Zapier vs Make vs n8n: Which One Should We Use in 2026?

Zapier vs Make vs n8n: which one should we use?

Choose between Zapier, Make and n8n on three things in this order: whether your apps are supported, how the platform counts what you run, and who is going to maintain the thing in six months.

App coverage first, because it is a hard constraint rather than a preference. Zapier's integration count is the largest of the three and the figure in circulation has moved: older write-ups say above 8,000, while three 2026 sources put it at 9,000 or more, among them a pricing tracker that logged the change in May 2026. Make and n8n both cover the mainstream business software and both let you call an API directly when a connector does not exist, which is fine if somebody on your side can read API documentation and a problem if nobody can.

Counting second, because it decides the bill, and the section below covers it properly.

Maintenance third, and it is the one owners skip. Every platform here is a tool for building things that run unattended, which means somebody has to notice when one stops. A no-code platform does not remove that job, it just makes the building part faster.

The short version of each: Zapier is the broadest and the most expensive per unit of work, Make is the cheapest per unit until a loop is involved, and n8n is the cheapest at volume and the only one that asks you to run software. The table below sets out what each one is and who it suits, and the section after it explains why their prices cannot be compared directly.

How do Zapier, Make and n8n compare on price and features?

Zapier, Make and n8n compare on four columns rather than three, because a built system is the alternative people are usually weighing them against.

Every price in that table came from third party pricing guides checked on October 16, 2026, not from the vendors' own pages, and those guides disagree with each other above the entry tier. Six or more of them agree on the three entry figures above, which is why those are the only ones here. Treat them as the shape of the market and check the live number at Zapier, Make and n8n before you commit to anything.

Why does the same workflow cost a different amount on each one?

Because the three platforms meter different units, and the difference is multiplicative rather than cosmetic. This is the part the comparison blogs skip and it is the only pricing fact that survives a price change.

Take one ordinary workflow: an email arrives, you check whether it is from a customer, you extract three fields, you look up the record, you update the CRM, you post a message to a channel, and if the lookup fails you send yourself a note. Call it seven steps with one branch.

On Zapier that is billed per successful action the Zap performs, so most of those steps count. On Make it is billed per credit, which is one module action, and filters, routers and iterators count too, which means a loop over ten line items does not cost one credit, it costs ten. On n8n the entire run is one execution whether it contained seven steps or seven hundred.

So the same workload lands in three different places. Low volume with many steps per run favours n8n's model heavily. High volume with few steps narrows the gap. Anything with a loop in it is where Make's counting surprises people, and reports of a cheap plan turning into a much larger bill almost always trace back to an iterator nobody counted.

Two practical consequences. First, you cannot compare these platforms on the monthly price, you have to estimate your own volume in each platform's unit, which takes twenty minutes and is the only honest way to do it. Second, a design that is cheap on one platform can be the expensive one on another, so a workflow ported across without redesign often costs more than it did before.

Is n8n actually open source?

No, n8n is not open source, and the distinction matters if you plan to run it for anybody other than yourself. n8n's own documentation describes its licence as the Sustainable Use Licence, which the company adopted in March 2022 to replace the Apache 2.0 with Commons Clause arrangement it had used until the 17th of that month, and which it describes as fair-code rather than open source.

What the licence allows, per that documentation: using and modifying the software for internal business purposes, non commercial use or personal use, and redistributing it free of charge for non commercial purposes. What it does not allow is selling a product or service whose value derives entirely or substantially from n8n's functionality. White labelling n8n for paying customers and hosting it for a fee are named as uses that are not permitted, and commercial embedding is handled through a separate commercial arrangement the company calls n8n Embed.

Internal use is clearly fine, and the documentation says so explicitly for the obvious case of syncing a company's own data between its own systems, including where that business makes money. If you are a business automating your own operations, the licence is not a problem.

Where people get caught is the agency case. A consultant who stands up n8n for a client and charges a monthly fee for running it is in the territory the licence names, and "it is open source" is not a defence because it is not open source. Anybody planning that should read the licence and talk to the company rather than taking a comparison blog's word for it, including this one.

