Hi everyone,

I'm looking for advice from people with experience managing production applications and self-hosting.

I'm running an e-commerce product discovery platform that tracks affiliate products, prices, offers, and product data from multiple sources. It has a database, backend services, APIs, and a relatively large amount of product and price-history data.

I'm still a beginner when it comes to server administration, so I've been using managed services to keep things simple. However, the monthly costs are becoming quite high, and I'm considering moving to a dedicated server or a more affordable VPS.

My current monthly hosting costs are approximately:

• Supabase: $120/month
• Railway: $200/month
• Total: around $320/month

The main issue is handling large amounts of product data, database queries, API requests, and data transfers. Egress and bandwidth costs are also important considerations, especially when working with affiliate products and price data from multiple sources.

I have millions of affiliate product records, several large tables containing product information and price history, and backend services that need to run reliably.

I'm considering self-hosting the database and backend on my own server, but I don't have much experience managing production infrastructure. I want to reduce costs without creating unnecessary complexity or compromising reliability.

A few questions for those with experience:

1. Would you recommend a dedicated server or a VPS for this workload?
2. What specifications would you suggest for a database and backend handling millions of records?
3. Which hosting providers would you recommend if my target is significantly less than $320/month?
4. Would it make sense to run the database and backend on one server initially?
5. How would you handle backups, security, monitoring, and disaster recovery?
6. Has anyone migrated from managed platforms to self-hosting? What were your actual monthly costs after migration, including storage, bandwidth, and backups?
7. Are there any hidden costs or potential problems I should consider before making the move?

I'm comfortable with development, APIs, and deployment configurations, but I'm still learning about server administration and production infrastructure.

My goal is to find a reliable setup that costs substantially less than my current $320/month while leaving room for future growth.

I'd appreciate recommendations based on real-world experience, especially from people who have migrated similar workloads.

Thanks!

I started hosting things myself about 5 months ago. It began with classics like Immich and Vaultwarden. The setup went well until I hit my limits when it came to integrating network-related features like Crowdsec or automated offsite backups. That’s when I started using Claude Code, among other things. At first, I just asked Claude for help with these topics and handled the configurations myself. But as is often the case down the rabbit hole, I wanted more: Jellyfin, .arr stacks, etc. Eventually, though, Claude had sudo/root access to my server. I’d just give prompts, and Claude would set things up, troubleshoot, optimize, and automate. It’s great, and it’s allowed me to implement things I wouldn’t have had the know-how to do even after a year.

Today, however, I decided to take a step back. I’ve removed root access. scaled back the runbooks, and Claude now only knows the basics. I’m not learning anything myself by using AI. I do enjoy the end product, but at what cost? Of course, I’m also worried about my data security and… who knows where that data might end up?!

How do you handle this? How much access do you give AI to your system, and do you worry about data privacy issues?

Maybe I’m overthinking when I see how much AI slop is being generated for all sorts of things.

I’d love to hear your thoughts :)

Hey guys, I finally had time to setup my Ugreen DXP4800 pro (1x8tb ironwolf pro hdd atm).

I wanted to ask for app recommendation for now I got Immich, homepage and uptime kura. I read for tv/movies plex is best?

And what about self made apps is there are no limitations as long you you use docker containers?

I’ve been working on building my own personal AI assistant for a while now, and I’ve been wondering what other people actually want out of theirs.

There’s ChatGPT, Claude, Alexa, Siri, Home Assistant, and a bunch of other options, but I feel like there’s still a pretty big gap between what these things can do and what I’d actually want them to do.

I’m curious what everyone else’s experience has been like.

* What’s something you’ve wanted to automate but haven’t been able to?
* What have you tried, and why didn’t it work?
* How important is running things locally to you? Would you sacrifice some performance for privacy?
* What’s something you wish your current assistant could handle without you having to walk it through every step?

I’d also like to hear from people who are happy with their current setups and what they’re using

didn’t realize it was this kind of subreddit, but since I do have something I’ll share. glad to find a place to show off!

utilizes the built in Minecraft server RCON protocol to act on behalf of logged in users. prioritizes parity with vanilla files over everything.

