Three automation tools.
Three different execution models.

Compare execution models, not feature lists.

Every comparison of Zapier, Make, and n8n ends up as a table of logos and check marks. That table is out of date the week it is published, and it hides the thing that actually decides whether a workflow is easy or painful to build: how each tool runs.

Zapier, Make, and n8n all connect a trigger to a sequence of actions. They differ in how they treat records, how they charge, how much of the work happens on their servers, and who on your team can safely edit them. Those four differences explain almost every regret I hear from teams that picked the wrong one.

This guide compares the three on those points. Where a detail changes often, such as a price or a plan limit, I say what to check rather than quoting a number that will be wrong by the time you read it.

Understand how each tool runs a workflow.

Zapier runs a zap once per record. A form submission arrives, the zap runs top to bottom for that one submission, and the next submission starts a fresh run. Paths add branching, and loops exist, but the mental model is one record at a time. This is what makes Zapier easy to reason about and what makes bulk work feel awkward.

Make runs a scenario on bundles. A trigger can return many bundles, and each module runs once per bundle that reaches it. Routers split the flow, iterators expand arrays, and aggregators collect results. It sits between the other two: more capable than a linear zap, more visual than n8n, and easier to get wrong when bundle counts multiply.

n8n runs nodes on items. Each node receives every item from the previous node and processes them as a set. Branching uses IF and Switch nodes, loops are explicit, and a Code node can reshape anything. The n8n data structure documentation explains the item model. Teams coming from Zapier are surprised by it for a week and then rarely want to go back.

Know what each one counts.

The three tools charge on different units, so a plan comparison is meaningless until you translate your own volume into each unit. Zapier counts tasks, which are action steps that actually run. A five step zap that runs a thousand times uses several thousand tasks. Make counts operations, which are module runs, so a scenario that touches many bundles consumes operations quickly.

n8n cloud counts workflow executions, and a single execution can contain as many nodes and items as it needs. That model favors workflows that do a lot of work per run. Self hosted n8n has no execution charge at all; you pay for the server and the time to run it. n8n is published under a fair code license that allows free self hosting for most internal business use, with conditions for reselling it. Read the license text before assuming your case is covered.

The honest way to compare cost is to take one month of real workflow runs, count steps and records per run, and price that volume on each vendor’s current plan page. Every time I have done this exercise with a client, the ranking changed depending on whether their workflows were many and small or few and heavy.

Weigh app coverage against API access.

Zapier has the largest catalog of prebuilt app connections, and for most software a small business uses, a Zapier integration exists and is maintained by someone. Make has a large catalog too, with deeper options in many modules than the Zapier equivalent. n8n has fewer app nodes, and some of them lag the vendor API.

That gap matters less than it looks, because n8n’s HTTP Request node reaches any API with a credential, and community nodes cover many gaps. In practice, the question is who will read the API documentation when the prebuilt connector does not do what you need. Zapier and Make also offer generic HTTP steps, but the rest of their tooling assumes you are staying inside the catalog.

For a team without an engineer, catalog size is a real advantage and Zapier wins on it. For a team that already calls APIs, the catalog stops mattering and the item model starts mattering more.

Look at what the AI features actually do.

All three now include AI steps: a model call inside a workflow, and higher level agent features layered on top. Zapier and Make position theirs as steps you configure inside the visual builder, which is enough for classification, drafting, and extraction in a fixed sequence.

n8n goes further with an AI Agent node that can call tools, hold memory, and loop until a task is done, alongside nodes for vector stores, document loaders, and structured output. See the n8n advanced AI documentation. For a custom agent that reads your systems and takes bounded actions, n8n is the most complete of the three without leaving the workflow builder.

None of them removes the need to evaluate the agent, control what it may write, and keep a person responsible for consequential actions. The guide to building AI agents for a business covers that side, whichever tool runs the workflow.

Decide where the data may run.

Zapier and Make are hosted services. Your data passes through their infrastructure, and your control is limited to the region options and the data processing terms they publish, which change over time and should be read directly.

n8n is the only one of the three you can run on your own infrastructure. Self hosting keeps workflow data, credentials, and execution logs inside your environment, which is the deciding factor for some regulated teams and for anyone who wants webhook URLs on their own domain. The hosting documentation describes the options.

Self hosting is also work. Updates, backups, TLS, the credential encryption key, and access control become yours. A team that will not maintain a server should use n8n cloud or stay with a hosted tool, and treat that as a sensible choice rather than a compromise.

Check what you can see when it breaks.

Zapier keeps a task history per zap, lets you replay failed runs, and can automatically retry some failures on paid plans. It tells you a step failed and shows the data, but the fix usually means editing the zap and replaying, one record at a time.

Make records every execution with the bundles that passed through each module, and it offers error handler routes per module plus a store of incomplete executions you can resume. That is more control than Zapier, at the cost of a scenario that is harder to read once handlers are attached.

n8n shows every execution with the input and output of every node, supports retries and error outputs per node, and can route any failure to an error workflow that alerts a person. The retries and rate limits guide covers those settings. For workflows that write to business systems, this visibility is the reason I usually end up on n8n.

Match the tool to who will maintain it.

Zapier is the tool a non technical operations person can own end to end. Make asks for comfort with data shapes and bundle counts. n8n asks for someone who can read a JSON payload, write an expression, and understand what an item is.

That is not a ranking of quality. A workflow that nobody on the team can edit is a liability regardless of how capable the tool is. The best tool is the most capable one that someone in your business will actually maintain, or that you have arranged for someone like me to maintain with a clear handoff.

If you are choosing for a team that will grow into engineering, start on the tool you will end on. Migrations are possible, as the migration guide shows, but they cost weeks that a good first choice avoids.

Choose by situation.

The table gives the situations where each tool is the right default. When a team sits across two rows, the answer is usually both: the simple tool for simple sequences and n8n for the work that crosses systems or needs state.

Choose Zapier when

Workflows are short, the apps are in its catalog, volume is modest, and the person maintaining them is not technical. Speed of setup matters more than cost per run.

Choose Make when

You need visual branching and array handling beyond Zapier, prefer a hosted service, and have someone comfortable with bundles and operations counts.

Choose n8n when

Workflows touch APIs without connectors, process many items per run, need stored state, call AI agents with tools, must run on your infrastructure, or must show exactly what happened when they fail.

Use two tools when

Most sequences are simple but a few are heavy. Keep the simple ones where they are and move the heavy ones to n8n, connected through a webhook.

Reconsider when

Your monthly bill is driven by a handful of high volume workflows, or your team has started writing scripts to work around the tool. Both are signals the execution model no longer fits.

Questions.