n8n vs Make vs Zapier

All three in 30 seconds

Zapier is the no-code automation pioneer — the easiest to start with, with 7,000+ ready-made integrations. You connect a "trigger" to an "action" in a few clicks. Perfect for non-technical people who want a fast result.

Make (formerly Integromat) is a cloud platform with a colorful visual editor based on "scenarios". It balances ease and power — more flexible than Zapier and usually cheaper for medium volume.

n8n is a node-based platform focused on developers and flexibility. You can self-host it, write custom code at any step, and build complex AI agents. The most powerful and cheapest at scale — but it requires more technical knowledge.

Comparison table

Pricing — where the real difference is

This is usually the deciding factor, and each one counts differently: Zapier by tasks (each action = a task), Make by operations, and n8n in the cloud by executions (running a whole workflow = 1). With n8n self-hosting you pay only for the server — a small VPS — with no limit on operations.

• Zapier: a limited free tier, then paid plans that climb steeply with volume — generally the most expensive at scale.
• Make: a free tier with a monthly operation allowance, then mid-tier plans that suit medium volume.
• n8n Cloud: paid tiers billed by executions. Self-hosted: server cost only — the cheapest at high volume.
• Check all three vendors' current pages before deciding. Tiers and allowances here are restructured often, and the counting model below outlives any figure.

When to choose each

Choose Zapier if…

• You're non-technical and want a working automation today
• You need a rare integration — Zapier has the most (7,000+)
• Your volume is low–medium and ease matters more than price

open_in_newTry Zapier

Choose Make if…

• You want a balance of ease and power, at a reasonable price
• You build visual, multi-step scenarios
• Your volume is medium and you're price-sensitive vs. Zapier

open_in_newTry Make

Choose n8n if…

• You're a developer/technical team that wants flexibility and custom code
• Data privacy (self-hosting) or low cost at scale matters to you
• You're building AI agents and complex pipelines

open_in_newTry n8n

Three answers to one question

These platforms look like competitors at different price points. They are better understood as three answers to a single question: how much of the complexity should the platform hide from you?

Hide almost all of it and you get something anyone can use on their first afternoon, at the cost of doing anything unusual. Hide none of it and you get a tool that can do anything and expects you to understand what you are doing. The middle option trades some of each.

That framing settles the choice faster than a feature table, because it turns on something you already know: who will be maintaining this in six months.

• A non-technical owner who needs it to keep working without help. Hiding complexity is the product, and paying more for it is rational.
• A technical person part-time. The middle sits well — enough power for real work, not enough operational burden to become a job.
• A team that runs its own infrastructure. The flexible option costs less and fits habits you already have.

The common mistake is choosing on capability and inheriting the maintenance. A powerful platform built by a contractor who has left is worse for a small business than a simpler one the owner can open and understand.

Tasks, operations, executions: count your own workload

All three bill for volume and all three count differently, which is the single largest source of surprise bills.

Per task or per operation means every individual action is billed — a trigger, a lookup, a filter, a write. Per execution means one workflow run counts once regardless of how many steps it contains.

Take a concrete job: a form arrives, you look up the customer, branch on whether they exist, write to a CRM, send an email, and post a notification. That is six actions. Run it a thousand times a month and the action-counting platforms bill six thousand units while the execution-counting one bills a thousand.

Now add a loop — process each of fifty line items — and the gap becomes an order of magnitude on the same work.

So before comparing prices, write down for your busiest workflow: how many steps, how many items it loops over, how often it runs. Multiply it out under each model. That number decides this, and it frequently reverses the ranking you would have guessed from the headline tiers.

One more counting subtlety worth checking on whichever you pick: whether filtered-out runs, polling checks and failed attempts consume your allowance. On some platforms a trigger that polls every five minutes and finds nothing is still spending budget all day.

Triggers: the difference you feel every day

Rarely compared, and it shapes both responsiveness and cost.

A webhook trigger fires the instant something happens: the source system calls you. Immediate, and it consumes nothing while idle.

A polling trigger asks "anything new?" on a schedule. It is what you get when a service offers no webhook, it introduces a delay equal to the polling interval, and on a platform that bills per action it can consume budget continuously while finding nothing.

The practical implications:

• Check which kind your key integrations use before committing. "Supports your CRM" is a different promise if it can only poll it every fifteen minutes.
• Polling frequency is usually tied to your plan tier, so "real-time" can be a pricing question rather than a technical one.
• Where a service offers webhooks and the connector does not use them, a generic webhook node plus the vendor's own configuration is often faster and cheaper than the ready-made integration.

What happens between the steps

Most of the difficulty in real automations is not connecting two apps — it is reshaping the data as it moves. This is where the three diverge most in practice and least in marketing.

The questions that decide whether a project is an afternoon or a fortnight:

• Can you iterate over a list cleanly, and what happens to the items that fail partway through?
• Can you transform arbitrarily — write a few lines of code when the visual mapper cannot express what you need? The moment your data needs restructuring rather than remapping, a platform without a code step becomes a wall.
• How are errors surfaced inside a branch, and can you catch and continue rather than halting the run?
• Can you test a single step with sample data without executing the whole workflow against live systems?

