n8n vs Zapier vs Make: an honest comparison for Indian businesses

Short answer: in the n8n vs Zapier vs Make decision, choose Zapier for the fastest setup and widest app list, Make for visual workflows at a low per-operation price, and n8n when you want to self-host, keep data on your own server and run AI agents without paying per step. The cost gap is usually bigger than the feature gap.

n8n vs Zapier vs Make: the comparison table

Prices are the advertised entry tiers as of September 2026, on annual billing where the vendor quotes it that way. Vendors change tiers often, so treat this as a shape-of-the-market guide and confirm on the official page.

How the three pricing models actually work

This is the part most comparison posts get wrong, and it is the part that decides your bill.

Zapier: per task

Zapier's documentation is explicit — tasks are used when a Zap successfully moves data or completes an action for you. Polling for new data does not cost a task, and several built-in tools (Filter, Formatter, Paths, Delay, Looping, Zapier Tables, Storage) do not consume tasks either. But every real action does. A Zap that reads a form entry, creates a CRM record, sends a WhatsApp message and writes a row to a sheet burns three tasks per submission, not one.

Make: per operation

Make counts an operation each time a module performs an action — reading, searching, creating, updating, deleting, transforming, aggregating or iterating. Routers and error handlers are free. Iterators are not, which is the trap: a scenario that loops over 50 line items in an order will consume roughly 50 operations in that branch alone. Make is cheap per operation, but operation counts grow quickly with loops.

n8n: per execution

n8n Cloud counts one execution as a single run of your entire workflow, regardless of how many steps it contains or how much data it moves. A 4-node workflow and a 40-node workflow both cost one execution. That is the structural advantage: as your automations get more sophisticated, your n8n bill does not move.

A worked example. Say you process 1,000 enquiries a month, and each one runs a 10-action workflow: capture the lead, enrich it, score it with an LLM, write to CRM, send a WhatsApp acknowledgement, notify sales, log to a sheet, and so on.

• Zapier: roughly 10,000 tasks a month. That is well past the entry tiers and into a mid-to-high task plan.
• Make: roughly 10,000 operations. That is exactly the Core plan's 10,000 credits, so you would be shopping for the next tier up almost immediately.
• n8n Cloud: 1,000 executions. That fits inside the Starter plan's 2,500 executions at €20/month.
• n8n self-hosted: the VPS cost, and nothing else.

The tools cost roughly the same for trivial automations. They diverge sharply the moment your workflows get real. If you are not sure a process is worth automating at all yet, start with our checklist on deciding whether a task is worth automating before you pick a platform.

What each tool is actually best at

Zapier is best at speed and coverage

Zapier has the largest connector library of the three and the shallowest learning curve. If a founder wants "new Razorpay payment goes into a Google Sheet and pings Slack" working before lunch, Zapier wins. It is also the safest choice when nobody on the team will ever look at the automation again, because there is very little to break.

Where it stops being the right answer: branching logic, loops over arrays, heavy data reshaping, and anything where the task count multiplies.

Make is best at visual, branching scenarios

Make's canvas shows you the whole scenario, including routers, filters and error handlers, in one picture. For operations people who think visually, it is the easiest of the three to debug. The per-operation price is genuinely low. It is a strong middle option when you want more power than Zapier without running a server.

Where it stops being the right answer: loops over large datasets, and any requirement to keep data inside your own infrastructure.

n8n is best at complexity, AI and control

n8n gives you code nodes in JavaScript and Python, full webhook control, API and CLI access, and the option to run the whole thing on your own server. For a business that is automating a lot — invoices, reports, WhatsApp, CRM hygiene, document processing — the economics stop being close. Our list of n8n automation ideas for business shows the kind of workflows this suits.

Where it stops being the right answer: you have one simple integration, nobody technical, and no appetite to maintain anything.

AI and LLM node support

All three vendors shipped AI features. They are not equivalent.

n8n has the most complete AI layer. Its documentation covers building AI workflows with LLM providers such as OpenAI, Anthropic and Google, plus agents, tools, memory, vector storage and MCP servers, and includes evaluation tooling to test AI workflows before you trust them. Because you connect your own provider keys, you pay the model provider directly at their rates instead of paying a markup in platform credits. See the official n8n AI documentation for the current node list.

Zapier offers AI steps and Agents inside the same task-based billing. It is the quickest way to add "summarise this email" to an existing Zap. Zapier notes that the AI model tier affects how many tasks a step consumes, so an AI-heavy Zap burns through a task allowance faster than a plain one.

Make offers AI modules and agents, and its documentation states that most actions consume one credit while some advanced features using Make's AI provider may use more. Again, convenient, but metered.

The practical rule: if AI is a garnish on an otherwise simple automation, Zapier or Make is fine. If the AI is the product — an agent that reads enquiries, decides, calls tools and replies — build it in n8n. That distinction is the subject of our piece on AI agents versus chatbots.

