Use Zapier for simple, linear automations your own non-technical team will maintain, because it has the largest app catalogue and the gentlest learning curve. Use Make for visual workflows with real branching, loops and error handling. Use n8n for complex or AI heavy work, or when personal data must stay on infrastructure you control, because it can be self hosted in a UK region. When an automation becomes core to how the business runs, move it to bespoke integration code.
The short version
| Tool | Best for | Watch out for |
|---|---|---|
| Zapier | Simple, linear automations. The largest app catalogue. Staff without a technical background can maintain it. | Gets awkward once the logic branches heavily. Cloud only, so data is processed on Zapier's infrastructure. |
| Make | Visual workflows with real branching, loops and proper error handling. | A steeper learning curve. Large scenarios become hard for anyone but their author to read. |
| n8n | Complex, high volume or AI heavy work. Code steps in JavaScript or Python. Can be self hosted. | Genuinely technical. Self hosting means someone owns patching, backups and uptime. |
None of them is the best in general. The right one depends on three questions: how complex the logic is, where the data is allowed to go, and who on your team will look after it in two years. The rest of this guide works through each.
Side by side: what each tool can do
| Capability | Zapier | Make | n8n |
|---|---|---|---|
| Triggers | Polling triggers that check apps on a schedule, plus instant triggers from webhooks | Scheduled runs, instant triggers and webhooks | Schedule, webhook, app event and manual triggers, plus a trigger that fires when another workflow fails |
| Branching | Filters and Paths | Routers with a filter on each route | IF and Switch nodes, with Merge to rejoin branches |
| Lists and loops | A looping step, with line items handled app by app | Iterators and aggregators, which are powerful once they click | Every node works on a list of items natively, with Loop Over Items for batching |
| Error handling | Retries, error alerts and run history, with error-handling paths added more recently | Error routes with Resume, Ignore, Break, Commit and Rollback directives, and stored incomplete runs | Retry on fail per node, continue on error, and separate error workflows |
| Code steps | JavaScript or Python steps, with limits on run time | Built-in functions inside field mapping; heavier logic is usually sent out to a webhook or cloud function | A Code node in JavaScript or Python with full access to the data passing through |
| AI | AI steps and agent features built into the platform | Modules for the main AI providers and newer agent features | An AI Agent node with tools and memory, vector store nodes, and your choice of model provider |
| App catalogue | Several thousand apps, the largest of the three | Thousands of apps, plus a strong generic HTTP module | Hundreds of built-in integrations, community nodes, and an HTTP Request node for any API |
| Hosting | Vendor cloud only | Vendor cloud only, with a choice of region | Vendor cloud, or self hosted on a server you control |
| Version control | Version history inside Zapier | Scenarios export as JSON blueprints | Workflows are JSON and can be kept in Git |
Feature lists change every few months, so read this as a picture of each tool's character rather than a spec sheet, and check the vendor's own documentation for anything that decides your choice.
Zapier: the one your team can own
Zapier is the default, and for many businesses that is the right call. Its catalogue covers almost every SaaS tool a UK business uses: Xero, HubSpot, Microsoft 365, Google Workspace, Shopify, Slack, Typeform, Calendly and thousands more. The editor reads top to bottom like a checklist, so an office manager can follow what a Zap does and change a field mapping without calling anyone.
That simplicity is also the limit. Zapier is at its best with one trigger and a few actions in a line. Paths give you branching, and Formatter, Filters and code steps cover a lot of edge cases, but a Zap with nested paths and several code steps is harder to follow than the same logic in Make or n8n. If you find yourself building Zaps that call other Zaps to get round the editor, the tool is telling you something.
Good fits for Zapier:
- A new enquiry from a website form creates a contact in HubSpot, posts to a Teams channel and books a follow-up task.
- A signed DocuSign envelope creates a project in your project tool and a folder in SharePoint.
- A new row in a Google Sheet sends a templated email and updates a status column.
Zapier has also added tables, simple interfaces and AI agent features, so it can now hold a little data and present basic forms. They are handy for light work. We would not build a business-critical process on them.
Make: the visual middle ground
Make (formerly Integromat, now part of Celonis) is the middle ground and, for many small and mid-sized businesses, the sweet spot. Scenarios are drawn as a canvas of modules, and routers, filters, iterators and aggregators let you model real logic: split an order into its lines, handle each line, gather the results and write a summary back to the original record.
Error handling is Make's best feature and its most underused one. Every module can have an error route, and a directive decides what happens next: resume with a fallback value, ignore the failure, commit what has worked so far, roll back, or break and store the run so it can be finished once the cause is fixed. That is the difference between an automation that fails quietly and one that recovers.
