Observability for AI systems
Ship models you can
actually explain.
SebyAI traces every inference your stack makes, so you find the drift, the cost spike and the bad answer before your customers do.
1.4B
Traces per month
42ms
Added p99 latency
99.98%
Collector uptime
310
Teams shipping
What you get
Three problems, one pane of glass.
01
Drift detection
Baselines are built from your own traffic, not a generic benchmark. When output distribution moves, you get the diff and the ten requests that moved it most.
02
Cost attribution
Every token is priced and tagged back to a feature, a customer and a deploy. Finance stops guessing and your team stops arguing about whose prompt got expensive.
03
Replay and audit
Replay any historical request against a new model or prompt and see the delta side by side. Every run is retained with full lineage for review.
Built for production
One collector. No rewrite.
The agent sits beside your inference layer and reads what is already there. No proxy in the hot path, no vendor SDK threaded through your business logic, no forked client library to maintain.
Most teams are reading their first traces inside an afternoon, and keep their existing dashboards pointed wherever they already point.
We had three dashboards and still could not answer why Tuesday cost four times what Monday did. SebyAI answered it in about twenty minutes.
Priya Raghunathan, Staff Engineer
See it against your own traffic.
Thirty minutes, screen shared, your data. No slide deck.