Deploy and Host FlowiseAI on Railway

Flowise is an open-source visual builder for LLM apps: drag nodes together to make chatbots, AI agents and RAG pipelines over your documents, then call them through an API or embed them in a website. This template runs it on Railway as one service, with its SQLite database, credentials and uploads on a volume, and the admin account created from variables before the app goes online.

This is a community template, not made or supported by FlowiseAI.

About Hosting FlowiseAI

A fresh Flowise opens a sign-up page that makes whoever loads it first the owner of the instance. This template closes that gap: on the first boot it starts Flowise on an internal port, creates the admin from ADMIN_EMAIL and ADMIN_PASSWORD, and only then starts it on the public domain. Sign-up is closed from then on.

It also:

keeps the database, the encryption key for your stored API keys, logs and uploaded files on a volume
sizes Node's memory to your plan, so Flowise collects garbage instead of being killed on small plans
tells Flowise it's behind Railway's proxy, so https and client addresses are read right

Log in: open the service's domain and sign in with ADMIN_EMAIL and the ADMIN_PASSWORD from the Variables tab. Then add your model provider's key under Credentials and build a flow.

For heavy traffic, Flowise also runs in queue mode with workers, Postgres and Redis; that's a separate template.

Common Use Cases

Chatbots that answer from your documents (RAG), embedded on a website
AI agents with tools: web search, APIs, code, databases
Prototyping LLM workflows and exposing them as an API for your app

Dependencies for FlowiseAI Hosting

An API key for a model provider (OpenAI, Anthropic, Google, OpenRouter and others), or a model server such as Ollama

Deployment Dependencies

Implementation Details

Tested on Railway: the first boot created the admin before the domain went live; a second sign-up was refused ("You can only have one organization"); the admin logged in through the API, a wrong password and anonymous API calls got 401; the account and data survived a redeploy. Building a flow with a real model provider wasn't tested.
Upstream image fixes: the official flowiseai/flowise:3.1.4 image installs connect-sqlite3 0.9.17, which breaks Flowise's SQLite login sessions at startup (this.db.exec is not a function); this image swaps in 0.9.18. Three nodes still fail to load in that image (ReAct Agent for Chat Models, ReAct Agent for LLMs and AWS Bedrock LLM); the tool-calling agents and the other nodes load.
Variables after the first boot: ADMIN_EMAIL and ADMIN_PASSWORD are read once. Change your email or password inside Flowise afterwards; editing the variables does nothing.
Memory: needs 1 GB. Flowise peaks at about 640 MB while it loads its nodes and idles around 500 MB, so it can't start within the Free plan's 0.5 GB; there the service stays up and its page says to raise the memory limit. At 1 GB it ran through a soak with no out-of-memory kills, and the template sizes Node's heap to the limit you set.
Image: ghcr.io/two-workspaces/flowiseai, built from this repository's Dockerfile on flowiseai/flowise:3.1.4.

Why Deploy FlowiseAI 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 FlowiseAI 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.