n8n vs Make vs Zapier for AI automation in 2026

n8n, Make and Zapier all promise the same thing: connect your tools and let workflows run on their own. Since all three added AI agents and LLM steps, the choice has become harder — and more important. The wrong platform doesn't just cost more; it decides where your data flows, how complex your automations can get, and how painful it is to switch later.

We build automations on all three. This comparison of n8n vs Make vs Zapier is based on what we see in real client projects — not on feature lists. It covers pricing models with a worked example, AI agent capabilities, GDPR, what self-hosting n8n really involves, three typical AI workflows, and a simple rule for which tool to pick when.

What is the difference between n8n, Make and Zapier?

All three are workflow automation platforms, but they target different users. Zapier is built for speed and simplicity: pick a trigger, add actions, done. Make offers a visual canvas where you can model branching, loops and data transformation without code. n8n is the most technical of the three: a node-based builder that can be self-hosted, extended with JavaScript or Python, and used to build complex AI agents.

How do n8n, Make and Zapier compare on pricing?

The three platforms charge in fundamentally different ways — and that matters more than the list price of any single plan. Plan prices and quotas change regularly, so check the vendors' current pages; the underlying billing models are far more stable:

A rule of thumb from our projects: for a handful of simple workflows, Zapier's convenience is worth the price. Once you run thousands of multi-step executions per month — typical for AI workflows that classify, enrich and route data — per-step pricing becomes the biggest cost driver, and n8n wins clearly.

What does per-step vs per-execution pricing mean for a real workflow?

Abstract pricing models are hard to compare, so here is the arithmetic for one workflow. No currency involved — just the billing units each platform would count.

Take an AI workflow with six billable steps: read a new form entry, look up the company in the CRM, ask an LLM to classify and summarise, write the result back, notify the sales channel and log the run. It runs 10,000 times a month.

• Per-step pricing (Zapier tasks, Make operations): 6 steps × 10,000 runs = 60,000 billable units per month. Details differ — Zapier doesn't count the trigger, Make counts every module that runs — but the order of magnitude is the same.
• Per-execution pricing (n8n Cloud): 10,000 runs = 10,000 executions, whether the workflow has six steps or sixty.
• Self-hosted n8n: 10,000 runs on your own server. The limit is CPU and memory, not a quota.

The gap widens when workflows loop. Say a document workflow processes invoices with an average of 20 line items, and runs two steps per line item (extract, validate). On per-step pricing that adds 40 units per invoice on top of the fixed steps; on per-execution pricing the invoice is still one execution. That is why AI workflows — which tend to iterate over items, retry and branch — are where the billing model matters most.

What the arithmetic doesn't show: per-execution plans have their own limits (concurrency, execution history, features by tier), and self-hosting has real staff cost. For an hours-based estimate of what automation saves you, use our ROI calculator.

Which tool is best for AI agents?

All three now offer AI features, but they differ in how deep you can go:

• n8n has dedicated AI agent nodes with tools, memory and vector stores, and you can connect any model — including local models running on your own servers. That makes it the most flexible choice for AI agents and retrieval workflows.
• Make offers AI agents and AI modules inside its visual builder. It's a good fit when the AI step is one part of a larger business process.
• Zapier focuses on making AI accessible: agents and AI actions that non-technical users can set up quickly across its huge app catalogue.

The deciding question is usually: does the AI need to work with sensitive data? If yes, the ability to self-host the workflow engine and use a local model is hard to beat.

Which tool is the most GDPR-friendly?

For companies in the EU, where data is processed matters as much as what the tool can do.

• n8n self-hosted runs entirely on your servers — in your data centre or an EU cloud. Workflow data never touches the vendor. This is the cleanest setup for personal or confidential data.
• Make offers EU hosting regions, which helps — but it remains a cloud service, so you need a data processing agreement and a clear view of sub-processors.
• Zapier is a US-based cloud service. It can be used under GDPR with the right agreements, but data flows through infrastructure outside your control, which many compliance teams view critically for sensitive data.

Keep in mind that the automation platform is only one link in the chain. If a workflow sends customer data to a cloud LLM API, that provider is a processor too. Combined with a local language model, self-hosted n8n lets you build AI automation where no personal data leaves your environment at all. This is a technical assessment, not legal advice — your data protection officer should sign off on the final setup.

