Benchmarks · Aug 2026
Measured, not estimated.
Every number below comes from a real test run against the live system — a k6 load test against the production ingestion endpoint, and validation metrics from the actual trained models. No projections.
Ingestion load test
k6, ramping 1,000 → 2,500 → 5,000 concurrent virtual users against /v1/ingest.
Model validation scores
Trained on a dataset of ~2,724,000 labeled sessions. See the whitepaper for the full methodology.
M3 — XGBoost bot classifier
Validation AUC
M2 — Siamese LSTM (keyboard identity)
AUROC
M4 — Autoencoder (mouse + scroll)
AUROC
M4 — Autoencoder (touch + motion)
AUROC
Training data composition
~1,974,000 human sessions (keyboard-only, mouse/touch/scroll modalities at zero — the empirically correct representation for single-page sessions) and ~750,000 bot sessions, spanning all six behavioral modalities.