How do I deploy n8n Enterprise-Ready Stack + Ollama on Railway?

You can deploy n8n Enterprise-Ready Stack + Ollama on Railway by clicking the "Deploy Now" button on this page. Railway will automatically set up all the necessary services and configurations for you.

What are the system requirements?

Railway handles all the infrastructure requirements. You only need a web browser to deploy and manage your application.

Is this template free to use?

Yes, this template is free to use within your existing Railway account.

Can I customize this template?

Yes, you can fully customize this template. After deployment, you will have full control of what you've deployed in your Railway project.

How do I get support for this template?

You can get support through our community forum: n8n-enterprise-ready-stack-ollama-cc3b7973, or click "Get template help" for this template for more assistance.

Deploy and Host Ollama for AI-Powered n8n on Railway

🤖 Local AI powered by Qwen 2.5 - Run AI workflows in n8n without external API costs!

About Hosting Ollama on Railway

Ollama allows you to run open-source large language models (LLMs) locally. By hosting it on Railway alongside n8n, you create a powerful, private, and cost-effective AI automation stack. This service is pre-configured to:

Run the highly-capable Qwen 2.5:3b model.
Communicate over Railway's private network for maximum security.
Store models in a persistent volume so they only download once.

Why Deploy Ollama with n8n?

Zero API Costs: Say goodbye to per-token billing from OpenAI or Claude.
Data Privacy: Your data never leaves your Railway project. Local AI means local data.
No Latency to External APIs: Fast communication over Railway's internal network (railway.internal).
Tool Use: Perfect for giving n8n AI agents the ability to query your PostgreSQL database directly.

Common Use Cases

Natural Language DB Query: "Show me all users who signed up today" -> AI generates SQL -> n8n executes.
Support Automation: Classify incoming tickets and generate draft replies.
Data Extraction: Extract structured data from messy emails or documents.
Smart Workflows: Use AI as a conditional logic engine to make decisions in your workflows.

Dependencies for Ollama Service

Dependency	Version	Purpose
Ollama	Latest	The engine that runs the LLM
Qwen 2.5	3b	The default intelligence model
Docker	Latest	Containerization for Railway

Deployment Dependencies

Resource	Requirement	Note
RAM	4GB (Min)	Required for 3b models
Disk	10GB	To store the model weights
CPU	2 vCPU+	Faster CPU = Faster responses

Default Configuration

Setting	Value
Model	`qwen2.5:3b`
API Endpoint	`http://ollama.railway.internal:11434`

Connecting to n8n

Step 1: Add Ollama Credentials in n8n

Open your n8n UI.
Go to Settings -> Credentials.
Create a new Ollama API credential.
Set the Base URL to: http://ollama.railway.internal:11434.

Step 2: Use in Workflows

Use the AI Agent or Ollama Chat Model nodes. Always ensure the "Model" name in the node matches qwen2.5:3b.

Resource Requirements & Cost

RAM	Est. Cost/mo	Performance
2GB	~$10	Struggling / High Latency
4GB	~$20	Balanced (Recommended)
8GB	~$40	Fast / Supports 7b models

Troubleshooting

❌ Connection Timed Out: Ollama might still be downloading the model weights on the first boot. Check logs.
❌ Out of Memory: Ensure the service has at least 4GB of RAM allocated in Railway.
❌ Slow Responses: Running on CPU is slower than GPU. Use smaller models like qwen2.5:1.5b if speed is critical.

Support This Project

If this template saves you time and money, consider supporting its development! ☕