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Shadow Deployment: Mirrored Traffic, Zero User Risk

Interactive 3D shadow-deployment simulator: mirror live requests to a candidate model alongside production, measure prediction agreement and latency overhead, and see why the shadow model's output never reaches a real user.

AI & Machine Learning3DModerate60 FPS📱 Mobile-adapted⇄ 2D version

Shadow deployment (dark launch) forks a share of live production traffic to a candidate model running in parallel, so its predictions can be logged and compared against the live model's answers without ever being shown to a real user. This simulator renders the two lanes in 3D: a steady stream of production requests flows to the live model and back to the client, while a sampled subset is mirrored down to a shadow model whose result is only ever scored — never served. Tune the request rate, shadow sampling percentage, the candidate's simulated disagreement rate and its compute overhead, and watch the live agreement rate, mirrored-traffic count and latency readouts update in real time.

⚙ Under the hood

Mirror a share of live production traffic to a candidate model running in parallel, and watch its predictions get scored against the live model without ever reaching a real user.

mlopsshadow deploymentmodel monitoringmachine learningdark launchThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install