n8n vs Zapier vs Make: The Honest 2026 Enterprise Comparison

I'll tell you upfront where this article is headed. If you run a company with 50 to 250 employees, process sensitive business data, and aren't willing to hand your entire process automation over to a US SaaS vendor, this comparison ends at n8n. Not because n8n is flawless. It's because the alternatives are simply unacceptable in a few dimensions that matter.

That would make for a very short article, though. So we'll walk through all three platforms: architecture, cost structure, GDPR compliance, the integration ecosystem, real-world scalability, and the thing vendors never put on their pricing pages, which is vendor lock-in. Real configuration examples for each one, not marketing slides.

The 2026 Starting Point: Why This Comparison Needs to Be Made Again

Automation tools stopped being optional a while ago. A typical mid-market business today syncs CRM data with ERP systems, triggers invoicing workflows from incoming emails, routes support tickets, and chews through webhook events from payment providers, none of which needs a developer in the loop. Gartner puts a number on it: by the end of 2026, more than 69% of routine tasks in mid-size companies will be handled by low-code automation (Gartner, Magic Quadrant for Integration Platform as a Service, 2025).

Three vendors dominate the segment. Zapier (founded 2011, currently more than 7,000 native integrations), Make (formerly Integromat, rebranded in 2022, headquartered in Prague), and n8n (open source, AGPL-3.0, founded 2019 in Berlin).

What's changed between 2022 and 2026 is bigger than it looks. AI-powered workflows, native LLM nodes, MCP integration, complex branching logic: none of these are niche features anymore, and that shifts the balance of the comparison considerably.

Quick Overview: The Three Platforms at a Glance

n8n: Self-Hosted Automation with Enterprise DNA

Architecture

n8n is a Node.js application. You can run it as a Docker container, a Kubernetes pod, or a managed cloud instance, whichever fits your stack. Workflows are stored as JSON documents and are fully versionable, and that's the decisive difference from every competitor here. Your automation logic lives in your infrastructure, in your Git repositories, under your control.

The execution engine is event-driven. Every node processes an array of items (INodeExecutionData[]), which means bulk processing without loop constructs. The Python node and the JavaScript node run in the same process context, with no sandbox overhead.

Practical example: automating incoming invoice processing

Here's a workflow you see constantly in mid-market shops. Incoming emails with PDF attachments are triggered via IMAP, the PDF goes to an OCR endpoint, invoice data is extracted and written straight into DATEV or SAP. In n8n, the Python Code node looks like this:

# n8n Code Node (Python) - Normalize invoice data
import re
from datetime import datetime

items = _input.all()
results = []

for item in items:
    raw = item.json.get("ocr_text", "")

    # Extract amount (DE format: 1.234,56 €)
    amount_match = re.search(r"(\d{1,3}(?:\.\d{3})*(?:,\d{2}))\s*€", raw)
    amount_raw = amount_match.group(1) if amount_match else None

    # Convert to float
    amount_float = None
    if amount_raw:
        amount_float = float(amount_raw.replace(".", "").replace(",", "."))

    # Invoice date
    date_match = re.search(r"(\d{2})\.(\d{2})\.(\d{4})", raw)
    invoice_date = None
    if date_match:
        d, m, y = date_match.groups()
        invoice_date = datetime(int(y), int(m), int(d)).isoformat()

    results.append({
        "json": {
            "amount": amount_float,
            "invoice_date": invoice_date,
            "raw_vendor": re.search(r"Lieferant:\s*(.+)", raw, re.I).group(1).strip()
                          if re.search(r"Lieferant:", raw, re.I) else None,
            "needs_review": amount_float is None or invoice_date is None
        }
    })

return results

This code runs on your infrastructure. It never leaves your network. No US company processes your supplier data.

Docker Deployment: Production Configuration

A clean docker-compose.yml for n8n in enterprise use. Rootless-compatible, PostgreSQL as the backend, Redis for queue mode:

# docker-compose.yml - n8n Enterprise Self-Hosted
version: "3.8"

services:
  n8n:
    image: n8nio/n8n:1.47.0
    restart: unless-stopped
    user: "1000:1000"
    ports:
      - "127.0.0.1:5678:5678"
    environment:
      - DB_TYPE=postgresdb
      - DB_POSTGRESDB_HOST=postgres
      - DB_POSTGRESDB_DATABASE=n8n
      - DB_POSTGRESDB_USER=n8n_user
      - DB_POSTGRESDB_PASSWORD_FILE=/run/secrets/postgres_password
      - N8N_ENCRYPTION_KEY_FILE=/run/secrets/n8n_encryption_key
      - N8N_PROTOCOL=https
      - N8N_HOST=automation.your-company.com
      - EXECUTIONS_MODE=queue
      - QUEUE_BULL_REDIS_HOST=redis
      - N8N_LOG_LEVEL=info
      - N8N_METRICS=true
    volumes:
      - n8n_data:/home/node/.n8n
    secrets:
      - postgres_password
      - n8n_encryption_key
    depends_on:
      postgres:
        condition: service_healthy
      redis:
        condition: service_healthy

