From Prototype to Production: Where Most AI Builders Actually Fail
📰 Dev.to AI
Learn how to bridge the gap between AI prototype and production, and why most AI builders fail to scale
Action Steps
- Identify potential scalability issues with your AI-built app
- Evaluate the infrastructure dependencies of your AI builder
- Develop a rollback mechanism for emergency situations
- Plan for database migration and ownership
- Configure monitoring and logging for production environments
Who Needs to Know This
AI engineers and product managers can benefit from understanding the limitations of AI builders and how to overcome them to ensure successful production deployment
Key Insight
💡 AI builders optimize for speed, not scale, leaving a gap between iteration and production
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🚨 Don't let your AI-built app fail in production! Learn how to bridge the gap between prototype and production 🚀
Key Takeaways
Learn how to bridge the gap between AI prototype and production, and why most AI builders fail to scale
Full Article
Why Your AI-Built App Works Until Users Show Up You built something in Lovable or Bolt in a weekend. It works. You're shipping it Monday. Then reality hits: your database lives on their servers, your code is locked in their proprietary format, and you have no rollback mechanism when something breaks at 2am. This is the gap between iteration and production. AI builders are optimized for speed, not scale. They let you move fast because they abstract away infrastructure
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