Make vs n8n: Which Wins in 2026?

Updated 2026-09-14 · 1040 words

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Quick take: if you want a polished visual builder with predictable operations-based pricing and zero infrastructure to manage, Make is the faster on-ramp. If you want to self-host for free, need custom code inside your workflows, or plan to build AI-agent automations with MCP support, n8n is the stronger long-term pick. We rebuilt the same three workflows on both platforms to see where each one actually held up outside the marketing page.

We built a lead-routing flow (webhook in, CRM lookup, conditional Slack alert), a scheduled weekly report pulling and reformatting fifty spreadsheet rows, and a webhook-triggered AI research assistant that summarizes a company and posts the result to Slack. Same source data, same three flows, both platforms.

What Make and n8n actually are

Make (rebranded from Integromat in 2022) is a cloud-only visual automation platform built around a canvas where you drag "modules" and connect them with routes that can branch, loop, and handle errors visually. Its billing counts individual "operations," meaning every action a scenario runs, not every completed run, which matters once you're processing many rows per execution.

n8n is open-source and self-hostable first, cloud-hosted second. You get the same drag-and-connect canvas model as Make, but every node can be extended with a full JavaScript or Python code step, and the entire platform can run on your own server for free under n8n's Sustainable Use License. It's also the more AI-native of the two: n8n ships dedicated AI Agent and LangChain nodes plus native MCP client and trigger support, letting workflows both call external AI tools and expose themselves as tools to other agents.

Comparison table

Building the same workflow twice

The lead-routing flow took about eighteen minutes to build in Make, most of it spent getting the router module's filter conditions exactly right for the branching logic, but the finished scenario reads clearly at a glance on Make's canvas. The same flow in n8n took roughly fifteen minutes, with a code node added to reshape the Slack message payload into a format Make's built-in formatting tools would have handled without custom code.

The scheduled report generator is where Make's operations billing became concrete: reformatting fifty spreadsheet rows on a schedule burns one operation per row processed inside the loop, and on a monthly cadence that adds up fast enough to matter for a cost-sensitive team. n8n has no equivalent per-row cost once it's self-hosted; the same workflow costs nothing beyond the fixed price of the VPS it runs on.

The AI research assistant is where the gap was widest when we first built these two workflows. In n8n, we wired the AI Agent node directly to a web-search tool and a Slack output node, and the agent handled its own tool-calling loop natively. Make has since shipped its own AI Agents through Maia, its conversational builder, closing some of that gap, but it's a newer product than n8n's node-level agent tooling, and we'd want to rebuild this test on Maia specifically before calling the two equivalent.

Pricing reality check

Make's free tier covers light use well, and its paid plans price by operations rather than completed scenarios, which can work out cheaper than task-based competitors for simple, low-volume flows. But operations scale with every row and every action inside a loop, so a workflow that processes a lot of data per run can burn through a plan's monthly allotment faster than the scenario count alone suggests. n8n is free to self-host indefinitely; if you'd rather not manage a server, its cloud plans start around $24 a month for a starter tier with capped active workflows and execution volume, a flat cost that doesn't move with row counts.

When NOT to use Make

Skip Make if you want to self-host for data residency or cost reasons; there's no path to that on any plan. It's also a weaker fit for workflows that process large volumes of rows or records in a single run, since operations billing scales directly with that volume in a way that can surprise a team used to task-based pricing.

When NOT to use n8n

Skip n8n if nobody on your team wants to own self-hosting, or if you're not willing to pay for n8n Cloud instead as the alternative. Its expression syntax and code-node model also ask more of a first-time non-technical user than Make's guided router-and-module builder, so a fully non-technical team will hit a steeper wall getting past basic flows.

Which should you pick?

Choose Make if your team wants a polished, fully managed visual builder, doesn't want to think about infrastructure, and your workflows don't process huge row volumes per run. Choose n8n if self-hosting for free matters to your budget, you need custom code inside your automations, or you're building AI-agent workflows that go beyond calling a single API as one step in a chain.

Check current plans and features directly from the source before committing: Make and n8n both publish up-to-date pricing and docs on their sites.

If you want the full three-way picture including Zapier, our Zapier vs Make vs n8n comparison covers all three head-to-head. For teams leaning toward self-hosting either way, our guide to self-hosting n8n walks through the exact production setup, and our n8n vs Activepieces comparison covers another open-source option worth weighing. And if cost is the main driver behind this search, our roundup of cheaper Zapier alternatives covers both of these tools alongside five more.

For teams still torn, our advice hasn't changed across dozens of these comparisons: build your single most common workflow on both platforms before committing to either. The operations bill or the self-hosting effort tends to reveal the right answer faster than any feature table, this one included.

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