The trade-off is that Make expects you to think like a systems person. Mapping data between modules, arrays and bundles takes a while to click. On a big scenario the canvas turns into a plate of spaghetti that only its author can read, so naming every module, adding notes and splitting large scenarios into smaller ones are not optional.
n8n: the developer's tool
n8n is the one we reach for when the work is complex, high volume or heavy on AI. It is a Berlin company that publishes its source under a fair-code model, so you can read the code and run it on your own server, although it is not open source in the strict sense.
Workflows are built from nodes, much like Make's modules, but n8n behaves more like a programming environment. A Code node runs JavaScript or Python against every item, expressions work in any field, and sub-workflows let you build reusable pieces such as a standard way of looking up a customer. Workflows are stored as JSON, so they can be reviewed and versioned like code.
The catch is that n8n is a developer's tool. If we build you something in n8n, you depend on someone technical to change it. Self hosting adds operational work: updates, backups, monitoring, and queue mode with workers when volumes grow. We are upfront about that rather than pretending it does not exist.
When things go wrong: error handling and monitoring
Every automation fails eventually. An API times out, a supplier renames a column, a connection token expires, someone types a date the wrong way round. What matters is whether you find out in five minutes or at month end.
- Zapier records failed runs in Zap history and emails you about errors, and failed steps can be replayed. That is fine at low volume. At high volume the alert emails become noise that people learn to ignore.
- Make lets you design recovery into the scenario itself with error routes, and keeps incomplete runs so they can be resumed after a fix rather than rebuilt by hand.
- n8n retries per node and lets you attach an error workflow that fires whenever a workflow fails, which can post to Teams or Slack, open a ticket or write to a log. Self hosted, it can also feed metrics to your own monitoring.
Whichever tool you pick, three habits prevent most incidents. Send failures to a channel someone actually watches. Log the record ID on every run so a failure can be traced to a customer or order. And make every step safe to run twice, so a retry never creates a duplicate contact or a second order. That last one, idempotency, is the habit most often missing when we are asked to look at an automation that has gone wrong.
Hosting and data residency under UK GDPR
When an automation moves customer, staff or patient data, the platform it runs on is a processor under UK GDPR. You need a data processing agreement, a list of its sub-processors and a clear answer on where data is processed. If it leaves the UK, there must be a lawful transfer mechanism, such as the UK extension to the EU-US Data Privacy Framework for certified US companies, or the ICO's international data transfer agreement. The ICO's guidance on international transfers sets out the options.
Do not forget the logs. All three tools keep a copy of the data that passed through each run so you can troubleshoot. That log is personal data too, so check how long it is kept and who can read it.
| Tool | Where it runs | What to check |
|---|---|---|
| Zapier | A US company whose service runs on US infrastructure, at the time of writing | Its data processing agreement, its transfer mechanism and how long run history is kept |
| Make | A cloud service with EU and US regions, chosen when your organisation is set up | That your organisation sits in the region you expect, and its sub-processor list |
| n8n cloud | Hosted by n8n, operated from the EU at the time of writing | Region and retention settings in n8n's own documentation |
| n8n self hosted | Wherever you put it, for example AWS London or Azure UK South | Patching, backups, access control and who can read execution data |
Vendors change their hosting arrangements, so confirm the current position in each vendor's documentation before relying on it.
For regulated work (clinics, law firms, financial advisers), self hosted n8n in a UK region is often what makes automation possible at all. Data stays on infrastructure you control, stored credentials are encrypted with a key you set, and you can switch off saved execution data for sensitive workflows. n8n's hosting documentation covers the setup, and our guide to AI and UK GDPR for small businesses works through the same questions for AI tools. Our own approach to hosting and access is on our security page.
Maintainability: who on your team can own it
The question that should decide most choices is not which tool is most powerful. It is who will look after the automation in two years, when the person who built it has moved on.
| Tool | Who can realistically own it | What they need |
|---|---|---|
| Zapier | An operations or admin person comfortable with web apps | An afternoon of learning, a naming convention, and a shared company login |
| Make | A technically minded operations person or analyst | A few weeks to get fluent with mapping, arrays and error routes |
| n8n | A developer, or an analyst who writes some JavaScript | Server access if self hosted, Git for workflow versions, and someone on call for incidents |
Whatever the tool, the same habits apply. Use one company account rather than personal logins. Connect apps with service accounts, not someone's own Microsoft 365 login. Give every workflow a clear name and a one-line note on what it does and who to ask. Keep a list of every live automation. The most common rescue job we see is an automation running on a leaver's personal account that stops the day their access is removed.
