Wardrowbe vs OpenWardrobe: Which Should You Self-Host?

If you're looking at open-source wardrobe apps, you've probably found two names: Wardrowbe and OpenWardrobe. Both are free, both can be self-hosted, and both promise to help you organize your closet. But they solve different problems at different levels of sophistication.

One thing to know up front: the original OpenWardrobe repository (github.com/OpenWardrobe/app) was archived by its owner in 2025. Forks such as github.com/Adeity/OpenWardrobe still exist, but the project is not actively maintained.

This comparison covers what each app actually does, where they diverge, and which one fits your use case. No marketing spin — just a straight feature comparison from people who obviously built one of them and want you to make an informed choice.

Quick Comparison

AI Capabilities

This is the biggest difference between the two apps.

Wardrowbe uses vision AI to analyze clothing photos automatically. Photograph an item, and the system detects its type (shirt, pants, jacket), color, pattern, style (casual, formal, streetwear), and formality level. No manual tagging required. The same AI powers outfit suggestions that factor in weather, occasion, and your personal preferences — and it gets better over time as it learns what you accept and reject.

OpenWardrobe has no AI clothing recognition, so you add items and tags yourself. Its README lists a virtual closet, outfit creator, outfit planner, weather-based suggestions and wardrobe insights, but it still says "Screenshots (Coming Soon!)" and I could not verify that any of those work, so I treat them as plans more than features.

If your goal is "I want to see my clothes on a screen," OpenWardrobe aims at that. If your goal is "I want an AI that tells me what to wear today based on the weather," that's Wardrowbe territory.

Self-Hosting Experience

Both apps can run on your own hardware, but the experience is different.

Wardrowbe

Four Docker containers: backend (FastAPI), frontend (Next.js), worker (background AI jobs), and PostgreSQL. Optionally, a fifth container for Ollama if you want local AI. The setup is a standard docker-compose up -d followed by a database migration. Total time: about five minutes.

The AI flexibility is worth emphasizing. You choose where image analysis happens — on your machine with Ollama for complete privacy, on a self-hosted LLM server, or via an external API. It's a runtime configuration change, not a code modification. This matters for self-hosters who want data to never leave their network.

OpenWardrobe

OpenWardrobe is a Flutter app with Supabase (PostgreSQL, Auth, Storage) as its backend. Self-hosting it means running your own Supabase backend and building the Flutter app yourself, which is more work than a single Docker image would suggest. I did not find a maintained Docker setup for it.

Neither app requires unusual hardware. Both run on a Raspberry Pi, NAS, or cheap VPS. Wardrowbe uses more resources because of the AI component, but the base app (without local AI) is still lightweight — roughly 1 GB RAM for the full stack.

Mobile Access

Wardrowbe ships a native mobile app built with Expo (React Native), available for both iOS and Android. The mobile app handles everything: photographing items, browsing your wardrobe, getting outfit suggestions, logging what you wear, and managing family groups. It connects directly to your self-hosted backend — same app whether you're on cloud or self-hosted.

OpenWardrobe is built with Flutter, which can target mobile, but I found no listing in the App Store or Google Play, so you would have to build the app yourself. For a wardrobe app, where the main interaction is "take a photo of this shirt", an installable mobile app matters more than it would for most self-hosted tools.

Weather and Outfit Intelligence

Wardrowbe integrates with Open-Meteo (free, no API key required) to pull current weather conditions. Outfit suggestions account for temperature, rain, wind, and humidity. The system won't suggest a wool blazer on a 35°C day or sandals when rain is forecast.

Beyond weather, the suggestion engine considers:

• What you've worn recently (to avoid repetition)
• Your style preferences and feedback history
• Item pairing compatibility (select one item, get complete outfits)
• Occasion (casual, work, formal)
• Family member ratings if you're in a household group

OpenWardrobe's README lists an outfit creator, outfit planner and weather-based suggestions, but with no screenshots and an archived repository I cannot confirm they work. It has no AI behind them.

Privacy Model

Both apps score well on privacy since both can be self-hosted. Your data stays on your hardware in either case.

Where they differ is in AI data handling. Wardrowbe gives you explicit control over where AI processing happens. Run Ollama locally and your clothing photos never touch an external server. Run a cloud API and you accept the trade-off knowingly. The choice is yours, and it's clearly documented.

OpenWardrobe sidesteps the AI privacy question by not having AI clothing recognition. This is a legitimate advantage for users who want zero complexity in their privacy model: if no AI exists, no AI data handling policy is needed.

When to Choose OpenWardrobe

OpenWardrobe makes sense if:

• You are comfortable running your own Supabase backend and building a Flutter app, and want to work from an archived codebase or a fork
• Manual tagging doesn't bother you (or you prefer it)
• You do not need AI tagging
• You want the lightest possible resource footprint
• You are fine with a project that is no longer maintained

It reads as an early-stage closet inventory project. Not every user needs AI, but with the original repository archived I would only pick it to learn from or fork.

When to Choose Wardrowbe

Wardrowbe is the better fit if:

• You want AI to handle tagging so you don't have to manually categorize every item
• Daily outfit suggestions based on weather and personal style matter to you
• You need a native mobile app for photographing clothes and checking suggestions on the go
• You share a household and want family wardrobe features
• You want the option to start self-hosted and move to cloud later (or vice versa)
• Active development and regular updates are important to you

Getting Started

1. Self-host Wardrowbe — Docker Compose, free forever, full feature set
2. Or start a free trial of the cloud version if you want zero infrastructure

Check out all features or compare pricing options.