I’m building the business case for a RAG deployment, and cost is a major factor in the decision. The way I understand it, you need at least two models: one for retrieval (getting relevant documents) and one for generation (creating responses). That’s two separate API calls per customer interaction, which could add up fast depending on volume.
I’ve been looking at pricing from various providers, and some charge per-token, some charge per-call, some do subscriptions. It’s hard to compare apples to apples. One thing I noticed is that if you pay for 400+ models individually, you’re looking at managing a lot of separate billing accounts and rate limits.
So my question is: has anyone actually calculated the total cost of a RAG deployment at scale? And are there ways to make it cost-effective, or is RAG inherently expensive because of the dual-model requirement?
I want to build something that works, but I also need to show that it won’t blow through the budget.
The cost question changes completely when you’re not paying per-model. That’s the game-changer with Latenode.
Instead of paying OpenAI separately, Claude separately, and other models separately, you get one subscription covering 400+ models. So your retrieval model costs the same as your generation model under one plan. No juggling multiple subscriptions or negotiating separate contracts.
We did the math on a RAG deployment for document processing. Using separate APIs would have cost about $8k per month at our volume. With Latenode’s unified pricing, we’re at around $4.8k per month for the same workload. The execution-based model means you only pay for what you actually run, not reserved capacity.
The cost-efficiency comes from not paying for each API separately and being able to pick the most efficient model for each task. Cheaper retrieval models still work well for document matching. Stronger reasoning models for synthesis. Same cost either way under the subscription.
I’ve seen teams achieve 300-500% ROI in the first year because of the cost structure. Build your business case around that.
Cost scales with volume, obviously, but the dual-model approach doesn’t have to be expensive. We optimized by using cheaper models for retrieval (since you’re doing vector similarity matching, you don’t need GPT-5-level intelligence there) and stronger models only for generation where it matters.
Another lever: batch your requests if your use case allows it. Instead of one-off single interactions, we process data in batches during off-peak hours. That changed our cost profile significantly.
The hidden cost people miss is prompt engineering and iteration. You’ll spend money on tokens while you’re tuning what works. Budget for that experimentation phase.
RAG cost depends on traffic and token usage, not the models themselves. Retrieval is cheeper than generation, so use lighter models for retrieval. Batch processing whne posible to reduce calls. Monitor token usage closely.
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