Pinepods

Self-Hosted

Open-source self-hosted podcast manager and audio streamer

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Overview

Pinepods is an open-source self-hosted podcast manager designed for privacy-focused audio streaming. It lets you subscribe to podcasts via RSS feeds, stream episodes directly or download them for offline listening, and organize content into custom playlists. With multi-user support, it works for families or teams. Deployment is simple using Docker and docker-compose, accessible to users with basic server knowledge. It prioritizes data ownership, keeping your library and listening history under your control instead of third-party services.

Self-Hosting Resources

Below is a reference structure for docker-compose.yml. ⚠️ Do NOT run blindly. Replace placeholders with official values.

docker-compose.template.yml TEMPLATE

version: '3'
services:
  pinepods:
    image: <OFFICIAL_IMAGE_NAME>:latest
    container_name: pinepods
    ports:
      - "8080:<APP_INTERNAL_PORT>"
    volumes:
      - ./data:/app/data
    restart: unless-stopped

Key Features

  • RSS podcast subscriptions
  • Stream/download audio episodes
  • Multi-user support with custom playlists

Frequently Asked Questions

? Is Pinepods hard to install?

Pinepods uses Docker and docker-compose for easy setup—most users can deploy it in minutes with a simple config file, though basic Docker/server knowledge helps.

? Is it a good alternative to Spotify Podcasts or Apple Podcasts?

Yes! It offers core podcast features while letting you own your data. It lacks proprietary extras like algorithmic suggestions but excels at privacy and customization.

? Is Pinepods completely free?

Absolutely—Pinepods is open-source under the MIT License, free to use, modify, and self-host with no hidden costs.

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People Also Ask about Pinepods

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Tool Info

Pricing Open Source
Platform Self-Hosted

Pros

  • Privacy-focused (self-hosted data ownership)
  • No subscription fees
  • Docker-friendly setup

Cons

  • Requires a server/VPS
  • Limited mobile app options (web-based primary)
  • Lacks algorithmic recommendations

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