n8n vs Make (Integromat): Which Automation Platform Should You Choose?

Make charges you for every module that touches your data. A workflow with 10 modules running 1,000 times costs 10,000 operations. That same workflow in n8n? Just 1,000 executions. Same automation, 10x different billing.

Both Make (formerly Integromat) and n8n are powerful Zapier alternatives with visual builders and serious automation capabilities. But they count usage differently, handle AI differently, and serve different types of users.

The Operation Counting Problem

Make’s operation-based pricing seems reasonable until you build real workflows. Each module that processes data counts as one operation. Routers, iterators, aggregators, filters that pass data through. They all count.

A simple CRM sync with 5 modules running hourly consumes 3,600 operations monthly. Add error handling and conditional logic? You’re looking at 8-10 modules easily. The operations add up faster than most users expect.

The math that changes everything: A 10-module workflow running 1,000 times monthly costs 10,000 operations in Make but only 1,000 executions in n8n. Self-hosted n8n? Unlimited, regardless of complexity.

Two Visual Builders, Different Philosophies

Make targets visual thinkers who want polished, drag-and-drop automation. The interface is beautiful. The learning curve is gentle. Marketing teams and operations managers can build sophisticated workflows without touching code.

n8n targets technical users who need flexibility and control. The interface is functional rather than flashy. Code nodes let you do anything JavaScript or Python can do. Self-hosting gives you complete ownership of your automation infrastructure.

Neither approach is wrong. They serve different users with different priorities.

What You’ll Learn

• How operation-based vs execution-based pricing actually works
• The true cost comparison at different scales
• AI and agent capabilities on both platforms
• When Make’s visual approach beats n8n’s flexibility
• Self-hosting advantages and requirements
• Testing and debugging differences
• A decision framework for your specific situation

Quick Comparison: n8n vs Make at a Glance

Factor	n8n	Make
Pricing Model	Per execution (or free self-hosted)	Per operation (each module counts)
Self-Hosting	Yes (free, unlimited)	No
Open Source	Yes (fair-code license)	No
Visual Builder	Node-based canvas	Module-based scenarios
Code Support	Full JavaScript/Python	Limited functions only
AI Capabilities	Native agents, LangChain integration	Basic AI modules
Integrations	500+ native, unlimited via HTTP	1,800+ native apps
Learning Curve	Moderate (technical users)	Easier (visual users)
Best For	Developers, complex workflows, AI	Visual thinkers, marketing teams

The verdict: n8n wins for technical teams needing flexibility, AI capabilities, and cost control at scale. Make wins for non-technical users who prioritize visual simplicity and don’t need self-hosting.

Pricing Deep Dive: Operations vs Executions

Understanding how each platform counts usage is fundamental to calculating real costs.

How Make Counts Operations

Make charges per “operation.” An operation is any module that successfully processes data in your scenario.

Example: Lead capture workflow

1. Webhook receives form data (1 operation)
2. Router checks lead source (1 operation)
3. Filter validates email format (1 operation if data passes)
4. HTTP module enriches data (1 operation)
5. CRM module creates contact (1 operation)
6. Email module sends notification (1 operation)

Total: 6 operations per form submission

If you receive 500 leads monthly, that’s 3,000 operations for this single workflow.

The iterator multiplier: When processing arrays, Make’s iterator runs each item through subsequent modules separately. Processing 50 items through 3 modules? That’s 150 operations, not 3.

How n8n Counts Executions

n8n charges per “execution.” An execution is one complete workflow run, regardless of how many nodes it contains.

The same lead capture workflow in n8n:

1. Webhook trigger receives data
2. IF node checks lead source
3. HTTP Request enriches data
4. HubSpot node creates contact
5. Email node sends notification

Total: 1 execution per form submission

500 leads = 500 executions. Not 3,000.

For self-hosted n8n, even this doesn’t matter. Executions are unlimited. You pay only for your server infrastructure.

Real Cost Scenarios

Scenario 1: Marketing automation (1,000 workflows/month, 8 modules average)

Platform	Calculation	Monthly Cost
Make	1,000 × 8 = 8,000 operations	$9/mo (Core plan)
n8n Cloud	1,000 executions	$20/mo (Starter)
n8n Self-Hosted	Unlimited	~$10-20/mo (VPS)

At this scale, Make is actually cheaper on cloud. But watch what happens as you grow.

