Ask HN: What's the 2025 stack for a self-hosted photo library with local AI?
My goal is to create a system with smart search capabilities, and one of the most important requirements is that it must run entirely on my local hardware. Privacy is key, but the main driver is the challenge and joy of building it myself (an obviously learn).
The key features I'm aiming for are:
Automatic identification and tagging of family members (local face recognition).
Generation of descriptive captions for each photo.
Natural language search (e.g., "Show me photos of us at the beach in Luquillo from last summer").
I've already prompted AI tools for a high-level project plan, and they provided a solid blueprint (eg, Ollama with LLaVA, a vector DB like ChromaDB, you know it). Now, I'm highly interested in the real-world human experience. I'm looking for advice, learning stories, and the little details that only come from building something similar.
What tools, models, and best practices would you recommend for a project like this in 2025? Specifically, I'm curious about combining structured metadata (EXIF), face recognition data, and semantic vector search into a single, cohesive application.
Any and all advice would be deeply appreciated. Thanks!
Stock NC gets you a very solid general purpose document management system and with a few addons, you basically get self hosted SharePoint and OneDrive without the baggage. The images/pictures side of things has seen quite a lot of development and with some addons you get image classification with fairly minimal effort.
The system as a whole will quite happily handle many 100,000 files with pretty rubbish hardware, if you are happy to wait for batch jobs to run or you throw more hardware at it and speed up the job schedules.
NC has a stock phone app which works very well these days, including camera folder uploads. There are several more apps that integrate with the main one to add optional functionality. For example notes and voip.
It is a very large and mature setup with loads of documentation and hence extensible by a determined hacker if something is missing.
https://immich.app/
We no longer are auto uploading to Google or Apple.
So far, I really like it. I haven't quite gone 100%, as we're still uploading with Synology's photo app, but Immich provides a much more refined, featured interface.
I think you can use Immich to just look at a folder and not use the backup from phone bits.
May I ask why? Just curious as the main reason I use Immich is for the auto upload
Edit: Ugh. Can’t read. I somehow read don’t auto upload to Immich.
No hate here, I'm really grateful for what they've achieved so far, but I think there's a lot of room for improvement (e.g: proper R/W query split, native S3 integration, faster endpoints, ...). I already mentioned it in their channel (they're a really welcoming community!) and I'm working on an alternative drop-in replacement backend (written in Go) [1] that will hopefully bring all the needed improvements.
TL;DR: It's definitely good, especially for an open-source project, and the team is very dedicated - but it's definitely not Postgres-good
[1]: https://github.com/denysvitali/immich-go-backend
A lot of existing tooling supports the s3 protocol, so it would simplify the storage picture (no pun intended).
Near zero maintenance stack, incredibly easy to update, the client mobile apps even notify you (unobtrusively) when your server has an update available. The UI is just so polished & features so stable it's hard to believe it's open source.
2. The software is provided without modification; I think it would be stranger to remove the encryption.
This is exactly how I self-host Ente and it has been great.
Machine leaning for image detection has worked really well for me, especially facial recognition for family members (easy to find that photo to share).
I have the client on my Android mobile, Fire tablet (via F-Droid), and my Windows laptop.
My initial motivation was to replace "cloud" storage for getting photos copied off the phone as soon as possible.
No. (I self-host Ente and use their published ios app.)
I've used gemma to process pictures and get descriptions and also to respond questions about the pictures (eg. is there a bicycle in the picture?). Haven't tried it for face recognition, but if you already have identified someone in one photo, it can probably tell you if the person in that photo is also in another photo
Just one caveat, if you are processing thousands of pictures, it will take a while to process them all (depending on your hardware and picture size). You could also try creating a processing pipeline, first extracting faces or bounding boxes of the faces with something like opencv, and then passing those to gemma3
Please post repo link if you ever decide to open source
And for sure, if I get this to a point where it's open-source, I'll post the link here!
The dev is really reluctant of accepting external contributions, which has driven away a lot of curious folks willing to contribute.
Immich seems to be the other extreme. Moving really fast with a lot of contributors, but stuff occasionally breaks, the setup is fiddly, but the Ai features are 100x more powerful. I just don't like the ui as much as photoprism. I with there was some kind of blend of the two, on a middle ground of their dev philosophies.
As of now, I use SentenceTransformer model to chunk files, blip for captioning (“Family vacation in Banff, February 2025”)) and mtcnn with InsightFace for face detection. My index stores captions, face embeddings, and EXIF metadata (date, GPS) for queries like “show photos of us in Banff last winter.” I’m working on integrating ChromaDB for faster searches.
Eventually, I aim to store indexes as:
{
}I also built an UI (like Spotlight Search) to search through these indexes.
Code (in progress): https://github.com/neberej/smart-search
I'm using docker compose to include some supporting containers like go-vod (for hardware transcoding), another nextcloud instance to handle push notifications to the clients, and redis (for caching). I can share some more details, foibles and pitfalls if you'd like.
I initiated a rescan last week, which stacks background jobs in a queue that gets called by cron 2 or 3 times a day. Recognize has been cranking through 10k-20k photos per day, with good results.
I've installed a desktop client on my dad's laptop so he can dump all of the family hard drives we've accumulated over the years. The client does a good job of clearing up disk space after uploading, which is a huge advantage in my setup. My dad has used the OneDrive client before, so he was able to pick up this process very quickly.
Nextcloud also has a decent mobile client that can auto-upload photos and videos, which I recently used to help my mother-in-law upload media from her 7-year-old iPhone.
Gonna check the apps that you mentioned. Feel free to share more details of your set up. Why are you running 2 instances? Edit: I see, probably for the memories app.
https://help.ente.io/self-hosting/
take my photo catalog stored in google photos, apple pictures, Onedrive, Amazon photos. collate into a single store, dedupe. Then build a proper timeline and geo/map view for all the photos.
Example: https://rclone.org/googlephotos/#limitations
Glaring example:
> The current google API does not allow photos to be downloaded at original resolution. This is very important if you are, for example, relying on "Google Photos" as a backup of your photos. You will not be able to use rclone to redownload original images. You could use 'google takeout' to recover the original photos as a last resort
In fact I would go so far as to say my personal photo management never really recovered from the transition.
The addition of an AI tool is a great idea.
I expect we will see a Qwen 3VL soon.
https://ente.io/
https://photonix.org/
https://github.com/LibrePhotos/librephotos
https://github.com/photoprism/photoprism
1. The Immich app's performance is awful. It is a well known problem and their current focus. I have pretty high confidence that it will be fixed within a few months. Web app is totally fine though.
2. Some background operations such as AI indexing, face detection and video conversion don't work gracefully when restarted from scratch. They all basically first delete all the old data, then start processing assets. So for many days (depending on your parallelism settings and server performance) you may be completely missing some assets from search or converted videos. But you only need to do this very rarely (change encoding settings and want to apply to the back catalog or switch AI search model). I don't upload at a particularly high rate but my sever can very easy handle the steady state.
1 is pretty major but being worked on and you can work around it by just opening the website. 2 is less important but I don't think there is any work on it.
It gives a sort of high level system overview that might provide some useful insights or inspiration for you.
Do you need the embeddings to be private? Or just the photos?
I pay them for service/storage as it’s e2ee and it doesn’t matter to me if they or I store the encrypted blobs.
They also have a CLI tool you can run from cron on your NAS or whatever to make sure you have a complete local copy of your data, too.
https://ente.io - if you use the referral code SNEAK we both get additional free storage.
The stack is hacky, since it was mostly for myself...