n8n vs Make.com vs Zapier for AI workflows in 2026

Clients ask me this question almost every week, usually as “which one is best?” There isn’t a best one. n8n, Make.com and Zapier are all good tools, and they fail in different places. What matters is where your workflow is likely to break and what that break would cost you.

This is how I compare them in 2026, what each one bills you for, and the questions I ask before I pick one for a client. One honest note first: SlickLab’s automation service builds on n8n, Make and custom code. The pricing facts below were read on each vendor’s own pages as of October 2026. Prices change, so treat the links as the source and this post as the map.

The short answer

• Zapier is the fastest way to connect two popular apps when you don’t want to think about hosting. Its app catalogue is huge and its learning curve is the gentlest of the three.
• Make is the best visual builder when the logic branches, loops over lists or reshapes data. Its error handlers are more explicit than Zapier’s.
• n8n is the one to choose when you want to host it yourself, keep data on your own server, or write real code inside a workflow. It asks more of you technically.
• Custom code is the right choice when one mistake costs real money and has to be prevented by design rather than noticed afterwards. I’ll come back to this, because it’s the choice people skip.

Where it runs, and who owns the risk

This is the first fork in the road, and it decides more than features do.

Zapier and Make run in the vendor’s cloud. You sign up and build. Uptime, updates and security patches are their job. The trade-off is that every record your workflow touches passes through their servers, and you live with their limits and their price changes.

n8n gives you a real choice. Its documentation lists two options: n8n Cloud, fully managed, or self-hosted on your own infrastructure. The self-hosted Community edition is free and, in n8n’s own words, has “almost the complete feature set”. Enterprise features such as SSO need a paid plan either way.

Self-hosting has a licence condition worth reading. n8n is not open source in the strict sense. It uses the Sustainable Use License, which n8n describes as a fair-code licence. It allows you to use and modify the software “only for your own internal business purposes or for non-commercial or personal use”. Running your own company’s workflows on it is what it’s for. If you plan to resell n8n, or host it as a paid service for other people, read n8n’s licence FAQ first, because that needs a separate commercial agreement.

What each one actually bills you for

The headline plan price is the least useful number on these pages. What matters is the unit they count, because that decides how your bill grows as the workflow gets longer or runs more often.

Tool	Billing unit	What counts
Zapier	Tasks	Each successful action step. Triggers, polling and built-in tools such as Filter, Formatter and Paths are free, and failed actions are not counted.
Make	Credits	Each action a scenario performs. Most cost 1 credit, but some features that use Make’s own AI provider can cost more.
n8n Cloud	Workflow executions	One run of the whole workflow, “regardless of complexity”: the number of steps doesn’t change the count.
n8n self-hosted	Your server	No per-run charge for the Community edition. You pay for hosting and your own time.

Sources, as of October 2026: Zapier pricing, Make pricing and Make’s credits and operations help page, and n8n pricing.

Here is why the unit matters. Take a workflow with eight action steps that runs 500 times a month. On Zapier that is up to 4,000 tasks, because each successful step counts. On Make it is roughly 4,000 credits or more, because each action costs at least one. On n8n Cloud it is 500 executions, because a run is a run. Double the steps and the first two bills roughly double, while the third stays the same.

That doesn’t make n8n the cheapest every time. Zapier’s free plan includes 100 tasks a month for two-step Zaps, and its Professional plan starts at 750 tasks a month. A small, simple workflow can live there comfortably. Make’s free plan includes 1,000 credits a month. The point is to work out your own number: steps times runs per month, in each tool’s unit, before you commit. Our cost calculator gives a rough range for a build. The vendor pages give you the running cost.

One more detail: Make recently changed its billing unit. It used to bill on operations. It now bills on credits, and its help page explains the difference: “Operations count the activities performed in your scenarios, while credits are what you pay for those activities.” If you’re reading an older comparison that talks about Make operations, the numbers in it may no longer match your invoice.

AI steps and agents

All three tools added AI features, and all three now have something they call an agent. The idea is the same in each: you give a language model a goal and a set of tools, and it decides which tool to call next. If that difference between an agent and a chatbot is new to you, I explain it in Agentic AI vs chatbots.

