Self-Host Flowise

Flowise is an open-source tool for building LLM-powered applications through a visual drag-and-drop interface. You can create chatbots, RAG pipelines, and multi-step agent workflows by connecting nodes, without writing application code.

Flowise runs on Railway as a CPU-based service. The LLM chains you build in Flowise call external APIs (OpenAI, Anthropic, HuggingFace Inference, etc.) for model inference. No models run locally on Railway.

What you will set up

• A Flowise instance running from the official Docker image
• Persistent storage via a Railway Volume
• Authentication to protect the Flowise editor
• A public domain for accessing the interface

One-click deploy from a template

Railway has a Flowise template that provisions the full setup in one step.

After deploying, skip to Configure authentication to secure your instance.

If you prefer to set things up manually, continue with the steps below.

Deploy from a Docker image

1. Create a new project in Railway.
2. Click + New and select Docker Image.
3. Enter flowiseai/flowise as the image name and press Enter.
4. Railway will pull the image and create a service.

Configure environment variables

Add the following variables to your Flowise service under the Variables tab:

Variable	Value	Description
PORT	${{PORT}}	Binds Flowise to Railway's injected port
FLOWISE_USERNAME	Your username	Username for editor authentication
FLOWISE_PASSWORD	Your password	Password for editor authentication
APIKEY_PATH	/root/.flowise	Path for storing API keys
SECRETKEY_ENCRYPT	A random string	Encryption key for stored credentials
LOG_LEVEL	info	Log verbosity (debug, info, warn, error)

Attach persistent storage

Flowise stores chatflow definitions, credentials, and configuration in /root/.flowise by default. Without a volume, this data is lost on every deployment.

1. Open the Flowise service in your project.
2. Go to the Volumes tab.
3. Click Add Volume.
4. Set the mount path to /root/.flowise.
5. Choose a size appropriate for your usage (1 GB is a reasonable starting point).
6. Click Add.

Set up a public domain

1. Go to the Settings tab of the Flowise service.
2. Under Networking, click Generate Domain.
3. Open the domain in your browser to access the Flowise editor.

Configure authentication

If you set FLOWISE_USERNAME and FLOWISE_PASSWORD, the editor requires authentication. Without these variables, anyone with the URL can access and modify your chatflows.

After logging in, you can also generate API keys within Flowise to protect your chatflow API endpoints for external integrations.

Add LLM API keys

Flowise does not include LLM models. You provide API keys for external providers within the Flowise editor:

1. Open a chatflow in the editor.
2. Add an LLM node (e.g., ChatOpenAI, ChatAnthropic).
3. Click the node and enter your API key in the credentials section.

Flowise encrypts stored credentials using SECRETKEY_ENCRYPT.

Next steps

• Manage Volumes: Resize or back up your persistent storage.
• Set up a custom domain: Use your own domain instead of the generated Railway domain.
• Monitor your app: View logs and metrics for your Flowise instance.