Zapier vs Make vs n8n: the short answer
Zapier, Make, and n8n all solve the same problem — move information between applications when something happens — and they charge for that work in three structurally different ways. Zapier meters per task. Make meters per operation. n8n is open source, can be self-hosted, and in self-hosted mode the cost of running a workflow stops tracking how often it runs.
That metering difference is the most decision-relevant thing on this page, because it is what surprises people on their first real bill. A workflow that feels like one automation in your head — new form submission, look up the customer, branch on deal size, update the CRM, post to Slack, send a confirmation email — is one automation to you and six billable units to a meter that counts steps.
The honest recommendation up front: most small businesses should start on a hosted tool, and most should start on Zapier specifically. Move to Make when your step counts get expensive or your logic gets branchy. Move to n8n when volume, custom integrations, or data control justify running a server. Self-hosting to save money on a handful of workflows is a bad trade — you replace a subscription with an operations job.
One deliberate omission: this article does not quote prices, plan names, task allowances, or operation quotas. All three vendors change those often enough that any specific figure in a comparison article ages badly and misleads someone. Check each vendor's current pricing page before you commit. The shape of how they charge changes far more slowly than the amounts, and the shape is what should drive your decision.
How each automation tool actually works
The three products look similar in a feature grid and feel completely different in your hands. The difference is not the number of integrations. It is what the editor asks you to think about.
Zapier: a trigger and a straight line of actions
Zapier is great at the first mile. You pick a trigger app, pick an action app, map fields in a form-shaped editor, and turn it on. Nothing about the interface asks you to understand data structures, loops, or how an API paginates. For a two-step connection between two mainstream SaaS products, it is the closest thing to plug and play that this category has produced.
A Zap is a trigger followed by a linear list of steps. Paths let you branch. Filters stop a run when a condition is not met. Formatter reshapes dates, text, and numbers without code. Code by Zapier gives you a small JavaScript or Python step when the built-in tools run out. Triggers are either instant, when the source app supports webhooks, or polled on an interval, which is where the surprise latency lives — a polled trigger does not fire the second something happens, it fires the next time Zapier checks.
The integration library is Zapier's real moat. If you need an obscure scheduling tool, a regional payment processor, or a niche CRM, Zapier is the most likely of the three to already have it, with the fields pre-mapped and OAuth handled. Usability is genuinely the product here, not a bullet point.
Make: a visual canvas with routers, iterators, and aggregators
Make gives you a canvas instead of a list. Modules are circles you connect with lines, arranged by drag and drop, and the data flowing between them is visible as bundles you can inspect run by run. When a workflow has real branching, that visual model is easier to reason about than a vertical list of steps with nested conditions.
The three pieces that matter most are the router, the iterator, and the aggregator. A Router splits one path into several that each run their own filter. An Iterator takes an array — line items on an order, rows in a spreadsheet, attachments on an email — and runs the following modules once per element. An Aggregator collapses many bundles back into one. Those three primitives are why e-commerce, invoicing, and reporting workflows tend to end up on Make.
Make occupies the middle of this comparison on purpose. It is more capable than Zapier at data manipulation and more approachable than n8n at operations, and it generally offers more headroom per dollar than Zapier at the same step count. The trade is a steeper first hour. Nothing about Make is hard, but it does expect you to know what an array is.
n8n: a node graph that can run on your own server
n8n is a Node.js application. You can use its cloud version like any other SaaS product, or you can run the same software yourself on a server you control — a cheap virtual machine, a container in your existing cloud computing account, or hardware in your own building. That second option is the entire reason n8n exists in this comparison.
The editor is a node graph, closer to Make than to Zapier, with two nodes doing most of the heavy lifting. The HTTP Request node calls any API, authenticated with credentials you store once, which means an integration that does not exist as a prebuilt node is still buildable in about the same time it takes to read the vendor's API docs. The Code node is a place to write real JavaScript — a map over incoming items, an anonymous function that reshapes a payload — without leaving the canvas.
One thing an honest n8n comparison has to say plainly: n8n's source is public, but it ships under a sustainable-use license rather than a standard permissive open-source license. Running it internally for your own business is squarely allowed. Reselling it as a hosted service to your customers is where the restrictions bite. Read the current license text before you build a product on top of it.