• Zapier has AI steps (“AI by Zapier”) inside Zaps, which use tasks per AI step and per tool call at rates that depend on the model tier. It also has Zapier Agents, which are billed separately in “activities” and, according to the pricing page, don’t use your task allowance.
• Make has a Make AI Agents app. Its help pages describe agents with instructions, knowledge and tools, where a tool can be a module, a whole scenario or an MCP tool.
• n8n has an AI Agent node: you connect a chat model and one or more tools, and “the agent decides which tools to call to complete a task.” You can bring your own model provider and keys.

The practical difference is control. In n8n you can see and change everything around the model call: what goes in, what comes out, and what happens when the answer is malformed. Zapier makes the common case very easy and hides more of the plumbing. Make sits in between.

Whichever you use, my rule doesn’t change: the model may suggest, but a rule decides anything that costs money or can’t be undone. A model reading an email and suggesting “this is a refund request” is fine. A model issuing the refund on its own is not.

Error handling: where workflows really break

Most of the work in a real automation is deciding what happens when something fails. The happy path takes an afternoon. The other paths take the rest of the project.

• Zapier doesn’t count failed actions as tasks, and on paid plans it can automatically replay a Zap run that errored (“Autoreplay”). That covers short outages well. It is less suited to logic like “if the CRM is down, keep the booking, queue the CRM write, and tell a person after the fifth failure.”
• Make has named error handlers you attach to a module: Skip, Retry, Resume, Commit and Rollback. Retry stores the incomplete execution so it can be retried automatically or by hand. This is the most explicit model of the three in a visual builder.
• n8n lets you name an error workflow, started by an Error Trigger node, that runs whenever an execution fails. It receives the error message and the last node that ran, so you can alert someone or log the failure. One error workflow can serve many workflows.

Here is the test I use. For each step, I ask: if this fails halfway, what state is the world in? If the answer is “a customer was charged but not booked” or “a message went to someone who opted out”, a retry button isn’t enough. That is the point where I stop using a no-code tool for that part.

When I don’t use any of them

The Lead Responder engine is a good example. Its job sounds simple: take an inbound lead, qualify it, book an appointment and confirm by text. I could have built a working version in any of the three tools in a day.

I wrote it as custom code instead, because the important rules are about what must never happen:

• The same webhook arriving twice must not create two bookings. In the engine, that is a database constraint on the event, not a check someone remembers to add.
• A CRM outage must not lose the lead. The booking goes through, and the CRM write waits in an outbox table until the CRM is back.
• An outgoing message that keeps failing must not retry forever or disappear. After five failed tries it becomes a callback task for a person.
• A number that has opted out is never texted. Suppression is checked before the message is even written.

Each of those has a test written for the failure, not just the success. You can approximate some of this in Make or n8n. But when the guarantee is “exactly once”, I want it enforced by the database and proven by tests. The same engine is behind the Lead Responder on our products page, which is still marked as in build.

The opposite is just as true. Posting a new blog entry to three social accounts, copying form submissions into a spreadsheet, or sending a Slack note when an invoice is paid doesn’t need custom code. If one run fails, you notice and fix it. Paying a developer to build that by hand would be wasting your money.

The questions I ask before choosing

1. What breaks if one run fails or runs twice? If it’s “nothing much”, use the simplest tool. If it’s money, consent or a promise to a customer, think about code.
2. Where must the data live? If client records can’t pass through a third-party cloud, that points to self-hosted n8n or custom code.
3. How many steps, how many runs? Work out the monthly units in each tool. Long, frequent workflows favour per-execution billing.
4. Who maintains it? A tool your team can open and understand beats a cleverer one only I can fix. This is why the automation service is a monthly subscription: workflows drift as the apps they connect change.
5. Are the apps supported? Check that every app you need has an integration with the triggers and actions you need, not just a logo on the integrations page.

If you’re not sure the workflow is worth automating at all, answer that first. My post on scoping an AI automation project walks through how I decide, and the free 2-minute automation audit gives you a quick first read. For the bigger picture of where agents fit in a small business, start with the guide Agentic AI for small business. And if your workflow needs an AI client to reach your internal tools directly, read What is an MCP server before you build connectors by hand.

Already know what you want automated? Tell me about the workflow and I’ll tell you which of the four I’d use, and why.

Questions people ask