---
title: "Deploy Flowise - Railway Template"
description: "Flowise with web UI, auth, and persistent storage. It just works 🤖"
category: "AI/ML"
url: https://railway.com/deploy/flowise-railway-template
---

# Deploy Flowise - Railway Template

Flowise with web UI, auth, and persistent storage. It just works 🤖

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

- **Creator:** codestorm
- **Category:** AI/ML
- **Total deploys:** 1

## Template content

### flowiseai https://i.imgur.com/PCQwxlP.png

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

## Documentation

![Railway](https://img.shields.io/badge/Railway-Supported-blue?logo=railway)

# Deploy and Host Flowise AI Agent on Railway

Flowise AI Agent is an open-source, low-code platform for building AI agents, chatbots, and LLM workflows through a visual drag-and-drop interface. It helps you connect language models, tools, memory, retrievers, vector stores, APIs, and custom logic without building a full AI application from scratch.

![](https://i.imgur.com/hbOkfpX.png)

## About Hosting Flowise AI Agent

Deploying Flowise AI Agent on Railway uses the prebuilt official Flowise Docker image for a faster and simpler setup. Instead of building Flowise from the GitHub repository, Railway pulls the ready-to-run image and starts the web UI on port `3000`. Railway manages infrastructure, networking, deployments, and scaling, while Flowise provides the browser-based interface for creating and managing AI agents. A Railway Volume should be mounted at `/root/.flowise` so saved flows, credentials, API keys, logs, and configuration files persist across restarts and redeployments.

> **Note**
> This template uses the prebuilt Flowise Docker image, so deployment is usually faster, lighter, and lower-cost than building Flowise from source.

## Common Use Cases

* Building AI agents and chatbot workflows with a visual web UI
* Creating RAG pipelines with document loaders, embeddings, and vector stores
* Prototyping LLM apps without building a custom frontend
* Connecting AI workflows to APIs, tools, memory, and custom logic
* Running a lightweight self-hosted Flowise instance with persistent storage

## Dependencies for Flowise AI Agent Hosting

* Official Flowise Docker image: `flowiseai/flowise:latest`
* Public HTTP access through Railway on port `3000`
* Railway Volume mounted at `/root/.flowise` for persistent storage
* Default dashboard credentials using `FLOWISE_USERNAME=admin` and `FLOWISE_PASSWORD=admin123`

### Deployment Dependencies

* Flowise GitHub repository: [https://github.com/FlowiseAI/Flowise](https://github.com/FlowiseAI/Flowise)
* Official Flowise Docker image: [https://hub.docker.com/r/flowiseai/flowise](https://hub.docker.com/r/flowiseai/flowise)
* Flowise documentation: [https://docs.flowiseai.com](https://docs.flowiseai.com)
* Flowise environment variables: [https://docs.flowiseai.com/configuration/environment-variables](https://docs.flowiseai.com/configuration/environment-variables)
* Railway Public Networking: [https://docs.railway.com/networking/public-networking](https://docs.railway.com/networking/public-networking)
* Railway Volumes: [https://docs.railway.com/reference/volumes](https://docs.railway.com/reference/volumes)
* Railway CLI: [https://docs.railway.com/guides/cli](https://docs.railway.com/guides/cli)
* Railway SSH guide: [https://docs.railway.com/guides/ssh](https://docs.railway.com/guides/ssh)

### Environment Variables

Use the following environment variables for this Railway template:

```bash
PORT="3000"
FLOWISE_USERNAME="admin"
FLOWISE_PASSWORD="admin123"
```

Default login credentials:

```text
Username: admin
Password: admin123
```

Users can change `FLOWISE_USERNAME` and `FLOWISE_PASSWORD` from Railway Variables before or after deployment. For public deployments, it is recommended to replace the default password with a stronger value before sharing the Flowise URL.

### Public Web Access

Flowise AI Agent provides a browser-based web UI. After deployment, expose the service using Railway Public Networking and open the generated Railway domain or your custom domain in a browser.

Recommended port:

```text
3000
```

The service should use:

```bash
PORT="3000"
```

Once the Railway domain is active, visit the public URL in your browser and log in using the configured `FLOWISE_USERNAME` and `FLOWISE_PASSWORD` values. The default credentials are `admin` and `admin123`, unless changed in Railway Variables.

### Volume Setup

Mount a Railway Volume at:

```text
/root/.flowise
```

This keeps Flowise data available across service restarts and redeployments, including saved flows, credentials, API keys, secret keys, logs, and configuration files.

### SSH Usage

SSH is optional and mainly useful for diagnostics. Most users should access Flowise AI Agent through the public web UI instead.

#### Prerequisites

Ensure you have the necessary setup in place:

* The Railway CLI installed on your local machine
* Logged in with your Railway account using:

```bash
railway login
```

#### Usage

You can copy the exact SSH command directly from the Railway dashboard:

1. Navigate to your project in the Railway dashboard.
2. Right-click on the Flowise AI Agent service you want to connect to.
3. Select **Copy SSH Command** from the dropdown menu.
4. Paste the command into your local terminal to connect to the running Flowise container.

Once connected, you can inspect files, review logs, check mounted storage, verify environment variables, or debug your Flowise deployment from inside the container.

### Implementation Details

This template deploys Flowise AI Agent using the prebuilt official Flowise Docker image:

```dockerfile
FROM flowiseai/flowise:latest
```

Because the image is already built, Railway does not need to install dependencies or compile Flowise from source during deployment. This makes the template faster to deploy and simpler to maintain than a source-build template.

This template includes default dashboard credentials so users can deploy quickly:

```text
FLOWISE_USERNAME=admin
FLOWISE_PASSWORD=admin123
```

Users can change both values by editing Railway Variables before deployment or after the service is running.

For persistent storage, mount the Railway Volume at `/root/.flowise`. This path keeps Flowise data available across restarts and redeployments.

## Why Deploy Flowise AI Agent 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 Agent 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.


## Similar templates

- [Chat Chat](https://railway.com/deploy/-WWW5r) — Chat Chat, your own unified chat and search to AI platform.
- [stella](https://railway.com/deploy/stella) — Self-host stella with web, API, Postgres, Redis, and object storage.
- [Hermes Agent | OpenClaw Alternative with Dashboard](https://railway.com/deploy/hermes-agent-or-openclaw-alternative-wit) — Self-Hosted Hermes AI Agent for Telegram, Discord & Slack

Open this page in a browser: https://railway.com/deploy/flowise-railway-template