I’ve been working on a workflow that models ROI across different AI model choices, and I’m discovering that customization has a non-obvious cost.
The setup is straightforward: we have a template for calculating automation ROI that works well for a standard use case. But different teams want different models for different tasks—one team prefers Claude for analysis, another wants OpenAI for general workflows, another is experimenting with Deepseek for cost optimization.
So I started building scenario comparisons: what if we use Model A for Task 1 and Model B for Task 2? What does the ROI look like? The initial workflow was clean, but then I realized the pricing-per-token varies wildly across models, and usage patterns aren’t linear.
That’s where it got complicated. To keep the ROI comparison fair, I needed to:
Normalize pricing across different token-counting methods. OpenAI counts differently than Claude, which counts differently than Deepseek. Small differences at the token level compound into real cost variance across a thousand-step workflow.
Account for performance variability. Some models are faster, which means fewer retries, which affects the actual cost per workflow run. That’s not baked into published pricing.
Model latency costs. If Model A takes two seconds per request and Model B takes half a second, that matters on a large scale—it changes parallelization possibilities, which affects infrastructure costs downstream.
The more granular I got with the customization, the harder it became to compare apples to apples. I ended up building a separate “normalization” layer that translates everything into a standardized cost per workflow execution.
My question: at what point does scenario modeling become so customized that the ROI comparison loses meaning? Has anyone built something similar and figured out where to draw the line between useful customization and noise?
We hit this same wall and eventually accepted that perfect comparison is impossible. What we did instead was set comparison bounds—we’d calculate ROI for each model with different assumptions (optimistic, realistic, pessimistic) and then just report the range.
For example: Model A might deliver five to fifteen percent savings depending on how we optimize for speed versus accuracy. We’d show that range rather than pretending we had an exact number. Stakeholders actually appreciated the honesty. It helped them see where customization was adding value and where we were just guessing.
The normalization layer is the right move. We built something similar but we called it a “cost translator.” Every AI model gets mapped to standard units: cost per task, time per task, accuracy per task. That way comparisons are actually meaningful because you’re comparing normalized metrics instead of raw pricing.
The key is making the translation methodology transparent. We documented exactly how we normalized each model’s pricing and shared that with the teams evaluating ROI. That way they could audit our assumptions and adjust if they wanted to.
This is actually a known problem in cost modeling—the “noise floor” issue. At a certain point, trying to be more precise actually adds error because you’re measuring things that naturally fluctuate. We handle it by defining a threshold: if the customization adds less than five percent variance to the ROI calculation, we don’t bother modeling it separately. Everything below that threshold gets lumped into a standard deviation metric.
Latenode actually handles this problem elegantly because you can run multiple AI models in parallel within a single workflow. Instead of trying to guess which model will perform best, we literally tested each one and measured actual performance.
We built a workflow that takes a sample of our real tasks, runs them through Claude, OpenAI, and Deepseek simultaneously, and measures actual token usage, latency, and quality. That gave us empirical data instead of theoretical comparison.
The ROI calculator then uses real performance data, not assumptions. So when we compare scenarios, we’re comparing actual behavior, not normalized guesses. That cuts through the customization noise immediately.
The workflow took maybe four days to build because we could describe what we wanted—“test these three models against our task library and measure cost and performance”—and the AI Copilot generated most of the scaffolding. We just connected it to our data sources and ran it.
Once it was live, scenario comparisons became dead simple. We could plug in different combinations of models and the ROI would automatically reflect the real performance we’d measured.