Short answer
Zapier is the fastest to start and the most expensive at scale. Make offers visual multi-step logic at mid-range prices. n8n is the most flexible and the cheapest to run at volume — self-hostable with no per-task fees — but needs more technical setup. Many businesses run Zapier or Make for simple links and n8n for heavy AI workflows.
We build client automations on all three of these platforms, so this comparison has no horse in the race — just the trade-offs we see in production, on the workflows small and mid-size businesses actually run: lead follow-up, document processing, reporting, and back-office sync.
What do they have in common?
All three connect the software you already use — CRM, inbox, spreadsheets, accounting — and run multi-step workflows when something happens: a form is submitted, an invoice arrives, a deal changes stage. All three can call AI models mid-workflow to read, classify, extract, or draft. The differences are cost structure, ceiling, and who can maintain them.
How do they compare at a glance?
| Zapier | Make | n8n | |
|---|---|---|---|
| Pricing model | Per task, metered | Per operation, metered | Flat if self-hosted; metered cloud tier available |
| Ease of setup | Easiest — minutes | Moderate — visual canvas | Steepest — technical setup pays off later |
| Multi-step logic | Basic branching | Strong visual branching | Full logic, loops, custom code |
| AI agent readiness | AI steps | AI steps + routers | Deepest: agents, memory, custom nodes |
| Data control | Cloud only | Cloud only | Self-hostable — data stays yours |
| Best for | Simple links, fast wins | Visual multi-step flows | High volume, AI-heavy, sensitive data |
When is Zapier the right call?
When speed matters more than run cost: connecting a form to a CRM, posting notifications, syncing two tools. Zapier's pricing is friendly at low volume and punishing at high volume — which is fine, because low-volume convenience links are exactly what it is best at.
When is Make the right call?
When a workflow has real branching — different paths for different customer types, error routes, approval splits — and you want to see the logic on a canvas. Make's per-operation pricing lands between the other two, and its visual builder is the most pleasant for a semi-technical operator to own.
When is n8n the right call?
When volume is high, the workflow is AI-heavy, or data control matters. n8n can run on your own infrastructure with no per-task fees, supports true agent patterns (memory, tools, retries), and handles custom code when an integration does not exist. The trade: someone has to set it up and keep it healthy — which is usually where a partner comes in.
What do we actually deploy for clients?
A mix, chosen per workflow. A typical stack: Zapier or Make for a handful of simple, low-volume links the team can self-serve — and n8n for the workhorse processes: inbound triage agents, document extraction, report assembly, CRM sync. The platform choice follows the workflow economics, not brand loyalty — and every platform gets the same guardrails: human approval on anything customer-facing, and logs for everything.
Frequently asked questions
Which platform is cheapest at high volume?
n8n, decisively — self-hosted n8n has no per-execution fees, so a workflow that runs 50,000 times a month costs the same infrastructure money as one that runs 500 times. Zapier and Make both meter usage.
Can these platforms run real AI agents?
All three can call AI models, but n8n has the deepest agent tooling — memory, branching logic, custom code, and self-hosted control over data flow. Zapier and Make handle simpler AI steps like drafting, classifying, and extracting well.
Should we migrate off Zapier if costs are climbing?
Only the workflows that hurt. A common pattern is keeping low-volume Zaps where they are and moving the high-volume, multi-step processes to n8n — migration effort goes where the savings are.
What if nobody on our team is technical?
Zapier or Make remain the safest DIY choices. If a partner builds and maintains the automation for you, platform difficulty stops mattering and total run cost becomes the deciding factor — which usually favors n8n.
