n8n vs Make vs Zapier for Business Automation

Zapier, Make and n8n all promise the same thing: connect your apps and let work move between them automatically. They overlap enough that teams often pick whichever one they heard of first. But they're built on different ideas about who builds automations, how they're priced and where they run — and those differences matter a lot once your workflows grow.

We build on all three. This is how we decide between them.

Pricing details change regularly on all three platforms, so check each vendor's current plans before committing. The pricing models, which matter more than the exact numbers, have been far more stable.

The one-line summary

• Zapier is the easiest to start with and has the largest catalogue of app integrations. It's best for simple, linear automations built by non-technical teams.
• Make offers a visual canvas with more control over logic and data, at a lower price per step. It's best for moderately complex workflows built by people comfortable with some technical detail.
• n8n is the most flexible and can be self-hosted. It's best for complex, high-volume or AI-heavy workflows, and for teams with technical skills or a technical partner.

Zapier

How it works

You build a "Zap" as a sequence: a trigger ("new form submission") followed by actions ("create a CRM contact," "send a Slack message"). The editor walks you through each step in plain language. Branching, filters and multi-step logic are available, but the model is fundamentally a straight line.

Strengths

• Fastest to learn. Most people build a working automation in their first session.
• The biggest integration library. Thousands of apps, including many niche tools that the others don't support natively.
• Reliable and well-documented. Errors are clearly reported and easy to replay.
• AI features built in, including AI steps and agent-style tools that can work across connected apps.

Weaknesses

• Cost at volume. Zapier charges per task, and every action step that runs counts. A workflow with eight steps running a thousand times a month uses eight thousand tasks. Costs climb quickly for high-volume or many-step automations.
• Complex logic gets awkward. Loops, heavy branching and data transformations are possible but harder to build and maintain than on the other two.
• Cloud only. Your data passes through Zapier's servers, which can matter for regulated industries.

Make

How it works

Make (formerly Integromat) uses a visual canvas. Each app action is a module, and you connect modules into a "scenario" that can branch, loop over lists, aggregate results and route data down different paths. You can see the whole workflow at a glance.

Strengths

• Powerful visual logic. Routers, iterators and aggregators make it easy to process lists of items and split a workflow into branches.
• Better data handling. Mapping, transforming and reshaping data between apps is more flexible than in Zapier.
• Lower cost per step for most workloads, because pricing is based on operations or credits at lower rates than Zapier tasks.
• Good AI integrations, including connections to major AI model providers and AI agent features.

Weaknesses

• Steeper learning curve. The canvas is intuitive once you understand bundles, iterators and mapping, but non-technical users often need help at first.
• Every module run counts. Make counts operations generously, and a scenario that polls frequently or loops over large lists can use more than expected. Watch scheduling and filters.
• Cloud only, like Zapier.

n8n

How it works

n8n also uses a visual node-based canvas, but it's built with developers in mind. Any node can be replaced or extended with JavaScript or Python, and you can call any API directly with an HTTP node. It's "fair-code" licensed: you can use the managed cloud version or run it on your own server.

Strengths

• Self-hosting. Running n8n on your own infrastructure keeps data inside your environment and can be dramatically cheaper at high volume, since you pay for the server rather than per step.
• Execution-based pricing on cloud. n8n's cloud plans count workflow executions rather than individual steps, so a long, complex workflow costs the same as a short one per run.
• Code where you need it. When a built-in node can't do something, a few lines of code can.
• Strong AI tooling. n8n has invested heavily in AI agent nodes, memory, vector stores and tool use, which makes it a popular choice for building AI agents and retrieval workflows.

Weaknesses

• Most technical of the three. You'll get more out of it with someone who understands APIs, JSON and basic scripting.
• Self-hosting is a responsibility. Updates, backups, security and uptime are yours to manage if you host it yourself.
• Smaller native integration library than Zapier, though the HTTP node means almost any API can be connected.

How they compare on what matters

Ease of use

• Zapier: easiest
• Make: moderate
• n8n: most technical

Pricing model

• Zapier: per task (each action step that runs)
• Make: per operation or credit (each module run)
• n8n: per workflow execution on cloud; server costs only when self-hosted

Complex logic, loops and data transformation

• Zapier: limited
• Make: strong
• n8n: strongest, with code when needed

Native app integrations

• Zapier: largest
• Make: large
• n8n: smaller, offset by direct API access

Hosting and data control

• Zapier: cloud only
• Make: cloud only
• n8n: cloud or self-hosted

AI agents and AI workflows

• All three now offer AI features. n8n gives the most control for custom agents; Zapier makes simple AI steps easiest.

A worked cost example

Consider a lead-handling workflow with eight steps — receive a form, enrich the lead, score it with AI, create the CRM record, assign an owner, send an email, post to Slack and log to a spreadsheet — running 3,000 times a month.

• On Zapier, that's roughly 24,000 tasks a month, because each of the action steps counts every time.
• On Make, it's a similar number of operations, but at a lower price per operation on most plans.
• On n8n cloud, it's 3,000 executions, regardless of the number of steps. Self-hosted, it's the cost of a small server.

The pattern holds generally: the more steps and the higher the volume, the more the pricing model favours Make and then n8n.

Which one should you choose?

Choose Zapier if:

• A non-technical team will build and maintain the automations themselves.
• Your workflows are simple and moderate in volume.
• You rely on niche apps that only Zapier supports.

Choose Make if:

• Your workflows involve branching, lists of items or reshaping data.
• You want lower running costs than Zapier without managing infrastructure.
• Someone on your team is comfortable with a more technical builder.

Choose n8n if:

• You're running high volumes or long, multi-step workflows.
• Data needs to stay on your own infrastructure.
• You're building AI agents, retrieval or other workflows that need custom logic.
• You have technical skills in-house or a partner to build and maintain it.

Migrating between platforms

Teams often start on Zapier and move later when costs or complexity grow. Migrating isn't a copy-and-paste job — each platform handles data and errors differently — but it's very manageable if you:

1. Inventory every automation, what triggers it and what it touches.
2. Prioritize by cost and importance, moving the most expensive or most critical workflows first.
3. Run old and new in parallel for a short period and compare results.
4. Improve as you go. Migration is a good moment to merge duplicate automations and add the error handling the originals lacked.

The platform matters less than the design

Whichever tool you pick, the same principles decide whether automation pays off: automate frequent, consistent work first, design for exceptions as well as the happy path, and make failures visible. We cover how to choose what to automate in workflow automation ROI.

If you'd like help choosing, building or migrating, our workflow automation team works across n8n, Make and Zapier and will recommend the platform that fits your volume, team and data requirements — not the one we happen to prefer.