Why Do These Three Platforms Look Cheap and Then Cost So Much?
Because they bill on three different units. Zapier counts tasks, Make counts operations, and n8n counts whole workflow executions. The same automation can cost 20 units on one platform and 1 unit on another, which is why headline monthly prices tell you almost nothing about your bill.
This is the single fact that decides which platform is cheapest for you, and it is buried on every pricing page.
A workflow with twenty steps costs twenty tasks on Zapier every time it runs. The same workflow on n8n costs one execution. That is not a small difference. It is the difference between a HK$230 month and a HK$2,300 month.
So the honest version of "which is cheapest" is: it depends entirely on how many steps your workflows have and how often they run. Below is what each one actually charges, followed by which buyer should pick which.
How Much Do Zapier, Make and n8n Cost in 2026?
Zapier Professional starts at US$29.99 per month for 750 tasks. Make Pro sits around US$16 to US$28 per month for 10,000 credits depending on billing cycle. n8n Cloud Starter is US$24 per month for 2,500 executions, and the self-hosted Community Edition is free apart from your server cost.
These are published list prices from 2026 pricing round-ups rather than live quotes for your account. Verify on the vendor page before you commit, because all three have changed tiers during 2026.
Free tiers, compared honestly
--- Zapier Free: 100 tasks per month, unlimited Zaps, but only two steps per Zap, which rules out most useful patterns
--- Make Free: 1,000 credits per month with multi-step scenarios and a 15-minute minimum run interval
--- n8n Community Edition: free, self-hosted, unlimited executions, every integration included, you pay only for the server
Paid entry points
--- Zapier Professional: US$29.99 per month monthly, or about US$19.99 per month billed annually, for 750 tasks. Unlocks multi-step Zaps, webhooks, filters, paths and premium apps
--- Make Pro: around US$16 per month for 10,000 credits when reported on the lower tier, rising to about US$28 per month at the 10,000-credit tier on monthly billing. Annual billing saves roughly 15%
--- n8n Cloud: US$24 per month for Starter with 2,500 executions, US$60 per month for Pro with 10,000, and US$800 per month for Business with 40,000. Priced in euros, with Starter at EUR24 and Pro at EUR60, dropping to EUR20 and EUR50 on annual billing
Where the ceiling bites
--- Zapier Professional runs on a slider from 750 tasks up to 2 million, reaching US$5,999 per month at the top
--- n8n removed active workflow limits across every plan in April 2026, so only execution volume moves your bill
--- Self-hosted n8n on a managed host runs about US$3.70 per month on PikaPods, roughly US$7 per month on InstaPods, or US$5 to US$6 on a raw VPS
The most quoted illustration of the gap: a customer service automation handling 10,000 tickets a month costs about US$299 per month on Zapier Professional, and about US$6 per month in VPS fees on self-hosted n8n. That comparison is real, and it is also the best case for n8n rather than the typical one.
Which Platform Should You Choose for AI Agent Workflows?
Choose Zapier if your stack is mainstream SaaS and nobody on the team writes code. Choose Make if you need multi-step logic on a small budget and want to see how the flow branches. Choose n8n if you want the lowest cost at volume, self-hosting for data control, or genuine multi-agent orchestration.
Verdict by buyer type
--- Solo marketer or freelancer, tight budget: start on Make's free 1,000 credits. It is roughly ten times Zapier's free volume and supports multi-step scenarios, so you can build something real before paying
--- Small non-technical team living in Gmail, Sheets, Slack, Notion and HubSpot: Zapier. It is the fastest to a working automation and has 7,000-plus app integrations. You will pay a premium for that speed
--- Ops or growth lead who wants to see and control the agent's reasoning path: Make. The visual scenario builder with conditional logic, error handling and scheduling is its strongest argument
--- Anyone running high volume or handling sensitive client data: n8n, self-hosted. n8n 2.0 launched in January 2026 with an AI Agent Tool Node for multi-agent orchestration, native LangChain integration across 70-plus AI nodes, persistent agent memory between executions, and vector database support for retrieval workflows
--- Anyone who does not actually want to build or maintain a workflow: skip all three for now. The next section covers that case
The AI-specific layer
Zapier launched Zapier Agents, autonomous AI systems that execute across 8,000-plus apps, plus an AI Copilot that builds Zaps from a plain-language description. Make connects Claude, GPT and Gemini into hundreds of business apps through its visual builder. n8n goes furthest on orchestration but expects more of you.
The honest constraint on n8n: for advanced use cases with custom API calls, data transformation logic or complex conditional flows, you need at least intermediate JavaScript or someone who has it. "No-code" holds for the first 80% and quietly stops holding after that.
Where Does Each Platform Beat the Other Two?
