Best Self-hosted Alternatives to Stats for Spotify

YourSpotify is a self-hosted application that tracks your Spotify listening activity and presents it in a web dashboard. It includes a backend that periodically polls the Spotify Web API and a frontend for exploring your personal listening statistics.

Key Features

• OAuth-based Spotify account connection using your own Spotify developer app keys
• Periodic synchronization of recently played tracks via the Spotify API
• Interactive dashboard to explore listening statistics and trends
• Import of historical listening data from Spotify privacy exports (StreamingHistory and extended endsong files)
• Multi-user support with optional control to disable new registrations
• Configurable timezone handling for accurate, user-specific statistics

Use Cases

• Personal analytics dashboard for Spotify listening habits (top tracks, artists, trends)
• Self-hosted alternative to third-party music tracking services for improved data control
• Import and visualize long-term listening history from Spotify GDPR/privacy exports

Limitations and Considerations

• Without imports, initial synchronization is limited to roughly the last 24 hours due to Spotify API constraints
• Requires a MongoDB instance and a Spotify developer application (client ID/secret and redirect URI)

YourSpotify is well-suited for users who want to self-host a private, dedicated dashboard for Spotify listening stats. With privacy-data imports and a simple Docker-based deployment, it can provide both recent and long-term insights into your streaming history.

Maloja is a self-hosted music scrobble database that stores your listening history and turns it into personal statistics, charts, and timelines. It focuses on private tracking and flexible tagging rather than social features.

Key Features

• Web interface with charts for top artists, tracks, and albums across time ranges
• Scrobble ingestion via a standard-compatible API (works with clients that support custom servers)
• Manual scrobbling from the web UI for offline listening (vinyl, radio, etc.)
• Support for associated artists and multi-artist tracks to improve chart accuracy
• Custom artist/track images upload and local management
• Proxy scrobbling to forward listens to other scrobble services
• Import tools for historical data (e.g., Last.fm exports, Spotify exports, ListenBrainz exports, and other Maloja instances)

Use Cases

• Run a private alternative to hosted scrobble platforms to track listening over time
• Build a personal “listening dashboard” for home servers and music setups
• Collect and analyze scrobbles from multiple players and devices via API keys

Limitations and Considerations

• Artwork enrichment typically requires external API keys (e.g., Last.fm/Spotify)
• The project recommends containerized deployment to avoid dependency/version mismatches

Maloja is well-suited for users who want ownership of their listening data and simple, accurate statistics without the social-networking features of larger scrobble platforms.

Koito is a modern, themeable scrobbler that implements the ListenBrainz-compatible scrobbling API and provides a web UI to explore listening history and trends. It is built for self-hosting with a focus on performance, theming, and interoperable relaying of scrobbles.

Key Features

• ListenBrainz-compatible scrobbling endpoint compatible with clients that support custom ListenBrainz URLs
• Scrobble relay to forward listens to other ListenBrainz-compatible servers
• Import support for Maloja, ListenBrainz, LastFM, and Spotify export formats
• Automatic metadata fetching from MusicBrainz and cover images from Deezer and Cover Art Archive
• Themeable web UI with multiple built-in themes and support for custom themes
• Docker-friendly deployment with example docker-compose and a PostgreSQL backend
• Focus on performance and a sleek TypeScript-based frontend for responsive dashboards

Use Cases

• Run a personal scrobbling server to store and visualize your listening history locally
• Migrate or import historical scrobbles from LastFM, Spotify, or other ListenBrainz-compatible sources
• Relay scrobbles to a public ListenBrainz instance or other compatible collectors while keeping a private copy

Limitations and Considerations

• The project is under active development and marked as unstable; users may encounter bugs and breaking changes
• Current deployments use PostgreSQL as the primary supported database, requiring a Postgres instance
• Feature set and testing coverage are still growing; some advanced analytics or integrations may be missing

Koito is a practical option for users who want a fast, themeable, self-hosted scrobbler with import and relay capabilities. It emphasizes interoperability with ListenBrainz and provides a modern UI for exploring listening data.

Lidify is a music discovery tool that generates artist recommendations from a Lidarr-managed collection. It queries external music metadata services and can add recommended artists back into Lidarr, running as a self-hosted web application.

