n8n vs Make vs Zapier: Which Automation Tool for Your Swiss SME?

You’ve decided to automate. Maybe because your sales reps still copy-paste LinkedIn leads into HubSpot. Or your accountant re-keys supplier invoices into Bexio. Or your team spends three hours a week nudging clients on Slack.

Three names keep coming up: Zapier, Make (formerly Integromat), and n8n. All three claim to solve the same problem. In practice, they target very different profiles — and the wrong choice can get expensive, especially for a Swiss SME dealing with FADP compliance.

This comparison is based on three years of SME deployments across French- and German-speaking Switzerland. No marketing fluff, just what works and what doesn’t.

Why this comparison?

The most common question we get: “We want to automate, which one should we pick?”. Honest answer: “it depends” — but six concrete criteria narrow it down.

The 6 evaluation criteria

1. Pricing: real monthly cost at your actual volume (not just the entry plan).
2. Self-hosting: can you host it yourself / in Switzerland?
3. Integrations: number and quality of native connectors.
4. Native AI integrations: OpenAI, Claude, local LLMs, RAG, vector DBs.
5. Learning curve: time to productivity.
6. French / Swiss support: docs, community, local partners.

n8n: the tool for technical teams

n8n is an open-source automation platform founded in Berlin in 2019. Hybrid model: paid Cloud, plus free self-hosting (Community Edition) or paid Enterprise.

Strengths

• Free self-hosting: an instance on a 10 CHF/month VPS runs without issues.
• Advanced AI integrations: OpenAI, Anthropic, Hugging Face, LangChain, vector DBs (Pinecone, Qdrant, Weaviate), native RAG.
• Code node: JavaScript or Python for anything not covered.
• Complex logic: loops, conditional branches, sub-workflows.
• Fair Code licence: commercial use is unrestricted as long as you don’t resell n8n as-is.
• Active community: forum, templates, custom nodes.

Weaknesses

• Learning curve: you need to understand webhooks, JSON, HTTP requests. Not for everyone.
• Self-hosting means responsibility: backups, updates, security are on you.
• Fewer “plug & play” integrations than Zapier (roughly 400 vs 6000).
• UI rougher than Make.

When to choose it

• You have an IT team or a technical partner.
• Strong FADP constraints (sensitive data, health, finance).
• Complex workflows with business logic.
• Heavy AI usage (RAG, LLM pipelines).

Make: the balanced visual choice

Make (formerly Integromat, acquired by Celonis) is a Czech SaaS. Its visual “bubble” interface is one of the most polished on the market.

Strengths

• Exceptional visual interface: you see data flow live.
• ~1800 integrations including most European tools.
• Per-operation pricing: more granular than Zapier.
• Advanced modules: iterators, aggregators, routers — great for data pipelines.
• Excellent English docs (partial in other languages).

Weaknesses

• No self-hosting: everything runs on their servers (mostly EU, not Switzerland).
• Limited AI integrations: they exist but less rich than n8n.
• Medium learning curve: simpler than n8n, but iterator/aggregator concepts take practice.
• Price scales quickly: at high volume, Make can exceed n8n Cloud.

When to choose it

• No IT team, but one motivated power user.
• Data-heavy workflows (list processing, JSON transformation).
• Moderate FADP constraints (EU hosting acceptable with DPA).
• Medium budget, predictable volume.

Zapier: simplicity for everyone

Zapier is the pioneer (founded 2011, US). Its pitch: automation accessible to any user.

Strengths

• ~6000 integrations: overwhelming coverage.
• Maximum simplicity: “when X happens, do Y”. Grasp it in 5 minutes.
• Reliability: mature infrastructure, rare outages.
• Fast, professional customer support.
• Native AI: Zapier AI, Copilot, natural-language zap generation.

Weaknesses

• High price: ~30 CHF/month base, explodes at volume (500 CHF+ for 50k tasks/month).
• Limited logic: basic branches, no real complex loops.
• No self-hosting: US servers only.
• FADP compliance: DPA available but systematic US transfer.
• Native AI lagging behind n8n.

