System requirements
The short answer: any Apple Silicon Mac running macOS 13 or later, with enough free disk for your retention plan.
macOS versions
Section titled “macOS versions”| Version | Status |
|---|---|
| macOS 13 Ventura | ✅ minimum |
| macOS 14 Sonoma | ✅ |
| macOS 15 Sequoia | ✅ |
| macOS 26 (current) | ✅ |
| macOS 12 Monterey or older | ❌ |
We track current macOS — the newest dot-release of the current major is the canonical platform we test on.
Memory
Section titled “Memory”| RAM | Usable for |
|---|---|
| 8 GB | 8+ cameras / 10 fps detection |
| 16 GB | Comfortable for 8–16 cameras, easily + other apps |
| 24+ GB | More cameras, GenAI descriptions, semantic search, run other memory-hungry apps |
Each ffmpeg decoder is the dominant memory consumer; budget 100– 250 MB per camera for ffmpeg + the detector pipeline. Fregata itself (the Python core, the Swift app, nginx, go2rtc) totals about 1 GB of RAM.
The detector model and the app are around 2 GB combined. Recordings dominate the disk usage:
| Cameras | Per day @ motion-only | 14-day disk budget |
|---|---|---|
| 1 | 4–10 GB | 60–140 GB |
| 4 | 16–40 GB | 250–560 GB |
| 8 | 32–80 GB | 500 GB – 1.2 TB |
| 16 | 64–160 GB | 1–2 TB |
External Thunderbolt SSDs work fine; external USB SSDs work if they’re 3.0 or better. A networked NAS is also a fine choice.
See Recordings & retention for how to dial these numbers up or down.
Network
Section titled “Network”- Cameras on Ethernet if at all possible. RTSP over Wi-Fi works but is sensitive to packet loss; a noisy Wi-Fi → Mac path manifests as detection drop-outs and choppy recordings.
- Mac on Ethernet for 4+ cameras. Multi-stream RTSP plus the HA integration’s MJPEG fan-out can saturate Wi-Fi quickly.
What we don’t run on
Section titled “What we don’t run on”Listed for completeness so you don’t waste time:
- Intel Macs — no ANE.
- iPads / iPhones — wrong app shape; we’d have to rewrite for iOS lifecycle and we’re not going to.
- Linux / Windows — that’s Frigate, the project we’re built on. Use it directly there, it’s excellent!
- Docker on macOS — possible, but nullifies every reason to use Fregata over Frigate. The hardware acceleration paths don’t cross the Hypervisor.framework boundary cleanly.
- A virtualized macOS guest — same problem; ANE access from a guest VM is impossible. Fregata will run, but you lose most of the benefits.