---
title: "Deploy Flowise"
description: "Build AI Agents, Visually"
category: "Other"
url: https://railway.com/deploy/flowise-1
---

# Deploy Flowise

Build AI Agents, Visually

**[Deploy Flowise on Railway](https://railway.com/template/flowise-1)**

- **Creator:** Techulus
- **Category:** Other
- **Total deploys:** 52

## Template content

### flowise https://cdn.jsdelivr.net/gh/homarr-labs/dashboard-icons/png/flowise.png

- **Image:** flowiseai/flowise:latest
- **Public domain:** Yes

## Buckets

- **storage**

## Documentation

# Deploy and Host Flowise AI on Railway

Flowise is an open-source visual builder for LLM applications and AI agents. Using a drag-and-drop interface, you can chain together prompts, models, APIs, vector stores, and tools to create chatbots, RAG pipelines, and multi-agent workflows—without writing boilerplate code. It integrates with OpenAI, Anthropic, Google, Mistral, and any OpenAI-compatible API.

## About Hosting Flowise AI

Flowise runs as a Node.js application with a React frontend. By default, it uses SQLite for storing chatflows, credentials, and API keys, requiring no external database. For production deployments, PostgreSQL is supported for better performance at scale.

## Common Use Cases

- Build conversational AI chatbots and customer support agents using visual drag-and-drop workflows with LangChain components
- Create RAG (Retrieval-Augmented Generation) pipelines connecting LLMs to vector databases like Pinecone, Chroma, or PostgreSQL with pgvector
- Design multi-agent systems and automated workflows that chain together multiple AI models, APIs, and custom tools

## Dependencies for Flowise AI Hosting

- None for basic setup — Flowise uses SQLite by default with no external database required

### Deployment Dependencies

- [Flowise Docker Image](https://hub.docker.com/r/flowiseai/flowise)
- [Flowise Documentation](https://docs.flowiseai.com/)
- [Flowise GitHub Repository](https://github.com/FlowiseAI/Flowise)
- [Environment Variables Reference](https://docs.flowiseai.com/configuration/environment-variables)

### Implementation Details

Essential environment variables:

```
# Paths (mount /root/.flowise as a persistent volume)
DATABASE_PATH=/root/.flowise
APIKEY_PATH=/root/.flowise
SECRETKEY_PATH=/root/.flowise
LOG_PATH=/root/.flowise/logs
```

Flowise exposes port 3000 for the web UI and API.

## Why Deploy Flowise AI 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 Flowise AI 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.

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