What is the difference between Make and n8n in short?
Both are platforms for connecting systems without programming everything yourself. You build a workflow from blocks: a trigger (a new order comes in), a few steps (look up the customer, transform the data) and an action (create an invoice, send a message). Visually, with ready-made connectors for widely used software.
The big difference is not in what you can build, but in three other things:
- How you pay. Make charges per step executed, n8n per complete run of a workflow.
- Where it runs. Make is a cloud service. n8n is available as a cloud service, but you can also run it on your own server.
- Who maintains it. With Make, the vendor handles updates, security and uptime. With self-hosted n8n, you (or your IT partner) are responsible for that.
We build workflows with Make and also work with n8n. Which one we recommend depends on the process and on who has to maintain it afterwards, not on a preference for one tool or the other.
What does it cost, and why does the pricing model matter so much?
Prices change regularly, so always check the current pricing pages. What changes less often is the model behind the price. And that model decides whether your bill grows with your usage or not. The information below was checked on 25 September 2026.
Make charges in credits. Every module action in a scenario, such as adding a row to a spreadsheet or fetching data from a mailbox, counts as one credit. There is a free plan with 1,000 credits per month and paid plans above it (Core, Pro, Teams and Enterprise). On the free plan a scenario can run at most every 15 minutes; on paid plans that can be every minute.
n8n Cloud charges per execution: one complete run of a workflow, regardless of how many steps it contains or how much data passes through. The plans are called Starter, Pro, Business and Enterprise and differ mainly in the number of executions per month. There is also the free Community Edition that you host yourself.
What does that mean in practice? A simple three-step workflow that rarely runs costs little on either platform. The difference shows up in workflows that take many steps per run, for example fetching a list of 200 orders and doing five things for each order. In Make every step for every order counts; in n8n it is one execution. The other way round: lots of small, short workflows that run very often are often perfectly affordable in Make.
Free self-hosting sounds attractive, but it is not free. You pay for a server, and above all in time: updates, backups, security and someone who notices when it has stopped.
Where is your data stored, and does it matter?
For many SMEs this is the deciding question, especially when personal data such as customer or employee records flows through a workflow.
Make runs in the cloud on AWS and offers hosting in the EU or North America. With n8n Cloud, according to n8n, data is stored in the EU on servers in Frankfurt. With self-hosted n8n you decide entirely yourself: on a server in your own environment or with a hosting provider of your choice.
That last option matters mainly if a customer, your sector or your own policy requires that data does not leave your own environment. Without such requirements, EU hosting with either cloud service is sufficient for most companies. In both cases, record which data goes through which workflow; you need that for your records of processing anyway.
Which is easier to build with and maintain?
Make is generally more accessible to someone without a technical background. The visual editor is clear and ready-made modules exist for many packages. A colleague who is handy with systems can, after some onboarding, read an existing scenario and make small changes.
n8n feels more technical. You can build a lot visually there too, but the platform invites more custom code steps and bespoke logic. That is an advantage if you have complex logic, and a drawback if nobody on your team understands that code later on.
So think not only about who builds it, but above all about who maintains it a year from now. A workflow only its builder can read is a risk. How to prevent a workflow from silently stopping is covered in our article on the first processes to automate; those points apply to both platforms.
What about integrations and AI?
Both platforms have a large library of connectors for widely used software, and both can talk to any system with an API through a generic HTTP module. Whether your specific package has a ready-made module differs per platform. Check that per system before you choose, because a missing module means extra build work. Our integrations page shows which systems we often connect.
On the AI side, both platforms offer building blocks to let a language model or AI agent perform a step in a workflow, such as summarising or classifying an incoming email. That is useful, but the platform is not the limiting factor here. The real question is which decisions you trust an agent with and where a person stays in the loop. More on that on our agentic AI page.
Step by step: how to make the choice
Go through these five questions. Usually the answer becomes clear on its own.
- Which systems need to be connected? Make a list and check per platform whether there is a ready-made connector or whether you need to go through the API.
- How many steps does a typical run take, and how often does it run? Many steps per run points towards n8n; many short runs are often fine in Make.
- Are there requirements on where the data is stored? If it has to stay in your own environment, self-hosted n8n is the obvious choice. If EU hosting is enough, either works.
- Who will maintain it? No in-house IT and no managed partner: choose a cloud service. That can be Make or n8n Cloud.
- What are you already using? If a platform is already running a few working workflows, that is often a reason to keep building on it rather than managing two platforms side by side.
An example to make it concrete. A wholesaler wants to fetch all new webshop orders every night, check each customer in the CRM and pass each order to accounting. That is one run with several steps per order, and order volume is growing. Here n8n's pricing model works in its favour, especially if someone takes on the maintenance. A consultancy that wants a CRM task and a Teams notification for every new contact form submission has a short workflow that runs now and then. That is quick to build in Make and easy to hand over.
When is neither the right choice?
Sometimes the Make or n8n question comes too early. If the data in your source systems is wrong, you mainly automate mistakes faster. And if you fetch and transform the same data again in five workflows, a central data foundation is often smarter than yet another workflow.
Also, if a package already has good built-in automation for exactly your process, it is usually wise to use that first. An external platform then mainly adds one more place where something can go wrong.
Need help choosing?
Torn between Make and n8n, or already running a few workflows that are not working smoothly? We are happy to look at your processes and systems and advise on what fits your situation and whoever will maintain it. See what we do in workflow automation or read how we work.