Zapier vs Make vs n8n: choosing an automation platform.

Zapier, Make and n8n all do the same basic job: when something happens in one app, they do something in another. A form submission becomes a CRM contact, a new order sends a message to the warehouse, a closed deal creates an invoice. Where they differ is in who they're designed for, how they charge, how they handle complex logic and where your data is processed.

All three are capable, well-established tools. This comparison sets out the trade-offs so you can match the tool to your team and your workflows.

Zapier

Zapier is the best known of the three and has a very large directory of app integrations. Many software vendors build and maintain their own Zapier connector, so it is often the first place a new app becomes available.

Strengths

• Easy to start: the step-by-step editor walks you through choosing a trigger app, an event and an action. Simple workflows can be set up without technical help.
• Breadth of integrations: if you use niche SaaS tools, Zapier is the most likely to have a ready-made connector.
• More than linear steps: Filters, Paths (branching), Formatter and Code steps cover a lot of more complex needs.
• Fully managed: no servers to look after.

Trade-offs

• Usage-based cost: Zapier bills by tasks, so long multi-step workflows running at high volume add up. Estimate how many tasks each run uses before you commit.
• Complex logic is less visual: heavily branched workflows with loops and data aggregation are possible, but can be harder to follow than on a canvas.

Make

Make (formerly Integromat) uses a visual canvas where each step is a module and you can see how data flows, branches and merges.

Strengths

• Visual design: the canvas makes complex scenarios easier to read and debug, and you can inspect the data that passed through each module on every run.
• Data handling: iterators, aggregators, routers and built-in JSON tools suit workflows that process lists, such as each line item in an order.
• Error handling: you can attach error-handler routes to modules, with directives such as Resume, Ignore, Commit, Rollback and Break, so a single failed API call doesn't silently break the whole scenario.
• Fully managed: Make runs in the cloud and lets you choose a hosting region. Check the current options if data location matters to you.

Trade-offs

• Learning curve: the extra power takes longer to learn, and it's easy to create scenarios that run more often than intended or loop over data unexpectedly.
• Usage-based cost: Make bills on its own usage unit, so model your expected volume against its current plans.
• No self-hosting: it's a cloud service.

n8n

n8n is a workflow tool aimed at technical teams. You can use n8n's own cloud service or host it yourself. Self-hosting is distributed under n8n's own licence rather than a standard open-source licence, so read the terms if you plan to use it commercially or offer it to clients.

Strengths

• Code where you need it: the Code node runs JavaScript or Python inside a workflow, and the HTTP Request node can call almost any API.
• Self-hosting option: running n8n on your own infrastructure means workflow data is processed on servers you control, which can help with data residency and internal security requirements.
• Different cost model when self-hosted: you pay for servers and maintenance time rather than per run, which can suit high-volume internal workflows.
• AI workflows: n8n has built-in nodes for working with AI models. Zapier and Make also offer AI features, so compare them for your use case.

Trade-offs

• Operations work if self-hosted: someone has to handle updates, security patches, the database, monitoring and backups that actually restore.
• Fewer ready-made connectors: for less common apps you may need to call the API directly with HTTP requests.
• More technical: non-developers can use it, but it suits teams who are comfortable with JSON and APIs.

Side-by-side comparison

How to choose

1. 1 List the workflows you need now and the ones you expect to need in a year.
2. 2 Check that each tool has connectors for the apps you rely on most, including your CRM and e-commerce platform.
3. 3 For each workflow, estimate the number of steps and how often it runs per month.
4. 4 Use those numbers to price each tool against its current published plans.
5. 5 Decide who will build and maintain the workflows, and choose a tool that suits their skills.
6. 6 Check data requirements: where data is processed, how long run logs are kept and whether you need to self-host.
7. 7 Build one real workflow in your top two choices before committing.

Don't forget what your existing platforms already do. If you're on HubSpot, many marketing and sales sequences can run as native HubSpot workflows without any third-party tool. See our list of HubSpot and Zapier automations every website needs for examples.

Good habits on any platform

• Store credentials properly: use each platform's connection or credential manager rather than pasting API keys into individual steps.
• Alert on failure: send failed runs to an email address or chat channel someone actually checks, and make sure failed records can be retried.
• Limit personal data in logs: check how long each tool keeps run history and whether you can shorten it. Confirm retention requirements with your data protection adviser. Our GDPR basics guide is a starting point.
• Document and export: name workflows clearly, note who owns each one, and export workflow definitions regularly. Make and n8n both support exporting scenarios and workflows as JSON.

If you'd like help choosing a platform or building and tidying up automations, our AI automation and integrations team works with all three. Get in touch with a list of the workflows you have in mind.