n8n vs Zapier vs Make is the decision most teams face the moment they get serious about business automation. The short answer: Zapier wins on simplicity and app coverage, Make wins on visual power for the money, and n8n wins on control, cost at scale, and AI agents. The honest answer takes longer, because the three platforms charge for different units of work, hit their limits at different points, and suit very different teams.
We build on all three platforms for clients, so we have no licence to sell and no horse in this race. This guide compares them on the dimensions that actually decide the outcome: pricing models, self-hosting, the complexity ceiling, and AI agent support, with a clear verdict for each scenario at the end.
n8n vs Zapier vs Make: The Short Answer
If you only take one thing from this comparison, take this:
- Zapier is the easiest to start with and connects to the most apps, around 8,000 at the time of writing. It gets expensive quickly as volume grows, and complex logic fights the interface.
- Make offers the most visual building experience and handles branching logic better than Zapier, at a typically lower price. It sits in the middle on both power and learning curve.
- n8n is the most capable and the cheapest at scale, with self-hosting and the strongest AI agent support of the three. It also demands the most technical confidence.
None of these is the best automation platform outright. Each one wins somewhere. The rest of this guide shows you where.
What Each Platform Actually Is
Zapier
Zapier is the household name of business automation. You build Zaps: a trigger in one app fires actions in others, with filters and paths for simple branching. It is cloud-only, priced per task, and designed so a non-technical operator can ship a working automation in minutes. The catalogue is its moat. If a SaaS tool exists, Zapier almost certainly connects to it, and the platform has expanded into tables, interfaces, and its own agent product.
Make
Make, formerly Integromat, is a visual automation platform where workflows are scenarios built on a drag-and-drop canvas. Routers split flows, iterators process lists, aggregators merge results, and error handlers catch failures. It connects to around 2,000 apps, runs cloud-only, and prices per operation. The canvas is genuinely good: you can see an entire complex workflow at a glance in a way Zapier never quite manages.
n8n
n8n is a source-available automation platform you can run on your own infrastructure or use as a managed cloud service. It ships hundreds of native integrations, an HTTP request node that talks to any API, and code nodes for JavaScript or Python when visual building runs out. It is licensed under a fair-code model rather than a classic open source licence, which in practice permits almost all internal business use. n8n was built developer-first, and it shows in both its power and its learning curve.
Pricing Models Compared: Where the Real Difference Hides
The pricing difference between n8n, Zapier, and Make is not the headline monthly fee. It is the unit each platform charges for, and that unit decides what your bill looks like once automation becomes core to your operations.
Zapier charges per task
Every action a Zap performs counts as a task. A 10-step workflow that runs once consumes 10 tasks. The free plan covers a small monthly allowance, and paid plans typically start around USD 20 a month and climb with volume. For simple, low-volume automations this model is painless. For complex, high-volume workflows it makes Zapier the most expensive option of the three by a wide margin.
Make charges per operation
Every module that executes within a scenario counts as an operation. The shape is similar to Zapier's model, but the typical cost per unit is lower, with a free tier around 1,000 operations a month and paid plans starting near USD 10. One caution: iterators multiply consumption fast. A scenario that processes 500 records can burn 500 operations in a single run, so high-volume data work needs careful design.
n8n charges per execution
One complete workflow run counts as one execution, regardless of how many steps it contains. A 50-step workflow that runs once costs exactly one execution. Cloud plans typically start in the low EUR 20s per month, and the self-hosted community edition is free apart from your server costs. This is the structural reason n8n wins on cost for complex automation.
A worked example
Take a 20-step order-processing workflow that runs 1,000 times a month. On Zapier that is 20,000 tasks, which lands you in a serious pricing tier. On Make it is roughly 20,000 operations, cheaper but still a paid plan with headroom. On n8n it is 1,000 executions, which fits inside an entry-level cloud plan or runs free on your own server. The more steps your workflows have, the harder this gap compounds.
Self-Hosting and Data Control
Only n8n can be self-hosted. Zapier and Make are cloud-only, and every record they process passes through their infrastructure.
For many businesses that is fine. For others it is the entire decision. If you operate under GDPR with strict data residency requirements, work in finance or healthcare, or face procurement teams that scrutinise every data processing agreement, running n8n on your own EU-hosted server removes a whole category of compliance friction. Customer data never leaves infrastructure you control.
Be honest about the cost, though. Self-hosting is not free in practice. Someone has to provision the server, apply updates, manage backups, and secure the instance. If nobody owns that work, the saved licence fees quietly disappear into maintenance debt. Teams without technical ownership should weigh n8n's managed cloud, which keeps most of the platform's advantages without the operational burden.
The Complexity Ceiling
Every no-code tool has a point where the next requirement stops being easy. Zapier hits that ceiling first, Make pushes it further, and n8n effectively removes it.
Zapier: built for linear flows
Trigger, filter, act. Zapier executes that pattern brilliantly. Push past it into loops, multi-branch logic, retries, or heavier data transformation and the workarounds start stacking up: helper Zaps, formatter chains, webhook gymnastics. Each workaround is another task on the bill and another thing to break silently.
