# Self-host Immich: I measured what Google Photos replacement actually costs in RAM

> Source: <https://dev.to/karthik_yk/self-host-immich-i-measured-what-google-photos-replacement-actually-costs-in-ram-506>
> Published: 2026-10-30 12:41:21+00:00

*Originally published on [FreeTierWatch](https://freetierwatch-eax.pages.dev/apps/immich-docker), where we actually deploy and measure what we write about.*

Immich is the self-hosted Google Photos replacement — 115,000+ GitHub stars, mobile apps with automatic backup, face recognition, and ML-powered search ("sunset over water" actually finds your sunset photos). It's the app that convinced a generation of self-hosters that leaving Google Photos was possible.

It's also the heaviest thing we've deployed in this series. Where [Vaultwarden runs in 14 MB](https://freetierwatch-eax.pages.dev/apps/vaultwarden-docker) and [Uptime Kuma in ~150 MB](https://freetierwatch-eax.pages.dev/apps/uptime-kuma-docker), Immich is a four-container stack that wants **gigabytes**. We deployed it, uploaded photos, ran the ML search, and measured what it really costs.

Immich ships its own Compose file — use it rather than hand-rolling:

```
mkdir immich && cd immich
curl -LO https://github.com/immich-app/immich/releases/latest/download/docker-compose.yml
curl -L -o .env https://github.com/immich-app/immich/releases/latest/download/example.env
```

Edit `.env` before starting — two changes are non-optional:

```
# generate a real DB password (alphanumeric only — special chars break it)
DB_PASSWORD=$(openssl rand -hex 16)

# set your timezone or every photo's date will be UTC
TZ=Asia/Kolkata
```

Also note `UPLOAD_LOCATION=./library` — point this at your biggest disk; it's where every photo lives.

```
docker compose up -d
```

First start pulled **4.7 GB of images** (server 2.44 GB, machine-learning 1.1 GB, Postgres-with-vector-extensions 989 MB, Valkey 180 MB) and took ~2.5 minutes on our connection. Then open `http://localhost:2283`, create the admin account, and click through onboarding.

| Container | RAM (settled, 5 photos processed) | Notes | 
|---|---|---|
| immich_server | 962 MB | Peaked at 1.3 GB during initial processing | 
| immich_machine_learning | 928 MB | ~200 MB idle; balloons when generating embeddings | 
| immich_postgres | 465 MB | Vector-enabled Postgres (pgvecto.rs/VectorChord) | 
| immich_redis (Valkey) | 14 MB |  | 
| **Total working set** | **~2.4 GB** |  | 

| Other metrics | Measured | 
|---|---|
| Images on disk | 4.7 GB | 
| Database size (5 photos) | 330 MB (ML models + vector indexes dominate) | 
| Upload speed | 5 photos (470 KB avg) in 235 ms via API | 
| **ML smart search** | "sunset over water" → correct photo ranked #1, **2.2 s** on CPU | 
| Server restart to ready | 5 s | 

The headline: **the machine-learning magic is real and runs fine on CPU** — but it's why this stack needs several GB of RAM where a plain photo gallery would need 200 MB. That's the trade: Google-Photos-grade search, Google-Photos-grade resource bill (except you pay in RAM, not rupees).

`DB_PASSWORD` break the connection string — the env file warns you; believe it. Alphanumeric only.`docker compose pull`.` immich-machine-learning` service entirely — uploads and browsing work; you lose smart search and face recognition.`library` folder AND a `pg_dump` — the official docs have a