The metering models: per task, per operation, per workflow run
This is where money is won and lost, and it is the section most comparison articles reduce to a price table that is wrong within a quarter. Ignore the amounts. Learn the counting rules, because those are stable.
Why one workflow bills as seven tasks
Zapier counts tasks. In practice a task is a successful action step, so the meter runs roughly once per thing your Zap does after it starts. Six actions means six tasks per run, not one. Multiply that by however many times the trigger fires in a month and the arithmetic gets uncomfortable fast — a workflow firing a few hundred times a day at six steps each is tens of thousands of tasks a month from a single automation.
This is the mechanism behind almost every story about an automation bill jumping a tier without the business doing anything differently. Volume did not necessarily change. Someone added two steps to an existing Zap, and every one of those steps now bills on every run. The definition of what counts and what does not — filtered-out runs, the trigger itself, failed steps, replays — is the fine print that actually determines your invoice, and it is worth reading Zapier's current task definition rather than trusting any third-party summary, including this one.
The practical defense is architectural, not commercial. Fewer, fatter steps cost less than many thin ones. Filter as early in the Zap as possible so cheap runs die before expensive steps execute. Move field formatting into the source system when you can. And when a Zap grows past roughly five or six steps and fires constantly, that specific workflow has become a candidate for a different tool, even if the rest of your account stays where it is.
How Make counts operations differently
Make counts operations, and an operation is one module doing one thing. On a straight-line workflow that is functionally similar to Zapier's task counting, and people who only skim the pricing pages conclude the two models are the same. They are not, in two places that matter.
First, iterators. If a module iterates over twenty line items, the modules after it run twenty times and consume twenty operations each. Loops are where Make bills can escape you, so watch anything that processes arrays. Second, and pulling the other direction, Make generally offers a much larger operation allowance for the money than Zapier offers in tasks. You can build a five-module scenario in Make without the per-run cost feeling like a reason not to add the fifth module.
Filters, routers, and the flow-control pieces are cheaper to reason about here too, because you can watch a single execution replay on the canvas and see exactly which modules consumed operations and which never ran. When you compare Zapier and Make on cost, do it with a real workflow and a real monthly run count, not with the headline number on the pricing page.
n8n vs Zapier for multi-step workflows
n8n changes the unit. Its cloud plans have metered full workflow executions rather than individual steps, which means the marginal cost of adding a node is zero — verify the current model on n8n's pricing page, but that has been the structural difference. A forty-node workflow and a three-node workflow consume the same allowance per run. If your automations are long, that inverts the entire cost comparison.
Self-hosting removes the meter completely. You pay for a server and your own time, and it does not matter whether that server runs a thousand executions a month or a million until you hit its actual capacity. Cost stops being a function of automation volume and becomes a function of infrastructure, which for a business running heavy automation is a much better curve to be on.
The comparison that decides it: take your busiest workflow, count its steps, and multiply by monthly runs. If that number is small, Zapier's convenience is worth its premium and you should stop optimizing. If that number is in the hundreds of thousands, you are paying a per-step tax on work that a single server would absorb without noticing.
How to estimate your real cost before you commit
Build the same workflow in the free tier of two of them before you subscribe to either. All three let you get far enough to count real consumption, and thirty minutes of building tells you more than any comparison table.
Then write down four numbers per workflow: how many times the trigger fires per month, how many steps run on a typical successful pass, how many steps run when a filter kills the run early, and whether any step loops over a list. Those four numbers, not the vendor's marketing tier names, determine what you will pay. Do this for your three busiest workflows and the choice usually makes itself.
Self-hosting n8n: what you are actually taking on
Self-hosting is where n8n's cost advantage comes from, and it is also the part that gets glossed over in every article that calls n8n free. The software is free. Running it is a job.
n8n offers the most control and the most operational work
Provisioning the server is the easy part — a small virtual machine and a container image gets you a running instance in an afternoon. The work starts the day after. You own updates, and n8n releases frequently enough that staying current is a recurring calendar item rather than an annual one. You own the database that stores your workflows, credentials, and execution history, which means you own its backups and, more importantly, you own testing that those backups actually restore.
You own uptime. If the machine reboots at 2am and your automation queue backs up, nobody pages you but your customers. You own the reverse proxy and the TLS certificate that webhooks arrive on, because inbound triggers from other services need a public HTTPS endpoint that stays reachable. You own log rotation and execution-history pruning, or the disk quietly fills and everything stops. You own the security posture of an application that holds API credentials for every system you have connected — which is exactly the box an attacker would most like to find.
