n8n vs Make vs Zapier: Which Platform Fits You?

Zapier is the platform most people try first, and for good reason. Setup is genuinely simple: pick a trigger, pick an action, done. It connects to more third-party apps than anything else on the market, without a line of code. For linear automations — when a form is submitted, add a row and send a message — it is hard to beat, and the breadth of its app directory means the connector you need probably already exists.

The simplicity becomes the limitation the moment workflows get serious. Branching is clunky, error handling is basic, and you end up splitting one logical process across several Zaps that have to be reasoned about together. That is a maintenance cost that does not show up until something breaks at the seam between them.

The pricing model matters more than the price. Zapier charges by task — roughly, by each action step a workflow executes. That is the model that punishes success: an automation that works, and therefore runs more often, costs proportionally more every month, and a workflow with six steps costs six times a workflow with one for the same trigger. Check the current price page before you commit, but check the model first, because that is the part that will not change.

The honest case for starting on Zapier anyway: the platform decision matters far less than getting one automation live and learning what you actually needed. Teams that spend three weeks choosing have lost more than the price difference. Start, then move deliberately — knowing that moving is a rebuild, not an export.

Make (formerly Integromat) is where most growing businesses land when they outgrow Zapier. The visual builder is genuinely powerful — modules, branching, loops, error routes and iterators let you build in one scenario what would take several stitched-together Zaps. Pricing is operations-based rather than task-based, which for equivalent work usually lands meaningfully lower, though how much depends entirely on how many operations your scenario burns per run.

Where Make earns its place is data transformation. JSON parsing, array operations, text manipulation — handled natively, without dropping into code. For agencies running client automations that someone else will eventually maintain, that visual, self-documenting quality is worth real money.

The limits appear when you need control rather than power. Make is a closed platform: you cannot self-host it, your data transits their infrastructure, and you inherit their uptime, their region choices and their roadmap. For a marketing workflow that is fine. For a workflow carrying client records, case files or health data, it is the constraint that ends the conversation.

One practical Make caveat that catches agencies: operations are consumed per module execution, so a scenario that loops over 200 line items burns 200 operations in that module, not one. Scenarios that look cheap in design can be expensive in production. Run one for a week on real volume before you size the plan.

n8n sits in a different category. It is source-available, self-hostable, and built for teams that want automation without vendor lock-in: run it on your own servers, keep the data inside your perimeter, extend it with JavaScript or Python nodes when a connector does not exist.

For AI work it is the clear leader. Native AI agent nodes let you build multi-step model workflows — chain models, add tool use, implement retrieval — inside the same builder as the rest of your automation. This is what makes it the backbone for AI orchestration: a whole multi-agent system can be one workflow, each node a specialised agent, with the routing and error handling visible rather than buried in an application.

The trade-off is that someone has to run it. Self-hosting means a server, updates, backups and an on-call answer when it stops. n8n's cloud offering removes most of that and keeps the extensibility, but then you are back to someone else's infrastructure — which, if data sovereignty was your reason for choosing it, defeats the point. Be honest about which of the two reasons brought you here.

Self-hosting cost is not the licence — it is the operations. Budget for a small server, an update cadence, backups you have actually restored once, and a named person who answers when a workflow stops overnight. For a team already running its own infrastructure this is close to free. For a team that is not, it is the real price of the decision, and it should be compared against a subscription rather than against zero.

Feature grids are the least useful way to make this choice, because all three do the common things. Three questions settle almost every case.

1. Does the data have to stay under your control?

If you are handling client files, case records, financial or health data under GDPR, this question comes first and it usually ends the comparison — a self-hostable platform is the only one that lets you say where the data physically sits and who can reach it. If your workflows move marketing data and public records, the question barely applies and you should optimise for speed instead. There is no middle answer here, which is why it goes first. More on how that constraint shapes a build in GDPR-compliant AI automation.

2. How complex will the workflows get in a year?

Not today — in a year. Linear trigger-action chains stay comfortable on any platform. The moment you need branching, retries with backoff, loops over collections, and a real error path that notifies someone rather than failing silently, Zapier starts costing you in workarounds and Make or n8n starts paying for itself. Ask what the third workflow will look like, not the first.

3. Does the volume curve bend up?

A per-task model and a per-execution model diverge slowly, then quickly. If your automation runs a few hundred times a month and always will, the difference is noise and you should pick on convenience. If a successful automation means ten thousand runs a month next year, the model you are on decides whether that success shows up as a line item that grows with it.

The migration cost nobody prices in

Automations do not port. There is no meaningful export from one platform into another: connectors differ, data shapes differ, error semantics differ, and the logic has to be re-expressed by someone who understands what the original was doing. Assume a migration costs roughly what the original build cost, plus the archaeology of working out why a step exists at all. This is the single strongest argument for choosing on a two-year view rather than a two-week one — and for documenting every workflow as you build it, whichever platform wins.

Is n8n better than Make and Zapier?

Not universally. n8n is better when you need self-hosting, data sovereignty, custom code inside a workflow, or native AI agent steps. Make is better for visual, code-free complexity at moderate cost. Zapier is better for breadth of connectors and speed to a first working automation.

Which is cheapest — n8n, Make or Zapier?

Over a year it is usually n8n self-hosted, then Make, then Zapier — but only because the pricing models differ, not because of the headline numbers. Zapier bills per task, Make per operation, n8n per execution or per server you run. Model your own expected run volume against each model rather than comparing entry plans, and re-check the current price pages before committing.

Can you self-host Make or Zapier?

No. Both are closed, cloud-only platforms. n8n is the only one of the three you can run on your own infrastructure, which is why it tends to be the default for regulated work in Europe.

Is n8n hard to learn compared to Zapier?

The builder is not much harder — the difference is what happens around it. Zapier hides the infrastructure entirely; n8n hands you more control and expects you to handle deployment, updates and monitoring if you self-host. If a technical person is available, the learning curve is a few days. If not, n8n cloud or Make is the more honest choice.

What is the best Zapier alternative?

Make if you want the same hosted convenience with more visual power and a cheaper unit model; n8n if the reason you are leaving is cost at scale, data control, or AI workflows. If the reason is a missing connector, check whether either alternative has it before switching — connector breadth is still Zapier's strongest card.

How do n8n, Make and Zapier compare for AI agents?

n8n leads: AI agent nodes, tool use, memory and retrieval are first-class parts of the builder, and you can point them at a self-hosted model. Make and Zapier both offer model steps, which is enough for single-call tasks like classification or drafting, and not enough for a multi-step agent that plans, calls tools and checks its own output. See what AI orchestration actually means for where that line sits.

There is no best automation platform. There is a best platform for one situation, and the situation is usually settled by three things: how complex the workflows get, how much data sovereignty you need, and how fast volume will grow.

• Zapier — results this afternoon, straightforward workflows, breadth of connectors over depth of control.
• Make — scaling operations, visual branching, no code, and no hard requirement to hold your own data.
• n8n — AI workflows, data sovereignty, or volume growing faster than the budget.

Most teams we work with start on one and evolve. That is fine, as long as the move is planned rather than forced. If you want the decision made against your actual workflows rather than a feature grid, an AI audit does exactly that — and the output is a plan you could hand to any implementer.