I’m at the point where I need to present something to our CFO on whether switching from Camunda to open source makes financial sense. The problem is, every ROI model I’ve tried to build feels like it’s missing something or requires too many assumptions.
I keep reading about people using no-code builders to prototype migrations and building comparisons that way. The theory sounds good—instead of speculating, you actually build the thing and measure what happens. But I’m wondering if that’s actually practical at our scale, or if we’d just end up needing our engineers to rebuild everything anyway.
Also, I’ve heard about consolidating AI model subscriptions into one platform—we’re currently juggling access to GPT-4, Claude, and a couple other models separately. If we could consolidate that and use it to rapidly test different migration approaches, would that actually simplify the business case math?
Does anyone have real experience building an ROI model that actually holds up to finance scrutiny, instead of being a best guess with a lot of caveats?
I built this for our company last year and it was messier than I expected, but the result was solid enough that it held up in budget meetings.
Honestly, the spreadsheet chaos is unavoidable at first. What made it workable was building prototypes in the actual platform instead of trying to model everything theoretically. We took three representative workflows—one simple data flow, one with complex rules, one with lots of integrations.
We built those three in the open source option and ran them for two weeks in parallel with our Camunda setup. Real execution costs, real error rates, real integration patterns. That gave us actual numbers instead of guesses.
On the AI model thing—we were managing four different subscriptions across teams. Consolidating into one platform for testing actually did simplify things because we could run scenario modeling using the same tools instead of context switching. We tested like ten different architectural approaches across one subscription, which made cost comparisons way more apples to apples.
Finance trusted the model because it was based on observed behavior, not predictions. That’s the key thing I’d emphasize.
The ROI model that actually works is the one grounded in actual execution, not theory. We made the mistake initially of trying to model costs without building anything real. The numbers were all over the place.
Once we committed to building actual prototypes—not full implementations, just enough to see patterns—the ROI became clear. Migration cost, platform cost, engineering time, integration effort. All based on what we actually observed.
What helped us avoid chaos was limiting scope. We didn’t try to model everything. Three representative workflows, realistic volume assumptions based on actual data, then we extrapolated. Finance understood that approach because it wasn’t magic, just math.
The critical insight is that ROI models for platform migrations fail when they’re based purely on theoretical cost comparisons. You need actual operating data. Here’s what worked for us: select five representative workflows that cover your process variety—high volume, low volume, complex logic, simple logic, heavy integration. Build those in your proposed platform. Run them for two weeks in parallel with current state. Measure infrastructure costs, operational overhead, integration friction. This gives you real scaling factors. If your high-volume workflow costs X to run on platform A and Y on platform B, you have actual math for your business case instead of estimates. The spreadsheet part becomes simple after you have real numbers to plug in.
ROI models that survive executive review are built on measured data, not assumptions. The practical approach involves building representative workflow samples in your target environment and capturing actual execution costs over a measurement period. This eliminates guessing about performance characteristics and integration overhead. For the consolidation question, having analytical capability under one subscription does reduce decision complexity because you’re looking at scenarios with consistent variables rather than different platforms with different strengths. Documentation becomes clearer. The finance presentation becomes straightforward because you’re showing they chose between measured outcomes, not competing consultant opinions.
Build actual prototypes, measure real costs for two weeks, use that data for ROI model. Theory fails, execution works. consolidating AI subscriptions helps because testing is consistent.
I went through this exact process and the breakthrough was realizing we were trying to build an ROI model without actually understanding our cost drivers. Once we stopped guessing.
We picked our five most important workflows. Built them in Latenode with access to multiple AI models through one subscription—that was key because we could test different decision logic approaches without managing separate contracts. Ran them against our actual data volumes for two weeks.
The ROI model that actually passed CFO review came directly from that exercise. Infrastructure costs, integration complexity, operational overhead. All measured, nothing theoretical.
What surprised us was how much the consolidation of AI models helped. Instead of testing decision flows with just one model or having to negotiate separate API access, we could rapidly prototype variations and compare outcomes. That speed of iteration actually changed the business case because we could show finance multiple scenarios with real cost differences instead of settling on one guess.
The result was a model that didn’t require caveats because it was grounded in observed execution. Finance approved the migration based on what we actually measured, not what consultants predicted.