None of that is exotic. Any competent systems person does it routinely, and n8n supports separating workers from the main instance when one machine stops keeping up. But if nobody at your company currently administers a Linux server, adopting self-hosted n8n means either hiring that skill or accepting that your automation platform is one unattended failure away from a very bad week. That is the real headache the free-software framing hides.
When self-hosting is genuinely worth it
Three situations justify it. High volume, where per-step or per-execution pricing has grown into a number that would fund a server several times over. Data control, where records genuinely cannot transit a third-party processor — regulated health, financial, or legal information, or a contractual commitment about where data lives. And deep customization, where you need custom nodes, long-running processes, or access to internal systems that never touch the public internet.
If none of those apply, hosted is the right call and n8n Cloud is a perfectly reasonable middle option — the same node graph and the same execution-based metering, without the server. Choosing it is not a compromise. It just means you kept the part of n8n that was actually about your workflows and skipped the part that was about infrastructure.
Integration breadth versus integration depth
Counting integrations is the laziest comparison in this category. What matters is whether the specific integrations you need exist, and how deep they go into the application's API.
Zapier wins breadth decisively. Its library is the largest, and the long tail is where that shows — the small vertical tools, the regional services, the software your industry uses and nobody else has heard of. If your stack includes three obscure applications, check Zapier first, because a prebuilt connector beats anything you would build yourself.
Depth is a different question. Prebuilt connectors expose a curated subset of an API, and the endpoint you need is sometimes not in the subset. A Slack connector might post messages beautifully and have no way to do the one administrative call you need. This happens on all three platforms, and the difference is what you do next.
On n8n, you drop in an HTTP Request node, paste the endpoint, reuse the credential you already stored, and move on. On Make, the HTTP module and custom apps get you to the same place. On Zapier, you have Webhooks by Zapier and the developer platform for private integrations, which works but feels more like leaving the product than extending it. When you compare Zapier against the other two on integration strategy, the honest framing is: Zapier gives you more roads already built, n8n makes it easiest to build the road yourself.
One test that settles it quickly: pick the least mainstream application in your stack and the least mainstream thing you need to do with it. Search each platform's integration list for that specific action, not the app name. That single search predicts your experience better than any feature comparison.
Error handling and reliability
Every automation fails eventually. An API times out, a rate limit trips, someone deletes a required field, a token expires. Exception handling is the difference between a workflow you can trust with revenue and one that silently drops records for three weeks before anyone notices.
Zapier's model is straightforward: failed steps surface in a history view, errors can notify you by email, and replaying failed runs is available depending on plan. It is adequate for simple workflows and thin for complex ones, because a Zap does not really have a concept of handling an error inside the workflow and continuing down a different path.
Make is the strongest of the three here for a specific reason: you can attach an error handler route to an individual module. When that module fails, execution follows the error branch instead of dying — log it, retry it, route it to a human, or ignore it deliberately. Combined with per-execution replay on the canvas, debugging a broken scenario is mostly a matter of looking at the bundle that went wrong.
n8n gives you error workflows: a separate workflow that runs whenever another one fails, receiving the failure details, which you can wire into anything — a Slack alert, a ticket, a retry with a delay. Self-hosted, you also get the execution log stored in a database you can query directly, which is genuinely useful when you need to answer a question like which records failed between Tuesday and Thursday.
Whichever you pick, build the same three habits. Alert on failure to a channel a human actually reads, not to an inbox rule. Make workflows idempotent where you can, so a replay does not create a duplicate record. And keep a filter early in the flow that drops malformed input before it reaches anything that writes data.
AI and LLM steps in Zapier, Make, and n8n
All three can call a language model, and the differences are smaller than the marketing suggests. The important part is what artificial intelligence is doing in the workflow: classifying an inbound message, extracting structured fields from unstructured text, drafting a reply for a human to approve, or summarizing a document. Those are the jobs that hold up in production.
