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Production deployment at scale: where most AI builders actually fail

DEV Community: api·Nometria·3 days ago
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The Gap Between "Built" and "Production Ready" You built something in Lovable or Bolt in a weekend. It works. Your users love it. Then reality hits: your database lives on their servers, you can't roll back a bad deploy, and scaling means rebuilding from scratch. This isn't a flaw in AI builders. It's by design. They're optimized for iteration, not infrastructure. But that doesn't mean you're stuck. Here's what actually happens when you try to move from builder to production without understanding the layers: The database problem. Your data lives in the builder's database. You can export it, but you don't own the infrastructure. If the builder changes pricing, deprecates a feature, or goes down, you're reactive, not in control. Most founders don't realize this until they have real revenue. The deployment problem. Builder platforms give you a URL. That's not a deployment strategy. It's a demo environment.…

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