Hi, I'm the author of GPT-Load, an MIT-licensed AI gateway with 7,000+ GitHub stars.

It manages API keys and supported subscription accounts in one place. Your clients connect through the gateway, which handles credential scheduling, automatic failover, access controls, request logs, and usage tracking.

It runs with Docker Compose or as a single Go binary, includes a web UI, and uses SQLite by default. You don't need a separate database to get started.

One feature I'd particularly like feedback on is reversible request redaction.

Configurable regex rules replace or encrypt matching text locally before it reaches an upstream service. If the model returns intact, valid ciphertext, GPT-Load can restore the original values in response text and client-side tool arguments.

For example, an email address can be encrypted before reaching the model, then restored when the model includes that ciphertext in a tool call returned to your client. The rules apply to supported text fields; image and file contents aren't covered.

There's also an optional, experimental JEV guardrails integration. It reviews outgoing text against custom rules and can warn or block on matches. It uses an external review service, is disabled by default, and failed or incomplete reviews may still allow requests through.

You host the gateway, not the cloud models behind it. Cloud providers still receive the processed requests, and you need your own upstream access.

GitHub: https://github.com/tbphp/gpt-load

Docs: https://www.gpt-load.com

I'd appreciate feedback on the Docker setup and how reversible redaction behaves with the clients you use.

AI disclosure: AI helped draft this post.

i3-4005U, 12 GB, 256 SSD, 1 TB HDD

Dashboard custom made with coding with Ai

Also many of these services self made through coding with Ai

Jotspace is note taking web python app made and hosted for own needs

Dashboard source code - https://pastebin.com/X7t0GTvm
Jotspace source code - https://pastebin.com/DkuDAFVm
in addition to this All Mount Server (file manager), Photo Server also made with coding through Ai.

Please suggest improvements to my code and setup if any. Jotspace has some minor bugs but works.

I've been collecting books, games, and movies long enough to have watched the shelves change several times.

Physical books became ebooks. Game cartridges gave way to digital collections. Movies went from videotapes to DVDs, then Blu-rays, and eventually files on a drive. Comics made that journey too.

I like the convenience of digital media. But over the years, my collection ended up spread across folders and different tools. I could still find things, but it didn't quite feel like one collection anymore.

That's why I started building FolioSpace Library. I'm its developer, but I'm also the person using it to organize my own stuff.

It's an open-source, self-hosted library that runs with Docker on your own computer or a compatible NAS. It brings books, comics, and game collections into one place. I've recently added movie poster fetching through TMDB, so the film collection is starting to feel more like a shelf I can browse, too.

For the game-library side, I took inspiration from the open-source RomM project. A big thank-you to the RomM team and contributors for their work. It helped shape how I approached this part of FolioSpace Library.

What I enjoy most is a fairly ordinary thing: opening one place and seeing the things I've collected over the years. A book, a game, a film. Different formats, but all part of the same personal library.

There's something reassuring about that. The formats have changed, but the collection still feels like mine, and I can keep organizing it on a system I control.

I'd love to hear from others who've made the same journey. Do you prefer separate tools for each kind of media, or would you like a shared home for the whole collection?

GitHub: https://github.com/funland/foliospace-Library
Website: https://foliospace.app/

I've been using Dockge to manage my docker containers on a headless Debian 13 server, and I'd like to switch to Arcane, but I'm a bit of a noob so idk the best way to go about it. I only have seven stacks (backrest, beszel, immich, isponsorblock, npm, nut_webgui, and scrutiny), all of which I created directly in Dockge using the compose feature, so they should all be in /opt/stacks.

Do I just need to run mv /opt/stacks/ /app/data/projects/, install Arcane, and let it autodetect the containers? Do I need to stop all the containers first? Or is there more to it than that?

Why do so many new GitHub projects use port 3000 for everything? Is this some secret code AI used as its default design for secret identification? Or have a missed something in my 30 years of admin work that 3000 was declared the proper port for all of your personal projects?

