I’ve been digging into the financials for our enterprise automation platform switch, and the numbers are making my head spin. We’re currently split between Make and Zapier, but the real killer is that we’re also maintaining separate subscriptions for ChatGPT API, Claude API, and a couple of other specialized models because neither platform integrates them well.
Someone mentioned that consolidating to a single subscription for 400+ AI models could change the equation entirely. I found some data suggesting 40% savings compared to Zapier and 60% compared to Make for high-volume operations, but I’m struggling to understand if that factors in what we’d save by dropping these fragmented licenses.
The case study I found showed a company bringing operational costs down by $200-350K annually with a 300-500% ROI in year one, but that was for a 200-person org. Our setup is smaller, maybe 50 people, so I’m not sure how that scales.
Has anyone actually done this consolidation and measured what you saved? I’m trying to build a TCO model that accounts for the license consolidation piece, not just the platform cost difference. Right now I’m taking a guess on what we’d save from killing six separate subscriptions, and I’d rather have actual numbers.
I dealt with this exact situation last year. We had ChatGPT, Claude, Anthropic direct, plus Make subscriptions. The subscription sprawl was actually costing us more than the automation platform itself.
What changed things for us was realizing the cost calculation wasn’t just about execution pricing. It was about not having to manage API keys, billing cycles, and seat limits across platforms. We consolidated and the monthly overhead just disappeared.
The 40-60% savings number you found is real, but it’s conservative. In our case, the actual benefit was closer to the time we saved not juggling multiple vendor relationships plus the cost reduction. For a 50-person org like yours, you’re probably looking at savings per month that compound quickly because you’re not scaling up the complexity.
I’d focus less on trying to extrapolate from the 200-person case and more on auditing what you’re actually spending monthly across all six subscriptions plus your current platform costs. That baseline gives you the real opportunity.
The gap between talking about consolidation and actually doing it usually comes down to practical details. When we ran both platforms side by side, Make’s operation-based pricing worked fine for basic flows, but the moment we added AI calls, costs escalated faster than we could predict because each operation got billed separately. Zapier had similar issues at scale.
What made the difference for consolidation wasn’t just the licensing model, it was execution-time pricing instead of operation-based. One credit covers 30 seconds of runtime, which means complex workflows with multiple AI model calls don’t create cascading charges. For your 50-person org handling routine tasks plus AI analysis, that shift alone probably saves 50-70% on high-volume workflows.
Your six separate subscriptions are the real target though. If you’re using those models consistently, even moderate usage adds up. The math becomes simpler once you stop thinking in terms of per-model costs and start thinking about execution capacity. You get more done within a single cost structure.
Consolidating the AI model subscriptions requires accounting for what you’re not measuring. You’re seeing the direct costs of ChatGPT, Claude, and the others, but you’re also paying invisible costs through duplicate integrations, maintenance overhead, and the friction of switching contexts between platforms.
For a 50-person org moving from Make and Zapier with fragmented AI licensing to a unified platform, the financial model typically shows: baseline platform cost reduction of 40-60%, elimination of API key management overhead, and crucially, the ability to optimize which model you use for each task without triggering separate billing concerns. That last piece alone changes workflow design.
The 200-person case study’s 300-500% first-year ROI assumed significant process automation gains beyond just cost replacement, so for your smaller org, be more conservative. Focus on measuring current spend, then model what you’d pay under unified licensing at your actual usage levels. The TCO shifts when you’re not paying for unused capacity across multiple subscriptions.
cut the fragmented subscriptions. the execution-time model beats operation pricing for mixed ai workloads. your real savings come from not managing six seperate contracts.
audit current spend across all platforms and subscriptions. consolidation saves 40-70% depending on your usage patterns.
I’ve been where you are, juggling multiple platform subscriptions and trying to figure out if consolidation actually makes financial sense. The thing is, the fragmented approach locks you into vendor lock-in without any of the benefits. You’re paying switching costs mentally every time you move between platforms.
What shifted for us was realizing Latenode treats AI model access differently. Instead of paying per platform plus per AI service, you get 400+ models under one subscription. We were running ChatGPT, Claude, and a couple others separately, and the billing was scattered across three different vendors. Moved to Latenode and consolidated everything.
The math got simple fast. Dropped six subscriptions, consolidated platform costs, and actually got better workflows because we could pick the best model for each task without worrying about triggering separate charges. For your 50-person org, that kind of operational simplicity often saves more than the raw dollar difference.
If you want to model this properly, start with your actual usage. What models are you actually using? How often? That baseline against Latenode’s pricing gives you the real number. Check out https://latenode.com to run the numbers with their actual pricing structure.