n8n vs Make: Why We Recommend n8n for Business Systems

Someone submits a Typeform. Their answers go into your customer management tool, and they receive a confirmation email. The process is clear, the tools already support the actions and there is little custom logic involved.

Make or Zapier can make sense for that kind of job. They offer an approachable way for a business user to connect familiar software. Make also has useful integrations for Swiss businesses, including a bexio connector maintained by Make.

The decision changes when several workflows need to act as one system. Customer information may need to be checked once, shared across departments and updated consistently. An approval in one workflow may release work in another. When something fails, the system needs to preserve what happened and recover without repeating an action that already succeeded.

Make can handle more than simple connections. Our recommendation comes from the combination of flexibility, operating model and cost that we want as those requirements grow.

A system needs more than a large collection of small automations. It needs parts that work together and can be changed without rebuilding the same logic everywhere.

In n8n, we can split a process into sub-workflows: smaller workflows called by a parent workflow. One might validate customer information. Another might prepare accounting data. A third might handle a notification or an exception. Those parts can be reused wherever the business needs them.

For example, imagine a customer onboarding process that checks a record, prepares billing information, creates a project and assigns delivery work. Keeping each substantial responsibility in its own workflow makes it easier to test a change and find the part that failed. It also avoids copying the same customer checks into every process.

n8n provides links between a parent execution and its sub-executions, so you can follow the process into those smaller parts when debugging.

This is how we want to build a business system: clear responsibilities, reusable logic and a process someone else can understand later.

Business processes rarely fit every assumption made by an off-the-shelf connector. You may need to combine records, calculate a value in a particular way or connect to an internal tool.

n8n gives us several ways to handle that. Its Code node supports JavaScript and Python. Expressions let us use small pieces of JavaScript-like logic directly in node parameters, for tasks such as choosing a value, formatting a date or making a calculation.

That means a custom requirement can become another part of the workflow, while the overall process remains visible. Self-hosting also allows additional code libraries when they are installed and permitted. Cloud has tighter library restrictions, so the hosting choice still matters.

Make also supports JavaScript and Python through Make Code. It is unavailable on the Free plan. Paid plans, including Core, provide access, but running code consumes two credits per second of execution time. That usage comes out of your credit allowance, and custom libraries are restricted to Enterprise. On n8n Cloud, a code step stays within the workflow execution, without a separate charge for each code node.

Ease of use is one of the appeals of Make and Zapier. But it is no longer a good reason to rule out n8n.

For a regular business user, AI changes what building in n8n involves. You can describe the intended process, have a connected assistant help build it and ask for changes in plain language. You can inspect the resulting workflow visually, without writing every line of code or configuring every step yourself.

n8n’s built-in MCP connection gives assistants such as Claude and Codex access to selected workflow tools. With the appropriate permissions, they can create and edit workflows, and test or run those made available to them.

That lowers the barrier to using n8n’s flexibility. You can start with a simple process and keep room to extend it as the business needs more.

You still need to understand the process you are automating: what should happen when information is missing, which actions need approval and what a correct result looks like. Before relying on the workflow, review it and test those situations. AI helps turn your instructions into a workflow. The business still decides what that workflow should do.

A useful advantage of a visual automation tool is that you can inspect the sequence of work. You can see where information enters, which decisions are made and what happens next. Custom code can sit inside a step without hiding the whole process in a codebase.

That matters when reviewing AI-built automations. You can point to a step and ask why it exists, check what it received and inspect what it returned. It also gives the business and the person building the system something concrete to discuss.

Make shares the benefit of a visual builder. With n8n, we particularly value having that visibility alongside the flexibility to extend the workflow as the business changes.

The connection also works when an assistant needs to do a task. You can make selected n8n workflows available to an MCP client, with access controlled through n8n’s settings and permissions.

For example, you could build a workflow that retrieves approved research from Ahrefs or gathers permitted company information. When the assistant needs that information, it can call the workflow and use the returned result. The assistant does not need to recreate the integration every time.

This gives us another reason to build reusable workflows. A useful business process can also become a tool for an AI assistant. We decide what that tool does and which actions it is allowed to take.

Make generally charges one credit for each ordinary non-AI operation. A module can run several times when it processes several separate items. AI features and some advanced apps have different charging rules.

n8n Cloud plans include a monthly allowance of workflow executions, with unlimited steps. Adding steps inside the same run does not add separate step charges.

Consider these simplified examples. Assume every Make operation shown is an ordinary one-credit operation, and all the n8n work happens within one parent execution:

The second example is an expense record for a business lunch involving five people. If each person passes through ten ordinary billable operations, Make uses 50 credits for that portion of the process. Processing those people within one n8n parent run still uses one execution from the plan allowance.

These examples exclude trigger checks, splitting or aggregating data, retries and external service costs. AI and code modules can have different Make credit rates. Credits and executions are different units, so the table is not a 20-fold or 50-fold price comparison. Price the complete workflow against the plan you need.

Sub-workflows are important here too. When a parent calls another workflow through n8n’s Execute Sub-workflow node, only the parent counts towards the paid-plan execution quota. A workflow started independently through its own production trigger is a separate run.

This is why we prefer n8n’s model for processes with many steps and repeated work across multiple records. We can structure the system into smaller, maintainable parts without adding a billable execution for each sub-workflow call.

We have worked with client systems running more than 50,000 workflow executions per week, with some approaching 100,000. In a high-volume self-hosted setup, our hosting bill was around US$30 per month.

That is an approximate example from our experience. It describes hosting costs, excluding development, maintenance, external APIs and any paid n8n licence. Your costs will depend on what the workflows do, how much data they process and the infrastructure they need.

n8n’s free Community edition lets you run the platform yourself. Some team and governance features require a paid plan, and paid self-hosted plans have execution allowances too. Budget for the edition and features your team actually needs.

For high-volume systems, the opportunity is substantial: you can choose infrastructure suited to the workload, rather than buying credits for every ordinary processing step. You also take responsibility for updates, backups, access and recovery. Those responsibilities need an owner.

One detail worth understanding is the Wait node. A workflow can pause until a later time, but a long pause does not mean it keeps actively consuming CPU throughout the wait. n8n offloads waiting execution data to its database and reloads it when the workflow resumes. Waits shorter than 65 seconds behave differently. Storage, retained data and the overall deployment still affect the bill.

Self-hosting also gives you a choice of provider and location. For a Swiss business, that can mean running the automation on infrastructure in Switzerland. A European business can choose infrastructure in its required region.

Make’s organisation regions are EU or US, and its documentation says the selected region cannot be changed after creation.

The whole data journey still matters. A workflow hosted in Switzerland can send information to an overseas AI provider or customer platform. Backups, logs and connected services need to be included in the decision. The business remains responsible for protecting personal data when it outsources processing, including how connected providers handle that data.

We value being able to make the hosting decision around the client’s requirements, then choose the right operating arrangement for that system.

For a straightforward form-to-tool automation, Make or Zapier can do the job. If you expect to connect processes across the business, reuse logic, involve AI assistants and handle significant volumes, our recommendation in 2026 is n8n.

That recommendation comes from the way we build: connected workflows, custom logic where it is useful, a visual process we can inspect and a choice between managed cloud and self-hosting.

Start with one meaningful process, but choose the tool with the larger system in mind. Test the missing information, repeated events and failed connections as carefully as the successful path. Then build the next process on parts you can trust and reuse.

If you want to turn separate automations into a connected business system, explore our process automation service.