n8n, Make or Zapier: which automation tool to choose

Why this choice matters

An automation tool becomes infrastructure: over time, it ends up carrying leads, orders, messages and payments. Switching later means rebuilding the workflows one by one. That's why it's worth choosing based on the volume and complexity you'll have a year from now, not just the first automation.

A disclosure: SINOON has no commercial partnership with any of the three. We use whichever makes sense for each project. Prices checked on the official pages in October 2026.

Quick comparison

The criteria that weigh most:

How each one charges (and why it changes everything)

The billing unit sets the bill at the end of the month. Think of a common workflow: a lead comes in through a form, the system checks the CRM, an AI classifies the interest, a message goes out on WhatsApp and the CRM is updated.

In n8n, each time this workflow runs counts as one execution, no matter how many steps it has. In Make, each module that acts consumes credits, so the same workflow uses about five per lead, and Make's own AI features may consume more than one credit per action. In Zapier, each completed action is a task: four per lead, since the trigger doesn't count.

With a thousand leads a month, that's a thousand executions in n8n (fits the Starter plan), about five thousand credits in Make (fits Core) and about four thousand tasks in Zapier, well above the 750 in the entry plan. Loops within the workflow, such as processing each item in an order, multiply credits and tasks, but not executions.

When to choose each one

A practical summary:

• ✓Zapier: to start fast, with a few automations and a non-technical team. Its catalog of ready-made integrations is the largest on the market. Cost becomes an issue as volume grows.
• ✓Make: for workflows with several steps, conditions and data transformations, with a visual editor that shows the path of every piece of information. It usually has the best cost at intermediate volume.
• ✓n8n: for AI agents, integrations with custom APIs, high volume or data that needs to stay on the company's server. It requires someone to install, update and monitor it, or a partner who does. We explain how it works in our guide to automation with n8n.

Where the data lives (and data protection)

In Make and Zapier, the data passing through your automations is processed in the vendor's cloud, often in another country. That doesn't prevent use, but you need to check the data processing agreement and the international transfer requirements of laws such as LGPD and GDPR.

n8n installed on the company's own server is the option with the most control: the data stays on the infrastructure you choose, including in your own country. In exchange, security, backups and updates become the responsibility of whoever runs the server.

WhatsApp and AI agents

All three connect to Meta's official WhatsApp Business platform. For business-initiated messages, such as reminders and confirmations, Meta requires approved message templates, whatever the tool.

In AI agents, n8n is currently the most complete of the three: it has an agent node, connections to MCP servers and human approval before the agent uses a tool. Make and Zapier also offer agents, and Zapier bills them by activities, separate from tasks.

Common pitfalls

The mistakes that make automations more expensive or bring them down:

• ✓Choosing by entry price without simulating real volume.
• ✓Loops within the workflow multiplying credits and tasks without anyone noticing.
• ✓Installing n8n on your own server with nobody to update, back up and secure it.
• ✓Automations without an owner: when they break, nobody knows who fixes them.
• ✓Workflows without error handling or alerts, which fail silently.
• ✓Credentials for important systems in personal accounts.

Can you switch tools later?

Yes, but there's no import between the three: workflows have to be rebuilt. Documenting each automation (what it does, which systems it uses, who owns it) greatly reduces the work of a migration, whatever the tool.

How SINOON helps

In our automation squads, we choose the tool by volume, data and who will maintain the workflow, and deliver it with documentation and monitoring. When the process calls for an agent connected to the company's systems, it's built as a custom AI agent.