Zapier vs Make vs n8n: which automation tool to choose

Zapier, Make and n8n all connect apps so that an event in one triggers actions in others, and all three now let you add AI steps and agents. The real differences are in who they are built for, how they count usage and where your workflows run. Picking the wrong model for your volume or your technical skills is the most common and most expensive mistake.

The short answer

• Choose Zapier if non-technical colleagues need to build automations across a very wide range of apps, with admin controls over AI use.
• Choose Make if you want a visual canvas for multi-branch scenarios with data transformation, and you are comfortable watching credit use.
• Choose n8n if your team is technical, wants to mix code with visual steps, inspect AI agents in detail or self-host for data control.

Side by side

How the pricing model changes the answer

The three tools meter usage differently, and that matters more than list prices. Zapier counts tasks, so a ten-step Zap that runs often consumes far more than a two-step one, and more capable AI models use more. Make counts credits per module action, with the same effect for long scenarios. n8n Cloud counts complete workflow executions, so a complex workflow costs the same as a simple one per run. If your automations are long and frequent, n8n’s model is usually the most predictable; if they are short and occasional, any of the three can be inexpensive.

Who can build with it

Zapier is the easiest to hand to colleagues without technical training: it lists more than 9,000 connected apps and keeps each automation readable. Make’s canvas suits people who think visually and need branches, filters and data mapping, and it adds an API and CLI for technical teams. n8n assumes more comfort with code and APIs, but rewards it with custom API calls for services that lack a ready-made node and with MCP connections to assistants such as Claude.

AI agents and control

All three now offer agents rather than single AI steps. Zapier lets administrators restrict model providers and apply checks for personal data, prompt injection and toxicity, which suits companies worried about governance. n8n makes each step of an agent’s reasoning visible and lets you swap between cloud and local models, which helps when you must explain what an agent did. Make has made Make AI available across plans to help build scenarios and add classification or generation steps.

Where your data runs

Only n8n can be self-hosted, including on air-gapped infrastructure. That is decisive for some regulated teams, but it moves responsibility for updates, backups and security onto you. n8n’s fair-code licence also has conditions if you embed it in a product you sell, so read it before building a commercial service on top.

How to decide

1. Write down the three automations you would build first, with their steps and how often they run.
2. Estimate monthly usage in each tool’s unit: tasks, credits or executions.
3. Build the most complex one on each free option and note where you got stuck.
4. Decide whether self-hosting or AI governance controls are requirements, not nice-to-haves.
5. Add error handling and a test run before any automation writes to customer data.

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Tools to explore

ClaudeProductivityFlowiseAutomationDeepSeekCodingDifyAutomationAnthropic APICodingOllamaCoding