For a developer or technical team choosing a platform to run an agentic AI workflow, n8n wins: it is the only one of the three you can self-host, it bills by full workflow execution rather than by step, and its AI Agent node supports human-in-the-loop approval plus MCP in both directions. Make is the better pick for non-technical operations teams who want every agent decision visible on a canvas. Zapier Agents only makes sense if your stack already lives inside Zapier's app catalogue, because agents are a paid add-on there.
TL;DR
- Winner for builders: n8n. Community Edition is free and self-hosted with unlimited executions (n8n pricing), cloud Starter is EUR 20/month billed annually, and you pay for full workflow runs, "not for each step", so branchy agent logic does not inflate the bill.
- Winner for non-technical teams: Make. Agents sit on the visual canvas with every decision reviewable; the free tier includes 1,000 credits per month (Make pricing overview).
- Make's catch: one credit equals one module execution, so routers and iterators multiply cost (Make credit metering).
- Zapier Agents is an add-on: Agents Pro runs about $33.33/month for 1,500 activities (Zapier Agents pricing summary), separate from the $19.99/month Professional plan (Zapier pricing).
- Last verified: 22 September 2026.
Which platform should you use for an agentic AI workflow?
Pick by constraint, not by feature list.
Choose n8n if you write code, need data residency, or expect your agent to loop, branch and retry. Self-hosting via Docker or Kubernetes is supported, JavaScript and Python steps are first-class, and an MCP client node ships alongside an MCP server trigger, so your workflow can consume external tools and expose itself as one (n8n AI agents).
Choose Make if the people maintaining the automation are not engineers. Make's next-generation AI Agents, announced on 11 February 2026, put the agent on the same visual canvas as your scenarios, keep each decision visible and reviewable, accept and produce PDFs, images and CSVs, and orchestrate more than 3,000 apps (Make announcement).
Choose Zapier Agents if breadth of integrations is the binding constraint. Zapier lists 9,000+ apps (Zapier pricing), which is the largest catalogue of the three, and that alone can justify the platform for a business drowning in niche SaaS.
How does pricing actually differ between n8n, Make and Zapier?
The billing unit matters more than the headline price, because agentic workflows loop.
| Platform | Billing unit | Entry paid tier | Self-host |
|---|---|---|---|
| n8n | Full workflow execution, regardless of complexity (source) | Starter EUR 20/mo annual; Pro EUR 50/mo annual; Business EUR 667/mo annual (source) | Yes, Community Edition free with unlimited executions (source) |
| Make | Credit = one module execution (source) | Core about $12/mo and Pro about $21/mo annually, 10,000 credits (source) | No (source) |
| Zapier | Tasks for Zaps; activities for Agents (source) | Professional from $19.99/mo annual; Team $69/mo annual (source) | Not published |
One concrete case: an agent that reads an inbound email, calls a model, branches three ways, writes to a database and posts to Slack is a single execution on n8n whether it takes 6 nodes or 26. On Make, each module is metered, and AI modules cost one credit plus your LLM provider's charges (Make AI module costs). Add-on credit packs bought since November 2025 carry roughly a 25% markup over in-plan rates (source).
On self-hosted n8n, the recurring cost is infrastructure rather than licence: a hobby-scale VPS runs roughly $3 to $7 per month (n8n cost breakdown). The tradeoff is real: you own upgrades, backups, queue mode and monitoring.
What does n8n give builders that hosted-only platforms cannot?
Four things, in the order they usually decide a migration.
- Data stays where you put it. Self-hosting on Docker or Kubernetes removes the vendor from the data path entirely; n8n's managed cloud is SOC 2 compliant if you would rather not run it (n8n AI agents).
- Code when the node runs out. JavaScript and Python steps sit next to 500+ integration nodes, so an unsupported API is an afternoon, not a blocker (n8n AI agents).
- Human-in-the-loop as a first-class step. Approval gates are built in, which is what makes an agent shippable in finance, HR or anything customer-facing. n8n's own guidance on agentic design patterns leans on validation, error recovery and governance gates rather than trusting a single model call (n8n design patterns).