That last one sounds minor and is the difference between building confidently and poking at production. Try it during a trial, on a real workflow, before deciding.

What makes an automation trustworthy

All three build something that works in a demo. What separates an automation a business relies on from one that quietly stops is the same short list on every platform, and it is worth knowing before you compare anything else — because none of them gives you these by default and all of them support them.

• An error path that reaches a person. The default everywhere is that a failed run stops and sits in a log nobody opens. A notification to somewhere you actually look is the single highest-value addition to any workflow.
• Retries with increasing delay on every external call. Transient failures and rate limits are normal, not exceptional.
• Idempotency. Search before creating, keyed on something stable. A retry that duplicates a customer record is worse than the original failure.
• A decision about partial failures. When item 47 of 200 fails, does it stop, skip, or finish and report? All three are defensible; not choosing is not.
• A run-count sanity check. The quiet killer is a trigger that stops firing — nothing errors, the work just stops. A weekly glance at whether the execution count looks normal catches it in days rather than months.

Judge the platforms on how easy they make these, rather than on how quickly the happy path comes together. Every one of them makes the happy path quick.

The platform holds the keys to everything

Whichever you choose, it accumulates credentials for your email, your CRM, your files, your payment system. That concentration is the point and it is also the largest security surface most small businesses acquire without noticing.

• Connect service accounts, not personal ones. An automation running on a departed colleague's login fails the day their account is disabled, and nobody remembers which one it was.
• Scope each connection down. A workflow that reads a calendar does not need permission to delete files; most integrations request more than they use.
• Restrict who can view run history — execution logs contain whatever flowed through, which is usually customer data.
• Keep a register of what is connected to what. When you need to rotate a key or answer a security question, this list is the difference between an hour and a week.
• If you self-host, never expose the editor to the internet without authentication in front of it. This is the most common and most serious self-hosting mistake.

Switching costs, honestly

None of the three can import another's workflows. The node types, the data shapes and the expression syntax all differ, so moving is a rebuild.

That is less alarming than it sounds — you already know what the workflow should do, which was the expensive part — but two things make it much easier if you plan for them from the start:

• Keep the business logic simple. A forty-node workflow with intricate branching is one you will never move. Several small workflows with clear boundaries are portable in a way one large one is not.
• Keep the credentials and the data outside the platform where you can — in your own database, your own storage — so the platform is orchestrating rather than owning.

The real lock-in is rarely technical anyway. It is that the automations work, nobody remembers exactly how, and the person who built them has moved on. Documentation and exported workflow files in a shared repository cost almost nothing and are what actually preserve your freedom to change.

You can use more than one

The comparison assumes a single choice, and plenty of working setups do not make one.

A common and sensible arrangement: the simplest platform for the handful of straightforward connections that non-technical colleagues need to see and edit, and the flexible one for the heavy pipelines and anything touching sensitive data. Two bills, clear ownership, and neither tool used outside its strengths.

The cost is fragmentation — two places to look when something breaks. Worth it when the split follows a real boundary, such as who maintains what; not worth it when it happens by accident because different people picked different tools.

The AI features, separated from the marketing

All three now advertise AI heavily, and most of what is on offer is the same thing wearing three logos: a node that calls a model's API. That is genuinely useful and it is not a differentiator, because you can make that call from any platform — and from a script.

Where they do differ is the machinery around the call, and it matters only if you are building something agentic:

• Multi-step agent loops — the model choosing which tool to call next, repeatedly, until a goal is met. Genuine support for this, with step limits and state, is a real capability gap between platforms.
• Conversation state held across turns rather than reconstructed by you each time.
• Vector storage and retrieval as first-class nodes, for building anything that answers from your own documents.
• Structured output handling — parsing and validating what the model returned before the next step acts on it, which is the difference between a pipeline and a hope.

Be honest about which you need. "When a form arrives, summarise it and file it" is a model call and a write — any platform does it well, and the AI marketing is irrelevant to your decision. A support agent that decides between six tools across several turns is a different project, and there the platform choice is real.

One caution common to all three: a workflow that sends data to a model is sending your data to a third party, and self-hosting the automation platform does not change where that call goes. The retention, training and region questions apply to the model provider separately.

A trial that actually decides it

All three offer a free tier or trial. Spend it on one thing rather than touring features.

1. Pick your genuinely hardest workflow, not the easy one. The easy one works everywhere and tells you nothing.
2. Build it end to end, including the error path and a notification when it fails.
3. Break it deliberately — revoke a credential, feed a malformed record, trigger it twice. Compare how clearly each platform tells you what went wrong.
4. Count the units consumed for one run, and multiply by your real monthly volume. This is the number the pricing page will not give you.
5. Hand it to the person who will maintain it and watch them change something. If they cannot, you have your answer regardless of what the comparison table says.

Practical notes

All three platforms have integrations for WhatsApp, Google Workspace, Telegram and webhooks — so you can build customer-service and lead automations for any business. n8n has an edge when you need to connect systems through a generic API, or keep customer data on your own server for regulatory and privacy reasons. Make and Zapier win when you need a ready-made integration for a popular service without writing code — Zapier with the widest selection, Make with a better price.