Kubernetes deployed apps can have complex failure modes that are hard to troubleshoot. Our machine learning proactively catches these failures and generates root cause reports. No more hunting through logs and dashboards.
See a summary of what happened in plain English!
We use the GPT-3 language model to construct simple to understand summaries of the root cause reports our machine learning generates.
See how Zebrium helps Sweetwater, the world's leading music technology and instrument retailer, reduce their Mean-Time-To-Resolution (MTTR) from 3 hours down to just minutes in their Kubernetes deployed apps.
Zebrium machine learning works by finding hotspots of anomalous patterns across logs and Prometheus metrics. Our open source forked instance of Prometheus achieves near real-time metrics updates, captures labels for correlating with logs, handles out of order samples and achieves > 500x bandwidth reduction.