  postgres:
    image: postgres:16-alpine
    restart: unless-stopped
    user: "1000:1000"
    environment:
      POSTGRES_DB: n8n
      POSTGRES_USER: n8n_user
      POSTGRES_PASSWORD_FILE: /run/secrets/postgres_password
    volumes:
      - postgres_data:/var/lib/postgresql/data
    secrets:
      - postgres_password
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U n8n_user -d n8n"]
      interval: 10s
      timeout: 5s
      retries: 5

  redis:
    image: redis:7-alpine
    restart: unless-stopped
    user: "1000:1000"
    command: redis-server --requirepass "${REDIS_PASSWORD}"
    volumes:
      - redis_data:/data
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 10s
      timeout: 5s
      retries: 5

volumes:
  n8n_data:
  postgres_data:
  redis_data:

secrets:
  postgres_password:
    file: ./secrets/postgres_password.txt
  n8n_encryption_key:
    file: ./secrets/n8n_encryption_key.txt

Note on secrets: Never use plain-text passwords in docker-compose.yml. Docker Secrets or a vault backend (HashiCorp Vault, Bitwarden Secrets Manager) are mandatory.

Cost Structure: n8n

The Community Edition is free. The Enterprise Edition (from $500/month, annual contract) unlocks SAML/SSO, audit logs, advanced user permissions, and SLA support. For most mid-market companies with a dedicated IT function, the Community Edition plus self-managed hosting is plenty.

Here's a real cost calculation for 50,000 workflow executions per month:

• n8n self-hosted (Hetzner CX31, 8 GB RAM): ~€12/month server + €0 license = €12/month
• n8n Cloud (Starter): $20/month for 2,500 executions, so it doesn't scale here
• Zapier Professional: approx. $73/month for 50,000 tasks
• Make Teams: approx. $29/month for 40,000 operations (rounded packages, overage gets expensive)

Even counting operational overhead, the break-even between n8n self-hosted and Zapier lands at just a few hundred executions per month.

Zapier: Market Leader with Structural Enterprise Weaknesses

What Zapier Does Well

Zapier is to the no-code world what Salesforce is to CRM: ubiquitous, deeply embedded in the daily work of business teams, with an ecosystem that's genuinely hard to match. Over 7,000 native app integrations, ready-made Zap templates for almost any use case, and an editor that non-technical staff can operate without a single training session.

For small teams with limited IT capacity, that's a real argument, and I won't pretend otherwise.

Where Zapier Falls Short for Mid-Market

Start with data sovereignty, because it's the one that ends most enterprise conversations. All workflow data flows through Zapier's US servers, and Zapier Inc. is subject to the US Cloud Act. If US authorities request access to data on Zapier's servers, Zapier cannot refuse, and it makes no difference whether your data is GDPR-relevant or not. For any company that processes personal data, financial data, or trade secrets in its workflows, that's a structural problem, and no DPA (Data Processing Agreement) papers over it.

Then there's pricing escalation. Zapier bills by "tasks," and every action in a Zap costs one task. Multi-step workflows with filter nodes, formatter steps, and lookup tables multiply task consumption fast. A workflow that processes 1,000 records per day across 8 steps burns 8,000 tasks daily, which is 240,000 tasks per month. On the Zapier Professional plan that's roughly $400/month. Nobody warns you in advance.

Code flexibility in the critical nodes is thin, too. The Zapier Code step supports only JavaScript (no Python, no shell), and access to external libraries is severely limited. The kind of complex data transformations that are simply standard practice in finance and ERP environments end up needing workarounds or external API calls.

And finally the proprietary format. Zapier workflows exist only as proprietary configurations in Zapier's database, with no export to an open format, no Git versioning, no review process. If Zapier suspends your account, doubles its prices, or shuts the service down, you walk away with nothing in hand.

That last point isn't hypothetical. Zapier raised its prices several times between 2021 and 2024 and gradually phased out grandfathering for existing plans.

Make: The Technical Middle Ground with European Roots

Positioning and Architecture

Make (formerly Integromat) was founded in 2012 in Prague and is now part of the Celonis group. Its visual interface is more powerful than Zapier's. Workflows are represented as data-flow graphs, with explicit routing, iterator modules, aggregators, and an error-handling system that comfortably outperforms Zapier's.

