What are n8n and Make?

n8n and Make are workflow automation platforms that let you connect apps, APIs, and services into automated workflows without writing full application code. n8n is an open-source, self-hostable platform built for technical users and developers. Make (formerly Integromat) is a cloud-based platform with a visual drag-and-drop interface designed for less technical teams. Both can power serious AI automation, but in very different ways.

At Kaizora.ai, we build production AI automation systems for B2B businesses. We've used both platforms across dozens of projects from Instagram DM sales agents and RAG knowledge systems to email automation and multi-step AI pipelines. This comparison reflects real production experience, not a feature matrix comparison.

Pricing: n8n vs Make

n8n's self-hosted version is free you run it on your own server and pay only for infrastructure, typically $10–30/month on a VPS. n8n Cloud starts at $20/month. Make's free plan covers 1,000 operations/month; paid plans start at $9/month but scale quickly with usage since every step in a scenario counts as an operation. For high-volume AI workflows, n8n self-hosted is significantly cheaper.

Here's where pricing gets important for AI workflows specifically: an AI automation system typically runs thousands of workflow executions per month. On Make, each execution can consume 5–20 operations depending on complexity. An active Instagram DM automation handling 500 conversations per day easily burns through 50,000–100,000 operations monthly which pushes you into Make's Enterprise tier.

On n8n self-hosted, that same workflow costs the same regardless of execution volume. The infrastructure cost is fixed. For any B2B business running high-frequency AI workflows, n8n self-hosted has a decisive cost advantage at scale.

AI capabilities: where each platform stands

n8n has native AI nodes for most major providers OpenAI, Anthropic Claude, Hugging Face, and others plus a built-in LangChain integration for building multi-agent systems. Make connects to AI APIs via HTTP modules, which works but requires more manual configuration. For complex AI workflows involving memory, tool use, or multi-agent coordination, n8n provides a meaningfully better development experience.

n8n's AI advantage in practice:

  • Native agent nodes: n8n's AI Agent node handles tool use, memory management, and multi-step reasoning with minimal configuration
  • Vector store integration: Direct connectors for Supabase Vector, Pinecone, and Qdrant essential for RAG systems
  • LangChain support: Build complex chains and agents using LangChain components directly within n8n
  • Streaming responses: n8n supports streaming AI responses for real-time chat experiences

Make can connect to the same AI APIs, but you're building the logic manually with HTTP request nodes. For simple AI workflows summarise this document, classify this email Make works fine. For multi-agent systems, RAG pipelines, or anything requiring state management, n8n is a substantially better choice.

Self-hosting: the data ownership question

n8n can be fully self-hosted on your own server your workflows, credentials, and execution logs never leave your infrastructure. Make is cloud-only; your data, workflow logic, and API credentials are stored on Make's servers. For businesses handling sensitive data, working in regulated industries, or serving clients who require data sovereignty, n8n self-hosted is the only viable option.

Self-hosting n8n requires a VPS (we use Hetzner or DigitalOcean $6–15/month for most workflows), Docker, and basic Linux knowledge. It's not zero-effort, but for a professional deployment it's a one-time setup. We set up and configure n8n servers as part of every project we deliver.

For businesses in the Maldives specifically, data residency and client data handling is increasingly important for enterprise and government clients. n8n self-hosted means you can credibly guarantee that no client data transits a third-party SaaS platform.

Make's advantages: where it wins

Make has a more polished visual interface, a broader library of pre-built app connectors (over 1,500 vs n8n's ~400), and a shorter learning curve for non-technical users. If your team needs to build and maintain workflows themselves without developer support, and the workflows are simple connecting Google Sheets to Slack, sending automated emails Make may be more practical than n8n.

Make is the right choice when:

  • The team building and maintaining workflows has no coding background
  • You need a specific SaaS integration that Make has and n8n doesn't
  • Workflow volume is low (under 10,000 operations/month)
  • The workflows don't involve AI, vector databases, or multi-agent coordination
  • Speed of setup matters more than long-term cost and control

Which one does Kaizora.ai use?

We build primarily on n8n for all AI automation projects. The reasons are consistent across projects: self-hosting for data ownership, lower total cost at production scale, superior native AI capabilities, and the ability to write custom JavaScript nodes when a workflow requires logic that no built-in node handles. For clients who already use Make for simple automations, we recommend keeping Make for those workflows and building AI-specific systems on n8n.

The decision isn't really n8n vs Make it's n8n for AI-heavy, high-volume, or data-sensitive workflows, and Make or Zapier for simple SaaS glue. Most serious B2B automation setups end up using both, with n8n handling the complex logic.

If you're starting from scratch and building AI automation that you intend to scale, n8n self-hosted is the right foundation. The learning curve is real but manageable with the right partner, and the long-term flexibility and cost profile is substantially better than any cloud-only alternative.