So we’ve been drowning in subscriptions. OpenAI API, Anthropic, a couple of smaller model providers—each one with its own account, its own billing cycle, its own API key management nightmare. Five different logins. Five different dashboards. Our finance team was losing their minds trying to track it all.
We started evaluating the cost difference between Make and Zapier for our enterprise workflows, and halfway through that conversation, we realized we were looking at the problem backwards. We weren’t just paying for Make or Zapier. We were paying for each AI model separately on top of whatever platform we chose.
I found that when you consolidate all your AI model access into one subscription, the whole financial picture actually shifts. The context I found showed that automations built with a unified subscription can be up to 7.67 times cheaper than running the same thing across Make alone, especially when you’re doing anything AI-heavy like generating emails with GPT and dumping them into sheets.
Here’s what I actually noticed: our operation costs dropped because we stopped paying per-operation for the AI models. We were doing roughly 2000 email generations a month using GPT, and that alone was bleeding us dry across five different platforms. With everything consolidated into one subscription where you get access to 300+ models, the math changed dramatically. One credit covers 30 seconds of runtime, and it costs $0.0019 per credit. For our workflow, that’s cents instead of dollars.
But here’s the thing that surprised us—it wasn’t just about the AI models. The licensing model itself matters. We were comparing Enterprise plans between Make and Zapier, and when you factor in that we’d be paying for each additional AI model integration on top of that, Make’s operation-based pricing started looking really expensive. Zapier’s per-task model became even worse.
The bigger learning: if you’re evaluating any workflow automation platform right now, don’t separate the platform cost from the AI model cost in your TCO calculation. They’re entangled. Our first-year ROI completely changed once we accounted for the consolidated AI subscription.
Has anyone else here actually run the numbers on what happens when you stop treating AI model subscriptions as separate line items and roll them into your platform pricing?
Yeah, this is exactly what we ran into. We were stuck on Make for two years and our bill kept climbing because every new AI integration meant another API key and another vendor relationship.
What shifted things for us was realizing that the per-operation model on Make meant every API call counted against our quota. So when we built workflows that called GPT multiple times per run, we’d hit our monthly limit fast. Then we’d upgrade and still feel like we were overpaying.
When we consolidated, it wasn’t just the cost. It was the simplicity. One dashboard, one bill, one set of credentials across Claude, GPT, Deepseek—everything. Our DevOps team actually had time to focus on other stuff instead of managing API key rotations.
The ROI math became clear pretty quick. We were running maybe 50,000 operations a month on Make. Just operations. When we switched, that translated to maybe 10-15 credits worth of runtime under the consolidated model. The difference was stupid—went from $8,000 a month to under $1,500.
One thing though: make sure you actually audit your current workflows before you migrate. Some of ours were built inefficiently because we were trying to minimize operations. Once we consolidated, we could refactor them properly, and that pushed the savings even higher.
I went through a similar evaluation last year. The biggest mistake I made was comparing just the platform costs. I ignored how much we were actually spending on AI integrations scattered across different services.
When I consolidated everything into one subscription where you get access to hundreds of models, the picture changed completely. Our team was spending roughly $200 a month on individual API keys across OpenAI, Anthropic, and a couple others. That’s on top of whatever automation platform we were using.
The consolidated approach eliminated that entire line item. So we weren’t just saving on the platform—we were reducing headcount overhead too. No more managing separate credentials, no more emergency API key rotations at 2am, no more hunting through five different dashboards to figure out which service was causing a bottleneck.
For TCO calculations, I’d recommend being really clear about what “total cost of ownership” actually includes: platform fees, AI model subscriptions, internal labor to manage integrations, and the cost of downtime when a vendor changes their API. Consolidating helps with most of those.
The licensing model fundamentally changes your cost structure, and most companies don’t account for that until they’re already locked in. Your observation about the 7.67x cost difference is accurate for workflows heavy in AI calls. The key variable is runtime efficiency.
Within your 30-second credit window, you can make multiple API calls, process datasets, and execute complex transformations without incurring additional charges. Make and Zapier charge per operation, so each step in your workflow costs money independently. That compounds fast with AI-heavy workflows.
For enterprise TCO comparisons, I suggest modeling three scenarios: baseline automation (simple integrations), AI-augmented workflows (moderate AI usage), and agent-heavy workflows (continuous AI decision-making). The consolidation advantage grows significantly in the latter two cases.
Also factor in management overhead. Centralized API access reduces security governance complexity. One authentication model, one audit trail, one compliance framework rather than scattered third-party integrations. That’s real cost when you have security and compliance teams.
Exact same experiance here. Cut our bill 60% when we stoped using 5 diferent ai subscriptions. The per-operation model was killing us. make consolidation changes everything.
Consolidate your AI subscriptions early. Platform choice less important than unified access.
You’ve hit on something really important here. The reason your consolidation worked so well is because you stopped treating AI models as external costs and actually integrated them into your workflow infrastructure.
What you’re describing—one subscription giving you access to 300+ models, paying for runtime instead of operations—that’s exactly the problem Latenode solves. Instead of managing separate credentials for OpenAI, Anthropic, Deepseek, and all the others, you get them all under one plan. One API key, one billing cycle.
For your specific scenario with email generation and sheets integration, Latenode’s execution-based pricing model would handle those 2000 monthly generations way more efficiently than Make’s operation counting. You’d be looking at roughly 67 seconds of total runtime, which at $0.0019 per 30-second credit comes out to literal pocket change compared to what you were paying.
The real win though is that your workflows become infinitely more flexible. You’re no longer optimizing to minimize operations—you optimize for logic and speed instead. Your refactored workflows would perform even better.
Check out Latenode’s pricing and see how your actual workflows map to their credit system. Your ROI math will probably shift even more favorably: https://latenode.com