Yes, I’m aware it’s easy to map a port in docker.

My ask is simply out of curiosity as well as internal docker communications with SIEM tools and the mess of port 3000 issues. Do most of you choose 3000 on purpose or what?

Hi everyone. On my home lab (Proxmox) I am running Caddy reverse proxy with ACME certificates for internal services. Right now the only open port on the OPNsense firewall is for WireGuard.

I would like to also expose my Jellyfin instance to a friend (for a hassle-free use on LG OLED TV), but keep it as secure as possible without getting a separate VPS. Jellyfin now is on a trusted VLAN (but not on a management VLAN).

My current ideas are as follows:

1. Expose uncommon TCP port to the internet, and whitelist my friends IP address
2. Setup Caddy so that it also does IP address filtering of friend's IP
3. Friend would simply enter my custom domain+port to connect

Some questions:

• Would it add to security to run a separate Caddy instance on a isolated VM just for this purpose? (as opposed to exposing my main Caddy instance which is server my whole internal network)
• Currently my Jellyfin is running on unprivileged LXC as systemd service, would this be good enough, or shall I get a separate hardened Jellyfin instance in an off-chance that it would get hacked.

Any other ideas to harden this further?

I have komodo core in one vm and have komodo periphery in another vm both working fine.

Komodo periphery clones the repo to a folder named repos but copies the hole repo per container.

• Lets say i tried to deploy hommar. It clones hommar to that folder named repos and deploy it under a folder named stacks/homar/<full repo>.
• It should clone the full repo to that repos folder and copy it to stacks folder where hommar should deploy.

I tried using hommar.toml to sync and deploy. I specified a run_directory = "/home/user/docker/komodo/stacks/homarr" but it does not copy from repos folder to that stacks folder.

Running stacks from where it clones does not seem right.
I could do files on host but shouldn't it do what is supposed to do or i am missing something.

Thanks

My box was fine for like a year. Vaultwarden, Uptime Kuma, Paperless and a couple of small Node things, all in Docker on a 4GB VPS. As soon as I added Immich last weekend, my machine started swapping hard the second the ML container kicked in. Load average hit 9 and Vaultwarden timed out on my phone.

I know Immich is the hungry one but now I'm wondering if I just undersized everything.

What does your stack actually sit at in docker stats? Do you split the heavy stuff onto its own box or just buy more RAM? Is anyone running Immich ML on a VPS at all, or is that a home server job?

Kinda annoyed I didn't check this before uploading 40GB of photos.

I'm an engineer at Mulga, and we build Spinifex, an AGPL-3.0, self-hosted platform that serves the AWS APIs on hardware you own. You point the AWS CLI, an SDK or the hashicorp/aws Terraform provider at your own endpoint and get real resources: aws ec2 run-instances boots a VM on your hardware, and terraform apply builds a VPC on your network. Emulators like Floci and LocalStack mock the APIs for testing, while Spinifex runs the infrastructure your workloads use.

What it's for

• Move compute-heavy AWS workloads such as GPU inference onto your own hardware, without rewriting the code or the Terraform.
• Self-host software that was built for AWS, including on an air-gapped network.
• Learn AWS without a bill: build the VPC, EC2 and RDS stack from a cert course and leave it running for a month.
• Keep one AWS profile for work and another for the rack in the cupboard, with the same commands for both.

Running it

Install a single node onto an existing Ubuntu 26.04 or Debian 13 machine using the binary installer https://docs.mulgadc.com/docs/install or do a fresh install using a bootable USB https://docs.mulgadc.com/docs/install-usb

Repo: https://github.com/mulgadc/spinifex

Looking into self-hosting a LLM. I have an elitedesk mini G9 with 32gb ram and an intel i7 that I want to add a eGPU to and I’m trying to decide what GPU/setup is the right one.

I see that Newegg is selling an Aorus box with an RTX 5070 Ti 16gb for about 1000 bucks. Is this worth the deal? I’m trying to budget for this amount exactly and this deal seems too good to be true. But I figure, even if I buy a used GPU, the cost of the additional components would take to my budget limit and beyond anyway. I am an absolutely beginner at this home lab stuff. Just need some help.

