Complete comparison of n8n and Make for AI-powered automation. Features, pricing, AI agents, RAG systems, and recommendations for building intelligent workflows in 2025.
n8n vs Make: Which Automation Tool is Best for AI Workflows in 2025?
AI is transforming automation. The question isn't whether to add AI to your workflows—it's which platform handles it best.
n8n and Make are the two leading alternatives to Zapier. But when it comes to AI capabilities, they're not equal.
Here's the complete comparison for building AI-powered automations in 2025.
The Quick Comparison
| Factor |
n8n |
Make |
| AI Agents |
Yes, built-in |
No |
| RAG Systems |
Native support |
No |
| Self-hosting |
Yes (free) |
No |
| Pricing |
From $20/mo or free self-hosted |
From $10.59/mo |
| AI Node Types |
15+ specialized |
5-6 basic |
| Learning Curve |
Steeper |
Moderate |
| Best For |
AI-first workflows |
Visual automations |
TL;DR:
- Choose n8n if you're building AI agents, RAG systems, or complex AI workflows
- Choose Make if you want simpler AI features with a friendlier interface
Why AI Changes Everything
Traditional automation: "When X happens, do Y."
AI-powered automation: "When X happens, understand it, decide what to do, and adapt."
The difference is intelligence. AI workflows can:
- Analyze incoming data and make decisions
- Generate personalized content on the fly
- Query knowledge bases for context
- Learn from interactions over time
Not every platform handles this well.
n8n's AI Capabilities
n8n has gone all-in on AI. Here's what makes it different:
AI Agents
n8n offers native AI agents that can:
- Make autonomous decisions
- Execute multi-step tasks
- Work with your data through RAG
- Call tools and APIs as needed
User Query → AI Agent → [Decides action] → Executes → Returns result
This isn't just "call GPT and return the response." It's agentic AI that reasons and acts.
RAG (Retrieval Augmented Generation)
n8n includes built-in RAG capabilities:
- Vector store integrations (Pinecone, Qdrant, Supabase)
- Document loaders for PDFs, websites, databases
- Embedding generation
- Semantic search
Example workflow:
- Customer asks a question
- n8n searches your knowledge base
- Retrieves relevant context
- Generates accurate, grounded response
AI Nodes Available
- AI Agent - Autonomous reasoning and action
- Chat Model - OpenAI, Anthropic, local LLMs
- Embeddings - Generate vector embeddings
- Vector Store - Store and query embeddings
- Document Loaders - PDF, CSV, web pages
- Text Splitters - Chunk documents intelligently
- Memory - Conversation history
- Output Parsers - Structure AI responses
Make's AI Capabilities
Make offers AI features, but they're more basic:
Built-in AI Tools
- Text categorization
- Language detection
- Document extraction
- Content summarization
- Translation
These are useful but limited. You're calling predefined AI functions, not building intelligent systems.
OpenAI Integration
Make has an OpenAI module that lets you:
- Send prompts to GPT models
- Generate images with DALL-E
- Create embeddings
But there's no agent framework, no RAG system, no autonomous decision-making.
What's Missing
- No AI agents
- No native RAG
- No vector store integrations
- No document processing pipeline
- No memory/context management
Head-to-Head: Building an AI Workflow
Let's compare building the same workflow on both platforms.
Task: Customer Support AI
Goal: Automatically answer customer questions using your knowledge base.
n8n Approach:
- Trigger: New support ticket
- Load relevant docs from vector store
- AI Agent analyzes question + context
- Agent decides: answer directly, escalate, or request more info
- Generate personalized response
- Update ticket system
Complexity: Medium
AI Intelligence: High (agent reasons about response)
Make Approach:
- Trigger: New support ticket
- Send question to OpenAI
- Get response
- Update ticket system
Complexity: Low
AI Intelligence: Low (no context, no reasoning)
Verdict: n8n creates a genuinely intelligent support system. Make creates a basic chatbot wrapper.
Head-to-Head: Pricing for AI Workloads
AI workflows often involve:
- Multiple API calls
- Large data processing
- Frequent executions
How do costs compare?
n8n Pricing
| Option |
Cost |
AI Executions |
| Self-hosted |
Free |
Unlimited |
| Starter |
$20/mo |
2,500 |
| Pro |
$50/mo |
10,000 |
| Enterprise |
Custom |
Unlimited |
Key advantage: Self-host n8n for free and run unlimited AI workflows. You only pay for the AI APIs (OpenAI, Anthropic, etc.).
Make Pricing
| Plan |
Cost |
Operations |
| Core |
$10.59/mo |
10,000 |
| Pro |
$18.82/mo |
10,000 |
| Teams |
$34.12/mo |
10,000 |
Note: Starting August 2025, Make uses credits instead of operations. AI modules consume more credits than standard modules.
