So we were in this weird spot where we needed OpenAI for some tasks, Claude for others, and we kept adding more specialized models as different teams discovered they needed them. Each one had its own subscription, its own billing cycle, its own API key management nightmare. We were looking at maybe $400-500/month across all of them, not counting the headaches of maintaining all those integrations.
When we started comparing Make and Zapier for enterprise automation, we kept running into the same problem—every AI model felt like a separate line item. We’d build a workflow in Zapier that needed GPT-4, then someone else wanted to use Claude in a different workflow, and suddenly we’re negotiating three different subscriptions just to get the job done.
We ended up consolidating everything into a single subscription. The math was straightforward—we went from juggling multiple vendors to one unified pricing model. But what surprised us was how much easier governance became. No more tracking which team was using which API key, no more accidental overspend when a workflow ran more than expected.
I’m genuinely curious how other teams are handling this. When you’re evaluating platforms against each other, are you factoring in the total cost of all those AI model subscriptions separately, or are you looking at it as part of the platform cost?
We did something similar but approached it differently. Instead of consolidating first, we mapped out exactly which models each workflow actually needed. Turns out we were paying for stuff nobody was using.
What helped us was building a quick audit—just tracked API calls for a month. Showed that about 60% of our OpenAI allocation was sitting idle, and we had Claude subscriptions that were barely touched. Once we killed the unused ones, the single subscription thing made even more sense.
The execution-based pricing model you mentioned matters more than people think. We were burned by per-task pricing before, so that was the real decision maker for us.
The governance piece you mentioned is actually huge. We spent way more time managing keys and permissions than we did on actual automation. Having everything under one roof simplified the whole chain—who has access, what they’re spending, audit trails for compliance.
One thing to watch though—make sure your platform actually supports model switching in the same workflow. Some setups lock you into one model per workflow, which defeats the purpose. We needed the flexibility to test different models without rebuilding.
This resonates. We were paying roughly what you mentioned monthly, spread across different services. The real insight for us wasn’t just cost reduction though. When everything funnels through one provider, you actually get better visibility into which AI models work best for which tasks. We could run A/B tests on the same automation using different models and measure the actual output quality versus cost trade-off. That kind of testing is almost impossible when you’re managing 12 separate subscriptions. The consolidated approach gave us data we didn’t have before.
The governance angle is critical here. We discovered that consolidating models also simplified our security posture significantly. Fewer API keys to rotate, reduced surface area for key exposure, centralized audit logging. From a compliance standpoint, this actually mattered more than the raw cost savings. Our security team was happier with one managed API surface than they were with a dozen separate keys floating around. The financial math was obvious, but the operational benefits were the real win.
we saved about 40% after consolidating. but the real value was time—no more managing keys n permissions across different services. automation just felt cleaner overall, honestly.
What you’re describing is exactly why Latenode’s unified approach makes sense. Instead of managing separate API keys and subscriptions for OpenAI, Claude, and 10 other models, you get access to 400+ models through one subscription. We went through the same audit you did, discovered half our subscriptions were waste, and realized how much time went into key rotation and access control.
The execution-based pricing model also changes the math. You’re not paying per task like Zapier—you pay for execution time, which actually favors complex workflows. We built some fairly intricate multi-step automations that would have been brutal cost-wise on other platforms.
The real unlock though was being able to test different AI models against the same workflow without rebuilding anything. You switch models in the UI, run it, compare outputs. Can’t do that when each model has its own subscription and keys scattered everywhere.