What does self-hosting n8n really involve?

"Self-hosted n8n is free" is true for the licence, not for the operation. In our experience, a production setup needs the following — none of it exotic, but all of it someone's job:

• A server. Typically a VM or container host in your data centre or an EU cloud, run with Docker. Small setups need modest resources; heavy AI workflows and local models need considerably more.
• A proper database. n8n defaults to SQLite, but for production we use PostgreSQL. It stores workflows, credentials and execution history — and execution history grows fast if you don't prune it.
• Reverse proxy and TLS. Webhooks need a stable HTTPS endpoint; the editor should not be openly reachable from the internet without access control.
• Updates. n8n releases frequently. We test updates on a staging instance and read the release notes for breaking changes before touching production.
• Backups. Back up the database and the encryption key. Without the key, stored credentials can't be decrypted — a restored database alone is not enough.
• Monitoring. Uptime checks, alerts on failed executions (an error workflow is a good start), disk and memory usage.
• Queue mode for volume. For high throughput, n8n can run in queue mode: a main instance accepts triggers, Redis holds the queue, and separate worker processes execute workflows. Workers can be scaled horizontally — at the price of more moving parts.

Some features, such as SSO and advanced permissions, depend on the n8n edition and licence — check this before you plan a rollout across departments. If nobody in your team can own these tasks, n8n Cloud or a managed setup by a partner is usually the better path. Our security page describes how we approach this for client infrastructure.

Which tool would we pick for typical AI workflows?

Three workflows we build regularly, and the platform we would choose for each — with the reasoning, because your constraints may differ.

1. Lead enrichment and scoring

A new lead arrives via a web form. The workflow looks up company information, lets an LLM summarise the fit against your ideal customer profile, writes a score and summary into the CRM and pings the right salesperson. Volume is usually moderate, the logic is linear, and the most important thing is good native connectors to your CRM and chat tool.

Our pick: Make for most teams, Zapier if a non-technical sales ops person should own it end to end. Move to n8n once volume grows or scoring logic gets complicated. Note that contact data of leads is still personal data under GDPR.

2. Invoice and document extraction

Invoices arrive by email. The workflow extracts the attachment, has a model read supplier, amounts and line items, validates totals and VAT logic, books clean documents into the accounting system and sends exceptions to a human for review. The data is financial and often confidential, and the line-item loops make per-step pricing expensive.

Our pick: self-hosted n8n, ideally with a local model for the extraction step — the documents never leave your infrastructure, and loops don't multiply the bill.

3. Support ticket triage

Every incoming ticket is classified by intent and urgency, enriched with customer data from the CRM, and then either answered with a drafted reply, handed to an AI agent or routed to the right team. Volume is high and the data is personal. A support triage flow like this typically runs on n8n and is the foundation for an AI support agent that resolves routine requests on its own.

Our pick: n8n, self-hosted or on n8n Cloud depending on data sensitivity — high volume plus agent logic with memory and tools is exactly where it is strongest.

What are the downsides of each tool?

None of these is a deal-breaker on its own. They become a problem when the tool doesn't match the job — Zapier for heavy data processing, or self-hosted n8n in a team without anyone to look after it.

Which tool fits which team?

The best platform depends less on features than on who will build and maintain the workflows:

In practice, most of our process automation projects end up on n8n — not because it's always best, but because AI workflows tend to get complex, run at volume and touch sensitive data. For teams that just need to connect a few tools quickly, we happily recommend Zapier or Make.

How do you migrate from Zapier or Make to n8n?

A switch doesn't have to be a big-bang project. We usually move workflows in order of cost and risk:

1. Inventory. List all workflows with run volume, steps per run, cost and business criticality.
2. Start with the expensive ones. High-volume, multi-step workflows deliver the biggest savings first.
3. Run in parallel. The new workflow runs alongside the old one until results match.
4. Switch and monitor. Turn off the old version, keep alerts on the new one.
5. Keep simple zaps if they work. Not everything needs to move — only what pays off.

Workflows can't be exported from Zapier or Make into n8n one-to-one, so expect to rebuild rather than convert. The upside: a rebuild is a good moment to merge duplicate workflows and remove steps nobody needs anymore.

Not sure which platform fits your processes? Book a free call and we'll map your workflows to the right tool.