I’ve been wrestling with this for a few months now. We’re a mid-size team of about 40 people, and right now we’re split between Make and Zapier. The thing that’s been bugging me is how much we’re hemorrhaging on separate AI subscriptions.
I just ran some numbers and found a case study where someone was generating 2000 emails with GPT and inserting them into Google Sheets. On Make, it cost significantly more than it should have. The person quoted something like 7.67x more expensive for the same task compared to a time-based pricing model.
That got me thinking—we’re probably doing something similar. We have GPT subscriptions, Claude subscriptions, and God knows what else scattered across different tools. Each one requires its own API key management and billing. Then on top of that, we’re paying per-task with Make and Zapier.
The execution-based pricing model at $19/month starting point seems interesting because you pay for actual runtime, not individual operations. So if a workflow runs for 30 seconds and processes a ton of data, it’s all one charge instead of being nickel-and-dimed by operation counts.
But here’s where I’m uncertain: when you consolidate licensing like that, does the math actually hold up for enterprise teams? We’re not just talking about hobby workflows here. We need reliability, audit trails, and the ability to scale without suddenly hitting unexpected cost walls.
Has anyone actually made this switch and tracked what changed? What does your TCO actually look like when you’re not paying separately for each AI model, and what kind of surprises did you hit?
We went through this exact exercise about eight months ago. The 7.67x number is real, but it’s not magic—it’s just how differently the pricing models work when you’re doing heavy AI stuff.
Here’s what actually changed for us. We had CloudFlare for some tasks, separate OpenAI keys for different departments, and we were using Make’s operation model where every step counted. When we ran a workflow that involved pulling data, transforming it with an AI model, then pushing it somewhere, that could be 8-10 operations. Times a hundred runs a day. Times 20 different workflows.
With time-based pricing, that same workflow doesn’t care about operation count. It runs for maybe 15-20 seconds total, processes whatever data it needs to during that window, and costs a fraction of what we were paying.
The tricky part was actually consolidating all those API keys and separate subscriptions. That took longer than the actual migration. We had to audit what we were actually using versus what we were just paying for.
For enterprise scale, the model holds up, but you need to be deliberate about it. You’re trading operation-based overages for execution time overages. Both can spiral if your workflows aren’t efficient. The difference is execution time overages are way harder to trigger accidentally.
The consolidation part is where most teams miss the actual value. It’s not just about switching platforms. It’s about stopping the bleed from having ten different billing relationships.
We had three teams using different AI models for different reasons. Finance was using one API for data entry, marketing was using another for content generation, and ops was using a third. None of them talking to each other. That’s three separate bills, three separate support relationships, three separate rate limits to manage.
When you move to a unified subscription for 300+ models, you’re not just getting cheaper pricing. You’re getting one billing cycle, one API management surface, one place to monitor usage. That alone is worth something because you’re not scrambling when one subscription expires or hits a weird limit.
The TCO equation changes because now your automation costs are predictable. We budget $X per month for execution time, and that’s it. Before, we’d get surprise bills when a process ran more often than planned.
One thing to watch out for though. Consolidation sounds great until you realize some tools are just better at certain tasks. GPT is overkill for simple data validation, but Claude is better at nuanced writing. If you’re genuinely using different models for different reasons, a unified subscription where you can pick the right tool for each task is a real advantage.
But if you’re consolidating just for the sake of consolidation, you might end up overpaying in a different way. We tried that initially—forced everything into one model type. It was cheaper on paper but slower in practice, which cost us way more in engineering time.
The sweet spot is having the option to choose. That’s where Latenode’s 300+ model approach actually makes sense. You’re not locked into one vendor’s interpretation of which model is best. You pick GPT for one step, Claude for another, Gemini for a third, all from the same subscription. That flexibility is worth money when you’re at enterprise scale.