When choosing an AI model for a project, it's tempting to look at benchmark rankings and pick the model with the highest score.

But real-world performance is rarely that simple.

A model that performs well on a public benchmark might not be the best choice for your actual application.

For example, imagine you're building an AI-powered customer support tool.

Hey Product Hunt community!

I'm sharing a project we've been working on: HefuAI.

As more developers start building with AI, one challenge keeps coming up: working with different AI providers often means managing separate APIs, integrations, and usage costs.

We're building HefuAI to make that process simpler.

As AI applications become more sophisticated, developers often need to work with multiple models for different tasks coding, reasoning, content generation, and more.

But managing multiple providers can introduce some practical challenges:

• Different API keys and endpoint configurations

• Different request formats and model parameters

• Switching providers when a model is unavailable

• Tracking usage and understanding the actual cost

• Maintaining integrations as models and providers change

I'm interested in how other developers approach this.