---

Scenario 2: E-commerce operations (5,000 workflows/month, 10 modules average)

Platform	Calculation	Monthly Cost
Make	5,000 × 10 = 50,000 operations	~$50-80/mo
n8n Cloud	5,000 executions	$50/mo (Pro)
n8n Self-Hosted	Unlimited	~$20-40/mo

The gap narrows, and self-hosted n8n becomes clearly advantageous.

---

Scenario 3: Agency/Enterprise (20,000 workflows/month, 12 modules average)

Platform	Calculation	Monthly Cost
Make	20,000 × 12 = 240,000 operations	$200-400+/mo
n8n Cloud	20,000 executions	~$100-150/mo
n8n Self-Hosted	Unlimited	~$50-100/mo

At scale, n8n’s pricing model wins decisively. The more complex your workflows (more modules/nodes), the greater the savings.

We’ve helped businesses cut automation costs significantly through migration to n8n. The same service works for Make migrations.

Visual Interface Comparison

Both platforms use visual builders, but the approaches differ fundamentally.

Make’s Scenario Builder

Make uses a module-based visual interface where you connect circular modules with lines. Data flows through the scenario from left to right.

Strengths:

• Exceptionally polished UI. Drag-and-drop is smooth and intuitive
• Visual data mapping. Point and click to map fields between modules
• Clear data flow. Easy to see how information moves through the scenario
• Beautiful visualizations. Scenarios look like professional flowcharts

Limitations:

• Complex logic becomes cluttered. Many branches create visual noise
• Limited code access. Can’t drop into JavaScript for custom logic
• Module dependency. You’re limited to what Make’s modules support

n8n’s Node Canvas

n8n uses a node-based canvas where rectangular nodes connect to form workflows. The interface is functional rather than flashy.

Strengths:

• Scales better visually. Large workflows remain manageable
• Data inspection at each node. Click any node to see exact input/output
• Code when needed. Drop into JavaScript or Python for any transformation
• Expression system. Powerful data manipulation without separate modules

Limitations:

• Less visually polished. The interface prioritizes function over form
• Expressions require learning. Data mapping uses syntax rather than pure point-and-click
• Higher initial friction. Takes longer to build your first workflow

Winner: Make for visual simplicity and non-technical users. n8n for complex workflows and teams comfortable with expressions.

AI and Automation Capabilities

AI integration is rapidly becoming a key differentiator between automation platforms. Here’s how they compare.

n8n’s AI Capabilities

n8n has invested heavily in AI-native features:

LangChain Integration:

• Build AI chains and agents directly in workflows
• Vector store connections for RAG applications
• Memory nodes for conversation context
• Tool-calling for autonomous agents

AI Agent Node: n8n’s standout feature lets you create agents that can:

• Receive a request and decide which tools to use
• Call APIs, query databases, or execute code autonomously
• Maintain context across multi-turn interactions
• Route to different workflow paths based on AI decisions

Native LLM Nodes:

• OpenAI
• Anthropic
• Google AI
• Ollama (for self-hosted models)
• Hugging Face and more

Example AI workflow:

1. Webhook receives customer question
2. AI agent classifies intent
3. Agent decides to search knowledge base OR escalate to human
4. If searching: queries vector store, generates response
5. Sends contextual reply

This entire workflow runs as one execution, regardless of how many AI calls it makes.

Make’s AI Capabilities

Make offers AI modules, but they’re more limited:

• OpenAI modules for completions and chat
• AI-powered formatters for basic transformations
• HTTP modules to call any AI API manually

Make’s AI is functional but not agent-native. You can call AI models, but building autonomous agents with tool selection requires significant workarounds.

Why This Matters

AI agents are becoming central to modern automation. The ability to build workflows where AI makes decisions and selects tools, rather than following rigid paths, is increasingly valuable.

n8n’s architecture supports this natively. Make requires manual orchestration of AI calls without the agent abstraction.

Winner: n8n, significantly. For AI-powered automation, n8n’s agent capabilities and LangChain integration provide substantial advantages.

Features Deep Dive

Integrations

Make: 1,800+ native app integrations. Strong coverage of marketing tools, CRMs, and popular SaaS products. If you need a specific integration, Make probably has it.

n8n: 500+ native nodes. Covers major tools comprehensively, but the raw count is lower. However, the HTTP Request node connects to any API, and custom nodes can be built for repeated integrations.

Practical difference:

• Need quick Calendly-to-Notion sync? Make has both native
• Need custom API integration with complex auth? n8n’s HTTP node handles it better

Data Handling

Make:

• Visual data mapping (point and click)
• Iterator and aggregator modules for arrays
• Built-in functions for transformations
• Data stores for persistent storage

n8n:

• Expression-based data mapping
• Full JavaScript for transformations via Code nodes
• Split In Batches for array processing
• Expressions for inline data manipulation

n8n’s approach is more powerful but requires understanding expressions. Make’s approach is more visual but less flexible.