Make: visual logic with real depth
Make's canvas handles branching, iteration, and aggregation natively, which covers most of what mid-complexity operations need. The honest limits: very large scenarios become hard to read, debugging intermediate state takes patience, and when you genuinely need custom code, your options are thinner than n8n's.
n8n: the ceiling is your team
Loops, sub-workflows, error workflows, queue mode for scale, and code nodes that accept JavaScript or Python. When the built-in nodes run out, you write a function instead of abandoning the workflow. The trade-off is real: building well in n8n is a developer-shaped task. Non-technical staff can run and lightly edit n8n workflows, but someone technical should own the architecture.
AI Agent Support
n8n has the strongest AI agent support of the three, and it is not close. Its native agent nodes handle model selection, memory, and tool calling, and an agent can invoke other workflows as tools, which turns your existing automations into capabilities the agent reasons over. That architecture is why n8n became the default platform for AI automation builders.
Make has added agent capabilities of its own, and they work well for bounded tasks inside scenarios where the inputs and outputs are predictable. Zapier offers agents and AI assistance across its enormous app catalogue, which suits light agentic work, though you accept less control over model behaviour and orchestration.
The distinction that matters: classic automation is deterministic, the same input always produces the same output, while agents apply judgement to messy inputs. Most businesses need both, and the platforms are converging on that reality at different speeds. Our guide to AI agents for business operations covers what agents can realistically take on today and how to deploy them without disrupting your team.
The Verdict, Scenario by Scenario
You need simple handoffs between popular SaaS tools
Verdict: Zapier. Form submissions into your CRM, deal alerts into Slack, invoices into a spreadsheet. At low volume the per-task pricing barely registers, the app coverage is unmatched, and anyone on the team can maintain it. Speed to value beats elegance here.
You want serious multi-step workflows on a lean budget
Verdict: Make. If you have an operations-minded builder who is not a developer, Make hits the sweet spot: visual logic strong enough for real workflows at a price a small business can absorb. Our guide to AI automation for small businesses covers which processes to automate first.
You run high-volume or complex operations
Verdict: n8n. Execution-based pricing rewards exactly the workflows that punish the other two: long, branching, high-frequency. Order processing, inventory syncs, and multi-system data flows are where it shines, which is why we lean on it heavily for AI automation in e-commerce.
You are building AI agents into your operations
Verdict: n8n, with Make as the runner-up. Native agent nodes, model flexibility, and workflows-as-tools give n8n a genuine architectural lead. Choose Make if your agent needs are modest and your team already lives there.
Your data cannot leave your own infrastructure
Verdict: n8n self-hosted. It is the only option of the three. There is no Zapier or Make equivalent, so if this constraint is real for you, the comparison is already over.
When You Need an Automation Partner Instead
Choose a partner over a platform when your bottleneck is design and ownership, not software. In our experience the tool is rarely what makes automation succeed or fail.
The common failure pattern looks like this: a business picks a platform, builds a handful of workflows in a burst of enthusiasm, then stalls. Nobody mapped the underlying processes, so the automations encode the mess instead of fixing it. Nobody built error handling, so failures go unnoticed until a customer complains. Nobody owns maintenance, so workflows rot as APIs change. Twelve months later the licence renews and the team quietly goes back to doing things manually.
A good partner closes those gaps. The work starts with a process audit to find where automation actually pays back, then moves through workflow design, build, systems integration with the tools you already run, documentation, and ongoing maintenance. As an AI automation agency, we work across n8n, Zapier, and Make and recommend whichever fits your team, your volume, and your data constraints, not whichever is fashionable this quarter. Our AI automation service runs that full arc, with projects typically starting from EUR 5,000, and our breakdown of what an AI automation agency does explains how the engagement model works.
If you want a second opinion on your stack before you commit, book a discovery call. We will tell you if a ten-dollar Make plan solves your problem, because sometimes it genuinely does.
Frequently Asked Questions
Is n8n actually free?
The self-hosted community edition is free under n8n's fair-code licence, and it covers almost all internal business use. Free refers to the licence, not the total cost: you still pay for a server and for the time someone spends updating, backing up, and securing the instance. If nobody on your team can own that, n8n's managed cloud plans, which typically start in the low EUR 20s per month, are the more honest comparison point.
Which platform is cheapest at scale: n8n, Zapier, or Make?
n8n, and it is structural rather than promotional. Zapier bills every action and Make bills every module execution, so costs grow with both volume and workflow complexity. n8n bills per complete workflow run regardless of step count, so complex workflows cost the same as simple ones. The more your automations do, the wider the gap becomes.
Can I migrate from Zapier to Make or n8n?
Yes, but treat it as a rebuild rather than an import. There is no clean converter between platforms, so each workflow needs to be recreated, tested, and cut over. The logic usually maps across faster than the original build took, because the thinking is already done. Migrate your highest-volume and most complex workflows first, since those carry the biggest savings, and leave cheap simple Zaps where they are until renewal.
Which platform is best for AI agents?
n8n leads clearly. Its agent nodes support multiple model providers, memory, and tool calling, and agents can trigger your existing workflows as tools, which is the architecture serious agent deployments end up needing. Make suits bounded agent tasks inside predictable scenarios, and Zapier suits light agentic work across a very wide app catalogue.
The Bottom Line
Zapier for simplicity and reach. Make for visual power on a budget. n8n for scale, control, and AI agents. Pick for the scenario you are actually in, not the one on the vendor's homepage, and remember that the platform is the cheapest part of the equation. The expensive part is designing workflows worth running, and that cost is the same whichever logo sits on the canvas.
