n8n vs Make, Which One for Your AI Agents?

n8n is the best choice for building complex AI agents in 2026, thanks to its native LangChain integration, self-hosting (data stays in the EU), and unbeatable fixed cost. Make remains superior for beginners and simple linear workflows. The deciding factor: operation volume (Make charges per unit, self-hosted n8n is unlimited) and data privacy.

This is the debate that’s been splitting the No-Code community for 3 years. On one side, the reigning champion: Make (formerly Integromat), with its colorful bubbles and polished UX. On the other, the open-source challenger: n8n, the darling of developers and data enthusiasts.

If you just want to connect a Google Form to Slack, pick whichever one looks prettier. But if you want to build robust AI Agents, the choice is critical. A mistake here can cost you dearly down the road.

The Analogy to Understand the Philosophy

• Make is Apple (iOS). It’s beautiful, it works right away, it’s smooth. But you’re on their turf. You pay their prices, follow their rules, and your data lives on their servers.
• n8n is Android (or even Linux). It’s a bit rougher around the edges (though it’s improved a lot!). But you can install it at home, modify it, and only pay for your server.

Round 1: Cost (AI Is Hungry)

This is where Make hurts.

The Make model: You pay per operation. An AI Agent is chatty. It thinks, it loops, it calls tools. Example: An agent processing 1 email can generate 50 operations. If you have 1,000 emails, do the math. The Make bill quickly climbs to 300 euros/month.

The n8n model: If you choose the Cloud version, it’s similar. BUT, if you choose the Self-Hosted version, you pay 0 euros in licensing (for internal use). You only pay for your server (e.g., Hetzner at 5 euros/month).

AI Winner: n8n (by KO).

Round 2: Technical Power (LangChain & Co)

In 2026, AI isn’t just an API call. We’re talking about vector memory, RAG (Retrieval Augmented Generation), and complex chains.

• Make: Very strong for standard API connections. Less intuitive for complex loops and Python code processing.
• n8n: Natively integrates LangChain nodes. You can build conversational agents with memory directly in the interface. It’s designed for modern AI.

That’s the stack we use at Node6 to build our agents like Zoe and Alison: self-hosted n8n with LangChain chains, connected to the Claude API.

Round 3: Compliance (GDPR & Security)

As we covered in our AI & GDPR piece, data location is key.

• Make: Servers in the US or Europe (on Enterprise plans). You don’t control everything.
• n8n: You install it on YOUR server in Paris. Data never passes through n8n.cloud. For a banking or medical client, it’s the only viable choice.

The Final Verdict: Which One Should You Pick?

Choose Make IF…

• You’re a beginner in automation.
• Your workflows are linear (If A then B).
• You have a comfortable budget and don’t want to manage a server.
• You’re a busy solopreneur.

Choose n8n IF…

• You’re building complex AI Agents (loops, memory).
• You process high volumes (and want to save money).
• Data privacy is non-negotiable (Healthcare, Finance).
• You have some technical knowledge (or a developer at hand).

To calculate the ROI of your automation regardless of which tool you choose, remember to use our calculator.

The Node6 Team’s Take We migrated 80% of our AI agents to n8n in 2025. The reason? Native LangChain management and fixed self-hosting costs. Once the server is configured, it’s “all you can eat.” For creating automated workflows, also check out our workflow generator app.ia-insights.fr that transforms your descriptions into ready-to-use n8n/Make templates.