Zebrium

Zebrium took a different approach to incident response: instead of asking engineers to write alert rules for every failure mode, it ingested logs and metrics and used unsupervised machine learning to find correlated anomalies, condensing them into root-cause reports that typically highlighted a handful of log lines out of millions. It layered plain-language summaries on top using GPT-3, and integrated with PagerDuty, Opsgenie, Slack, and the Elastic Stack so reports landed where responders already worked. Third-party case studies cited in its ecosystem claimed automatic root-cause identification in over 95 percent of incidents.

ScienceLogic acquired Zebrium, and zebrium.com now redirects to ScienceLogic's Skylar Advisor page, where the technology continues as part of the ScienceLogic platform. That is the honest caveat: you can no longer buy Zebrium as a standalone product, and any evaluation should start from ScienceLogic's current offering. The underlying idea, unsupervised root-cause detection without rule authoring, remains one of the more credible ML-for-ops stories of the last few years.

1. Note: Zebrium as a standalone product was acquired by ScienceLogic; evaluate Skylar Advisor on the ScienceLogic platform instead.
2. Historical setup: connect log sources (or the Logstash output plugin for the Elastic Stack) and metrics to Zebrium.
3. The ML models built baselines unsupervised, with no alert rules to author.
4. When incidents fired, review the auto-generated root-cause report: the first event in the correlated sequence, worst symptoms, and the plain-language summary.