Error Handling

Make:

• Visual error routes with dedicated paths
• Break and Resume directives for retries
• Built-in error notifications
• Incomplete execution handling

n8n:

• Error Trigger node for global error handling
• Per-node error outputs
• Configurable retry logic
• Custom error workflows

Both handle errors well. Make’s visual error routing is easier to understand. n8n’s approach is more customizable for production systems. See our workflow testing guide for error handling patterns.

Code and Customization

Make:

• Limited JavaScript functions
• No custom module creation
• Relies entirely on built-in modules
• Some transformations require workarounds

n8n:

• Full JavaScript and Python in Code nodes
• Execute Command node for system operations
• Custom node development supported
• No limitations on logic complexity

If you need custom business logic beyond what built-in modules support, n8n wins decisively.

Self-Hosting and Data Control

This is where n8n offers something Make simply cannot match.

Why Self-Hosting Matters

Cost control: Unlimited executions for the price of a VPS. No per-execution billing regardless of volume.

Data sovereignty: Your workflows and data never leave your infrastructure. Critical for regulated industries.

Performance: Dedicated resources without shared infrastructure throttling.

Compliance: For businesses with HIPAA, GDPR, or other requirements, self-hosting often isn’t optional.

Getting Started

Basic Docker deployment:

docker run -it --rm \
  --name n8n \
  -p 5678:5678 \
  -v n8n_data:/home/node/.n8n \
  docker.n8n.io/n8nio/n8n

For production, add PostgreSQL and proper configuration:

version: '3.8'
services:
  n8n:
    image: docker.n8n.io/n8nio/n8n
    restart: always
    ports:
      - "5678:5678"
    environment:
      - DB_TYPE=postgresdb
      - DB_POSTGRESDB_HOST=postgres
      - DB_POSTGRESDB_DATABASE=n8n
      - DB_POSTGRESDB_USER=n8n
      - DB_POSTGRESDB_PASSWORD=${POSTGRES_PASSWORD}
      - N8N_ENCRYPTION_KEY=${ENCRYPTION_KEY}
    volumes:
      - n8n_data:/home/node/.n8n
    depends_on:
      - postgres

  postgres:
    image: postgres:15
    restart: always
    environment:
      - POSTGRES_USER=n8n
      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
      - POSTGRES_DB=n8n
    volumes:
      - postgres_data:/var/lib/postgresql/data

volumes:
  n8n_data:
  postgres_data:

See our Docker setup guide and PostgreSQL configuration for complete instructions. Avoid common pitfalls with our self-hosting mistakes guide.

Make has no self-hosting option. If you need on-premise deployment, n8n is your only choice between these two platforms.

Testing and Debugging

How you troubleshoot failing workflows matters for production reliability.

n8n’s Debugging Experience

Detailed execution logs: Each execution shows exactly what data entered and exited every node. You see the complete data transformation chain.

One-click node testing: Test individual nodes without running the entire workflow. Isolate problems quickly.

Error output inspection: When nodes fail, you see the exact error message, stack trace, and input data that caused the failure.

Execution replays: Re-run failed executions with modified data to test fixes.

Make’s Debugging Experience

Visual execution history: See which modules ran and which failed. Basic error messages indicate what went wrong.

Data inspector: Click modules to see input/output data, though less detailed than n8n.

Manual testing: Run scenarios manually to test, but isolating specific modules requires unlinking others.

Winner: n8n for debugging complex workflows. The detailed logs and node-level testing make troubleshooting significantly faster. Use our workflow debugger tool for additional help.

Use Case Comparison

Marketing Automation

Make excels here with native integrations for:

• Facebook/Meta ads
• Google Ads
• Email platforms (Mailchimp, ActiveCampaign, etc.)
• CRM systems

The visual interface makes campaign automation accessible to marketing teams without developer support.

n8n handles marketing workflows but may require more setup for some integrations. For complex multi-channel campaigns with custom logic, n8n’s flexibility becomes advantageous.

Recommendation: Make for typical marketing automation. n8n for complex, multi-system marketing operations.

Data Pipelines and ETL

Make handles basic data movement but struggles with:

• Large datasets
• Complex transformations
• Direct database access

n8n excels with:

• Native database nodes (MySQL, PostgreSQL, MongoDB)
• Code nodes for complex transformations
• Better handling of large data volumes
• Self-hosting for data privacy

Recommendation: n8n for data pipelines, especially with sensitive data.

