I’ve been thinking about building a RAG system for our internal knowledge base, but I keep hitting the same wall: cost. Everyone talks about how having access to 400+ AI models is great, but that’s also kind of paralyzing? Do I use the fastest (most expensive) model for everything? Do I use cheaper models and accept lower quality? How do you actually decide?
I understand the concept of using different models for different tasks—maybe a lightweight model for retrieval ranking and a heavier one for answer generation. But in practice, how do you make that choice without just trial and error forever?
I’m specifically thinking: if you have all those models available in one subscription, is there actually a strategy for routing tasks to minimize cost while keeping quality acceptable? Or is it just “pick a model and see how it goes”?
Has anyone actually mapped out what works best where? Like, are there certain models that are particularly good at retrieval ranking without costing a fortune? Or certain ones that excel at synthesis without being overkill?
The model picking strategy changes everything for cost. I went through this exact exercise last year.
Here’s what I learned: retrieval ranking doesn’t need your most expensive model. Use something fast and cheap for scoring relevance—even smaller models rank documents effectively. Save your heavy models for answer generation where nuance matters.
I run retrieval through a smaller model that costs pennies per thousand calls. Then I only send top candidates to my generation model. That split alone cut my costs by 60% while keeping quality stable.
The subscription model in Latenode is the secret advantage here. You’re not paying per-API-call, so you can experiment freely with different models. I tested combinations for a week, found what worked for our documents, then locked it in. That experimentation would’ve been expensive on traditional APIs.
Start with a mid-tier model for everything, then split tasks once you understand where your bottlenecks are. Measure cost per query and quality separately. You’ll find the sweet spot.
I approached this pretty systematically. I benchmarked three model combinations before going live.
First combo was using our most capable model everywhere—expensive, but guaranteed quality. Second was budget models everywhere—fast and cheap, but output suffered. Third split tasks: faster model for retrieval, better model for generation.
The third option won by a lot. It outperformed budget-everywhere on quality and beat expensive-everywhere on cost. Retrieval ranking is basically scoring documents, and honestly, most models do that fine. Generation is where you want sophistication.
What helped: I tracked cost per query and accuracy metrics separately for a month. That data made the model selection obvious. Retrieval alone doesn’t need premium models, but synthesis does.
The optimal strategy involves task-specific model selection based on complexity requirements. Retrieval ranking objectives—relevance scoring, candidate filtering—perform adequately with efficient models. Answer generation demands higher model quality due to synthesis complexity and citation accuracy requirements. My analysis showed approximately 40% cost reduction by using targeted model selection: efficient models for retrieval, capable models for generation. Testing specific model combinations against your document corpus provides empirical guidance. Establish baseline metrics for both cost and quality, then optimize iteratively.
Task complexity determines model selection efficiency. Retrieval operations require ranking capability but not semantic sophistication; faster, less expensive models suffice. Generation tasks demand contextual understanding and coherence; higher-capability models justify their cost here. Practical deployments showing 35-50% cost reduction employ this stratification. Within a single subscription framework, model experimentation is cost-free, enabling rapid identification of optimal task-model mappings. Empirical testing against production document characteristics informs selection more reliably than theoretical assumptions.
use cheaper models 4 retrieval ranking, better models 4 generation. thats the main cost lever. split tasks by what they actually need, not by using premium models everywhere.