In today’s fast-paced digital landscape, workflow automation has transcended from a luxury to a cornerstone of business efficiency. Among the frontrunners are Make (formerly Integromat) and n8n, two powerful yet distinctly different solutions.
However, their unique philosophies can create a dilemma. Make is often seen as the accessible choice, while n8n is the developer’s favorite.
This article provides a comprehensive comparison of Make vs n8n for 2025, delving into pricing, AI capabilities, and hosting to help you make the right choice.
At a Glance: The Core Differences
Before diving deep, here is the high-level breakdown of the “Simplicity vs. Power” dynamic.
| Feature | Make | n8n |
|---|---|---|
| Ideal User | Marketers, SMBs, Non-technical | Developers, Data-heavy teams |
| Ease of Use | High (Visual drag-and-drop) | Moderate (Steeper learning curve) |
| Pricing Model | Per Operation (Action) | Per Execution (Workflow run) |
| AI Capabilities | AI Assistants, 300+ Apps | Advanced (LangChain, Local LLMs) |
| Integrations | 1,500+ pre-built apps | 1,000+ (Unlimited via Code) |
| Hosting | Cloud SaaS only | Cloud or Self-Hosted |
Make: The Visual No-Code Automator
Make champions a visual, no-code approach. It is designed for users who want to visualize data flow without touching a terminal.
Pros:
- Intuitive: The “bubble” visual builder is excellent for understanding logic at a glance.
- Vast Library: 1,500+ pre-built apps mean less time reading API docs.
- Low Barrier to Entry: You can build a complex flow in minutes with zero coding knowledge.
Cons:
- Pricing at Scale: The “Per Operation” model punishes inefficient workflows. Loops and heavy data processing can burn through credits quickly.
- Complexity: While easy to start, error handling in massive scenarios can get messy.
n8n: The Developer’s Choice
n8n uses a “fair-code” model, emphasizing flexibility and control. It allows you to write JavaScript or Python directly within nodes, making it infinitely extensible.
Pros:
- Self-Hosting: You can host n8n on your own servers for complete data sovereignty and privacy.
- Cost Effective at Scale: Pricing is based on workflow executions, not individual steps. A 100-step workflow costs the same to run as a 1-step workflow.
- Advanced AI: It has native support for LangChain and local models (like Ollama), making it superior for building AI agents.
Cons:
- Technical Requirement: To get the most out of it (especially self-hosting), you need some technical know-how.
- Smaller Library: Fewer pre-built apps, though the generic HTTP Request node can connect to anything.
Deep Dive: Pricing & Cost Effectiveness
This is often the deciding factor. Make charges for every action (operation), while n8n charges for the workflow run (execution).
| Scenario Type | Est. Monthly Runs | Est. Make Cost | Est. n8n Cloud Cost | n8n Self-Hosted |
|---|---|---|---|---|
| Simple (Tweet new blog post) | 30 | $0 (Free Plan) | $0 (Community) | ~$5 (Server cost) |
| Medium (Lead enrichment) | 500 | $9 (Core) | $24 (Starter) | ~$5 (Server cost) |
| High Volume (Data syncing) | 100,000+ | $29+ (Teams/Ent) | $24 (Starter) | ~$10 (Server cost) |
Verdict: Make is cheaper for low-volume, simple tasks. n8n becomes significantly cheaper as soon as you scale up to complex, data-heavy operations.
The AI Factor: Agents vs. Customization
Make has introduced “Make AI Agents” and an AI Assistant to help build workflows. It’s great for connecting standard AI apps (like OpenAI or Claude) to your other tools.
n8n is currently the leader for building AI products. With built-in memory management, RAG (Retrieval-Augmented Generation) support, and the ability to run local LLMs, n8n is effectively an IDE for AI agents.
Final Verdict: Which Should You Choose?
Choose Make if:
- You are a marketing or sales team without engineering support.
- You need to connect standard SaaS tools quickly.
- Your workflows are linear and low-volume.
Choose n8n if:
- You are comfortable with technical concepts or have access to developers.
- You need to process large datasets without breaking the bank.
- Data privacy is paramount (Self-hosting).
- You are building complex AI agents using LangChain or local models.
Is Custom Automation Too Complex?
Both Make and n8n require you to understand logic, APIs, and data structures. If you are looking to build AI applications without managing nodes, webhooks, or server infrastructure, there is a third option.
Waterflai simplifies the creation of AI applications. We abstract the technical complexity so you can focus on the outcome, not the wiring.