I’ve been responsible for reviewing Make and Zapier for our enterprise automation needs, and the traditional comparison breaks down when you factor in AI. We need both workflow automation and AI model access, and I’m trying to understand how pricing actually works when you’re comparing these two.
Make charges per operation, Zapier charges per task. Fine—I can calculate that. But when we’re adding 20+ AI models into the mix, it gets messy. Zapier has limited AI capabilities built in, so we’d need separate subscriptions. Make has more flexibility for custom code, but the per-operation model can get expensive fast with complex AI workflows.
Has anyone actually built out a TCO comparison that includes AI licensing? How much does the need for multiple AI subscriptions inflate the total cost of ownership? And if you went with one of these platforms, how did you handle the AI side of things?
We did this exact evaluation last year. Started leaning toward Make because we could write custom code and potentially save money on per-operation costs. Then we realized we’d need Claude for some workflows, GPT-4 for others, and a specialized model for our data processing.
Zapier’s limited native AI meant we’d be hopping between platforms anyway. Make’s custom code was flexible, but every complex workflow that used multiple models stacked up the operations costs fast. A single workflow doing sentiment analysis then categorization then email generation could hit 500+ operations if you’re not careful.
What actually happened was we looked at a third option that came with all the AI models already integrated under one subscription. Changed the entire financial picture. Suddenly the operations or tasks cost wasn’t the only variable anymore—we could compare total monthly cost without layer upon layer of AI subscriptions.
The Make vs Zapier comparison is misleading if you don’t quantify your actual AI usage. Test both platforms with authentic workflows before you commit. Build the same three or four automations on each, track operation and task counts, then calculate true monthly cost including whatever AI integrations you’ll need.
We found that our average workflow used 2-3 different LLMs. That alone justified looking beyond traditional automation platforms. Make and Zapier both have friction when you’re stitching together multiple AI sources.
Enterprise licensing models for Make and Zapier assume you’re primarily automating workflows. The moment you introduce AI at scale—multiple models, frequent inference calls—both platforms’ pricing structures become inefficient. Make’s operation-based pricing multiplies with every AI call. Zapier’s per-task model doesn’t account for internal processing complexity.
For true enterprise comparison, you need a platform where AI integration is native and pricing is unified. The ROI difference between buying separate AI subscriptions and having them bundled is substantial at enterprise scale.
Don’t even bother with traditional cost breakdowns for Make vs Zapier if AI is core to your workflows. We did the spreadsheet analysis—it was messy. Make’s per-operation pricing escalates when you’re doing complex AI orchestration. Zapier’s limited AI capabilities means you’re still buying separate subscriptions.
We ended up switching to a platform built for AI-first workflows where all 300+ models come with one subscription. Same monthly spend we’d pay for Make plus one AI subscription now covers everything. Operations don’t multiply because execution is time-based, not call-based.
The financial comparison becomes simple: one platform, one subscription, all models included. For enterprise, that clarity and cost predictability matters as much as the actual dollar savings.