n8n vs Make vs Zapier: Choosing an Automation Platform

n8n vs Make vs Zapier comes down to two questions most comparisons skip: what unit each platform charges you for, and who is going to look after the thing once it is running. The feature lists overlap heavily. All three connect apps, all three handle webhooks, all three now have AI steps. The billing model and the hosting model are where they genuinely differ, and those two things decide what you pay and what breaks.

This article puts all three on the same worked example and is opinionated about which business fits which. Once you have picked a platform, adding AI steps to n8n and Make workflows covers the practical side of wiring in an LLM call.

How each platform charges you

This is the single most important row in any comparison, so it comes first. Figures are at the time of writing (October 2026) and change often — check the official pages before deciding.

Sources: Zapier pricing, Make pricing, n8n pricing. At the time of writing n8n's cloud plans start at €20 a month for 2,500 executions on monthly billing, and Make's paid plans start from around $9 a month for 10,000 credits.

The consequence is easy to miss. Zapier and Make get more expensive as your workflows get longer. n8n gets more expensive as your workflows run more often, and does not care how many steps each one has.

A worked cost example

Say a business runs one enquiry workflow. Each run does six things: receive the form webhook, store the enquiry in a Google Sheet, ask an AI model to summarise it, create a CRM contact, send an internal Slack alert and send an acknowledgement email. It runs 1,500 times a month.

These are hypothetical numbers to show the shape, not a quote. The point is that the same workflow lands in very different tiers on each platform. A team running a handful of long workflows at modest volume will find n8n cloud or Make cheaper than Zapier. A team running many tiny two-step automations may barely notice the difference.

There is a second hidden cost on Make: a scenario that polls an app every few minutes reads data on every check, so it can spend credits even on days nothing happens. Webhook triggers avoid that, and are better practice anyway because they are immediate.

Where each platform is genuinely strong

Zapier

Zapier's strength is breadth and approachability. It has the largest catalogue of app connections of the three, and the editor is a straight list of steps that a non-technical person can read top to bottom. If an obscure industry app has an integration with anything, it is usually Zapier.

Where it is weaker: complex branching gets awkward, data transformation is limited compared with the other two, and the per-task model makes long, high-volume workflows expensive. It is cloud-only.

Make

Make's visual canvas is the best of the three for seeing a branching scenario at a glance. Routers, iterators and aggregators make it good at the messy middle of a workflow — splitting an order into line items, grouping results, handling several outcomes. Error-handler routes are a first-class part of the design, which matters more than people think.

Where it is weaker: the credit model rewards you for keeping scenarios short and punishes polling, and like Zapier it is cloud-only, so customer data passes through Make's infrastructure.

n8n

n8n is the most flexible. You can drop into JavaScript or Python inside a node, call any HTTP API directly, and self-host the whole thing so data never leaves infrastructure you control. Per-execution billing makes long workflows cheap, and its AI Agent node is well suited to the tool-calling patterns covered in AI agents for small businesses.

Where it is weaker: it assumes more technical comfort, and self-hosting turns you into a server administrator. Updates, backups, SSL, monitoring and the occasional breaking change between versions are now your job.

Error handling compared

Error handling deserves its own section, because it decides whether you find out about a failure in five minutes or five weeks.

The practical difference: Make and n8n make it natural to design what happens when a specific step fails, for example "if the CRM is down, write to a backup sheet and alert me". Zapier can do some of this, but its default model is closer to "it failed, here is an email". For simple automations that is fine. For anything where losing a single run costs money, the ability to route around a failure matters.

Whichever you choose, send failure alerts somewhere a person already looks — a shared inbox, a Slack channel, a text — not just the platform's own dashboard.

AI features compared

All three now offer AI steps, and all three let you call OpenAI, Anthropic and other providers. The differences are in how much control you get:

• Zapier has built-in AI actions and its own agents product, and you can bring your own API key for some providers. Easiest to start, least control over prompts and structured output.
• Make has AI modules and agent features, plus direct modules for the major model providers, so you can choose the model and shape the output.
• n8n has the deepest AI tooling of the three: an AI Agent node, memory, tool calling, vector store nodes and the option to run against self-hosted models. That flexibility is why it is popular for agent-style builds.

If the AI step is a single summarise-or-classify call, all three are adequate. If you are building something that calls tools and loops, n8n is the strongest fit.

Hosting and where your data goes

Zapier and Make are cloud-only, so every record that passes through a workflow is processed on their infrastructure. Both publish security and data-processing terms, and for most small businesses that is an acceptable arrangement, but it needs to appear in your privacy notice and your records of processing. GDPR and AI automation for UK businesses covers what to check.

n8n can run on a server you rent in the UK, which keeps workflow data on infrastructure you control. That is a real advantage for businesses handling sensitive records — provided the server is patched, backed up and secured. A self-hosted n8n instance left unpatched for a year is a worse data protection position than a well-run cloud service.

Which business fits which platform

That last row is worth taking seriously. A lot of external automation exists to make up for a CRM's own workflow builder never having been set up. CRM automation for small businesses covers what belongs inside the CRM, and if you run GoHighLevel, GoHighLevel workflow examples shows how much it handles natively.

What matters more than the platform

I have rebuilt workflows on all three. The ones that failed did not fail because of the platform. They failed for reasons covered in AI automation mistakes to avoid:

1. No error alerting. All three platforms can notify a person when a run fails. Most setups never turn it on.
2. No durable first step. The input was processed before it was stored, so a failure lost it permanently. How to build an AI-powered workflow explains why storing first matters.
3. Nobody owns it. The person who built it left, and nobody else knows what it does.
4. Unclear process underneath. The automation encoded confusion rather than removing it.

Pick the platform that fits your billing profile and your team's skills, then spend the real effort on those four.

Questions to answer before you choose

1. Roughly how many times a month will each workflow run, and how many steps does each have?
2. Who will fix it when it breaks — and do they understand the platform?
3. Does any of the data need to stay on infrastructure you control?
4. Which apps must it connect to, and does each platform support them natively or only via raw HTTP?
5. Do you need AI steps, and will you supply your own API key or use the platform's built-in AI?

Answer those and the choice is usually obvious. Working out automation cost and ROI covers how to put the platform fee alongside the build and maintenance costs, which are usually larger.

If you would rather not choose alone, the AI automation service starts by mapping the process and estimating volumes, then picks the platform to fit — and sometimes the answer is that your existing CRM already does the job. The pillar guide, what is AI business automation, is the place to start if you are earlier in the thinking.