n8n vs Make.com: Side-by-Side
A direct breakdown of what each platform does — and where each one falls short.
| Feature | n8n | Make.com | Winner |
|---|---|---|---|
| Pricing | Self-hosted free; cloud from $20/mo flat | Per-operation — grows fast with complex scenarios | n8n |
| Visual builder | Node-graph canvas — powerful but technical | Polished scenario builder — intuitive | Make |
| Self-hosting | Yes — Docker, VPS, AWS, full control | Cloud-only — no self-hosted option | n8n |
| Data privacy | All data stays on your infrastructure | Data processed on Make's cloud | n8n |
| Custom code | JavaScript + Python execution nodes | Basic code module (limited) | n8n |
| AI & LLM nodes | OpenAI, Claude, LangChain, Ollama native | OpenAI via HTTP module | n8n |
| Learning curve | Steeper — needs technical comfort | Faster for non-technical builders | Make |
| Error handling | Error workflows with full routing control | Error handlers per module | n8n |
| Custom integrations | Custom nodes in JavaScript | HTTP module for unlisted apps | n8n |
| Open source | Yes — MIT/Apache license | No — proprietary | n8n |
Where Make.com Has a Real Advantage
Make's visual scenario builder is genuinely good. Modules snap together cleanly, the data mapping UI is intuitive, and routers/filters make logic legible at a glance. For teams without a developer, Make reduces the time from "I have an idea" to "it's running" significantly. Make also has 1,500+ app connectors and predictable bundle-based pricing for moderate-volume workflows.
Where n8n Pulls Ahead
The gap becomes clear at three inflection points: volume (Make charges per operation — at 100,000+ ops/month, costs climb sharply; n8n self-hosted processes the same volume for a flat $12–24/month), data sensitivity (Make is cloud-only; self-hosted n8n keeps data inside your own infrastructure), and code complexity (n8n's Code node runs real JavaScript with npm access; Make's code module is basic by comparison).
AI Automation: n8n vs Make
n8n ships with native LangChain nodes, a dedicated AI Agent node, and direct integrations with OpenAI, Claude, Gemini, and Ollama. You can build a complete AI agent — with memory, tools, and decision loops — entirely inside n8n. Make connects to OpenAI via HTTP modules, which works but requires more setup for anything beyond simple prompt-response calls. For teams building AI workflow automation, n8n has a structural advantage today.
Choose Make if your team builds visually, volume is under 50k ops/month, and cloud-hosted is fine. Choose n8n if you want self-hosting, AI-native workflows, JavaScript-level control, or you're scaling past the point where per-operation pricing makes sense.
Real Migration Experience
Make.com scenarios rebuilt in n8n — designed for n8n's architecture, not just translated from Make's module chain.
Make Is Good.
n8n Is Yours.
Book a free 30-minute audit — I'll review your current scenarios, calculate migration cost, and tell you honestly if the switch makes sense.
n8n vs Make.com: The Definitive Automation Platform Comparison
Make.com built its reputation on visual automation that handles complexity without code. Its scenario builder — with modules, routers, aggregators, and iterators — gives non-technical teams significant power without requiring a developer. n8n built its reputation on giving developers the automation infrastructure they actually want: open-source, self-hostable, code-extensible, and priced at the infrastructure level. Both platforms have matured. The right choice depends on your team's technical capacity, data sensitivity, and long-term volume expectations.
Architecture Differences: Module Chains vs Node Graphs
Make uses a sequential module chain model. Modules connect linearly, data flows forward, routers branch the flow, and aggregators collect it back. The visual metaphor is a flowchart — legible for non-technical users, but constrained in flexibility. n8n uses a node graph. Nodes connect in any direction. A single node can receive input from multiple upstream nodes. Loops are first-class. Sub-workflows modularize complex logic. Code nodes run real JavaScript with npm access.
Simple Make scenarios translate cleanly to n8n. Complex ones — especially those using Make-specific aggregator patterns at high volume — get redesigned rather than translated, which usually produces a better n8n workflow than a literal port would.
Pricing Reality: Per-Operation vs Flat Infrastructure Cost
Make's Core plan: 10,000 operations/month for $9/month. Operations accumulate quickly — each module in a scenario that processes a bundle counts as one operation. A 15-module scenario processing 1,000 items consumes 15,000 operations in a single run. At 100,000+ monthly operations, Make costs $50–150/month depending on plan.
n8n self-hosted costs $12–24/month regardless of how many operations run. The break-even is around 40,000–60,000 monthly operations. Teams processing significant data volume almost always find self-hosted n8n dramatically cheaper after 6–12 months. Make's operation limits also create artificial ceilings on automation ambition — teams avoid building automations because they'd consume too many operations. Self-hosted n8n removes that ceiling entirely.
- Make Core: $9/mo, 10k ops — good for light automation
- Make Growth: $16/mo, 10k ops with advanced features
- n8n Cloud Starter: $20/mo, 2,500 executions (flat, not per-module)
- n8n self-hosted: $12–24/mo server, unlimited executions
AI Workflow Automation: Where n8n Has a Structural Advantage
n8n ships native nodes for OpenAI, Anthropic Claude, Google Gemini, Ollama, HuggingFace, and the full LangChain ecosystem. The AI Agent node implements a reasoning loop with tool calling — you give it tools and it figures out how to use them to complete a task. Building a multi-step AI agent that connects to your CRM and makes routing decisions based on LLM output is hours of work in n8n.
Make connects to OpenAI via an HTTP module, which works but requires manual prompt engineering for everything beyond basic calls. Multi-step agent architectures with tool use require significant workaround creativity. For teams building AI-native workflows — AI workflow automation, AI agent systems — n8n's native AI infrastructure is significantly more capable and maintainable. Full migration service: Make.com to n8n Migration.