AI-Powered Workflows

Make can call AI APIs but lacks agent capabilities. Building intelligent automation requires manual orchestration.

n8n provides native AI agent functionality. Build workflows where AI decides what actions to take based on context.

Recommendation: n8n for any AI-centric automation.

Developer and DevOps Workflows

Make wasn’t built for technical workflows. Limited GitHub/GitLab support, no SSH access, no CLI integration.

n8n fits developer workflows with:

• SSH node for server commands
• GitHub/GitLab nodes
• Docker-friendly deployment
• API-first architecture

Recommendation: n8n for developer and DevOps automation.

Decision Framework

Choose Make If:

You’re non-technical and want purely visual automation without learning expressions or code.

Your team needs quick adoption. Make’s interface is easier to learn, especially for marketing and operations teams.

You need specific marketing integrations that Make has native modules for.

Workflows are moderate complexity. Linear or simple branching logic without heavy data transformation.

Cloud-only is acceptable. You don’t have compliance requirements mandating data control.

Volume stays moderate. Under 50,000 operations monthly where Make’s pricing remains competitive.

Choose n8n If:

You have technical capability or partner with developers. n8n rewards technical investment with flexibility.

Cost efficiency matters at scale. As workflows grow in complexity and volume, n8n’s model saves significantly.

You need custom code for business logic that built-in modules can’t handle.

Self-hosting is required for compliance, data privacy, or cost control.

AI integration is important. Building agents and intelligent workflows is dramatically easier in n8n.

You’re connecting to databases or internal APIs that need direct access.

When NOT to Switch

Be honest about switching costs:

• Don’t migrate if Make’s costs are acceptable and your team is productive
• Don’t migrate if you have dozens of complex scenarios with no one to rebuild them
• Don’t migrate if your team strongly resists learning new tools
• Don’t migrate if you need Make-specific integrations without HTTP alternatives

Sometimes staying put is the right choice. That’s okay.

Getting Started

Ready to try n8n?

• Self-hosting: Follow our self-hosting guide or use our setup service
• Cloud: Start at n8n.io/cloud
• Migration help: Our migration services cover Make-to-n8n transitions
• Questions: Consulting for architecture and strategy decisions

Staying with Make? That’s a valid choice. Make is a capable platform for the right use cases.

Either platform can power serious automation. The key is choosing the one that fits your team’s skills, your technical requirements, and where your automation needs are headed.

Frequently Asked Questions

Is n8n more powerful than Make?

For code-heavy workflows, AI agents, and self-hosting, yes. n8n offers full JavaScript/Python support, native AI agent capabilities with LangChain integration, and complete infrastructure control. Make is more powerful for visual-only users who prefer purely point-and-click configuration without code.

Can I migrate from Make to n8n?

Yes. Make scenarios translate to n8n workflows with similar concepts. Modules become nodes, routers become IF nodes, iterators become Split In Batches. Some Make-specific integrations may need HTTP Request alternatives. Our migration playbook covers the process for both Zapier and Make.

Which is cheaper: n8n or Make?

It depends on scale and complexity. For simple workflows under 10,000 operations monthly, Make can be cheaper. As workflows grow in complexity (more modules) and volume, n8n’s execution-based pricing becomes significantly more economical. Self-hosted n8n is always cheapest at scale since executions are unlimited.

Does Make have self-hosting?

No. Make is cloud-only with no self-hosting option. If you need on-premise deployment for compliance, data privacy, or cost control at high volume, n8n is the only choice between these two platforms.

Which has better error handling?

Both handle errors well but differently. Make uses visual error routes and break/resume functionality that’s easier to understand visually. n8n uses Error Trigger nodes and customizable error workflows that offer more flexibility for production systems. For complex error logic, n8n is more capable. See our best practices guide for error handling patterns.

Can n8n replace Make for marketing automation?

Yes, though some marketing integrations may require more initial setup using the HTTP Request node. n8n handles CRM, email, ads, and analytics platforms well. For pure marketing workflows with popular tools, both work equally. For marketing automation that requires custom logic or AI, n8n provides more capability.

How do AI capabilities compare between n8n and Make?

n8n is significantly ahead for AI automation. It offers native AI agent nodes that can decide which tools to use, LangChain integration for building sophisticated AI chains, and vector store connections for RAG applications. Make offers basic AI modules for calling LLMs but lacks agent capabilities. For building intelligent, autonomous workflows, n8n is the clear choice.