Deploy and Host AudioMuse-AI on Railway

AudioMuse-AI brings AI-powered sonic analysis to your self-hosted music library. It analyzes every track's audio (tempo, key, mood, energy, embeddings) and uses it to generate smart playlists, "instant mix" song paths, similar-song search, clustering and natural-language playlist creation — for Navidrome, Jellyfin, Emby, Plex, LMS and Lyrion, even several servers at once.

About Hosting AudioMuse-AI

This template mirrors the official Docker Compose deployment:

Service	Purpose
audiomuse	Flask web UI + API (`SERVICE_TYPE=flask`), public HTTPS domain
audiomuse-worker	Background analysis / clustering queue workers (`SERVICE_TYPE=worker`)
Postgres	Stores analysis results, configuration and users

Both app services use the official ghcr.io/neptunehub/audiomuse-ai image and connect to Postgres over Railway's private network. Authentication is on by default; a random admin password, JWT secret and API token are generated for every deployment.

After deploy — do this right away:

Open the audiomuse domain. On a fresh install the Setup Wizard opens.
Connect your media server (URL + credentials/API token) and finish the wizard. Until a media server is configured the wizard is reachable without login, so complete it immediately.
Log in as admin with the password from the audiomuse service's AUDIOMUSE_PASSWORD variable.
Start an analysis, then explore playlists, Instant Mix and similar-song search.

Common Use Cases

Automatic mood/energy-based playlists for Navidrome or Jellyfin
"Song path" mixes that smoothly transition between two tracks
Similar-song and natural-language search over your own library
Feeding AudioMuse plugins for Navidrome, Jellyfin and other clients via the API token

Dependencies for AudioMuse-AI Hosting

A reachable media server (Navidrome, Jellyfin, Emby, Plex, LMS or Lyrion)
PostgreSQL (included)

Deployment Dependencies

Implementation Details

Audio analysis is CPU-heavy. The project recommends ~8 GB RAM and 4 vCPUs for large libraries; scale the audiomuse-worker service's resources (or replicas) in Railway to speed up analysis. Plugins that call the API use Authorization: Bearer with the API_TOKEN value.

Why Deploy AudioMuse-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 AudioMuse-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.