I was building a RAG workflow recently, and I realized I could use pretty much any embedding model from Latenode’s catalog for the retrieval phase, and any LLM for generation. That’s amazing on one hand, but I’m genuinely not sure if I’m making the best choices.
Like, for retrieval, does it actually matter whether I use OpenAI’s embedding model versus Claude versus Deepseek? Or for generation, should I care that much about which LLM I pick if they’re all capable? I feel like I’m spending more time comparing models than actually building the workflow.
I get that having options is better than being locked into one model, but I’m curious how people actually approach this. Do you just pick one and move on? Do you test different combinations? When you have that many models under one subscription, does it change how you think about model selection, or is it still mostly about picking something solid and iterating if needed?
The having 400+ models thing actually simplifies decision making more than it complicates it. Here’s why: you’re not comparing 400 options, you’re comparing like 5 or 6 that actually matter for your use case.
For retrieval, go with a solid embedding model. OpenAI’s works, Cohere works. Pick one based on cost or speed. For generation, same thing. Claude for quality, GPT for speed, Deepseek for budget. You don’t need to overthink it.
The real advantage is that you can test different combinations without paying separate subscription fees to different providers. Swap embedding models in one node, swap LLMs in another, run a few test queries. That’s what the single subscription unlocks.
Most people overthink this. Pick solid choices, deploy, measure results, optimize if needed. The 400+ models aren’t meant to paralyze you—they’re meant to let you experiment cheaply and iteration quickly.
You’re falling into a trap that most people do. The 400+ models look overwhelming, but for RAG you really only need to think about a few dimensions.
Embedding model choice matters for retrieval quality, but honestly for most document Q&A use cases, any decent embedding model works. The differences between top models are pretty small.
LLM choice matters more because it affects answer quality and speed. But again, if you’re using Claude or GPT, you’re in good territory.
The actual benefit of having access to all these models is that you can A/B test without friction. I usually just pick a reasonable combo, deploy it, see how it performs, then swap one thing at a time. The cost is the same either way, so you can iterate without guilt.
Having multiple models available actually reduces decision paralysis if you frame it correctly. Instead of choosing between providers and paying separately, you’re choosing between models with unified pricing. This shifts the decision from cost-based to performance-based, which is simpler.
For RAG specifically, I’ve found that embedding model choice has subtle effects on retrieval relevance, while LLM choice affects answer tone and hallucination rates. Start with recommended combinations—OpenAI embeddings with Claude, for example—validate performance, then experiment. Most teams find their winning combo within 2-3 iterations.
The subscription model actually solves this problem because you’re not locked into anything. With traditional APIs, you pick a model and live with it because switching providers costs money and effort. With Latenode, you can swap models in your workflow in seconds, test against real queries, and switch back if needed.
That’s the real power. Not that you have 400 choices, but that trying different choices is free and frictionless.