Zapier has the most packaged approach, with AI-flavored actions and assistant-style features layered on the same trigger-and-action model. It is the fastest way to get a GPT call inside a workflow with no setup, and the least flexible when you want to control the exact prompt, model, and retry behavior. Make AI modules connect to the major model providers and fit naturally into the canvas, so you can route on the model's output and iterate over results like any other data.
n8n goes furthest, with agent-style nodes built on the common LLM orchestration patterns — tool calling, vector stores, chained model steps. If you are building something closer to an application than an integration, that matters. If you are classifying support tickets, it does not, and the simpler tool will get you there sooner.
Two practical notes that apply everywhere. First, you supply your own model credentials on all three, so the model spend is a separate bill from the automation platform — a workflow calling GPT models, Claude, or whatever your team standardizes on will run somewhere in the tens to low hundreds of dollars a month for typical small-business volume, paid to the model provider directly. Second, if your team already lives in ChatGPT or Microsoft Copilot, an automation that hands a draft to a person inside a tool they already have open gets used. One that produces output nobody sees does not, no matter how good the prompt is.
Learning curve and who maintains it after launch
The learning curve is a real cost and it is usually underweighted, because the person choosing the tool is rarely the person maintaining it in month seven.
Zapier is learnable in an afternoon by anyone comfortable with a spreadsheet. Make takes a few days to feel natural and rewards understanding arrays, JSON, and how APIs return data. n8n asks for the same conceptual knowledge as Make plus, if you self-host, server administration — that is not one curve, it is two.
Ask a blunt question before you decide: if the person who builds this leaves, who fixes it? A Zap that an office manager can open, read, and repair is worth more to a ten-person company than an elegant n8n graph that only one developer understands. Complexity you cannot staff is technical debt with a monthly subscription attached.
Documentation is the cheap insurance here. For each workflow, write down what triggers it, what systems it writes to, what it does when it fails, and who owns it. Three sentences per workflow. Most automation disasters are not software failures, they are a bit of undocumented logic nobody knew was running.
How to choose the right automation tool
Match the tool to the workflow, not to the company. Plenty of businesses correctly run Zapier for the simple connections and n8n for the two workflows that would cost a fortune per step. Paying for two platforms is often cheaper than forcing everything onto one.
When to use Zapier, and when it starts to hurt
Use Zapier when the workflow is short, the volume is modest, and at least one of the applications is niche. Use it when the person maintaining the automation is not technical. Use it when you need the thing working this afternoon and the cost of a few extra dollars a month is irrelevant next to the cost of a delay.
It starts to hurt at the intersection of many steps and high frequency. If you are adding steps to a Zap and pausing to think about the bill, that is the signal. It also gets awkward when a workflow needs to loop over a list, because the linear model was never designed for that.
What Zapier allows a non-technical team to build
Realistically: form submission into CRM, new customer into an onboarding email sequence, closed deal into an invoice, calendar booking into a reminder sequence, inbound lead into a Slack channel, spreadsheet row into a task. These are the automations that recover the most hours per dollar in a small business, and none of them require anyone to understand an API.
That is not a small category. It is most of the automation value available to a company under fifty people, and it is available without a developer. The mistake is assuming that because the easy tier is easy, the hard tier is also easy — the second half of an automation program, where systems that were never designed to talk to each other have to reconcile, is where you either grow the skill or hire it.
Make vs Zapier at the same step count
This is the most common real decision, and at equal step counts Make usually wins on cost and on capability while Zapier wins on setup speed and integration breadth. If your workflows involve line items, arrays, multiple branches, or data reshaping between systems, Make's router and iterator model will save you both money and workarounds.
Choose Make when you are automating an operational process rather than connecting two apps — order processing, invoice reconciliation, multi-channel inventory, anything where one incoming event produces a variable number of downstream actions. You can build a five-module scenario in Make without the cost of the fifth module changing the decision, and that freedom shows up in better-designed workflows.
When n8n earns its keep
Pick n8n when at least one of these is true: your per-step billing has become a real line item, you need integrations that do not exist as prebuilt connectors, your data cannot leave infrastructure you control, or you are building something with enough logic in it that it is closer to an internal application than an integration.
Pick n8n Cloud rather than self-hosting unless the volume or the data-residency requirement specifically demands otherwise. You keep the execution-based metering and the extensibility, and you skip owning a server. The number of teams that self-host for ideological reasons and then run an unpatched instance for eighteen months is not small.
Migrating between automation platforms
There is no automatic converter between these three, and any tool claiming otherwise handles trivial cases only. Migration is a rebuild, and planning for that up front makes it a manageable project rather than a bad surprise.
