Migrating from Gemini API to OpenRouter: Building a More Flexible AI Platform

As SiteGen AI continued to grow, I started testing it with different types of prompts and larger projects.

The application was working well, but one issue kept interrupting development.

The AI service frequently returned quota-related errors, making it difficult to continue testing and improving the project.

At first, I assumed there was something wrong with my implementation.

After debugging the application, checking API requests, and verifying my credentials, I realized the issue wasn't in my code.

The limitation was related to the AI service configuration and usage quotas.

Why I Decided to Migrate

One lesson became very clear.

An application should never depend entirely on a single AI provider.

If the provider changes its pricing, limits requests, or introduces new restrictions, development slows down immediately.

I wanted SiteGen AI to remain flexible regardless of which AI model it used.

Instead of designing the project around one provider, I decided to redesign it around a common interface that could support multiple providers.

That decision led me to OpenRouter.

Why OpenRouter

OpenRouter provides access to multiple language models through a single API.

Instead of rewriting my backend every time I wanted to test a different model, I could simply change the model name while keeping the rest of the application unchanged.

This approach makes experimentation much easier.

Instead of being tied to one provider, SiteGen AI can evolve alongside the AI ecosystem.

Refactoring the Backend

The migration required more than replacing an API endpoint.

I reorganized the backend to separate AI communication from the rest of the application.

Instead of embedding AI logic directly inside the website generation workflow, I created a dedicated service responsible for handling AI requests.

This separation makes the codebase easier to maintain and prepares the project for future integrations.

Benefits of the Migration

After completing the migration, several improvements became immediately noticeable.

• Easier model switching
• Cleaner backend architecture
• Better separation of responsibilities
• Improved maintainability
• Greater flexibility for future development

Most importantly, SiteGen AI is no longer tightly coupled to a single AI provider.

Looking Beyond Website Generation

While working on this migration, another idea started taking shape.

I realized that changing the AI provider solved only one part of the problem.

The bigger opportunity was changing what the application actually generates.

Instead of producing only HTML, CSS, and JavaScript, I wanted SiteGen AI to understand complete software requirements and generate structured projects.

That idea eventually became the foundation for SiteGen AI v2.

Lessons Learned

Building AI-powered applications isn't only about choosing the most capable language model.

Architecture matters.

Abstraction matters.

Designing software that can adapt to new technologies is often more valuable than optimizing for a single provider.

This migration reminded me that investing time in a flexible architecture always pays off in the long run.

What's Next

In the next article, I'll introduce the vision behind SiteGen AI v2 and explain why I decided to move beyond website generation toward building an AI-powered full-stack project generator.