Key Features

• Generates artist recommendations based on selected artists tracked in Lidarr
• Integrates with Last.fm for discovery (Last.fm is the primary supported data source)
• Can add recommended artists to Lidarr via the Lidarr API with configurable search and quality settings
• Distributed as a Docker image and configurable via environment variables
• Configurable options include API keys, Lidarr address and API key, quality/metadata profiles, auto-start and dry-run modes
• Simple web UI for selecting artists, reviewing recommendations, and managing integration behavior

Use Cases

• Expand a Lidarr-managed music library by discovering similar artists to add automatically
• Seed a new music collection based on existing favorite artists tracked in Lidarr
• Curate and validate recommended artists before importing into a Lidarr workflow

Limitations and Considerations

• Spotify integration is no longer supported due to API changes; Last.fm is the supported discovery provider
• Requires valid Last.fm API credentials and a reachable Lidarr instance with an API key
• Matching accuracy depends on external metadata quality and may require manual review for ambiguous artist results

Lidify is a lightweight tool for automating music discovery around a Lidarr installation, focusing on Last.fm-based recommendations and easy Docker-based deployment.

Jellyfin Rewind is a web-based tool that creates a Spotify Wrapped–style report from your Jellyfin music library. It connects to a Jellyfin server, pulls playback and library data, processes it in the browser, and produces aggregated listening statistics and a downloadable report.

Key Features

• Client-side data processing: most aggregation and analytics run in the user's browser so no server-side data collection occurs
• Aggregates listening stats such as top artists, tracks, albums and other summary metrics
• Downloadable Rewind report for offline storage or future use
• Deployable as a static site or via a provided Docker image for local network hosting
• Designed to work with standard Jellyfin APIs and typical Jellyfin playback data

Use Cases

• Personal annual listening recap for users who host music on Jellyfin
• Self-hosted privacy-focused alternative to commercial "year in review" services
• Generate shareable or archival reports of listening habits for personal record-keeping

Limitations and Considerations

• Browser CORS and mixed-content restrictions may require self-hosting if the Jellyfin server is on a local/private address
• Accuracy depends on the availability and completeness of Jellyfin playback data; data is more comprehensive if server-side playback reporting plugins are enabled
• Large libraries can require noticeable client-side processing time and memory during report generation

Jellyfin Rewind is a lightweight, privacy-oriented way to visualize and export personal music listening statistics from a Jellyfin server. It is optimized for self-hosting and local network use and is focused on delivering an easy-to-read yearly recap.

Plex Rewind is a self-hosted web application that presents Plex usage statistics and viewing habits in a clean, organized dashboard. It aggregates data from Tautulli, Overseerr, and Plex to provide up-to-date insights, with an optional configurable rewind window for reviewing historical activity.

Key Features

• Fully responsive: viewable on desktop, tablet and mobile via Tailwind.css.
• Fully dynamic: data is always the latest available.
• Anonymization: anonymize user information to protect privacy.
• Rewind: view statistics for a customizable time period.
• Dashboard: glanceable overview of library activity for the server.
• Real-time activity: live updates powered by TanStack Query.
• Integrates with Tautulli and Overseerr: show statistics and request data.
• Plex login: secure login using NextAuth.js.
• PWA support: installable on devices thanks to Serwist.
• Easy deployment: runs in a Docker container.
• Animations: subtle UI motion for a refined experience.
• Multi-language support: English, Estonian, French and German.

Use Cases

• Home Plex server analysis: monitor viewing habits, library activity, and trends.
• Shared Plex environments: provide a sharable dashboard for family or friends.
• Server maintenance and capacity planning: spot popular libraries or genres to plan storage.

Conclusion: Plex Rewind is designed to be self-hosted and privacy-conscious, offering a centralized view of Plex activity with an optional rewind window. It leverages Tautulli/Overseerr data and modern web technologies to deliver a responsive, extensible analytics dashboard.

Trackly is a web application that enhances a Jellyfin music library with a polished, responsive interface and automated release tracking. It scans artists in a single music library, looks up new releases via the MusicBrainz API, and can post notifications to Discord using webhooks.

Key Features

• Modern React + Vite frontend with Tailwind CSS for a responsive music-focused UI
• Integrates with Jellyfin music library folder structure to surface artists, albums, and artwork
• Periodic automatic scans for new artist releases using the MusicBrainz API with configurable cron schedule
• Optional Discord webhook notifications for new releases and scan completion, with role mention support
• Docker and Docker Compose support with multi-architecture images (amd64 and arm64) for easy deployment
• Simple persistent data directory for application state; one Trackly container is intended per music library

Use Cases

• Maintain a browsable visual catalog of a Jellyfin music collection with artist pages and album artwork
• Automatically detect and notify a community (via Discord) when tracked artists release new music
• Run on small single-board computers (Raspberry Pi) or servers using Docker for lightweight library tracking

Limitations and Considerations

• Each Trackly container tracks a single music library; multiple libraries require separate containers
• Release detection depends on the MusicBrainz API and matching accuracy may vary; API rate limits apply
• Requires music folders to follow the expected Jellyfin-oriented structure and include backdrop/cover images

Trackly is suited for Jellyfin users who want a focused release-tracking UI and Discord notifications. It is lightweight, container-friendly, and optimized for single-library deployments.