n8n vs Make vs Zapier: the honest comparison

The three dominant automation platforms embody three different philosophies. Zapier optimizes for zero learning curve, Make for visual power at low cost, n8n for control and ownership. None is "best" — but for a given team, one of them is usually clearly right. Here's the comparison we'd want to read, with the caveat that prices are approximate and worth re-checking at the source.

The pricing models, decoded

This is where the philosophies show most clearly:

	Zapier	Make	n8n
Billing unit	Task (each action step)	Operation (each module run)	Execution (one workflow run, any size)
Entry paid tier	~$20–30/mo	~$9–11/mo	~€20–24/mo (Cloud)
Self-hosted option	No	No	Yes, free (sustainable-use license)
10k-step workflow, 1k runs/mo	Very expensive	Moderate	One plan — or ~€10 VPS

The structural point: Zapier and Make charge you per unit of work done inside the workflow; n8n charges per workflow run (Cloud) or flat (self-hosted). Complex multi-step automations — exactly what AI pipelines are — get disproportionately expensive on per-task pricing. A 15-node RAG query is 1 n8n execution but ~15 Make operations.

Learning curve and building experience

Zapier remains the gentlest on-ramp: linear trigger→actions, superb app catalog (7,000+ integrations), and their AI assistant scaffolds simple zaps from a sentence. The ceiling arrives fast: branching, loops and data transformation feel bolted on.

Make has the most pleasant visual canvas of the three, first-class routers and iterators, and handles medium complexity gracefully. Its data-mapping UI is genuinely excellent.

n8n asks more of you upfront — expressions are JavaScript, data flows as JSON items, and you'll meet the occasional raw error object. In exchange, nothing is off-limits: a Code node, custom HTTP calls with any auth scheme, sub-workflows, and version-controllable JSON exports. Developers and technical operators tend to feel at home within a day.

AI capabilities, the 2026 differentiator

All three bolted AI onto their platforms; the depth differs.

• Zapier: AI steps and an agent product, convenient for simple "summarize and send" patterns. Limited control over prompts, models and structure.
• Make: solid AI app modules (OpenAI, Anthropic…) treated like any other module. Fine for single calls; assembling RAG or agents is manual.
• n8n: a real LangChain layer as native nodes — chat models as swappable sub-nodes, vector stores (Supabase, Pinecone, Qdrant), retrievers, agents with tools and memory, structured output parsers. It's the only one of the three where a complete RAG pipeline or a tool-calling agent is a first-class, visual citizen.

If your automation roadmap is AI-heavy, this section should probably decide the whole comparison.

Data control

Zapier and Make are US-based SaaS (with EU data-residency options on higher tiers). n8n is the only one you can run entirely on your own infrastructure, which matters for GDPR-sensitive workloads and for anyone whose workflows touch customer PII at scale. It also means your automations survive vendor pricing changes — the JSON files are yours.

Migrating from Make or Zapier: the method

If the verdict leans towards n8n, you can migrate without breaking production by following four rules that apply to Make and Zapier alike:

1. Map everything first: every active scenario or Zap (trigger, steps, volume, criticality). This step cannot be skipped and it prevents discovering a forgotten automation halfway through.
2. Migrate one at a time, starting with the least critical, run the n8n version in parallel for a few days to a few weeks, compare results, then switch the old one off. A big-bang migration lets mapping errors slip through.
3. Rebuild error handling: Zapier silently handles some retries, n8n does not. Without an Error Workflow and Retry On Fail on the relevant nodes, an automation that used to fail quietly can stop dead.
4. Recreate credentials: neither Make nor Zapier exports keys or tokens. OAuth connections (Google, Slack…) must be redone from n8n.

As for effort, a simple three- or four-module scenario is rebuilt in under an hour; one with routers, an iterator/aggregator and a Data Store can take half a day. Make Data Stores are exported as CSV then imported into Postgres, Supabase or an n8n Data Table. And operation counting disappears: an iterator unpacking 50 rows followed by 3 modules burns more than 150 operations on Make, whereas self-hosted n8n charges nothing per execution and n8n Cloud bills per whole workflow execution.

Looking only at tools you can run yourself or that target developers? We compare n8n with Activepieces (no-code, MIT core), Pipedream (code-first, cloud-only, now owned by Workday) and Node-RED (IoT and devices).

The honest recommendations

• Choose Zapier if non-technical colleagues must build and own automations, volumes are low, and integration breadth matters more than cost.
• Choose Make if you want strong visual automation at the lowest SaaS price and your needs stop short of custom code and advanced AI orchestration.
• Choose n8n if you have any technical comfort, your workflows are AI-centric or high-volume, or data ownership is non-negotiable. Budget a real learning curve — or shortcut it with ready-made workflows: that's the exact gap FlowKit packs fill, shipping production n8n pipelines you import instead of building.

Migrating between platforms is rarely worth it for working automations; picking right at the start is. For most readers of this blog — technical freelancers and SMBs betting on AI automation — n8n is the platform we'd start on today.