What carries over is the logic — the sequence of decisions and actions you already worked out, which is the genuinely hard part and it is already done. What does not carry over: credentials must be re-authorized system by system, and every OAuth connection is a fresh consent screen. Triggers may change character, because an instant webhook trigger on one platform can be a polled trigger on another, which changes timing your downstream systems may quietly depend on. Code steps need rewriting, since Code by Zapier scripts and n8n Code node scripts have different input and output shapes. Field mappings are all manual, and this is where mistakes hide, because a mismapped field does not error — it writes wrong data silently.
Run both platforms in parallel for a full billing cycle. Point the new one at test records or a staging environment, compare the outputs against the live system, and only then cut over and disable the original. Migrate the highest-volume workflow first, since that is the one paying for the migration, and leave the long tail of small Zaps where they are until there is a reason to move them.
Budget more time than the build itself for testing edge cases, especially anything that touches money, contracts, or customer communications. The workflow that runs correctly on your five test records is not the same as the workflow that runs correctly on the malformed record someone submitted in 2023.
Frequently asked questions
Short answers to what people ask most when comparing these three.
Is n8n really free?
The software is free to run yourself. The server, the updates, the backups, and the person doing all of that are not. For a business already running infrastructure, self-hosted n8n is close to free in practice. For a business that would need to hire or learn systems administration to support it, the true cost is higher than a hosted subscription and should be counted honestly. n8n also sells a cloud version, which is a normal paid product.
Is n8n better than Zapier?
For high-volume, multi-step, or custom-integration work, yes, and usually by a wide margin on cost. For a five-person company connecting a form to a CRM, no — Zapier will be running before n8n is installed, and the money saved would not cover the extra hour. Any n8n comparison that answers this without asking about your step count and run volume is not answering the question.
Can I self-host Zapier or Make?
No. Both are sold only as software as a service, with no self-hosted edition. If running the automation engine on your own infrastructure is a hard requirement — for data residency, for an air-gapped network, for a compliance commitment — n8n is the only one of these three that can do it, which is often the entire reason it ends up on the shortlist.
Which is cheapest for high-volume automation?
Self-hosted n8n, and it is not close, because volume stops driving the bill. Among the hosted options, n8n Cloud's execution-based metering favors long workflows and Make's operation allowance generally beats Zapier's task allowance at the same step count. Zapier is the most expensive per unit of work at scale and the cheapest way to get the first workflow live, which is a coherent product strategy rather than a flaw.
Do I need to know how to code?
Not for Zapier, and not for most of Make. n8n does not require code either, but its ceiling is much higher if you can write a little JavaScript, and self-hosting requires comfort with servers regardless of coding ability. A useful rule: if you can read a JSON response and understand what a nested array is, you can use any of the three. If you cannot yet, start on Zapier and learn on real workflows.
Can I move my Zaps to Make or n8n automatically?
No. Expect to rebuild each workflow by hand, re-authorize every connection, and rewrite any code steps. The logic transfers; the configuration does not. n8n workflows export and import as JSON between n8n instances, which makes moving between your own environments straightforward, but that format has nothing to do with Zaps or Make scenarios.
What are the security trade-offs of self-hosting?
Self-hosting gives you control over where data sits, how it is encrypted, and who can reach the instance, and it lets you keep automation credentials inside your own network. In exchange you own patching, backups, monitoring, and access control. Hosted platforms take that work off your plate and ask you to trust their security posture and compliance certifications instead. Neither is automatically safer. A well-run hosted account beats a neglected self-hosted server every time, and the honest question is which one you will actually maintain.
Getting the workflow right matters more than the tool
The same automation can cost wildly different amounts on these three platforms, and it is usually the design, not the vendor, that decides. Fewer steps, earlier filters, and less data shuttling between systems make a workflow cheaper on every platform at once.
If you would rather have someone work through the decision with your actual volumes and step counts, that is a large part of what we do at AutomateNexus. A paid audit is $2,500 and produces a mapped workflow inventory with tooling recommendations. Full builds start at $7,500 and typically take about 30 days, with larger MVP-style projects running four to eight weeks. Where AI steps are involved, you bring your own model keys and pay the provider directly — usually somewhere in the range of $30 to $150 a month — so there is no markup on model usage sitting inside your invoice.