I’ve been thinking about this for a while. RAG is supposed to make your AI systems smarter by retrieving relevant information before generating answers. But when you’re looking at 400+ models available in one subscription, the decision-making feels harder, not easier.
Like, do I pick one model for retrieval because it’s really good at understanding relevance, then pick a different generator that’s better at writing? Or do I go with a model that’s decent at both? Does the platform help you make those choices, or are you expected to know the differences between all these models?
I started exploring this because we’re building a support system that needs to pull information from multiple internal data sources. The retrieval part is critical—we can’t generate good answers if we’re pulling wrong documents. But we also need the generation to be accurate and natural-sounding.
What I’ve noticed is that having access to all these models at once does change something. You’re not locked into one vendor’s model or paying per API call. But I’m still not sure if the choice helps or hurts when you’re trying to just get something working.
Has anyone figured out a practical way to think about model selection for RAG workflows? Like, are there patterns that actually work, or do you just have to experiment and see what gives better results?
The 400+ models aren’t actually a problem if you think about it differently. You’re not supposed to evaluate all of them. The platform helps you pick the right ones for your specific task.
For RAG, you typically want a retrieval model that’s good at understanding relevance and a generation model that can reason over the retrieved context. The beauty of having all these models in one subscription is that you can test different combinations without worrying about API costs or managing separate credentials.
What I’ve found works best is starting with a model pairing that’s known to work well for your use case. Popular retrieval models are good at ranking document relevance. Popular generation models are good at synthesizing information. The platform makes it easy to swap models if you’re not happy with results.
The real advantage is that you’re not paying per model or per API call. You access everything through one subscription, which means price per execution stays flat no matter which model you pick. That takes the financial pressure off experimentation.
I’d rather have 400+ options than be locked into one vendor’s offerings. Start simple, test combinations, and optimize from there.
Choice paralysis is real, but it’s actually easier to manage than you’d think. For RAG specifically, you’re making two key decisions: which model handles retrieval and which handles generation.
The retrieval part is about finding relevant documents fast. Generation is about turning those documents into a good answer. Those are different tasks, so it makes sense to potentially use different models.
What I’ve learned is that the platform doesn’t force you to figure this all out upfront. You can start with reasonable defaults, deploy your workflow, and iterate. If your retrieval is pulling wrong documents, swap the retrieval model. If your answers aren’t good enough, adjust the generation model.
Having 400+ models available means you can experiment without the cost barrier most people face. You’re not choosing between models based on price—you’re choosing based on what works best for your problem.
The practical approach is to think about model roles rather than trying to compare all 400 models. For RAG, you need models that excel at specific tasks: retrieval (understanding which documents are relevant) and generation (creating coherent responses based on retrieved documents).
The advantage of having many models available is that you can test combinations quickly. If your first attempt doesn’t work well, switching to different models is straightforward. This experimentation capability actually simplifies RAG building because you’re not locked into a single choice.
Most people don’t need to understand every model’s nuances. You pick models from reputable providers, test them, and keep what works. The cost stays the same regardless of which model you choose, so there’s no financial penalty for trying different combinations.
Don’t worry about all 400 models. Pick one for retrieval, one for generation. Test. Swap if needed. Fixed costs mean no financial penalty for experimenting with different combinations.