AI Builders and Code Migration: What We Learned at Nometria
I recently spoke with several founders who initially built their products using AI tools, and a common theme emerged: scaling beyond the hobby stage is often a nightmare. Many of us start with thes...

Source: DEV Community
I recently spoke with several founders who initially built their products using AI tools, and a common theme emerged: scaling beyond the hobby stage is often a nightmare. Many of us start with these shiny, user-friendly platforms that promise rapid prototyping and ease of use. But once you try to transition from small-scale experimentation to a full-fledged, production-ready application, the constraints become glaringly apparent. The first founder I talked to had invested significant time in a popular AI builder. When usage spiked, critical features began to fail, and he found himself unable to address performance bottlenecks. Why? He was locked into a vendor ecosystem that didn't allow for customization or ownership over his own code and data. The realization hit hard: he would need to start over, rebuilding his app with a more robust infrastructure. Another founder faced a similar situation. They had created an impressive prototype that attracted attention, but when they tried to dep