Make also offers an EU data region, which resolves the most pressing GDPR concern, at least as long as Celonis stays a European company and no US corporation buys it. That risk is real and worth keeping in view.

Strengths Over Zapier

A few things genuinely set Make apart. Billing is scenario-based: Make charges by "operations," and filter modules don't consume operations the way they do in Zapier, which makes it much cheaper for complex workflows with heavy conditional logic. The workflow logic is more sophisticated, with router, error handler, iterator, and aggregator as native concepts that handle demanding processes well. And the API integration is better, with an HTTP module that gives you full header control, OAuth2 flows, and webhook response manipulation.

Weaknesses in Enterprise Use

Make is not a self-hosted product. There's no officially supported on-premises version, and while the EU region offering reduces risk, it doesn't eliminate it.

The JavaScript code module is severely restricted, the same story as Zapier, and Python execution isn't supported at all.

There's also no native Git backend for workflow versioning. You can export scenarios as blueprints (JSON), but that isn't a genuine versioning workflow.

Running the same use case through Make (50,000 executions/month) lands at Make Teams: $29/month for 40,000 operations, plus overage of roughly $9 for 10,000 additional operations, so $38/month. Not bad at all. The catch is that the data sits on someone else's server.

GDPR Compliance: The Comparison Vendors Don't Want to Have

This is where a lot of IT leaders have a blind spot. "We signed a DPA" isn't a sufficient answer. A Data Processing Agreement shifts responsibility around, but it does nothing to prevent data access by US authorities.

For companies in regulated industries (financial services, healthcare, public sector), n8n self-hosted is the only one of the three that passes a GDPR audit without structural gaps.

Integration Ecosystem: Numbers and Reality

Zapier leads with over 7,000 native integrations, and that number sounds impressive until you look at how it plays out. A typical mid-market business leans on maybe 20 to 30 integrations, no more. And the critical systems (SAP, DATEV, Lexware, specific ERP systems, industry software) aren't fully natively integrated by any of the three vendors anyway.

So the integration count matters far less than the marketing implies. What actually matters is HTTP request flexibility. Can I connect arbitrary REST APIs? Can I transform SOAP web services? Can I implement custom auth flows, whether OAuth2, API key, or certificate-based?

On that axis, n8n gives you full HTTP flexibility, custom nodes via npm packages, and a community node repository with over 400 community nodes (as of May 2026). Zapier can do HTTP requests through "Webhooks by Zapier," but the authentication options are limited and custom app integration requires a Zapier Platform account. Make sits in between, with a strong HTTP module, good OAuth2 support, and custom apps via the Make developer portal.

Take SAP integration as the concrete case. All three platforms lack a native, production-ready SAP integration, so in every case the answer is an HTTP module against SAP's REST APIs or a middleware layer. This is where n8n pulls ahead, because the Python nodes let you run production-grade code directly instead of bending the tool around the gap.

AI Workflows in 2026: Who Is Really Ready?

This is the topic that has defined the market in 2025 and 2026. All three vendors now ship AI capabilities, but the quality differs a lot.

n8n is technically out front. It has native LLM nodes for OpenAI, Anthropic Claude, Google Gemini, and Ollama (local), plus LangChain integration. There's an MCP client node (since n8n 1.35) for Model Context Protocol, an Agent node with tool calling, and workflow-based RAG using Pinecone, Qdrant, and Weaviate nodes.

One example shows how far ahead it is. Picture a workflow that reads incoming support emails, classifies them through a local Ollama model (no data exchange with external APIs at all), works out the priority, and creates tickets in your ticketing system automatically. Fully on-premises, without a single line of customer data leaving your network.

{
  "nodes": [
    {
      "name": "Email Trigger",
      "type": "n8n-nodes-base.emailReadImap",
      "parameters": {
        "mailbox": "support@your-company.com",
        "format": "resolved"
      }
    },
    {
      "name": "Classification via Ollama",
      "type": "@n8n/n8n-nodes-langchain.lmChatOllama",
      "parameters": {
        "model": "llama3.2:3b",
        "baseUrl": "http://ollama.internal:11434",
        "prompt": "Classify this support request as: [critical, high, medium, low]. Reply with the priority level only.\n\nRequest: {{ $json.text }}"
      }
    }
  ]
}

Zapier's offering, "Zapier AI," does natural-language Zap creation and simple LLM calls via OpenAI. That's fine for simple use cases, but it won't carry agentic workflows with tool use or local LLM operation.

Make's "Make AI" lands in roughly the same place as Zapier: OpenAI integration and text manipulation, with no local LLM support and no MCP.