When to choose it

• Small non-technical team, low volume.
• Low-sensitivity data (no identifiable client data).
• Need for rare integrations not covered elsewhere.
• Budget not a constraint.

Summary table

Criterion	n8n	Make	Zapier
Entry price	Free (self-hosted) / €20/mo Cloud	€9/mo	$20/mo
Volume price	Server cost ~10-30 CHF/mo	€50-300/mo	$300-1000/mo
Self-hosting	Yes (free)	No	No
Swiss hosting	Yes (Infomaniak VPS)	No (EU)	No (US)
Integrations	~400	~1800	~6000
AI integrations	Excellent	Good	Average
Learning curve	Steep	Medium	Low
FADP compliance	Excellent (self-host)	Good (DPA)	Average (US)
Swiss local support	Yes (partners)	Rare	Rare
Custom code	JS + Python	JS only	Limited
Licence	Fair Code (open)	Proprietary SaaS	Proprietary SaaS

Decision matrix by SME profile

Micro-business (1-10 people), low-sensitivity data

Recommendation: Zapier or Make. You want simple, working immediately. Start with free Zapier, move to Make when price becomes painful.

SME 10-50 people, mixed team

Recommendation: Make. Best simplicity/power ratio. One motivated person can build 80% of workflows without an outside partner.

SME >50 people or strong CH constraint

Recommendation: self-hosted n8n. Maximum compliance, marginal cost at scale, leading AI integration. Needs a technical partner for setup, then in-house takes over.

SME with advanced AI projects (RAG, agents, LLM pipelines)

Recommendation: self-hosted n8n, no contest. Make and Zapier bolt AI on, n8n embeds it natively via LangChain.

Our default recommendation

For most Swiss SMEs, we recommend self-hosted n8n for three reasons:

1. Native FADP compliance: your data never leaves Switzerland if you host in Geneva or Zurich.
2. Unbeatable long-term cost: 15 CHF/month VPS vs 500 CHF/month Zapier at equivalent volume.
3. AI-ready: when you want to plug in an LLM, RAG, a vector DB, it’s already in place.

Initial setup takes 1-2 days with a partner. Afterwards, your team runs it.

3 concrete workflow examples

Workflow 1: LinkedIn lead → HubSpot + Slack

Trigger: new qualified lead on LinkedIn Sales Navigator.

LinkedIn Webhook
  ↓
Enrichment (Clearbit / Apollo)
  ↓
HubSpot contact creation
  ↓
Slack notification to sales team
  ↓
CRM follow-up task

Time saved: 5-10 min per lead. At 20 leads/week, that’s ~2 h/week back per rep.

Workflow 2: PDF invoice → Bexio

Trigger: incoming email with supplier invoice PDF.

Gmail Trigger (label "Invoices")
  ↓
OCR extraction (Mistral OCR / Azure Document AI)
  ↓
LLM parsing (structured extraction)
  ↓
Validation (amounts, VAT, account #)
  ↓
Bexio journal entry creation
  ↓
PDF archive to Infomaniak kDrive

Gain: ~3 min per invoice, with human validation on edge cases. For 200 invoices/month, that’s 10 hours saved.

Workflow 3: Customer review → AI reply + CRM

Trigger: new Trustpilot or Google Reviews entry.

Trustpilot Webhook
  ↓
Sentiment analysis (LLM)
  ↓
If negative → urgent Slack + manager escalation
If positive → AI-generated reply
  ↓
Draft pending validation
  ↓
Published after 1-click human approval

This last workflow makes an important point: AI proposes, humans validate. That’s the right architecture for most sensitive use cases (client replies, public publishing).

Mistakes to avoid

• Choosing the tool before the use case: start with a well-defined workflow, not the tool.
• Automating a broken process: if the manual process is broken, automation breaks it faster.
• Forgetting monitoring: a silently failing workflow is worse than no workflow.
• Underestimating upkeep: “no-code” still needs maintenance. Budget 10% of setup time annually.

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No tool is objectively better. Your choice depends on your team, compliance constraints, and AI ambitions.

Still undecided? We run a 1-hour workshop to scope your first workflow and pick the right tool. Reach out for an automation maturity audit.