
Dify
An open-source LLM app development platform
Web
langgenius/dify-web
Just deployed
Sandbox
langgenius/dify-sandbox
Just deployed
/dependencies
Redis
redis:6-alpine
Just deployed
/data
Api
langgenius/dify-api
Just deployed
Weaviate
semitechnologies/weaviate
Just deployed
/var/lib/weaviate
Worker
langgenius/dify-api
Just deployed
Postgres
postgres:15-alpine
Just deployed
/var/lib/postgresql/data
Storage provision
minio/mc
Just deployed
Storage
minio/minio
Just deployed
/data
Deploy and Host Dify on Railway
Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.
About Hosting Dify
Hosting Dify means running a comprehensive LLM application platform that orchestrates AI workflows, manages multiple model integrations, and provides development tools for building AI applications. The platform requires coordinating database connections, managing LLM provider APIs, handling file storage for documents and models, and maintaining user authentication and workspace management. Production deployment involves configuring environment variables for model providers, setting up persistent storage for workflows and data, and managing the complex multi-service architecture. Railway simplifies Dify deployment by providing integrated database hosting, managing environment variable configuration for LLM providers, handling persistent storage requirements, and coordinating the multi-container setup with automatic service discovery.
⚠️ Important Setup Note: After deploying for the first time, you'll need the auto-generated INIT_PASSWORD
variable in the Api
service to setup the admin account. Expect possible loading delays as a result of cache on fresh deployments.
Common Use Cases
- AI Application Development: Build and test powerful AI workflows on a visual canvas, leveraging comprehensive LLM capabilities
- RAG Pipeline Implementation: Create document ingestion and retrieval systems with out-of-box support for PDFs, PPTs, and other document formats
- AI Agent Development: Define agents based on LLM Function Calling or ReAct with 50+ built-in tools like Google Search and Stable Diffusion
Dependencies for Dify Hosting
The Railway template includes the required Python runtime, database systems, and Dify platform services with pre-configured multi-container orchestration.
Deployment Dependencies
- Dify Documentation
- Model Providers List
- Environment Variables Guide
- Docker Compose Configuration
- GitHub Repository
Implementation Details
Core Features:
1. Workflow: Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.
2. Comprehensive model support: Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models.
3. Prompt IDE: Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.
4. RAG Pipeline: Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.
5. Agent capabilities: You can define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools for the agent. Dify provides 50+ built-in tools for AI agents, such as Google Search, DELL·E, Stable Diffusion and WolframAlpha.
6. LLMOps: Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.
7. Backend-as-a-Service: All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.
Platform Comparison:
Feature | Dify.AI | LangChain | Flowise | OpenAI Assistants API |
---|---|---|---|---|
Programming Approach | API + App-oriented | Python Code | App-oriented | API-oriented |
Supported LLMs | Rich Variety | Rich Variety | Rich Variety | OpenAI-only |
RAG Engine | ✅ | ✅ | ✅ | ✅ |
Agent | ✅ | ✅ | ❌ | ✅ |
Workflow | ✅ | ❌ | ✅ | ❌ |
Observability | ✅ | ✅ | ❌ | ❌ |
Enterprise Features (SSO/Access control) | ✅ | ❌ | ❌ | ❌ |
Local Deployment | ✅ | ✅ | ✅ | ❌ |
Configuration:
If you need to customize the configuration, please refer to the comments in the docker-compose.yml file and manually set the environment configuration. You can see the full list of environment variables in the documentation.
Community & Contact:
- Github Discussion - Best for: sharing feedback and asking questions
- GitHub Issues - Best for: bugs you encounter using Dify.AI, and feature proposals
- Email - Best for: questions you have about using Dify.AI
- Discord - Best for: sharing your applications and hanging out with the community
- Twitter - Best for: sharing your applications and hanging out with the community
License:
This repository is available under the Dify Open Source License, which is essentially Apache 2.0 with a few additional restrictions.
Why Deploy Dify 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 Dify 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.
Template Content
Sandbox
langgenius/dify-sandboxRedis
redis:6-alpineWeaviate
semitechnologies/weaviateWorker
langgenius/dify-apiPostgres
postgres:15-alpineStorage provision
minio/mcStorage
minio/minio