AI steps in each tool
All three now offer AI steps: summarise an email, classify a support ticket, pull fields out of a PDF, draft a reply. For single-shot tasks like these, any of them will do, and Zapier is the quickest to set up.
The gap opens with agents, where an AI decides which tool to call, loops until a task is done and remembers context. n8n is the most capable here. It gives you control over prompts, tools, memory and the model used, and the option to keep the whole chain self hosted. Make and Zapier have added agent features too, but give you less control over each step.
Whatever you use, keep a human approval step before anything an AI writes reaches a customer or your accounts, log prompts and outputs, and carry out a data protection impact assessment before sending special category data to any model provider. When the AI part becomes the product rather than a helper, our AI development team builds it as proper software.
How we choose
- Will non-technical staff maintain it? Zapier, unless one of the answers below rules it out.
- Does the workflow need real branching, loops or recovery from failure? Make or n8n.
- Is there personal or sensitive data that should not pass through a third-party platform? Self hosted n8n in a UK region.
- Is the automation heavily AI driven, or will it be? n8n.
- Will it handle thousands of runs a day or large files? Self hosted n8n with queue mode, or bespoke code.
- Are the apps mainstream and the logic simple? Zapier or Make. Pick the one your team finds easier to read.
The real mistake is not choosing the wrong platform. It is choosing based on what your agency happens to like rather than on who will own the thing in two years and how much it will be doing by then. Ask any agency why they picked the platform they picked. If the answer is not about your data, your volumes and your team, be suspicious.
When to move to bespoke integration code
Sometimes the answer is none of them. These are the signs an automation has outgrown its platform:
- It has become core to how the business runs, and an outage stops work.
- The logic is spread across dozens of linked workflows that nobody fully understands.
- It needs its own data store, or screens for staff to review and approve records.
- Volumes or response times are beyond what the platform handles comfortably.
- You need automated tests, code review and controlled releases.
- Auditors or customers ask questions about data handling that the platform cannot answer.
At that point a direct API integration written in code, or a small bespoke application, is easier to test, change and secure than an ever larger scenario. It lives in a repository in your account from day one, runs on hosting in your name in a UK or EU region, has automated tests, and does not depend on a third-party editor. That is the standard we think the UK's leading software development company should hold itself to, and it is how every Fixology integration is handed over.
Moving does not need to be a big bang. We usually rebuild the busiest or riskiest workflow first, run it alongside the old one for a week or two, then switch over, keeping the simple automations where they are. Our workflow automation service covers both routes, and our guide to bespoke vs off-the-shelf software explains the wider decision.
Questions
Is n8n better than Zapier?
For complex, high volume or AI heavy workflows, usually yes. n8n handles branching, code and error workflows like a programming environment and can be self hosted in a UK region. For simple automations that non-technical staff will maintain, Zapier is easier to learn and has far more ready-made app connections. The better tool is the one your team can actually look after.
Is Make easier to use than n8n?
For most non-developers, yes. Make's visual canvas, routers and built-in functions let a technically minded operations person build real logic without writing code. n8n rewards people who can write some JavaScript or Python and are comfortable with servers if self hosting. Both are harder to learn than Zapier, and both handle errors and branching far better.
Can n8n be self hosted in the UK?
Yes. n8n can run on a server you control, for example in AWS London or Azure UK South, using Docker. That keeps workflow data and execution logs on infrastructure in the UK, which helps regulated businesses meet UK GDPR expectations. Someone has to keep the server updated, backed up and secure, so decide who owns that before you start.
Can I use Zapier or Make under UK GDPR?
Yes, provided you treat them as processors: sign the vendor's data processing agreement, check its sub-processors, confirm where data is processed and which transfer mechanism covers anything leaving the UK. Check how long run history is kept, because it contains the personal data that passed through. For special category data, many firms prefer self hosted n8n or bespoke code.
Which is best for AI automation?
For single steps such as summarising an email or classifying a ticket, any of the three works and Zapier is quickest to set up. For AI agents that call tools, loop and keep memory, n8n gives the most control over prompts, models and data, and can run self hosted. Always keep a human approval step before AI output reaches customers.
When should I stop using Zapier or Make and build custom software?
When the automation has become core to how you work: lots of logic spread across many workflows, its own data, staff who need screens to review records, or audit questions the platform cannot answer. At that point a direct API integration or small bespoke application is easier to test, change and secure, and it lives in your own repository.