Orbit | ML model monitoring dashboard | Dark UI

Orbit gives ML teams a single view of every deployed model. Latency p99, accuracy, drift score, error rate. Each model gets its own row. When fraud-detector-v3 starts slipping, the row changes. 847ms p99. 2.4 sigma drift. Accuracy down to 91.2%. No report to pull. No log to parse. The change is just there.

The overview keeps all seven models visible at once. Fraud-detector-v3 sits at the top, flagged ALERTING. The three active alerts are collected in their own panel, each one timestamped and assigned to a model. Clicking into fraud-detector-v3 pulls up the detail view: 90-day accuracy timeline, inference volume history, and drift delta from baseline. The explorer goes deeper: latency distribution, confidence spread, error rate over the last 24 hours plotted together so you can see if the issue is isolated or systemic.

Setup is one command. First reading in under a minute.

Most ML teams find out about model degradation from a support ticket. Orbit moves that discovery from the user's inbox to Marcus's dashboard. The gap between those two is usually days.

How long does it take your team to catch a model regression after it starts?

The full product is live. Landing page and web dashboard built in code, deployed on Vercel.

Real interactions, real data states, real responsive layout. Click around.

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