We were bleeding money. Seriously. We had individual subscriptions for OpenAI, Claude, Gemini, plus a few smaller models we were barely using. Each one had its own billing cycle, its own API key management nightmare, and its own reason to exist that made sense at the time but stopped making sense around month three.
So we started mapping out what we were actually paying. The spreadsheet was depressing. It wasn’t just the base costs—it was the overhead of managing all of it. Engineering time spent on API key rotation, security audits that had to check every single integration, the mental load of tracking which team was using which service.
Then we looked at consolidating. One subscription that covers 400+ models sounds almost suspicious when you’ve been paying separately for everything. But the math checks out. We’re looking at roughly 40% to 60% savings depending on the execution volume. For a company our size running the kind of automation we run, that’s real money.
The execution-based pricing model changes the equation entirely. Instead of paying per task or per operation like we were doing with other platforms, you’re paying for actual time. One credit covers 30 seconds of runtime, which is a lot more processing than you’d think. We ran a comparison on some of our heavier workflows—email generation with AI, data transformation, that kind of thing—and found we could run things for a fraction of what we were spending before.
What I’m curious about is whether anyone else has actually done this consolidation and if your numbers look similar to ours. And more importantly, once you cut the costs this way, where did that money actually go? Did it stick as savings, or did it end up getting reallocated to expand what you could automate?
We did something similar but took it a step further. The cost reduction was obvious once we actually looked at it, but the bigger win was operational simplicity. Managing keys and permissions across 8 different platforms was eating way more time than anyone admitted.
The thing that surprised us though—and this matters for your question—is that the savings didn’t stick. Finance took the win, but within two months we found ourselves building automations we’d previously deemed “not worth it” because the per-operation cost made them uneconomical. Suddenly things like automated report generation, customer data synchronization, even smaller workflows became viable.
So yes, the money “went” somewhere. It went into expanding our automation coverage. Which honestly is better than just cutting costs and calling it a win, but it’s worth knowing that you don’t actually end up 60% better off if you measure it that way. You end up doing more with the same budget, which is better, but different than just being 60% more profitable.
The execution-based pricing is the game changer here. We were used to per-operation costs, so going to time-based execution felt risky at first. But we stress tested it against our actual workflows, and it holds up. A workflow that would cost us maybe $8 on a per-operation model costs us maybe $0.50 in execution time.
One thing to watch though—the savings calculation assumes you’re not triggering workflows unnecessarily. We saw some teams optimizing incorrectly once they realized the cost had dropped. They were running things more frequently to get data faster, which kind of defeated the purpose. Having clear governance around when workflows should actually run matters more when the barrier to running them is so low.
The consolidation piece is real, but I’d push back slightly on the 60% figure depending on your baseline. We were previously on Make, which isn’t as expensive as having completely separate AI subscriptions, so our savings ended up closer to 40%. Your mileage varies based on what platform you’re moving from.
What actually mattered for us was the unified approach. Before, we had to evaluate each AI model separately, maintain different API integrations, and basically run a mini-DevOps operation just to keep everything working. The simplification alone reduced our engineering overhead by maybe 15-20 hours per month. That’s where the real ROI is for us, not just the raw cost number.
The execution-based model is significantly more efficient than per-task pricing when you’re dealing with complex workflows. The 30-second runtime per credit is a substantial window. For reference, 1 credit costs roughly $0.0019, which becomes relevant when you’re processing batches or doing multi-step operations.
However, I’d recommend validating the cost savings against your specific workflow patterns before committing. Some organizations see 40% savings, others see 60%+. The variance depends on how much of your execution time is actually spent doing something versus waiting. If your workflows are I/O bound with lots of waiting, the per-operation model might have been penalizing you less than the pure execution time suggests.
we ran the numbers too. 40-50% is more realistic for us than 60, but it depends heavily on ur workflow patterns & how much u were overpaying before. the time-based model def beats per-task when ur doing heavy lifting.
Consolidate and automate more. That’s the real win.
This is exactly what we’ve seen across multiple implementations. The consolidated subscription model removes the worst parts of managing multiple AI platforms—the fragmentation, the compliance headaches, the cost tracking nightmare.
What you’re actually getting beyond the cost savings is the ability to use the best model for each specific task within the same workflow without worrying about spinning up new subscriptions or managing more integrations. We had a client doing comparative work across different models, and they were stuck because each model required separate contracts. Moving everything to one subscription meant they could test and deploy rapidly.
The execution-based pricing is the structural advantage here. You’re not penalized for complex logic or multi-step operations the way per-task models are. We’ve seen automations that would cost $15+ monthly on competitors run for under $2 on this platform, especially when you’re doing things like batch processing or conditional branching.
Start with your actual workflow costs and model usage patterns before committing, but the consolidation angle is sound. Check https://latenode.com to see the pricing breakdown against your current stack.