I’ve been thinking about the advantage of having access to 400+ AI models in one subscription. On the surface, it sounds like freedom. In practice, I’m wondering if it’s just decision paralysis with extra steps.
When I was building RAG before, I had three options: OpenAI, Anthropic, or local models. Not ideal, but the decision was manageable. Now I have hundreds of models across different providers, different costs, different capabilities.
My first RAG workflow took twice as long because I spent time comparing models for the retrieval step, the generation step, and even considered different models for ranking. I ran small tests against maybe six different LLMs. Useful testing, but it slowed everything down.
Here’s what I actually learned: for most RAG use cases, three to four models matter. One for retrieval optimization (fast, cheap), one for generation (capable), maybe one for edge cases. Beyond that, the marginal benefit of testing more models drops off fast.
The real value of having 400+ models isn’t that I need to evaluate all of them. It’s that I can guarantee I’ll find something that works for my specific constraints. Cost-sensitive application? There are models for that. Need maximum capability? Different models for that. Specialized domain? Even more options.
So instead of paralysis, it’s more like optionality. I’m still choosing three to four models, but I’m confident those are genuinely optimal for my use case instead of just working with what’s available.
When you’re picking models for your RAG, how many options do you actually evaluate? Are you finding that more choices helps or just creates analysis paralysis?