to share it with someone else. Now you're staring at terms like "hosting," "environment variables," and "production deployment"—and the godlike feeling has evaporated. You're not alone. This is the deployment wall, and thousands of AI-generated apps are sitting unused because their creators hit it.

Here's the uncomfortable truth: AI tools have gotten remarkably good at generating code. But they've created a new bottleneck—the gap between "it works on my laptop" and "real users can access it." At thelaunch.space, we've shipped 65+ projects using AI-assisted development, and we've learned that deployment is where most non-technical founders get stuck. Not because they lack capability, but because the existing guides are written for developers, not domain experts.

This guide is different. We're going to walk through the actual decision you need to make—not a tutorial on Docker commands you'll forget, but a framework for figuring out what path makes sense for your situation, your skills, and your timeline.

Why AI-Generated Code Feels Like a Trap

A Reddit user captured the emotional arc perfectly: "Felt like a god for about 10 minutes. Then reality set in." That post got over 1,400 upvotes because it describes an experience thousands of non-technical founders have had.

Think of it like this: AI built you a beautiful restaurant kitchen, fully equipped, recipes ready to go. But the kitchen is in your basement. There's no front door, no address, no way for customers to walk in. Deployment is building the front door and putting up a sign.

The problem compounds because AI tools are optimized for that initial "wow" moment. Bolt.new users have reported persistent deployment issues—previews that work locally but fail on Netlify, wrong build commands, synchronization problems that have remained unresolved for months. The tool that made you feel like a god doesn't have a clear path to production.

This isn't a tutorial problem. It's a decision problem disguised as a technical one. The question isn't "how do I deploy?" The question is "should I deploy THIS, or validate my idea differently?"

The Tool Reality Check: What Actually Ships to Production

Not all AI tools produce equally deployable code. After shipping 65+ projects, we've developed a rough production-readiness ranking based on how much additional work is needed to go from "it runs locally" to "it's live for users."

Tools That Ship Faster

Tools That Create Deployment Friction

Three Paths Forward: DIY, Hire, or Pivot

You have AI-generated code sitting on your laptop. Here are your realistic options, with honest assessments of each.

Path 1: Learn Enough to Deploy Yourself

Time investment: 25-55 hours to learn the basics (Vercel/Netlify: 2-4 hours, databases: 5-10 hours, debugging and troubleshooting: 10-30 hours).

Who this works for: Founders who enjoy learning technical concepts, have flexible timelines, and plan to iterate on this product long-term. If you're going to ship multiple AI-built products, this investment pays off.

The honest reality: A METR study published in July 2025 found that developers using AI tools took 19% longer to complete tasks than working without AI—even though they believed they were faster. The gap between perceived and actual productivity is real. Plan for more time than you think you'll need.

Best starting path: If you generated code with Cursor, push it to GitHub, then connect that repository to Netlify or Vercel. For database-backed apps, add Supabase. Start with their free tiers—you won't hit limits during validation.

Path 2: Pay Someone to Deploy It

Cost range: $499-$1,850 for deployment services, or $1,500-$4,000 for a team like thelaunch.space to handle both deployment and necessary code fixes.

Who this works for: Founders whose time is worth more than the cost of hiring. If you bill $200/hour consulting and deployment would take you 30 hours to learn, the math is obvious.

What to look for: Services like ShipMyAI specialize in exactly this problem—taking AI-generated code and shipping it to production in 72 hours. They offer tiers from $499 (code audit and cloud setup) to $1,850 (full deployment with 30 days of support).

Path 3: Step Back and Validate Differently

Who this works for: Founders who haven't validated demand yet. If you don't know whether customers will pay for this solution, deployment might be premature.

The uncomfortable question: Did you build this app because AI made building easy, or because you have evidence people want it? We've talked to many founders who built first, validated second—and discovered they'd built something nobody wanted to pay for.

If validation is your real next step, skip deployment entirely. Use the AI-generated code as a demo. Screen-record it. Show it to potential customers. Get commitments before investing in infrastructure. You can validate your startup idea as a domain expert without a live production app.

The $1,000 Decision Calculator

Most founders underestimate the opportunity cost of deployment struggles. Here's a framework we use:

This isn't about being unable to learn deployment. It's about whether learning deployment is the highest-value use of your time right now.

What We've Learned Shipping 65+ AI-Generated Apps

After 14 months of shipping AI-assisted projects, a few patterns have emerged:

The tools that feel magical often aren't production-ready. Bolt.new creates impressive demos in minutes. But the gap between demo and deployed product is measured in weeks, not hours. Tools optimized for "wow" moments don't prioritize deployment paths.

Cursor + Supabase is our workhorse. Not the flashiest combination, but it ships. Cursor generates code with deployment in mind. Supabase handles the infrastructure pieces that trip people up—databases, auth, hosting. We've reduced typical deployment time to 2-3 days with this stack.

Debugging AI code is its own skill. AI generates code that mostly works, then breaks in subtle ways. Learning to debug AI output—understanding what it got wrong and why—is often harder than writing the original prompt. Budget time for this.

The real question isn't "can I deploy?" It's "should I deploy this?" We've talked founders out of deploying apps that weren't validated. A local demo + screen recording is often enough to test demand. Save deployment for when you have paying customers waiting.

The Rescue Pathway: If You're Already Stuck

You've been wrestling with deployment for days or weeks. Here's a 5-step process to get unstuck:

The Real Question Isn't Technical

You came here looking for deployment help. But if you're honest, the deeper question might be: "Is this thing I built worth deploying?"

AI tools make building so easy that we skip the validation step. We build because we can, then justify it afterward. The deployment wall forces a pause—and that pause might be useful.

If you know people want this and are ready to pay, deploy it. Use the framework above to choose your path.

If you're not sure whether people want it, validate first. The local version running on your laptop is enough to test demand. You don't need production infrastructure to show someone a demo.

AI tools are rocket ships. They'll get you somewhere fast. But most founders don't realize they also need a launch pad—the infrastructure, deployment path, and validation that turns a demo into a product.

The good news: once you've shipped one AI-generated app to production, the second is dramatically easier. The deployment wall is steep, but it's also climbable. And if you'd rather have someone else handle it, that option exists too.