I’ve been noticing that more options doesn’t always mean better outcomes. When we only had two or three commercial models to choose from, decisions were easy. Use OpenAI, move on.
Now there are hundreds. GPT, Claude, Deepseek, Llama, Mistral, on and on. And I find myself frozen trying to figure out which is actually best for my specific retrieval-augmented problem.
So I’m curious: does having access to 400+ models in one subscription actually make your RAG better? Or are you just picking one and running with it like we did before? Is there a meaningful way to experiment without spending a fortune on API calls?
I feel like I’m missing something about how to actually use all those options strategically instead of just feeling overwhelmed by them.
The paradox is real, but the answer is simpler than you think. You don’t test all 400. You test 3-4 of the right ones.
Hiding behind that choice is actually freedom. You’re not locked into one vendor’s pricing, rate limits, or feature set. If a model doesn’t perform, you swap it. Two minutes, swap a node, run another test.
That’s where the value sits. Not in having 400 options, but in the freedom to experiment without consequences. With Latenode, testing different models costs the same per query regardless of which model you pick. So the friction that normally keeps you locked into one choice vanishes.
The practical process: pick a promising model, run it against 20 test questions, measure quality and speed, move on if it works or try another if it doesn’t. That’s fast enough to do weekly.
Paralysis comes from overthinking. Access to choice is only paralyzing if you feel obligated to find the perfect answer. You don’t. You need good enough, tested, and deployable.
We use maybe 4 models actively. One for retrieval, two we swap between for generation depending on the task, and one backup. The 400 others don’t really matter.
What matters is that if our main retrieval model has issues, we switch. If Claude stops fitting the use case, we try GPT. The flexibility prevents lock-in and lets us optimize gradually without being stuck.
The way we handled choice paralysis: we picked one solid model per stage based on benchmarks, ran it for a month, measured quality. Then we scheduled a check-in to experiment with alternates. Treating it as a periodic review instead of a constant optimization hunt made it manageable.
Choice paralysis is real, but it’s solved by constraints. You don’t need to evaluate 400 models. You need to identify which 3-5 are actually viable for your use case, test them systematically, and pick the best. The real advantage of having 400 available is that you’re not dependent on any single vendor if priorities shift or pricing changes. That’s the insurance value.
Most teams ovethink model selection. A solid mid-tier model works fine for most RAG tasks. The value in having multiple options is flexibility for edge cases and negotiating power with vendors. You don’t need to use it constantly, just knowing you could move if needed changes your position.