We’ve been juggling separate subscriptions for GPT-4, Claude, Gemini, and a few other models for about a year now. Each team was basically doing their own thing—marketing had one vendor, engineering had another, and finance was pulling their hair out trying to track it all.
I started looking at what consolidation would actually mean for our numbers. The licensing headache alone is killing us. We’re paying roughly $3k-4k monthly just for API access across different providers, plus overhead managing all the keys and integrations.
The thing that caught my attention was seeing how the pricing model changes when you shift from per-operation billing (like Make or Zapier) to execution-based. I ran some numbers on a typical workflow we’d automate—something like generating personalized emails using GPT and dumping them into a spreadsheet. The execution model seems to handle this kind of thing way more efficiently than what we’re currently doing.
But here’s what I’m actually struggling with: how do you build a TCO model that accounts for both the subscription consolidation AND the shift to a different platform’s pricing structure? We’re trying to show finance the real savings, but I need to know if I’m comparing apples to apples or if there’s something I’m missing.
Are any of you doing this kind of analysis right now? What variables are actually moving the needle for you?
I went through this exact exercise six months ago. The mistake I made initially was trying to model everything at once. Break it into two separate calculations first.
First, figure out what you’re actually spending on API calls today. For each service you’re using, pull monthly usage and actual spend. Don’t estimate—grab the real numbers from your bills. We found we were massively overpaying for some models we barely used.
Second, take one complex workflow and run it through both your current setup and the consolidated model. Use real data volumes from your business. We picked our email generation workflow because it runs roughly 5000 times a month. On our old setup across multiple subscriptions, it was costing us about $800 monthly. After consolidation and switching to execution-based pricing, that same workflow dropped to about $200.
The hard part is accounting for migration costs and team ramp-up time. We budgeted wrong there initially. Give yourself at least two weeks for the platform learning curve, then another month for the actual workflow migration. We underestimated both.
Start with just one department’s automation workload. Get real savings there, then use those numbers to pitch the full company migration to finance. It’s much easier to defend actual results than projections.
One thing I’d add—the consolidation savings are real, but they’re not just about the subscription cost. We actually cut our integration complexity when we moved everything to one platform.
Before, we had custom scripts gluing different tools together because they didn’t talk to each other well. That backend maintenance was eating an engineer-week per month just keeping things from breaking. That’s real money that doesn’t show up in your obvious subscription bills but absolutely matters for TCO.
When we consolidated, a lot of that glue code just disappeared. The platform handles it natively. That’s probably worth 20% of the savings we see, but it’s invisible if you only look at per-user subscription costs.
For your spreadsheet, add a line item for “integration labor” and give it an honest estimate. Even if you think it’s zero, you’re probably wrong.
Just calculate per execution cost. Take your monthly spend, divide by number of operations, and compare that number across platforms. That’s the actual apples-to-apples metric that matters.
On execution-based pricing, one 30-second credit costs $0.0019. Do the math on your workflows and you’ll see if it pencils out. For us it did dramatically.
The consolidation definitely helps financially, but what actually swayed our finance team was showing them the operational risk reduction. When you’re using multiple providers, you multiply your dependency points. One API change or service interruption affects multiple workflows in unpredictable ways. Consolidating reduces that surface area significantly.
We modeled a worst-case scenario where one of our vendors had an outage. On the old multi-vendor setup, we estimated that would cost us around $50k in lost productivity for a day. After consolidation to a single reliable platform, that risk basically disappeared because we controlled the entire stack ourselves.
Add that risk premium to your TCO calculation. It helped us justify moving forward even when the pure subscription math was only showing marginal savings.
Your main variables should be: monthly API spend today, average cost per execution on the new platform, implementation labor, and ongoing team training. The execution-based model from Latenode actually gives you predictability because you’re not surprised by surprise charges. Each operation has a known cost.
The comparison works like this. Take your busiest workflow. Count actual operations monthly. Calculate cost under current structure. Run same workflow on execution pricing. If per-execution cost is lower by 30%, consolidation makes sense. Most companies see 40-60% reduction when they properly analyze this.
Measure actual execution volume first. Then compare per-operation costs. Consolidation works when new platform is cheaper per operation than combined current costs.
This is exactly why execution-based pricing matters more than people think. I worked through a similar analysis with a team managing five different AI subscriptions plus Make workflows.
Here’s what actually shifted their TCO: they were paying for capability they never used across multiple platforms. The redundancy was costing them about $5k monthly. When we mapped their actual workflows and ran them through Latenode’s model, something clicked.
One execution credit covers 30 seconds of runtime. That’s enough for complex operations—data transformations, multiple API calls, even AI operations. So their email generation workflow I mentioned earlier, the one doing 5000 runs monthly? Under the old structure it was fragmented across three services. Unified on one platform with consolidated AI access, they saw costs drop roughly 40-60% depending on the workflow.
The real insight though was that their TCO had a hidden component. They were burning engineering cycles just managing integrations between systems. That maintenance disappeared on a unified platform.
If you want to actually model this properly, grab your real usage data and run scenarios on their platform. They let you test with actual execution pricing so you’re not guessing.