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Fregata vs Frigate

Fregata is built on the excellent open-source Frigate NVR. The short pitch: Frigate is the Python core; Fregata is a native macOS shell around it that swaps in Apple-native acceleration.

Fregata is Frigate plus an Apple-Neural-Engine detector, plus VideoToolbox decode/encode, plus hundreds of macOS-specific performance optimizations and tweaks, bundled as a signed Mac app with all dependencies packaged in, with a $10 license.

Frigate detects on the CPU, an EdgeTPU, an NVIDIA GPU (tensorrt), an Intel iGPU (openvino), or a couple of more exotic accelerators. None of those help on Apple Silicon.

Fregata adds a coreml detector — a Rust dylib that owns ONNX Runtime’s CoreML execution provider, configured to prefer the ANE. Bundled YOLOv9-tiny inference is in the 1–2 ms range per frame on the Neural Engine, versus 40–80 ms on the CPU path.

The detector type and how to swap models (nearly any ONNX model, including Frigate+ models are supported) is documented at Detection tuning.

Frigate-on-Linux uses CPU ffmpeg by default and lets you opt into hardware accel. Fregata-on-macOS flips that around: every input is -hwaccel videotoolbox, recording defaults to h264_videotoolbox, the RTSP re-stream uses h264_videotoolbox. Hardware H.264 / HEVC decode and encode runs on the dedicated media engine, leaving the CPU free.

You can override the presets exactly as on Linux Frigate; the mechanism is identical. The defaults just match the platform.

Bandwidth-adaptive transcoding for recordings and live view

Section titled “Bandwidth-adaptive transcoding for recordings and live view”

Upstream Frigate serves recordings as fixed-quality MP4 segments — the player has no quality ladder and can’t adapt to a slow connection. On a weak LTE or 5G signal a 4K or high-bitrate stream simply won’t load.

Fregata adds an opt-in HLS adaptive bitrate layer that is Fregata-exclusive and not available in upstream Frigate. When enabled, for recordings the web player automatically switches between quality rungs (default: 1080p / 720p / 360p) based on measured throughput — no manual quality selection, no buffering from overshooting available bandwidth. 360p at 600 kbps loads cleanly on even a poor cellular signal.

The top rung is always a passthrough copy (-c:v copy) for H.264 sources — free, no encode cycles. Lower rungs are transcoded entirely on the Apple Silicon dedicated Media Engine via VideoToolbox — zero CPU. Clients on the local network bypass the ladder and receive the full-resolution source stream.

live: true also brings each rung into the live view stream dropdown allowing you to manually switch qualities for live streams.

This feature is macOS-only and not in upstream Frigate. See Adaptive transcoding for the full configuration reference.

Frigate ships as a Docker image. Fregata ships as a .app bundle:

  • Menu-bar tray for live status, performance metrics, log access, the license screen, settings.
  • Supervisor that launches the Python core and all the necessary processes, watches them, restarts on crash with exponential backoff, captures early stderr to a survivable place when something goes wrong before the log redirect kicks in.
  • Welcome wizard for permission grants and folder choices.
  • Native Login Item so Fregata can always start on boot.
  • Custom Update Checker so you always know when new versions are available.

None of this is Frigate; it’s the macOS-native experience layer.

Fregata is paid software ($10/year for the update window). The licensing system is local-first — the activation token lives on disk, the Mac runs offline indefinitely while the app is up (a 7-day window applies only to restarting with a stale token), and an expired paid license just stops new updates. Existing versions keep running forever.

The whole thing is described in Activation and Updates & renewals.

Fregata’s .app bundle ships its own ffmpeg, ffprobe, nginx, and go2rtc, signed with our Developer ID. You don’t install Homebrew, don’t brew install ffmpeg, don’t manage a Python virtualenv. The app is self-contained.

The detectors and acceleration paths that don’t apply on macOS:

  • EdgeTPU — Coral USB / M.2 module. Not what Apple Silicon users would buy.
  • TensorRT — NVIDIA GPU acceleration. Macs don’t have NVIDIA GPUs.
  • OpenVINO — Intel iGPU / NPU. Not on Apple Silicon.
  • ROCm — AMD GPU. Not on Apple Silicon.
  • Hailo-8L / RKNN / Synaptics / Teflon — Linux- only NPU integrations.
  • DeepStack / DeGirum / ZMQ-IPC detectors — external inference servers, not the macOS-native shape.

If you need any of those, you want Frigate-on-Linux, not Fregata-on-macOS.

A non-trivial chunk of Frigate is identical:

  • The web UI — same React app, same tabs, same recording scrubber. We’ve made several performance improvements and added the adaptive transcoding player for recordings and live view.
  • config.yml schema — every key Frigate understands works in Fregata, with the exception of the removed detector types.
  • MQTT topics, HTTP API, WebSocket events — the wire surface Home Assistant and other integrations target is identical.
  • The Frigate+ workflow — your account, your training data, your trained models. Fregata reads them via the same plus://... identifier.
  • Recordings format — segmented MP4s, same naming convention, same SQLite events table. You can rsync recordings between a Frigate-on-Linux install and a Fregata-on-macOS install and the web UI will show them.

When the docs cover macOS-specific ground — the menu-bar app, licensing, the CoreML detector, VideoToolbox presets, system requirements, supported Mac models, the Welcome wizard — read Fregata’s docs (you’re here).

When the docs cover the upstream Frigate engine — every non-detector config key, MQTT topic taxonomy, HTTP API, zone geometry, GenAI integration, semantic search, face recognition, LPR, PTZ tracking — read the Frigate docs. We don’t mirror them, the upstream Frigate docs are excellent and are always up to date.

This page is the only one that tries to keep both halves fresh — if Frigate adds a detector type and we add support, the “What Fregata removes” list shortens.