Make or n8n for AI Agents?

As soon as a workflow no longer just passes data along but decides for itself which tool comes next, Make and n8n call it an AI agent. Both platforms offer their own building block for this, both promote it as a core feature for 2026, but the differences lie in the details: how the agent is built, what a single run costs, and how much control you retain over the infrastructure your data passes through via a language model.

This article deliberately focuses only on the AI agent angle, not on a general feature comparison of the two tools. If you're weighing Make against n8n as an automation platform in general, you'll find a separate comparison for that; here we focus specifically on what happens once an agent independently calls tools and makes decisions.

How AI agents are built in n8n and Make

In n8n, you build an agent using the AI Agent Node. The node connects a chat model with at least one tool sub-node; only this lets the agent choose for itself which tool fits the task at hand, instead of working through a fixed chain of steps. Since version 1.82.0, all agents run as a so-called Tools Agent, and older configuration variants have been unified. n8n explains how it works like this: an agent is essentially a chain that can additionally make decisions, including calling a tool, evaluating the tool's response, and formulating the final answer, sometimes across multiple iterations. You can find details in the n8n documentation on the AI Agent Node and in the explanation of what agents in n8n actually do.

Make takes a similar approach, but with its own terminology: Make AI Agents rely on goals, prompts, and real-time data instead of rigid logic paths, according to the provider with step-by-step logging directly in the canvas. You can choose between Make's own AI provider and your own API key, for example from OpenAI or Anthropic Claude. Make also lets you factor unstructured inputs, files, and conversation history into the decision; the principle resembles n8n's tool-calling pattern, just within Make's own scenario logic.

What an AI agent run really costs

This is where the pricing models diverge noticeably. n8n Cloud charges per execution: a complete workflow run counts as one execution, regardless of how many nodes or tool calls it contains. According to the current n8n pricing page, the Starter plan costs 20 euros per month for 2,500 executions, and the Pro plan costs 50 euros for 10,000 executions; both cloud plans also include a quota of AI credits for the AI features built into n8n Cloud (Starter around 2,300, Pro up to 13,700). For the Business plan starting at 667 euros per month, it still says "AI Assistant coming soon" at this point.

At Make, billing isn't per execution but per credit, and an agent run consumes credits in a much more fine-grained way than a classic scenario. According to the Make Help Center on credit usage for AI Agents, running an agent with Make's own AI provider costs 1 credit per operation plus additional credits based on AI tokens consumed; if you use your own provider access instead, only 1 credit per operation applies, but you bear the model costs separately with the respective provider. For chat interactions, an additional credit is added per tool called. In practice, this means every reasoning step and every tool call is counted individually, so an agent run can noticeably consume more credits than a comparable classic scenario without AI. You can find the current rates themselves on the Make pricing page: Free with 1,000 credits, Core from 9 dollars per month, Pro from 16 dollars, Teams from 29 dollars, each for 10,000 credits per month; Make AI Agents are already available on the free plan.

Control over data and infrastructure

For AI agents, this question is especially relevant because prompts, intermediate results, and tool responses often contain sensitive business data. n8n gives you a choice here: besides n8n Cloud, you can self-host the Community Edition entirely, free of charge and with unlimited executions, either via npm, Docker, or with the usual cloud providers. The n8n documentation on self-hosting describes the installation paths in detail; it's important to know that all editions are based on the same core product, and Business or Enterprise features are only added via a license key. If the agent runs on your own infrastructure, only the actual model call stays with the chosen AI provider, while the rest of the processing happens in your own environment. This lets you retain control over where your data resides and who has access to the execution history.

Make doesn't offer this option. Scenarios and agents run exclusively in the Make cloud; you can choose the AI provider and thus where the model processing happens, but the automation platform itself always remains Make's own infrastructure. For many use cases, this isn't a problem, but for especially sensitive processes or strict internal data processing requirements, it's a point you should clarify in advance.

Which tool suits whom

• Make scores when you want to get started quickly, don't want to run your own hosting, and want the agent embedded in an existing set of over 400 connected apps.
• n8n pays off if you expect to run many agent runs, want predictable costs per execution instead of granular credit billing, or need to self-host for data protection or compliance reasons.
• For both tools, the actual cost driver is usually not the platform fee but the language model chosen behind it, so it's worth taking a look at the model choice before an agent goes into production.

If you're unsure which setup fits your use case, take a look at our AI agent consulting: there, we clarify in advance whether cloud or self-hosting pays off for your case, before the first line of workflow is built.

Frequently asked questions

Can I use my own OpenAI or Claude key at Make instead of Make's AI provider?

Yes. If you select your own provider access in the Make AI Agents app, according to the Make Help Center you only pay 1 credit per operation instead of additional token credits, but you bear the model costs directly with the respective AI provider.

Does every tool call by an agent count as a separate execution in n8n?

No. A complete workflow run counts as one execution, no matter how often the agent calls tools or goes through intermediate steps within that run. This makes the cost per agent action more predictable than with pure credit billing.

Can I self-host Make like n8n?

No. Make runs exclusively in the Make cloud. n8n, on the other hand, offers a free, self-hosted variant with the Community Edition, which you can run via npm, Docker, or common cloud providers.

Do I already need a paid plan for a simple AI agent?

Not necessarily with Make: according to the pricing page, Make AI Agents are already available on the free plan with 1,000 credits per month, though limited to two active scenarios. With n8n, the free, self-hosted Community Edition is enough for initial tests; in n8n Cloud, an execution quota including AI credits is included starting with the Starter plan.

What costs more: an agent run at Make or an execution at n8n?

This can't be answered across the board, because the billing units differ: n8n counts entire workflow runs, Make counts individual operations plus token and tool calls. For complex agents with many tool calls per run, Make usually consumes more credits than a comparably elaborate n8n workflow does in executions, because every intermediate step is counted separately.