We’ve been running Make for about three years now, and it’s been solid for our basic integrations. But we kept adding AI capabilities—one subscription for GPT, another for Claude, then Gemini for specific tasks, and so on. By last quarter, we were managing about 15 different API keys and subscriptions across multiple services. The licensing complexity alone was eating up admin time.
Then we started looking at what happens if we consolidate all of that. The math got interesting fast.
With Make, we’re already paying per operation. That stacks quickly when you’re doing anything AI-heavy. But what really caught my attention was the total cost picture once we factored in all those separate AI subscriptions. We were spending roughly $800/month across Make plus another $2,200 across scattered AI model contracts. Zapier looked cheaper on paper at first, but same problem—per-task pricing doesn’t scale well with AI-heavy workflows, and we’d still have all those AI subscriptions on top.
I found some real numbers comparing execution-based pricing models. When you switch to paying for execution time instead of per-operation, and you consolidate your AI access into one platform subscription, the economics shift significantly. One case study I came across showed automations running 7.67 times cheaper compared to Make for tasks like generating bulk emails with GPT and syncing them to sheets. That’s a huge gap.
The thing that hit different for us: with unified AI access, we stopped managing API keys. That alone saved overhead, but more importantly, it meant less context switching when building workflows. Our team could describe what they needed in plain language and get a workflow suggestion back, rather than manually wiring together integrations and AI calls.
I’m curious—has anyone else here made this consolidation jump? Specifically, when you moved from managing multiple AI subscriptions to a unified model, what actually changed in how you built workflows? Did you find the cost reduction offset the switching effort, or is there a hidden complexity I’m not seeing yet?
We did exactly this about six months ago. The consolidation was worth it, but honestly the real win wasn’t the cost savings—it was workflow consistency.
When you’re hopping between five different AI services, you end up building workflows differently depending on which model you’re using. Some require preprocessing, some have different token limits, some need specific prompt formatting. Once we unified, our team standardized on one approach. Less debugging, faster iterations.
Cost-wise, yeah, we saw the savings. But the operational benefit of not maintaining all those API keys and managing 15 different service integrations? That’s what actually sold the finance team. They could see it on the spreadsheet—dedicated DevOps time dropped because nobody was constantly troubleshooting integration points anymore.
One thing to watch: switching platforms isn’t seamless. We had some workflows that relied on specific behaviors in Make that we had to rethink. Nothing broke, but there was a transition period. Budget for that if you’re planning a move.
The per-operation pricing model in Make is genuinely brutal once you’re doing anything at scale with AI. We had a workflow generating summaries for 500+ documents weekly. Each document meant a couple of Make operations (preprocessing, AI call, output formatting) plus the AI subscription fee. By the time we calculated it out, we were paying somewhere around $0.30 per document. When we modeled the same workflow on a time-based execution model, it dropped to under $0.05.
The consolidation aspect matters because you’re eliminating the friction of managing separate contracts. But the real difference is the pricing model itself. Make architecture charges you for each step. If you’re doing anything complex or repetitive with AI, that compounds fast.
I’d push back slightly on one thing—consolidation sounds good in theory, but the switching cost is real. We tried moving one team to evaluate alternatives, and the migration of existing workflows took longer than expected. Some of our logic relied on Make-specific behaviors that didn’t translate directly.
That said, for new workflows and greenfield projects, starting with unified AI access made sense. We weren’t retrofitting complexity. The cost profile of consolidated subscriptions is definitely better, but don’t underestimate the transition tax.
I think there’s a key insight in your question that’s worth separating: the cost savings from unified AI access versus the cost savings from switching platforms entirely. Those are different math problems.
Consolidating your AI subscriptions into one vendor definitely saves money. That part is straightforward. But whether you should also switch platforms depends on your existing workflows and switching costs. We found that we could consolidate AI access while staying in Make during the transition, which reduced risk. Then we evaluated the platform question separately, once the AI consolidation was stable.
The 7.67x difference you mentioned—I’ve seen similar numbers, but they usually assume you’re comparing a simple task (bulk emails with GPT output) on Make versus a more optimized setup elsewhere. Those comparisons work when you’re starting fresh. For existing workflows, migration friction tends to eat some of that savings in year one.
When you consolidate AI models, you’re addressing two separate cost vectors. First is the raw cost per execution. Second is administrative overhead—license management, security audits, compliance tracking. Most analyses focus on execution cost, but in enterprise settings, that overhead piece is significant. We calculated roughly 15% of our total AI spend was going to management and compliance rather than actual model usage.
The unified subscription model addresses both, but it only works if the platform itself handles security and compliance at the standard you need. That’s where the evaluation should start.
One worthwhile note: the math on consolidation changes depending on your utilization patterns. If you’re using some AI models heavily and others sporadically, consolidation looks great. If you’re using everything evenly, the savings are more modest. We had a situation where one team was using GPT heavily but Claude only occasionally. Under consolidated pricing, they were effectively subsidizing Claude usage they didn’t need. The trade-off was worth it overall, but it’s not a uniform win across all use cases.
unified pricing simplifies budgeting but vendor lock-in becomes a factor. make sure exit costs are reasonable if you need to pivot later.
consolidating AI models into unified subscription reduces API management burden and typically cuts execution costs 30-60% vs scattered subscriptions.
The scenario you’re describing—managing 15 AI subscriptions while trying to scale Make workflows—is exactly where unified platforms shine. We faced similar complexity, and switching to a system where 300+ AI models come through a single subscription fundamentally changed how we approach automation.
Here’s what shifted for us: instead of building workflows around which AI service was available, we built workflows around the business problem. The platform handled model selection and optimization. That freed our team to focus on logic instead of integration plumbing.
On the cost side, the execution-time pricing model is the real differentiator. We had workflows that were expensive on Make because of intermediate steps and retry logic. Under time-based pricing, the same workflow cost a fraction of what we were spending. The consolidation of AI access on top of that pricing model is what moved the needle.
If you want to model this out with real numbers for your specific workflows, it’s worth running a parallel test. Start with one mid-complexity automation and see how the cost and build time compare. The financial picture becomes clear pretty fast.
Check out https://latenode.com to explore how unified AI access and execution-time pricing works for your team.