We’re in the middle of a platform evaluation for our team, and I keep getting stuck on the same question: does the pricing model actually move the needle enough to matter?
Right now we’re looking at Make and Zapier, but every time I try to do an honest cost comparison, I feel like I’m missing something. Both platforms publish their per-operation costs, but they structure them differently enough that direct comparison is almost impossible.
I ran into a case study recently where someone compared automations on Latenode to Make for a workflow that generates 2000 emails using GPT and inserts them into Google Sheets. The Latenode version came out to about 7.67 times cheaper than Make. That’s not a rounding error—that’s a real difference.
But here’s what I can’t figure out: is that difference because of the pricing model itself, or because of something else about how the platform works? With Make, you pay per operation. With an execution-based model, you pay for how long the workflow actually runs. Theoretically, if your workflow does a ton of work in a short time window, that should cost less.
The thing I’m struggling with is whether that difference holds up across different types of workflows, or if it’s specific to heavy API-call scenarios.
How much has the pricing model actually influenced your decision when you were comparing platforms? Did you find that the per-operation model genuinely cost more, or did the numbers end up feeling roughly similar once you dug into the details?
The pricing model difference is real, but it shows up differently depending on what you’re actually automating.
I dealt with this directly when we were deciding between Make and something with execution-based pricing. We had a workflow that pulled data from an API, transformed it, and sent results to Slack. With Make’s per-operation model, every single step counted as a billable operation, including conditional logic branches that usually didn’t execute.
With execution-based pricing, that same workflow ran for maybe 15 seconds and we paid a flat rate for that time window. The difference wasn’t subtle—we saved about 60% on that particular automation.
But here’s the catch: if your workflow is mostly waiting (like checking a queue every few minutes and usually finding nothing), execution-based pricing might actually cost you more because you’re still paying for those check cycles even when nothing happens.
The model that wins depends on what your workflows actually do. Heavy processing in short bursts? Execution-based wins. Lots of small, frequent checks with occasional payloads? Per-operation might be cheaper.
I’ve seen teams get burned by assuming the pricing model difference doesn’t matter until they actually built it out.
The real issue is that Make and Zapier structure their operations so differently that you can’t just compare raw cost per operation. Make charges for module execution. Zapier charges for task runs. A single “task” in Zapier might involve multiple steps that would count as multiple operations in Make.
What actually helped us was building out one representative workflow on each platform and running it for a week. That gave us real numbers instead of theoretical calculations. For us, the execution-based model won because our workflows did a lot of data transformation work, which doesn’t translate well to per-operation pricing in Make.
The pricing model absolutely matters, but you have to measure it against your actual use case.
We basically did this evaluation last year. We took our three most complex automations and modeled them on both platforms. Make came out about 40% cheaper for us because our workflows are actually pretty sparse—lots of conditional logic that prevents unnecessary steps from running.
But I talked to another team that runs real-time data processing and they said execution-based pricing killed it for them because every second of compute time added up fast.
The honest answer is that the pricing model only matters relative to what your workflows look like. If you’re mostly doing integrations between apps with light data transformation, the difference might be 10-15%. If you’re doing heavy compute, it could be 50%+.
The execution-based model does change the math significantly, but not in the way you might think. I helped our team model this out, and the real savings came from understanding exactly what each platform actually charges for.
Make bills per operation. Every step in your workflow uses operations. Zapier bills per task, but a task can contain multiple actions that don’t get billed separately. An execution-based model bills based on runtime duration.
What we found was that for our use case—data transformations and API calls—the execution model was about 40% cheaper annually. But that specifically applies to workflows that do a lot of computation work quickly. If your workflows are mostly orchestration with waiting periods, the advantage disappears.
The pricing model matters, but only if you understand your workflow’s actual execution profile: time spent computing, number of operations, and frequency of execution.
The execution-based pricing model does matter for enterprise decisions, though many teams underestimate how much of the savings comes from the model versus other factors.
For workflows with substantial data processing or complex transformations, execution-based pricing typically costs 30-50% less than per-operation models. This is because per-operation models charge for every conditional branch and iteration, while execution-based models charge only for actual runtime.
However, this advantage diminishes for orchestration-heavy workflows where the platform spends more time waiting for external services to respond than actually processing data.
When evaluating this for enterprise, I’d recommend running a representative workflow sample on each platform for approximately two weeks under normal processing volumes. This gives you accurate data rather than theoretical comparisons. The pricing model advantage varies significantly by use case—some teams see 15% savings, others see 70%.
Yes pricing model matters. Execution based beats per-operation for compute heavy workflows. We saw 40-60% savings. But depends on ur workflow type. Lite orchestration? hardly any difference.
Execution-based beats per-operation for heavy compute. Test ur actual workflows on both platforms for real numbers.
The execution-based model actually does move the needle—we’ve seen it firsthand.
The key insight is that platforms charging per operation end up nickel-and-diming you on conditional logic, iterations, and error handling steps. You’re paying for stuff that might not even execute. With execution-based pricing, you pay for what actually runs.
Here’s what made this click for us: we had a workflow generating 2000 emails through GPT and inserting them into Google Sheets. On Make’s per-operation model, it cost significantly more because every email generation, every sheet interaction, every conditional check added up. On an execution-based system, we were paying for how long the actual processing took—much cheaper.
The real win is when you’re doing heavy data transformation or working with multiple AI models in one workflow. That’s where execution-based pricing really shines because you’re not double-paying for compute time.
If you want to see exactly how this plays out, you can model your workflows and test the actual costs: https://latenode.com