- MCP both ways. The MCP server trigger lets other agents call your workflow as a tool, which is how multi-agent systems stop being brittle glue code. We have covered the practical wiring in connecting Claude to n8n over MCP.
n8n also reports over 200,000 GitHub stars, which translates into a deep well of shared workflow templates (n8n AI agents).
Where does Make genuinely beat n8n?
Transparency for non-engineers, and time-to-first-agent.
Make's agents are assembled in the canvas you already use for scenarios, with an in-canvas chat to refine behaviour and a Library of Agents to start from (Make announcement). When an operations lead must explain to a compliance reviewer why the agent refunded a customer, a visual trace beats reading JSON. See our comparison of Make and LangGraph as an agentic operating system for the code-first contrast.
When is Zapier Agents the right call?
When the integration you need exists only on Zapier, or when Zapier is already your system of record for automation.
Be clear-eyed about the cost structure: Agents Free covers 400 activities per month and Agents Pro is $400/year for 1,500 (Zapier Agents pricing summary), billed in activities rather than the tasks your Zaps consume (Zapier help). For a team already paying Team's $69/month (Zapier pricing), agents are an additional line item. Defensible if the alternative is rebuilding hundreds of working Zaps; hard to defend as a greenfield choice for a technical team. Our 2026 agentic AI roadmap walks through where Zapier Agents fits against code frameworks.
What should you check before committing?
Run a two-week pilot on the ugliest workflow you have, not the cleanest. Count executions and modules separately, project a month at real volume, force a mid-run failure to see the retry semantics, and test the approval path with a real reviewer.
One note on how we choose topics: "We priced 656 keywords in the AI and developer-tooling space using DataForSEO volume and difficulty data. Only 72 (11.0%) cleared a winnable bar of 150-6,000 monthly searches, difficulty 20 or below, a genuine technical term, and at least three words" (our methodology, n=656, measured 2026-09-21). Platform choice is the same exercise: most options look viable until you apply your actual constraints.
Still deciding whether agents belong in your stack? Start with agentic AI versus traditional automation and RAG versus agentic AI.
FAQ
Q: Is n8n really free?
A: The Community Edition is free and self-hosted with unlimited executions; you pay only for the server, roughly $3 to $7 per month at hobby scale (n8n cost breakdown). Managed cloud starts at EUR 20 per month billed annually (n8n pricing).
Q: Why does per-execution pricing matter for an agentic AI workflow?
A: Agents loop, branch and retry, so step count is unpredictable. n8n charges for full executions, paying "not for each step" (n8n pricing), while Make meters one credit per module (source), so the same agent costs more as it gets smarter.
Q: Can I self-host Make or Zapier?
A: No. Make is cloud-only (source) and Zapier does not publish a self-hosting option on its pricing page (Zapier pricing).
Q: How many integrations does each platform offer?
A: Zapier lists 9,000+ apps (Zapier pricing), Make orchestrates more than 3,000 (Make announcement), and n8n ships 500+ integration nodes plus code steps and MCP for anything missing (n8n AI agents).
Q: Do I still pay for the LLM separately?
A: Usually yes. Make's AI modules cost one credit plus your own model API charges (source). n8n cloud includes AI Assistant credits, 2,300 per month on Starter and up to 13,700 on Pro (n8n pricing), separate from the model provider you connect for reasoning.
Q: What is the fastest way to validate the choice?
A: Rebuild one production workflow on two platforms and run both for two weeks at real volume. Metering differences only become visible under load.
Last verified: 22 September 2026. Pricing on all three platforms changes without notice; confirm current figures on the vendors' own pricing pages before committing.
Corrections log: No corrections to date. Spotted an error? Tell us and we will fix it in place with a dated note.
Edited and published by Sham, editor of The Tech Archive. Researched and drafted with AI assistance; every claim fact-checked against primary vendor documentation.
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