Observability best practices, anomaly detection techniques, incident postmortems, and product updates.
Static thresholds fail when your traffic patterns change seasonally. Learn how AI-powered anomaly detection adapts to your data's natural rhythms and catches the anomalies that fixed rules miss.
Read More →A deep dive into our streaming architecture that processes 500K data points per second while maintaining sub-100ms anomaly scoring. Rust, SIMD, and quantized models.
How a subtle memory leak in a cache layer cascaded into a multi-region outage. What we learned, how AnomalyWatch detected the early signals, and our remediation steps.
We benchmarked Google's TimesFM and Amazon's Chronos on 50 real-world time series datasets. Here's when each model excels and how we ensemble them for better accuracy.
Alert fatigue is the silent killer of incident response. We analyzed 10,000 alert configurations and found 5 patterns that cause 80% of false positives.
Introducing AnomalyWatch Forecasting — use AI models to predict metric values up to 7 days ahead. Set proactive alerts on forecasted anomalies and prevent incidents.
A technical walkthrough of our data ingestion pipeline: from HTTP endpoint to anomaly score in under 100ms. Covering async I/O, ring buffers, and zero-copy deserialization.
How a manufacturing customer uses AnomalyWatch to monitor 100,000 IoT sensors. Edge-based scoring, bandwidth optimization, and detecting equipment degradation patterns.