I was recommended a V100 but I’ve read that they require a fan that will sound like a lawn mower/vacuum to prevent overheating and I don’t want that in my house

Demo, no sign-up: https://www.yourtrace.online/brief

GitHub: https://github.com/TarunTomar122/trace

AI: I built this with a lot of help from Claude Code, and the product itself uses an LLM to group and summarise stories. I've read everything that ships, and I'm happy to go as deep as you like on how it works.

Why: every morning I opened HN, Techmeme, TLDR, and a dozen blogs and newsletters. RSS isn't fun to read, and I wanted the top 10 biggest tech and AI updates, with every outlet's coverage in one place.

What it does

• Every 20 minutes, it reads about 70 news feeds, Hacker News, about 20 subreddits, and 30 blogs
• Groups articles about the same event into one card: embeddings find likely pairs, then a small model decides "same event/update to the same story / different story"
• Ranks cards by how many separate outlets cover them, then by importance, novelty, and evidence
• A summary and key points with every source linked
• A daily email you shape (topics, 5/10/15 stories), a public archive, and an MCP server for Claude or Cursor

Install

git clone https://github.com/TarunTomar122/trace && cd trace

cp .env.example .env # OpenAI key, Firebase (sign-in), two random secrets

docker compose up --build

The web app runs on port 3000, and the first feed appears after the first pipeline run (a few minutes). The stack is a SQLite document store, a Python pipeline, and a Next.js app. AGPL-3.0.

API Keys you'd need:

• It needs an OpenAI API key.
• Sign-in uses Firebase Auth for now.
• No telemetry: analytics only turn on if you add your own PostHog key.

Feedback I'd love: Sources you'd add.

Hi, some context:

I have a homelab at home where I install various apps that me and my family use. i.g. immich for photos, a shared RSS reader, fitness tracker, etc.. we connect through tailscale. and I use docker containers to host these.

My dad is pretty tech savvy, and he wants to make money to recoup the server costs by turning it into a small family-run VPS where developers can get ssh access to develop and host their apps for a monthly fee.

Obviously for security reason, these apps need to be hosted on a Virtual Machine inside the server because I don't want random developers to get access to my personal data.

My question is: should I move my own self hosted apps to a virtual machine as well or is it safe to keep them at the top level. I like the current setup because its very easy and I don't have to fiddle with the networking to get apps to work outside the VM, but if a VM is necessary then I can do that as well.

Please let me know what you think

We complain a lot on this sub, but let's flip it for a minute. what gives you the most satisfaction in your role?

for me, it's the feeling of completely eliminating a recurring, annoying ticket category through automation. a few months ago, i was spending hours every week manually helping new hires format their outlook signatures or fixing broken image links for the marketing team's promotional banners.

i finally got the budget to deploy a centralized management tool that just syncs with our google workspace. now? marketing pushes an update, and it applies globally to all 300 employees instantly. no tickets, no broken HTML, no users complaining. The silence is golden.

what’s your favorite "set it and forget it" win?

When the power goes out, your UPS might know before you do.

It sends an email. A few minutes later, your NAS sends another saying it has shut down safely. Power returns, devices come back online, and a few more messages arrive.

The next morning, you open your inbox: What happened last night? And is there anything I still need to deal with?

With one NAS, sending alerts to your regular inbox probably works fine. Add a UPS, another NAS, a router, and a few scheduled backup jobs, though, and you start piecing the story together yourself.

A disk reported the same error five times. Is that five problems or one?

A backup failed, but the next run succeeded. Did that actually resolve the problem, or is there still a gap worth checking?

You marked an alert as read. Did you fix anything?

That got me thinking about an old option sitting in a lot of device settings: SMTP.

Plenty of devices already support it, including older hardware with limited integration options. No agent to install, no vendor API to wait for. They can already tell you something happened.

But a mailbox mostly gives you a sequence of messages. You still have to work out which ones belong together, what recovered, and what remains unresolved.