Zapier wins on time-to-first-automation and integration breadth. Make wins on price-to-capability for multi-step logic. n8n wins on cost at volume, data control and AI orchestration depth. No platform wins every row, and any comparison claiming otherwise is selling something.
Zapier's real advantages
--- 7,000-plus integrations, the broadest of the three
--- The most approachable interface, which matters more than feature lists when nobody on the team is technical
--- Zapier Agents and AI Copilot lower the build effort further
Make's real advantages
--- The free tier is genuinely usable at 1,000 credits with multi-step scenarios
--- The visual scenario builder makes branching logic legible, which is how you debug an agent that made a strange decision
--- Priority execution, custom variables and full log access on Pro
n8n's real advantages
--- Billing per execution rather than per step, so a twenty-node workflow costs the same as a one-node workflow
--- Self-hosting means your client data never leaves infrastructure you control, which is the deciding factor for regulated work
--- Unlimited workflows and unlimited users on every Cloud plan, with active workflow limits removed in April 2026
What If You Do Not Want to Build the Workflow at All?
Then none of these three is your answer yet. All three are builders: they give you a canvas and expect you to design, test and maintain the automation. If what you want is a working outcome rather than a canvas, a pre-configured AI worker gets you there without the build phase.
That is the gap the UD AI Employee Hub occupies. It offers eight pre-built AI employees covering social media, sales support, customer service, SEO and content, administration, data analysis, e-commerce operations and information security, with 24 skills between them.
Verified facts, from the product page
--- Monthly cost: HK$0. Skills are free to download
--- Eight AI employees, 24 professional skills, provided free to Hong Kong businesses and selected by UDomain
--- Runs 24 hours a day, in contrast to a HK$15,000 to HK$35,000 monthly salary for the equivalent human role
--- Setup measured in minutes rather than the 2 to 3 months a new hire needs to reach competence
--- Installation help available over WhatsApp if you get stuck
The decision between these options is simpler than it looks. If your problem is plumbing between apps you already pay for, buy a builder. If your problem is that a job function is not being done at all, start with a ready-made worker and add plumbing later.
Many practitioners end up running both: an AI employee handling a whole function, and a small number of Make or n8n workflows moving data between systems that the employee does not touch.
What Are the Honest Limitations of Each Option?
Every option here has a real weakness. Zapier gets expensive fast, Make's credit accounting is hard to forecast, n8n moves maintenance work onto you, and a pre-built AI employee is not a workflow builder and will not connect two systems for you. Knowing which weakness you can live with is the actual decision.
Zapier
Task-based billing punishes exactly the multi-step workflows that make AI automation useful. The 750-task entry tier disappears quickly once an agent runs on a schedule, and the slider climbs to US$5,999 per month.
Make
Credits are harder to reason about than tasks or executions, and public reports of Pro pricing vary between roughly US$16 and US$28 per month depending on billing cycle and tier. Budget with a margin, because the number you find quoted may not be the number you are charged.
n8n
Self-hosting is only free if your time is free. You own updates, backups, uptime and security. The Starter plan's 2,500 executions can be exhausted in around 11 days on typical usage patterns, which pushes you to Pro sooner than the price list suggests. Advanced flows expect intermediate JavaScript.
UD AI Employee Hub
It is a set of pre-configured AI workers, not an automation canvas. If you need a custom trigger, a bespoke API call or conditional branching between five systems, this is the wrong tool and one of the three builders above is the right one. The "replaces HK$15,000 to HK$35,000 per month" figures on the page are UD's own comparison against local salary ranges, not independent research, and your result depends on how much of that role is genuinely repeatable.
All published prices
Every figure in this article is a list price from 2026 sources and vendor pages, not a quote. All three platforms adjusted tiers during 2026. Check the vendor pricing page on the day you buy.
What Is the Right Next Step This Week?
Count the steps in your most important workflow and multiply by how often it runs each month. That single number tells you whether Zapier's task billing is affordable or ruinous, and it takes about five minutes. Do that before you pay anyone.
Then take the cheapest path to proof. Make's free 1,000 credits or n8n Community Edition both let you build the real thing before money changes hands, and a working prototype answers the pricing question better than any comparison table.
If the honest answer is that you do not have a workflow problem but a capacity problem, a job nobody is doing, start with a ready-made AI employee instead and revisit the builders once you know exactly what needs connecting.
We know AI's cold edges. We know your real challenges. 28 years with UD, turning technology into a partnership with warmth. Pricing pages are designed to be compared; your actual workload is not, and that is usually where people need a second pair of eyes.
Not Sure Which One Fits Your Workload?
Before you commit to a monthly plan, it helps to map what you actually need automated against what each platform charges you for. UD has been doing that arithmetic with Hong Kong businesses for 28 years, and we'll walk you through every step, from counting your real execution volume to choosing between a builder and a ready-made AI employee.
Reviewed by the UD AI team.