Self-hosting n8n on a cheap Indian VPS

This is the option most Indian businesses do not know they have, and it changes the maths completely.

n8n's Community Edition is published under the Sustainable Use License. In plain terms, the licence permits use and modification for your own internal business purposes, or for non-commercial or personal use; what it restricts is commercially redistributing n8n itself. Running it inside your own company to automate your own operations is squarely within that. Read the official licence text before you deploy, and note that files marked .ee. are under a separate enterprise licence.

What it costs. A KVM VPS is enough. As of September 2026, Hostinger's India-facing VPS listing starts around ₹599/month promotional for 1 vCPU and 4 GB RAM, and ₹799/month for 2 vCPU and 8 GB RAM, with renewal prices noticeably higher — ₹999 and ₹1,199 respectively. Check the official page for today's rates. Budget realistically for the renewal price, not the first-year offer. For most small businesses, a 2 vCPU / 8 GB box comfortably runs n8n plus PostgreSQL.

Practical setup checklist:

• Deploy with Docker or Docker Compose — n8n documents one-line, Docker, Docker Compose, npm and cloud-provider installs.
• Use PostgreSQL, not the default SQLite, once you pass a few hundred executions a day.
• Put it behind a reverse proxy with HTTPS and a real domain, never a bare IP.
• Turn on basic auth or SSO at the very least; an open n8n instance holds every API key you own.
• Set execution data pruning so the database does not grow forever.
• Back up the database and the encryption key. Losing the encryption key means losing every stored credential.
• Set up failure alerts on a separate channel — a workflow that silently stops is worse than no workflow.
• Use queue mode only when you actually need concurrency; it adds Redis and moving parts.

The honest trade-off. Self-hosting removes the per-execution bill and adds an operations job: patching, upgrades, monitoring, restores. If nobody in the business will own that, either pay for n8n Cloud or have someone run it for you. When we build automations at LZ Worth's AI automation service, the client owns the server, the code, the keys and the accounts — so this decision stays yours, not ours.

Data control and compliance

With Zapier and Make, your data passes through and is stored on the vendor's infrastructure, largely outside India. With self-hosted n8n, it sits on a server you chose, in a region you chose.

That matters more than it used to. India's Digital Personal Data Protection Act, 2023 was enacted on 11 August 2023, and the Digital Personal Data Protection Rules were notified on 14 November 2025 with a phased compliance period of eighteen months. The Rules also contemplate government direction on restricted categories of data, including local storage where required, for Significant Data Fiduciaries. Sector rules can be stricter — payments data in particular has its own storage requirements. This is not legal advice; if you handle customer personal data at scale, get an opinion. But when someone asks you where your customers' phone numbers and order histories physically live, "on our own server, in our own account" is a much shorter conversation than "somewhere in a SaaS vendor's estate."

Three questions worth answering before you pick:

1. Does this workflow touch identifiable customer data, or only internal operational data?
2. If a customer asked us to delete everything, could we actually find it?
3. Who holds the API keys — us, or an agency?

Pick this if

• Pick Zapier if you need one or two simple integrations live this week, nobody on the team is technical, the app you need has a Zapier connector and not an n8n one, and your monthly action count is in the hundreds, not thousands.
• Pick Make if you want visual multi-branch scenarios, you are cost-conscious but do not want to run a server, and your scenarios do not loop over large datasets.
• Pick n8n Cloud if your workflows have many steps each, you want AI agents with your own model keys, and you want predictable pricing without managing infrastructure.
• Pick self-hosted n8n if volume is high, data control matters, or you are running more than a handful of automations — and someone will own the server.
• Use two of them if that is genuinely cheaper. Plenty of businesses keep three simple Zaps and run everything heavy in n8n. Nothing forces you to standardise.

Mistakes to avoid

• Comparing sticker prices instead of your own volume. Count the actions in one real workflow, multiply by monthly volume, then price it on all three.
• Forgetting the annual-billing asterisk. Most headline prices assume you pay for a year upfront. Monthly billing costs more.
• Ignoring iterators and loops in Make. They are the single biggest cause of surprise operation bills.
• Assuming self-hosted means free. It means no execution fee. Server, backups, monitoring and someone's time are real costs.
• Letting an agency hold the keys. If you cannot log into the automation platform yourself, you do not own the automation.
• No failure alerting. The expensive failure is not the workflow that crashes loudly; it is the one that quietly stops sending invoices for eleven days.
• Automating a broken process. Fix the process on paper first, then automate it.

How to decide in one afternoon

1. Write down the single process costing you the most hours per week.
2. List every action it needs, step by step, in plain English.
3. Count the actions. Multiply by monthly volume. That is your task/operation number; your execution number is just the volume.
4. Price that on all three official pricing pages today.
5. Ask: does this touch customer personal data? If yes, weight self-hosting heavily.
6. Build one workflow end to end before you buy an annual plan.

If you would rather not run that experiment yourself, we do this for a living. Browse what we build or see recent work, and note that at handover you get a recorded walkthrough, failure alerts, and ownership of all code, keys and accounts.

Deepanshu Singh co-founded LZ Worth, a Delhi studio that builds AI automations, apps and content systems. He has tested more than 150 AI tools and would rather tell you which ones to skip. More about him.