This is also a reason the self hosted option is less free than it looks for a business. The software costs nothing and the server is cheap. What you are taking on is the upgrades, the backups, the credential rotation and the pager, which is a job rather than a line item.

Who owns these platforms, and does it matter?

Ownership matters for one reason: it tells you which direction a platform is likely to be pushed, and whether your workflows are sitting on somebody else's strategic bet.

Make is the clearest case. It launched in Prague in 2016 as Integromat and was acquired by the process mining company Celonis in a deal TechCrunch reported on October 14, 2020, with the price undisclosed and Celonis's chief executive describing it as a three digit million dollar sum. The product was rebranded Make in a February 2022 announcement that also said it would run as a business unit within Celonis, whose core business is selling process intelligence to large enterprises. That is a reasonable home for an automation platform and it is not a neutral one.

Zapier is the independent of the three, founded in 2011 and still private, and its strategy has been breadth: more apps than anybody, aimed at the business user rather than the engineer. The pricing follows from that, because per action billing is what you charge when your value is the catalogue.

For n8n the governing fact is the licence described above rather than who owns the company. Source available software with a commercial company behind it gives you the code and the ability to run it yourself, within terms the company sets and can revise for future versions.

What to do with all that as a buyer is modest and worth doing. Ask yourself how much of your operation would stop if the platform changed its pricing model or its terms next year, and keep a written description of what each workflow does outside the platform that runs it. That is cheap insurance and almost nobody has it, which is why platform migrations turn into archaeology.

What happens when one of these breaks?

What happens when one of these breaks is that you find out eventually, and how fast depends on what you built around it rather than on the platform. All three send failure notifications and keep execution logs, which catches hard errors where something threw an exception and stopped.

The failures that cost money are the quiet ones. A supplier changes an invoice layout and every extracted total now reads zero. A filter stops matching because somebody renamed a field in the CRM. A credential expires and the run fails silently at step two for eleven days. The platform reports those as either success or a single failed run, and nobody is reading the log.

What catches them is a report of what the workflow produced, what it escalated and what it skipped, read by somebody who knows roughly what the numbers should be. None of these platforms gives you that out of the box, because no platform ships it. Somebody designs it for your process.

The other thing that breaks is the person. A no-code workflow built by the operations manager who has since left is a liability nobody can read, and that happens constantly. If a workflow matters, somebody other than its author needs to understand it, and the written description above is how.

These platforms are good at the building and silent about the operating, and the operating is where automation either earns its money or quietly stops. Who maintains an AI automation? covers what that job actually involves.

When is the platform the wrong question?

When you have not decided what to automate yet, which is most of the time somebody is comparing these three.

Picking a platform first is choosing the tool before the job. The expensive decision is which process is worth the engineering, and that answer is the same whichever platform you land on: find the process that recurs, follows rules somebody could describe, and eats skilled hours. Get that wrong and the platform choice is irrelevant, because you have automated something that was not costing you anything.

Three situations where a platform is clearly the right buy. The workflow is simple and the apps are mainstream, so a few connected steps do the job. You have somebody technical who enjoys this work and will still be there next year. Or you want to prove the process is worth automating before anybody spends real money on it, which is a good reason to build a rough version on a platform first.

Three situations where it is not. The inputs vary a lot, which is where no-code workflows turn into a maze of branches nobody can maintain. The job has to write into several systems in one transaction and be correct every time. Or nobody internally is going to own it, in which case you are buying a subscription and a future orphan.

Where BLACKSIG fits is the second set. We decide what is worth automating first, our engineering team builds it into the tools you already run, and we operate it from there, on our infrastructure. We use whatever the job needs underneath, platforms included where they are the right tool. If what you want is to own and run the workflows yourself, pick the platform that fits your volume from the table above and hire a contractor who knows it, and that is a legitimate answer rather than a consolation prize. For a worked example of the other kind, the Universidad Maimonides case study covers fifty to a hundred data files per batch becoming records built automatically, about fifteen hours a week back, running on our infrastructure ever since.

Frequently asked questions

Related resources

• Should we buy an AI tool or have one built?
• Who maintains an AI automation?
• What happens when AI gets it wrong?
• Does AI automation work with the software I already use?
• AI automation vs traditional automation
• Universidad Maimonides case study