Zapier bills per successful action, not per run, so the bill tracks how many steps your Zaps take rather than how often they fire.
The billable unit decides whether your bill grows with the complexity of an automation or with how often it runs. Zapier and Make grow with complexity. n8n grows with frequency.
This page explains the shape of the pricing model rather than quoting figures, because plan limits and task allowances change. Check zapier.com for current numbers before deciding.
| Zapier | Make | n8n Cloud | Aisle | |
|---|---|---|---|---|
| Billable unit | Task, per successful action | Credit, per module call | Workflow execution | Run, plus model calls |
| Trigger firing | Free | One credit, whatever it returns | Counts as the execution | Counts as the run |
| Adding a step | More per run | More per run | Nothing | Nothing |
| Doubling frequency | Doubles | Doubles | Doubles | Doubles the model calls |
| Iterating 1,000 rows | Roughly 1,000 tasks | Roughly 1,000 credits | Depends on loop shape | One run |
| Filters and branching | Free | Branch modules still count | Nothing | Nothing, it is an if statement |
| Cost known before deploy | Calculable, steps times runs | Calculable, modules times runs | Roughly | Yes, calls sit in the code |
Zapier charges for the actions it sends to other apps, and not for the routing and preparation around them, which is why cost estimates go wrong in both directions. A Zap that looks expensive on the canvas is often cheaper than it appears.
Per-task pricing is easy to forecast and easy to underestimate, because the two numbers multiply. A six-action Zap firing a thousand times a month is six thousand tasks, and the way to halve it is usually to remove an action rather than to reduce the trigger volume.
Most of the useful optimisations are structural rather than commercial, which is why they survive a plan change.
The Zaps that cost the most are often the ones that have been split, patched and split again because nobody could safely change the original. A lower per-task rate leaves every bit of that in place.
A Task is a Python program on a managed runtime. There is no per-action meter, so the number of things an automation does is a design decision rather than a pricing one.
Model calls are the cost that scales, and they happen where you wrote them, so the per-run spend is arithmetic you can do before you deploy rather than something you learn from a usage graph.
lead = aisle.integrations.hubspot.get_contact( payload["email"], id_property="email", ) score = aisle.ai.run_prompt(slug="score-lead", variables=lead) aisle.integrations.hubspot.update_contact( lead["id"], properties={"lead_tier": score["tier"]}, ) aisle.integrations.slack.create_message("#sales", text=score["summary"]) # Four calls, one run. The step count is not the bill.
A task is a successful action step, meaning something the Zap sent to another app. Triggers do not count no matter how often Zapier polls for them, and neither do Filter, Paths, Formatter, Delay, Looping, Digest, Storage, Tables and Forms steps. Steps that fail or are skipped are not billed. Check zapier.com for current allowances, since plans change.
No. Filters and Paths are free, and a Zap that a filter stops has performed no actions, so it costs nothing. This makes filter placement one of the few genuinely free optimisations available: a filter above an action decides before you pay, while the same filter below it pays for every record it then discards.
The usual causes are a Zap with more actions than you remember firing more often than you think, loops turning one run into hundreds of actions, replays recounting every step they repeat, and Sub-Zaps billing for the call and the return on top of the actions inside them. Counting actions per run and multiplying by trigger volume normally explains the gap.
Often, on the per-unit price, because Make charges a credit per module call and prices those credits lower. The shape is the same though: both bill per thing your automation does, so both grow with complexity. Two differences run the other way: Make charges a credit for the trigger where Zapier does not, and modules inside each active branch count, so a scenario with routers and iterators can consume faster than its step count suggests.
Yes, by changing the billable unit rather than the vendor. Self-hosted tools move the cost to servers and the engineering time to run them. Runtime-based platforms bill for the run rather than the steps inside it, which makes the step count a design decision again. Neither is automatically cheaper, but both stop the bill scaling with how much an automation does.
Competitor details reviewed . Vendors change plans and features without notice, so check theirs before deciding.
Python on a managed runtime, with a cost you can work out before you deploy.