How do I deploy Open WebUI on Railway?

You can deploy Open WebUI 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: open-web-ui-13158fe5, or join our Discord community for more assistance.

Deploy and Host Open WebUI on Railway

Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It supports various LLM runners like Ollama and OpenAI-compatible APIs, with built-in inference engine for RAG, making it a powerful AI deployment solution for teams and individuals.

About Hosting Open WebUI

Hosting Open WebUI involves deploying a comprehensive AI chat interface that can connect to multiple LLM backends. The platform requires a persistent database (SQLite, PostgreSQL, or cloud storage), optional vector database for RAG functionality, and compute resources to handle web requests and AI model interactions. Open WebUI supports horizontal scalability through Redis-backed sessions, making it suitable for both single-user installations and enterprise deployments. The application can be configured with various authentication methods, custom theming, and integration with external services like cloud storage providers. Resource requirements scale based on usage patterns and enabled features like RAG, image generation, and voice capabilities.

Common Use Cases

Enterprise AI Chat Platform: Deploy a centralized, self-hosted AI interface for your organization with role-based access control, LDAP/Active Directory integration, and support for multiple LLM providers
Local AI Development Environment: Create a powerful offline-capable AI workspace with RAG capabilities, document processing, and custom function calling for prototyping and testing AI applications
Multi-Model AI Assistant: Build a versatile AI system that can engage with various models simultaneously, perform web searches, generate images, and integrate custom Python tools for specialized workflows

Dependencies for Open WebUI Hosting

Database: PostgreSQL (recommended for production) or SQLite with optional encryption
Vector Database (optional for RAG): ChromaDB, PGVector, Qdrant, Milvus, or other supported options
Redis (optional): Required for horizontal scaling across multiple instances
Storage: Persistent volume for user data, documents, and model configurations
Python 3.11+: Runtime environment for the application

Deployment Dependencies

Implementation Details

For Railway deployment, configure the following environment variables:

# Database Configuration
DATABASE_URL=postgresql://user:password@host:5432/openwebui

# Optional: Ollama Integration
OLLAMA_BASE_URL=http://your-ollama-instance:11434

# Optional: OpenAI API
OPENAI_API_KEY=your_secret_key

# Optional: Redis for Scaling
REDIS_URL=redis://redis:6379

# Optional: Vector Database
VECTOR_DB=pgvector

Ensure persistent volume is mounted at /app/backend/data to preserve user data, configurations, and uploaded documents across deployments.

Why Deploy Open WebUI on Railway?

Railway is a singular platform to deploy your infrastructure stack. Railway will host your infrastructure so you don't have to deal with configuration, while allowing you to vertically and horizontally scale it.

By deploying Open WebUI on Railway, you are one step closer to supporting a complete full-stack application with minimal burden. Host your servers, databases, AI agents, and more on Railway. Railway's automatic HTTPS, seamless PostgreSQL integration, and straightforward environment variable management make it ideal for deploying Open WebUI with all its features—from RAG capabilities to multi-model conversations—without the complexity of traditional infrastructure management.