Zapier bills per action step, Make per module action. On the same 10-step workflow run 1,000 times a month, that is $129 against $9. Where each still wins.
Most Zapier-versus-Make comparisons lead with "Zapier is easier, Make is more powerful." That is true, and it is not what decides your bill. The meter is. Zapier counts tasks, one per successful action step. Make counts credits, one per module action. They sound equivalent and stop behaving alike once a workflow has more than a few steps or loops over lists.
Take one realistic automation: a 10-step workflow that runs 1,000 times a month, for example processing each new order into a CRM, a spreadsheet, an invoice tool and two notifications in Slack.
| Zapier | Make | |
|---|---|---|
| Units per run | 9 tasks, since the trigger is free | About 10 credits, one per module |
| Units a month | About 9,000 tasks | About 10,000 credits |
| Plan needed | Professional, 10,000 tasks | Core, 10,000 credits |
| Price billed yearly | $129 a month | $9 a month |
| Price billed monthly | About 1.5 times the yearly price | $10 a month |
Zapier and Make pricing pages, checked October 1, 2026.
The gap is not a rounding error. Zapier's advertised entry price of $19.99 a month buys a much smaller task allowance, and the price climbs steeply with volume on its slider. Make's price also scales with credits, from $9 for 10,000 to $29 for 40,000 and $99 for 150,000 on Core, but from a far lower base. Two things narrow the gap in practice: Zapier doesn't charge for filters, paths and formatter steps, and Make charges for every bundle that passes through later modules, so a scenario that loops over 50 records multiplies its credits.
On Zapier, every action step that runs successfully is a task, and since 2026 AI steps, code steps, MCP calls and SDK calls draw from the same pool, with more powerful AI models using more tasks per action. The trigger, filters, paths and formatter steps are free.
On Make, each module action costs a credit, including searches and every item an iterator processes. Code runs at 2 credits per second, and AI modules consume credits according to the work they do. The diagram shows why the two diverge:
| Zapier | Make | |
|---|---|---|
| Free plan | 100 tasks a month, two-step Zaps | 1,000 credits a month, runs at least 15 minutes apart |
| Entry paid plan | Professional from $19.99 a month yearly, $29.99 monthly | Core $9 a month yearly for 10,000 credits, $10 monthly |
| Team plan | Team from $69 yearly, up to 25 users, SAML SSO | Teams $29 yearly for 10,000 credits, roles and shared templates |
| Enterprise | By quote, unlimited users | By quote, SSO, overage protection |
| When you run out | Optional pay-per-task at 1.25x, or Zaps stop | Scenarios stop, webhooks queue, auto top-ups optional |
| AI agents | Zapier Agents add-on, 400 activities free | Make AI Agents in beta, on every plan |
| MCP | Zapier MCP | Make MCP Server |
Zapier's free plan is a demo; Make's is enough for a few real automations, as long as a 15-minute schedule is acceptable.
Zapier's lasting advantage is reach: over 9,000 apps, including niche industry tools that Make doesn't cover, plus a linear builder that anyone can use in minutes. Make covers more than 3,000 apps, and both can call any API through HTTP and webhooks, so check your own stack before deciding.
Make asks more of its users. Routers, iterators, aggregators and data mapping are powerful, and the canvas makes them visible, but a first complex scenario takes longer to build. Teams with a technical person usually find the learning time pays for itself in the first month's bill.
| If you | Choose | Why |
|---|---|---|
| Run multi-step automations at any real volume | Make | A fraction of the cost per action |
| Need an app only Zapier supports | Zapier | 9,000+ apps |
| Want non-technical colleagues building automations | Zapier | The simplest builder |
| Need branching, loops and data transformation | Make | The visual canvas is built for it |
| Want AI agents acting across many apps | Either | Zapier Agents as an add-on, Make AI Agents in beta |
| Need self-hosting or per-execution pricing | Neither | Look at n8n |
Make takes the verdict: for the same work it costs a small fraction of Zapier, and its builder handles complex logic better. Zapier earns its premium when its catalog or its simplicity is the deciding factor. For technical teams, n8n vs Make and n8n vs Zapier cover a third option priced per full execution.
Make is the better value for most multi-step automations, and its visual canvas handles branching, loops and data transformation well. Zapier is better when you need an app only it supports, when the people building automations are not technical, or when you want a simple two-step automation running in minutes.
Much cheaper at the same volume. Make costs $9 a month billed yearly for 10,000 credits on Core, while Zapier Professional costs $129 a month billed yearly for 10,000 tasks. Zapier's entry price of $19.99 covers a far smaller task allowance, so the gap grows as workflows get longer.
A Zapier task is one successful action step; the trigger, filters, paths and formatter steps are free. A Make credit is one module action, so every module that processes data, including searches and iterations over lists, uses credits. AI steps, code and connectors can use more than one unit on both platforms.
Zapier, with over 9,000 apps against Make's 3,000-plus. Both can call any API through HTTP or webhook steps, so the practical test is whether your specific tools have ready-made connectors on each, especially niche industry software.
On Zapier, paid plans can opt into pay-per-task billing, which keeps Zaps running at 1.25 times the plan's task price; otherwise Zaps stop. On Make, scenarios stop until you add credits, but incoming webhooks are queued and polling scenarios catch up from their last run, and paid plans can buy 10,000 extra credits automatically.
Yes, but by hand. There is no automatic converter, so each Zap has to be rebuilt as a Make scenario. For a handful of automations that takes an afternoon; for dozens of business-critical workflows, plan the move as a project and run both in parallel while you test.