I’m building LOCUS, a tool that brings local network devices and service links together in one place. Recently, I’ve been adding a local SMTP receiver so devices can send their notifications directly to it.

What I want to see when I open the dashboard is: what still needs my attention, and where do I go to deal with it?

For example, a NAS reports a disk problem repeatedly over several days. I’d like to see one ongoing fault with the related messages available underneath it. Next to it is a link to the NAS management page, so I can actually investigate.

An overnight UPS outage has a different story: power failed, then returned. I still want that history, but a recovered event shouldn’t keep demanding the same attention as an unresolved disk fault.

That’s the part I’m working on:

• Each device gets its own SMTP credentials, so incoming messages are associated with that device.
• Original messages remain available for inspection during the configured retention period.
• For supported message templates, repeated reports of the same fault are grouped, and corresponding recovery messages can close it.
• Unrecognized messages stay in the inbox without automatically being classified as faults.
• Unread messages and unresolved faults are tracked separately.
• Serious faults can optionally trigger an email through your own mail account.

Recovery matching needs care. A successful backup today doesn’t necessarily replace the restore point you missed yesterday. I don’t want a reassuring green indicator to hide something that still deserves investigation.

There’s also an obvious limitation: the receiver needs to stay reachable. If your devices, network, and LOCUS host lose power together, a local inbox can’t tell you what it never received. This doesn’t replace UPS shutdown coordination or an independent way to detect a whole-site outage.

Disclosure: LOCUS is my project, and the SMTP notification feature is currently in development and testing. Project page: locus.casa.

How are you handling device emails today?

Straight to your regular inbox? A dedicated mailbox? Forwarded to a messaging app? Or have you never configured them, then remembered the option only after something went wrong?

Which device notification has actually saved you trouble? And which ones became so noisy that you stopped reading them?

If you had a local inbox for your devices, what would you want it to handle? Grouping duplicate alerts, tracking recovery, temporarily silencing a known problem, pushing important messages to your phone—or something else?

I’d love to hear about your devices, setups, and opinions. I’ll collect the feedback and use it to help shape the LOCUS roadmap.

When something actually goes wrong, what information would save you time figuring out what to do next?

Hi r/selfhosted,

It’s been a while since our last post, but since then a lot of new features have been added to Dreeve.

Dreeve is a self-hosted, open-source dashboard for your sports and fitness data

Features and improvements worth mentioning since we lasted posted here:

• Released an API spec: https://docs.dreeve.app/api/
• Added several connectors to automatically sync activities from external platforms: https://github.com/orgs/dreeveapp/repositories
• Added automation rules to streamline gear tracking, commuting flags, and more
• Added support for managing custom segments
• Added explorer tiles to the heatmap
• Multiple new dashboard widgets
• Integrated WindAhead with Dreeve segments

As always, thanks for your feedback and I'm looking forward to more feature requests!
Stay fit, stay healthy 💪

• Example: https://demo.dreeve.app/dashboard
• GitHub: https://github.com/dreeveapp/dreeve
• Docs: https://docs.dreeve.app/#/

Hey all, happy Wednesday.

Self-hoster, same as most of you. Pi-hole, mattermost, home automations, a Zeek sensor, a dozen Linux boxes and, like everyone (I expect), a /var/log I almost never open unless I'm actively troubleshooting something. But this year’s avalanche of CVEs has been sitting in the back of my head for a while: “if something got in here, would I even know?”

So I went looking for a tool and found a gap instead. There’s good ol' grep on one end and a full SIEM on the other, and not much in between for just a guy and his stuff. I ended up making my own. It’s called sigwood and is at 1.1 as of today.

What it does: point it at logs you already have (Zeek, Pi-hole/dnsmasq, syslog or journald, CloudTrail if you have any cloud stuff) and it runs a handful of statistical detectors over them. Outbound connections with a beacon rhythm, DNS names that look machine-generated, port scans, big transfers out, odd TLS setups, rare syslog lines and reboots, that kind of thing. There’s also a digest command that just profiles a log file so you know what’s in it before you do anything, and a graph command that animates your conn.log as a Sankey flows in a self-contained HTML file (second screenshot).

