Sophie
July 13, 2025
n8n vs Apache Airflow: Choosing the Right Workflow Tool for 3D, AI, and AR Projects
At ar-go.co, we combine n8n for easy, visual automations with Airflow for complex data workflows to keep our 3D, AI, and AR projects running smoothly.
n8n vs Apache Airflow: Choosing the Right Workflow Tool for 3D, AI, and AR Projects
At ar-go.co, we build advanced solutions that blend 3D, AI, and augmented reality (AR). Our work includes dynamic product visualizations, AR configurators, and AI-driven content workflows.
As our projects grow, we often need to connect different systems and automate tasks. Two popular tools stand out: n8n and Apache Airflow. They help us manage data, run automations, and ensure smooth delivery. But they serve different needs.
What are these tools?
n8n is a visual automation tool. You can connect APIs and SaaS apps using a simple drag-and-drop editor. It works well for business processes and marketing automations.
Apache Airflow is a code-based orchestration tool. You write workflows as Python scripts called DAGs (Directed Acyclic Graphs). Airflow is great for complex data tasks and large batch jobs.
Why does this matter for 3D, AI, and AR?
Our projects often combine data from product databases, AI models, and AR rendering engines. We need workflows that handle tasks like:
Sending 3D model updates to web viewers
Processing new product textures with AI tools
Automating asset updates across AR apps
Our experience
When we need to link different cloud services — like pushing new product images to a CMS, updating marketing assets, or sending client approvals — we use n8n. Its visual editor makes it fast. Non-technical team members can also understand and update workflows without code.
For example:
We built a workflow that connects a 3D configurator to Slack and Google Sheets. When a client creates a custom design, n8n saves the data, generates a preview, and notifies the sales team in Slack. No developer time needed after setup.
On the other hand, when we process large data batches — for example, rendering thousands of AR assets overnight or running AI image upscaling pipelines — we choose Airflow. It handles complex dependencies and retries, and it scales well.
For example:
We run a nightly Airflow job to fetch new 3D model files, run AI-based optimizations, export AR-ready assets, and update the web portal. If one step fails, Airflow can retry or alert us.
Strengths and weaknesses
n8n
Strengths
Easy to learn and set up
Great for API and SaaS integrations
Works well for smaller automations
Weaknesses
Less suited for big data processing
Limited support for code-heavy pipelines
Testing large flows is harder
Airflow
Strengths
Handles complex, code-based workflows
Supports large-scale data tasks
Strong monitoring and error handling
Weaknesses
Requires more setup and expertise
Not friendly for non-developers
Heavier infrastructure needs
Our recommendation
At ar-go.co, we use both tools depending on the task.
Choose n8n for marketing automations, client notifications, and light API integrations.
Choose Airflow for heavy data processing, large rendering tasks, or when you need detailed control.
Final thoughts
In 3D, AI, and AR projects, fast iteration and reliable data flow are key. Using the right workflow tool saves time and reduces errors.
We believe teams should stay flexible and choose tools that match their technical skills and project needs. In our experience, combining n8n and Airflow gives us the best of both worlds.
References
✅ Let us know if you'd like help mapping out your own automation stack or seeing more AR-specific use cases!
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