Real Cost Comparison
Scenario: 1,000 AI-powered customer responses per month
n8n (self-hosted):
- Platform: $0
- OpenAI API: ~$50-100 (depending on model)
- Total: $50-100/month
Make:
- Platform: $18.82+ (Pro for better limits)
- OpenAI API: ~$50-100
- Total: $70-120/month
Savings with n8n self-hosting: 30-50%
Head-to-Head: Ease of Use
n8n Learning Curve
n8n requires more upfront learning:
- Understanding node-based workflows
- Configuring AI components
- Setting up vector stores
- Managing prompts and context
Time to proficiency: 1-2 weeks for basic, 1 month for AI features
But: Once learned, you can build sophisticated AI systems that would require custom development elsewhere.
Make Learning Curve
Make's visual canvas is intuitive:
- Drag-and-drop interface
- Clear data flow visualization
- Good documentation
- Templates available
Time to proficiency: A few days for basic, 1-2 weeks for advanced
But: You'll hit a ceiling. Complex AI use cases simply aren't possible.
When to Choose n8n
Choose n8n for AI workflows if:
1. You're Building AI Agents
If your automation needs to reason, decide, and act autonomously, n8n is the only choice between these two.
2. You Need RAG
Grounding AI responses in your own data requires RAG. n8n has it. Make doesn't.
3. Budget is a Concern
Self-hosted n8n is free. For AI-heavy workloads, this saves thousands annually.
4. You Want Full Control
Self-hosting means your data never leaves your infrastructure. Critical for sensitive AI applications.
5. You're Technical (or Have Technical Resources)
n8n's power requires some technical comfort. If you have it, the capabilities are unmatched.
When to Choose Make
Choose Make for AI workflows if:
1. Simple AI Features Are Enough
Summarization, categorization, translation—Make handles these fine.
2. You Prioritize Ease of Use
Make's interface is genuinely easier. If learning n8n's AI features feels daunting, Make gets you started faster.
3. AI is a Small Part of Your Automation
If you're mostly doing traditional automation with occasional AI calls, Make's simpler approach works.
4. You Need Specific Integrations
Check both platforms. If Make has a critical integration n8n lacks, that matters.
The Hybrid Approach
Some teams use both:
- Make for simple, non-AI automations (form processing, basic routing)
- n8n for AI-powered workflows (intelligent responses, document processing)
This maximizes each platform's strengths.
Real-World AI Workflow Examples
Example 1: Intelligent Lead Scoring (n8n)
New Lead → Enrich Data → AI Agent Analyzes →
Scores Lead → Routes to Sales/Nurture → Updates CRM
The AI agent considers: company size, industry, engagement signals, ideal customer profile match. It doesn't just score—it explains why.
Example 2: Content Repurposing (Make)
New Blog Post → Summarize → Create Social Posts → Schedule
Basic AI summarization. Works fine in Make.
Example 3: Document Processing Pipeline (n8n)
PDF Upload → Extract Text → Chunk → Embed →
Store in Vector DB → Ready for RAG queries
This entire pipeline is native to n8n. Impossible in Make without custom code.
Migration Considerations
Make to n8n
If you're upgrading for AI capabilities:
- Expect 2-4 weeks to rebuild and learn
- Most integrations exist in both
- AI workflows will be significantly more powerful
- Consider self-hosting for cost savings
n8n to Make
Not recommended if AI is your goal. You'd be downgrading capabilities.
Frequently Asked Questions
Does n8n have AI agents?
Yes, n8n offers native AI agents that can reason, make decisions, and execute multi-step tasks autonomously. These agents can use tools, query knowledge bases via RAG, and adapt their behavior based on context—capabilities that Make doesn't offer.
Can Make build RAG systems?
No, Make doesn't have native RAG (Retrieval Augmented Generation) capabilities. You would need to use external services and complex workarounds. n8n includes built-in vector store integrations, document loaders, and embedding generation for true RAG workflows.
Is n8n harder to learn than Make?
Yes, n8n has a steeper learning curve, especially for AI features. Expect 1-2 weeks for basic proficiency and about a month to master AI capabilities. However, the investment unlocks sophisticated AI workflows that would require custom development on other platforms.
Which is cheaper for AI workflows?
n8n is significantly cheaper, especially if you self-host (which is free). For AI-heavy workloads, self-hosted n8n with direct API access to OpenAI/Anthropic can save 30-50% compared to Make's pricing plus the same API costs.
Can I use local LLMs with n8n?
Yes, n8n supports local LLMs through Ollama and other providers. This means you can run AI workflows entirely on your own infrastructure with no external API costs—impossible with Make.
What AI features does Make actually have?
Make offers basic AI capabilities: text categorization, language detection, document extraction, summarization, and translation. It also has an OpenAI module for GPT calls. However, it lacks agents, RAG, vector stores, and the ability to build truly intelligent systems.
The Bottom Line
For AI workflows, n8n is the clear winner.
Make is a great automation platform for traditional workflows. But AI capabilities aren't just a feature difference—they're a category difference.
If you're building:
- AI agents that reason and act
- Knowledge-grounded chatbots
- Intelligent document processing
- Autonomous decision systems
n8n is your platform.
If you just need occasional AI text processing within simpler automations, Make works fine.
The future of automation is intelligent. Choose the platform that's ready for it.
Building AI-powered workflows for your business? Cedar Operations designs automation strategies that leverage the latest AI capabilities. Let's discuss your automation needs →
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