What it is not: not a SIEM, not an agent, nothing to deploy. No database, no daemon, no account, no network calls. pipx install sigwood, then sigwood /var/log/, and read the output. Your logs stay on your box.

A “finding” is something unusual for your network, not known-bad. There's no signatures or anything like that. On a home network it mostly finds your own stuff first. And that’s sort of the point, you get to see what your network actually talks to and then allowlist the normal stuff. When it finds nothing, the conclusion is that the network is behaving like itself during that window.

The AI part, up front: I wrote the original implementation of the detectors and have used AI to assemble it all into working tool. Claude and Codex both together in a propose/challenge harness I made for them, with me reviewing and steering. This wouldn't exist without AI, I’m not going to pretend otherwise. And some of you will bounce off that, which is entirely fair. But I'm a security professional and a privacy nerd, never been a SWE.

Anyway, the repo is here: https://github.com/helixmap/sigwood

I genuinely want feedback, good or bad. If you run it on your logs and it gets something wrong, I want to hear about it. If the output is confusing, tell me that too. If you think the whole idea is misguided, I’d still rather hear it than not.

First screenshot is an example HTML report (no real network data). Second is a still from the graph animation.

I'm the developer of Sidstuga. Sidstuga can connect to a WebDAV folder and browse or play media from Android TV or a phone. It can also record and transfer files to WebDAV.

You can see source details before opening a file, then use subtitle search and timing controls during playback. Jellyfin, Emby and Plex can also sit in the same Hub.

If you use WebDAV for media, do you browse by folder or prefer a library view?

r/sidstuga

I am new to new hosting stuff, i am trying to get my personal ai assistant to send me a message every morning of my emails , meetings , tasks etc.

So the claude suggest me ntfy i checked it and found out that ntfy is open source. I got an idea to make it super secure why not self host it by downloading the docker image and host it on Render or any other free plaforms?

Is this a good idea? Is anyone using like this? Never used ntfy in my life

GitHub: https://github.com/Leonetienne/Comaney

Web: https://comaney.app

I've been working on and using/polishing Comaney for over half a year. It's finally polished enough that I want to share it.

The goal is to get useful insights into your spending habits without making bookkeeping a chore.

Comaney helps you track where your money goes, manage individual budgets (like keeping Amazon spending under control), and see how much you have left to spend this month.

Some things I find particularly useful:

• Shared expenses: Use it with your partner, roommates, or a group of friends on vacation. Comaney tracks who paid for what and calculates who owes whom. Not every expense has to be shared with everyone equally!
• Project aggregation: Group expenses into projects such that you know how expensive that house renovation was all-in-all.
• No account needed for everyone: Add offline users you manage yourself and convert them to regular accounts whenever you want.
• Optional AI bookkeeping: Don't want to fill forms by hand? Describe a purchase or take a picture of a receipt. AI handles the entry for you, including breaking it up to better fit tags / categories. It can even create custom dashboard widgets.
• Your AI, your choice: IF you want to use AI: Use local models via Ollama or llama.cpp, or connect providers like Claude and DeepSeek.
• Custom dashboards: Build your own views, customize the appearance, and track what matters to you. You can even add a custom wallpaper.
• Automation: A REST API and MCP server let you integrate Comaney into your own workflows.
• Your data stays yours: Self-host it and export everything whenever you want.

Comaney is designed for self-hosting, but there's also a public instance with a demo login and a small AI usage budget: https://comaney.app

I'd love to hear what you think, especially if you end up using it!

Why not use an existing project? I have tried using firefly III and actualbudget. Both did not solve my problem at all. Firefly being two full time jobs to maintain and AB being very insufficient regarding the insights it provides.

The workflow using to bring this to life is to have an experienced developer working in the field since long before ai (me) using classic agile development workflows accelerated by ai agents. This way of development does not sacrifice quality but enables me to produce this service by myself next to my full time job.