n8n vs Make: An Honest Comparison of Two Automation Platforms (2026)

n8n and Make are two platforms for automating processes without writing all the code from scratch. The main difference: n8n is self-hostable under a fair-code license and billed per full workflow execution, while Make is a cloud SaaS billed per individual operation. n8n Cloud starts at 20 EUR/mo, Make at 12 USD/mo (as of July 2026).

Quick answer: when to use which

This is not a "better vs worse" ranking, but two different models for two different contexts:

• n8n when you want control: self-host, data on your own infrastructure, cheaper high-volume operation, custom-built integrations. The price is a steeper learning curve and the fact that someone has to maintain that server.
• Make when you want to start fast: a ready cloud, a flat learning curve, thousands of prebuilt integrations. The price is per-operation billing, which can grow on large, multi-step volume.

The whole text below unpacks that one sentence. And if the real question is where a workflow stops being enough, that boundary is the one we draw in the guide on what an AI agent is.

n8n vs Make: six differences in one table

This is not a table of the whole market, just the six axes where these two tools genuinely differ. Every entry is a public fact, as of July 2026.

License model and self-hosting: "run it yourself" vs "take it from the cloud"

The hardest difference is here, not in the price list. n8n is fair-code: the source is visible (source-available) under the Sustainable Use License, which n8n introduced in 2022, and you can run Community Edition on your own server for free, for your company's internal purposes (n8n docs, as of July 2026). The license also lets you offer n8n workflow-building services to clients. Files marked .ee. are covered by a separate Enterprise license.

Make works the other way: it is a proprietary cloud SaaS owned by Celonis. You do not host the code yourself; you use a ready platform in the provider's cloud. You get a dedicated instance only as Private Cloud on the Enterprise plan (Make pricing and docs, as of July 2026).

The practical upshot: if you want the whole automation on your own server, inside your network, n8n is built for it and Make is not. If you do not want to maintain any server, it is the reverse.

Pricing: two different meters, not two different amounts

Comparing raw prices misleads here, because the two platforms count different things. n8n counts workflow executions: one run of a process from start to finish is one execution, whether it has 3 steps or 30. n8n Cloud: Starter 20 EUR/mo (2,500 executions, annual billing), Pro 50 EUR/mo (10,000 executions), Business 667 EUR/mo (40,000 executions, self-host, SSO/SAML). Community Edition self-hosts for free, paying only for the server (n8n.io pricing, as of July 2026).

Make counts operations, renamed credits in August 2025 (1:1 conversion). One module run is one credit, and triggers, filters, routers, and iterators each count separately: an iterator over 10 items is 10 credits on that step. The Free plan gives 1,000 credits, Core is 12 USD/mo for 10,000 credits, Pro 21 USD, Teams 38 USD (Make.com pricing, as of July 2026). From November 2025, overage credits above your plan limit carry a 25% surcharge (Make, November 2025).

So before you compare plans, calculate your volume in Make's unit. This is your substitution, not our promise:

Monthly operations (credits) in Make ≈
  number of cases (records) per month
  x number of modules per case
  x number of runs per month

The same process in n8n is usually one execution per case, regardless of step count. The more modules per case and the higher the frequency, the more the bill diverges in n8n's favor (especially self-hosted). On simple, infrequent processes the difference is negligible and Make's convenience wins.

GDPR and EU data: where the data physically sits

Both tools can be set up GDPR-compliant. They differ in where the data sits and how much control you have over it.

Self-hosted n8n gives the fullest control: you put the instance wherever you want, e.g. on a server in the EU, the data never leaves your infrastructure, and you sign the DPA with your hosting provider. That is the default choice when a process touches especially sensitive data.

Make is cloud-based but has a real answer for EU data: when you create the organization you choose a region (EU at eu1.make.com or US), and EU customers' data is processed on servers in the Union (AWS, EU region). Watch two things: the region choice is permanent for a given organization, and the DPA is available from the Team plan up, so on the Free plan you should not process personal data through Make in a business context (source: Make docs and community, as of 2025-2026).

Short version: for ordinary operational data both are fine; for sensitive data, self-hosted n8n gives control the cloud by nature cannot.

Learning curve and when to actually choose which

Make is faster off the line. A visual builder, thousands of prebuilt integrations, and no server of your own mean you can build a working scenario in hours, even without an IT team.

n8n asks for more. It gives you code steps (JavaScript/Python) and full flexibility, but the self-host has to be set up and maintained, and unusual integrations are more often written yourself. It is a tool for someone who values control over the shortest possible start.

An honest rule of thumb from the market: at low, standard volume (roughly up to a dozen-odd thousand operations a month) Make is simpler and cheap enough; above that, on multi-step, high-frequency processes, self-hosted n8n usually starts to pay off (Nordsteg, May 2026). It is a rule of thumb, not a law of physics: run the numbers on your own volume.

When neither is enough

Both tools run workflows beautifully: an event comes in, steps execute, a result comes out along a fixed path. A workflow ends exactly where a decision begins.

If a process needs something to judge an ambiguous case, stop at low confidence, stay within the boundaries you set, escalate an exception to a human, and leave a trail you can check after the fact, that is no longer a workflow but an AI agent. A rigid n8n or Make scenario will handle the happy path and known exceptions; it will not cope where every case needs a judgment about what to do. This boundary is the same distinction we describe in the guide on what an AI agent is.

This is where we come in, and only here. If you want your team to learn to build such systems themselves, we run AI-native courses. If you would rather someone else build and maintain a process with boundaries, escalation, and a trail in production, that is our AI automations (from €3,500 net per process, an agent that runs a process from €6,000 net; what that automation really costs and how to calculate the return, we break down in a separate article on pricing and ROI).

When NOT to reach for any automation

Honestly: sometimes neither n8n, nor Make, nor an agent is the answer.

• The process happens rarely. A few cases a month are cheaper for a human to handle than the cost of building and maintaining automation.
• The rules live in someone's head. If no one can describe when a case is routine and when it is an exception, write the process down on paper first. That is usually most of the work before any tool enters the picture.
• A ready SaaS already does it. If an off-the-shelf tool solves the problem, there is no point rebuilding the same thing from blocks.

If any of these fits your situation, we will say so plainly at the scan, before you spend anything.

How to start

The cheapest sensible first step is to calculate the process, not pick a tool.

1. Book a free process scan and show one specific process.
2. Prepare: how many cases per month, how many steps one case has, which systems are in the path, where the exceptions appear, and where the data has to sit.
3. After the call you get a recommendation: n8n, Make, automation with boundaries and a trail, an agent, or an honest "not worth it yet."

Book a free process scan | AI-native courses | AI automations

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