This is something I wrestle with every time I build a RAG workflow.
When you have access to 400+ AI models through one subscription, it sounds like freedom. But honestly, it can feel paralyzing. Do I use GPT-4 for retrieval? Is Claude better at ranking? Should I use a smaller, faster model for generation to save latency?
Traditionally, you’d be limited by what API keys you managed—maybe three or four model options. You’d pick one and move on. That constraint was actually useful.
Now, with so many options available together, I’ve noticed something: you can actually experiment without juggling API keys, billing dashboards, and credentials. You can A/B test in the same workflow.
But I’m still not great at making those choices strategically. I know retrieval benefits from models that excel at semantic understanding, and generation benefits from models that are good at coherence and tone. But when you have this many models, how do you actually make the decision?
Do you just pick one and stick with it, or are there patterns I should know about? Has anyone built something where they switched models dynamically based on the task type?
The 400+ models thing changes the game if you use it right. You don’t pick one model and stick with it. You pair them strategically.
For retrieval, I use Claude for semantic matching because it’s solid at understanding context. For generation, I use GPT-4 when quality matters, but I’ll switch to a faster model like Mistral for high-volume requests where latency is the constraint.
In Latenode, switching models is just a dropdown change. I can set up conditions—if retrieval confidence is low, escalate to a more powerful model. If latency matters, route to a faster one.
The real advantage isn’t having 400 options. It’s not having to manage 400 different subscriptions. Everything’s unified billing, no credential juggling.
Start with model pairs that make sense for your use case, then optimize from there. Within Latenode, you can test different combinations in minutes.
I approached this by establishing a decision framework instead of picking blindly. For my retrieval step, I tested three models—Claude, GPT-4, and a smaller open-source model—against the same set of queries. Claude won on semantic relevance. For generation, GPT-4 produced better summaries, but for routine questions, a cheaper model was sufficient.
Once I had that baseline, I didn’t spend time reconsidering. I just set up rules in the workflow. The framework simplified the decision paralysis. Without it, I would’ve been optimizing forever.
The 400 options are valuable mainly for having flexibility when your initial choice isn’t working. You can switch without changing infrastructure.
Decision paralysis is real, but it’s solves by treating model selection as a testing problem, not a guessing game. I ran small pilot workflows with different retriever-generator pairs on representative queries and measured latency and quality. Claude + GPT-4 was best but slow. Claude + Mistral was faster with acceptable quality. Cost mattered too, so I chose based on acceptable latency thresholds and budget. Once I had that data, the choice was obvious. Don’t overthink it—test a few combinations on your actual use case and pick the winner.