n8n vs Make vs Zapier: An Honest Comparison from Someone Who Uses All Three

I build automation for a living, and I have production workflows running on all three of these platforms right now. So when people ask me about n8n vs Make vs Zapier, I can answer from invoices and incident logs, not marketing pages.

The verdict up front

If you just want the answer: Zapier if you're non-technical and want it working today, Make if you want power per dollar and can handle a learning curve, n8n if you care about AI agents, data privacy, or high volume. My default for client work has shifted to n8n over the past year, mostly because of its AI capabilities and self-hosting.

Now the details.

Pricing: the part everyone gets wrong

The three platforms bill in completely different units, which makes comparison shopping miserable. Here's the translation:

• Zapier bills per task. Every action step that runs counts as a task. A 5-step workflow processing one record = 4-5 tasks. At 10,000 runs/month of a multi-step workflow, you're easily at 40,000+ tasks — which lands you in plans costing $100–400+/month.
• Make bills per operation. Similar idea, but operations are cheaper. That same 10k-run scenario typically costs $16–50/month depending on how many modules each run touches. Make is consistently the best sticker price of the hosted options.
• n8n self-hosted bills nothing. You pay for the server. A $10–20/month VPS handles 10k runs without breaking a sweat. n8n Cloud exists too (per-workflow-execution pricing, roughly Zapier-tier), but self-hosting is where the economics get silly in your favor.

Real example: a client running ~300k operations/month moved from Zapier (quoted in the hundreds per month) to a $24 Hetzner box running n8n. Same workflows. The catch is that you now own a server — updates, backups, monitoring. That trade is worth it at volume and a waste of time at 500 runs a month.

AI and LLM capabilities

This is where the gap has widened most, and it's why the best automation platform question has a different answer in 2026 than it did two years ago.

• n8n has first-class AI agent nodes: an agent with memory, tool use, and model choice (OpenAI, Anthropic, Gemini, or local models via Ollama) built into the canvas. You can build a genuine tool-calling agent — classify an email, look up the customer, draft a reply, escalate on Slack — without leaving the platform. It's the closest thing to LangChain-with-a-GUI that exists.
• Make has solid AI modules (OpenAI, Anthropic, and others) plus Make AI agents, which have matured a lot. Great for "LLM as a step" workflows — summarize, extract, classify. Full agentic loops are possible but feel bolted on compared to n8n.
• Zapier has AI actions, chatbots, and agents that are genuinely easy to set up — the easiest of the three. But they're the least controllable, and per-task pricing makes token-hungry loops expensive fast.

If your roadmap is "LLM does one step in a pipeline," all three work. If it's "an agent that decides what to do next," n8n is a full generation ahead.

Self-hosting and data privacy

Simple: n8n wins, and it's not close. It's source-available and self-hostable with a single Docker container. Your customer data, API keys, and prompts never leave your infrastructure. For clients in healthcare, legal, or EU-regulated industries, this alone ends the discussion.

Make and Zapier are hosted-only. Your data flows through their servers, full stop. For most marketing and ops workflows that's fine. For anything sensitive, it isn't.

Learning curve

• Zapier: easiest by far. A non-technical person ships a working workflow in an hour. The ceiling is also the lowest — complex branching logic gets painful.
• Make: steeper. The visual canvas is powerful (routers, iterators, error handlers) but you need to think in data structures. A weekend of tinkering gets most people productive.
• n8n: steepest of the three, because it exposes the most — expressions, JSON handling, error workflows, code nodes. If you've ever written code, it feels natural. If you haven't, budget a week.

Who should pick which

My own rule of thumb for clients: prototype in whatever you'll learn fastest, but if the workflow involves an LLM making decisions or touches sensitive data, build it in n8n from day one. Migrating later is more painful than starting there.