Live end-to-end performance of the Lucy 2.1 realtime endpoint, measured from a fleet of real browser clients across Google Cloud regions. Each chart shows one metric over time, per region - p50 solid, p95 dashed, and p99 dotted where available. Click regions in the legend to pick which to show (choose several to compare); double-click to reset to all.
This dashboard reports the real user experience of the Lucy 2.1 realtime video model - measured end-to-end from a browser, not from server-side timers. Everything you see is what an actual client integration would observe.
A fleet of probes runs continuously across several Google Cloud
regions. Each probe is a headless Chromium browser (driven by Playwright) running the
production @decartai/sdk - the same SDK customers use. It connects to
lucy-2.1 at wss://api.decart.ai over WebRTC, streams a looped
synthetic camera feed, and decodes the model's returned video.
The probes execute as Google Cloud Run Jobs - one job per region, each sized at
cpu=8 / 16 GiB. A central scheduler fires one region per tick
on a rotation, so the load placed on production Lucy stays flat no matter how many regions are
monitored. Every run opens several short back-to-back realtime sessions and aggregates them.
Results are bucketed over time and plotted per region (p50 = typical, p95/p99 = tail).
connect() invocation until the connection resolves.connect() invocation until the first non-black model-generated frame is decoded — setup included.
Definition changed in probe 0.12.0: earlier points measured the warm prompt-to-frame window (setup excluded), so the series steps up at the cutover.setPrompt() call start until the prompt ack is received.getStats().getStats()) - higher jitter means less even delivery.