Performance and Scalability: Benchmarks

These figures are based on internal tests conducted as part of client projects at NextGen IT (test environment: Hetzner CX41, 16 GB RAM, 4 vCPU, Debian 12, n8n 1.47.0 in queue mode with 4 workers).

SaaS vendors throttle on purpose. Their whole business model rests on volume packages. n8n on your own hardware simply has no artificial ceiling like that.

Vendor Lock-in: The Hidden Costs

This is the topic nobody discusses until it's too late.

With Zapier, your entire automation logic lives as proprietary configuration on Zapier's servers. If, three years in, you've built 200 Zaps and Zapier doubles its prices, your options are to pay or to rebuild everything from scratch. There's no export and no portability.

Several other forms of lock-in stack on top of that. There's cognitive dependency, where your team learns Zapier concepts (Zap, trigger, action) rather than generic automation concepts, and that knowledge doesn't transfer anywhere. There's integration lock-in, because some Zapier integrations rely on Zapier-specific API access you can't reproduce outside the platform. And there's process lock-in: when workflows aren't documented or versioned, the company quietly loses institutional knowledge about its own processes, so a vendor switch or a key person leaving takes those processes with it.

n8n workflows, by contrast, are JSON documents. They live in your Git repository, every change is traceable. If n8n vanished tomorrow (the company is profitable and growing fast, but hypothetically), you export your JSON and reimplement the logic somewhere else. The processes are documented and the data is yours.

Migration Paths: What a Switch Actually Costs

From Zapier to n8n

For simple trigger-action Zaps, migration is manageable. For complex multi-step workflows that lean on Zapier-specific built-in apps (Formatter, Filter, Delay), budget 2 to 4 hours per workflow for documentation and re-implementation.

One thing matters more than any of this: export all your Zap configurations now, as screenshots or written documentation, because Zapier offers no machine-readable export. Do it today, regardless of whether you've decided to migrate.

From Make to n8n

Make offers blueprint exports as JSON. The format is proprietary, but it's structured enough to allow semi-automated conversion, and community tools for Make-to-n8n migration do exist (GitHub: make-to-n8n-converter, plus various community contributions). Even so, expect to hand-adjust every workflow.

Migration Checklist

1. Document all active workflows (trigger, steps, business logic, error behavior).
2. Inventory dependent credentials and API keys.
3. Set priority, with critical workflows first.
4. Plan a 2-to-4-week parallel operation period (dual-run).
5. Set up monitoring on both systems to catch divergences.
6. Decommission the old system gradually, once functionality is verified.

Decision Matrix: Which Tool for Which Context?

Legend: ✅ = suitable, ⚠️ = conditionally suitable, ❌ = not suitable

The Honest Recommendation

For German-speaking mid-market companies with an internal IT function (even a team of one), with data processing workflows that fall under GDPR, and with a horizon beyond 24 months, the answer is n8n self-hosted.

The upfront costs (server setup, initial configuration, staff onboarding) usually amortize within 6 to 12 months against Zapier. After that, the cost structure scales linearly with your infrastructure rather than exponentially with your automation volume, and that difference compounds every year.

Zapier still makes sense in a narrow case: no internal IT, exclusively non-sensitive data, and speed of entry as the top priority. If that's you, fine, but plan the migration path explicitly for the day automation becomes business-critical, because that day comes.

Make is a viable compromise if you can live with EU data residency, would rather not self-host, and need more complex workflows than Zapier offers. Just keep the M&A risk on your radar.

What You Can Do Tomorrow

1. Inventory your existing Zapier/Make workflows today, before anything gets lost.
2. Spin up an n8n test instance on a Hetzner server. The docker-compose.yml above is production-ready in about 30 minutes.
3. Migrate one non-critical workflow as a proof of concept.
4. Define your data sensitivity tiers, and be honest about what must never leave the corporate perimeter.

Automation isn't a tooling question. It's an architecture question. And architectures that run on servers you don't control are fragile architectures.

Further Resources

• n8n documentation: docs.n8n.io (community nodes, self-hosting guide, queue mode configuration)
• n8n GitHub: github.com/n8n-io/n8n (issues, roadmap, community node directory)
• Gartner, Magic Quadrant for Integration Platform as a Service, Worldwide, October 2025
• BSI IT-Grundschutz Compendium 2025, Module CON.2 (Data Protection), Federal Office for Information Security (BSI)
• European Data Protection Board, Guidelines 01/2021 on the use of cloud services by the public sector (applicable as a reference for private companies)

Niklas is a Backend Engineer at NextGen IT and manages the automation infrastructure for mid-market clients across DACH. This article reflects hands-on experience from production deployments.