n8n vs Make for AI agent workflows: a practical comparison

We build agent workflows on both n8n and Make, often for clients who already use one of them. They overlap a lot, but they are built on different assumptions, and those differences matter more once AI agents are involved. This comparison sticks to verifiable differences and notes where we have a preference.

The short answer

Make is a polished cloud platform with a very large integration library, priced by the actions your scenarios perform. n8n is source-available, can run on your own servers, and is priced by workflow executions rather than steps. If your agents need to stay inside your infrastructure or talk to a privately hosted model, n8n is usually the better fit. If speed of setup across many SaaS tools matters most, Make often wins.

Side-by-side

Integration counts are as reported in March 2026 and change frequently.

How the pricing models differ

The pricing unit shapes the cost of agent workflows more than the headline plan price. An AI agent that reads a ticket, looks up the customer, checks two systems, drafts a reply and logs the result may take ten or more steps. On Make, each of those actions consumes credits. On n8n, the whole run counts as one execution. For long, branching agent workflows, n8n's model is usually easier to forecast. For short, simple automations the difference is small.

Remember the other half of the bill: the language model itself. In both tools the model calls are paid to your AI provider separately, and they are often the larger cost. The levers in our guide to reducing LLM API costs apply regardless of which automation tool you use.

AI and agent features

Both platforms have moved quickly. As of early 2026, n8n offers an AI Agent node with chat, LangChain integration, self-hosted models via Ollama, human-in-the-loop approvals, guardrails, MCP support and built-in evaluations. Make offers an AI Agent builder, Make Grid for coordinating multiple agents, an MCP server, and AI content extraction and web search modules, some of them in beta.

Whichever you choose, agents that read untrusted text, such as emails, web pages or documents, need protection against prompt injection. See our article on defending production LLM apps.

Which to choose

Choose n8n when

• Data must stay in your own cloud account or on-premise.
• You want agents to call a private or self-hosted model.
• Workflows are long, branching or rely heavily on custom code.
• You have someone technical to run it.

Choose Make when

• You need many SaaS integrations working quickly.
• The team building workflows is non-technical.
• Cloud hosting is acceptable for the data involved.
• Workflows are short and numerous rather than long and complex.

How we use them

We extend whichever platform a client already runs rather than migrating for its own sake. For new agent projects handling private documents, we usually start on self-hosted n8n so the workflow and the model can sit in the same environment. Either way, every agent we deliver gets the same basics: structured outputs, step limits, a human hand-off and a cost-per-task meter.