Application monitoring is experiencing a sea-change. Root Cause is a bottleneck and the only way forward is automation. Enter... Root Cause as a Service. | Continue reading
Zebrium uses machine learning to find the root cause of software incidents. GPT-3 is then used to produce intuitive plain language root cause summaries. | Continue reading
ZELK Stack (AIOps for Elastic Stack) uses a new kind of machine learning approach to catch software incidents and show root cause right inside Kibana. | Continue reading
Try Zebrium with a cloud-native microservices application. In less than 30 minutes you'll experience the power of machine learning incident detection. | Continue reading
Read about how Zebrium is using OpenAI GPT-3 to achieve some amazing results with plain language incident root cause reports from logs. | Continue reading
Virtual tracing provides a much simpler alternative to distributed tracing for troubleshooting by bringing autonomous incident and root cause detection to an instrumented trace. | Continue reading
Use Grafana to construct dashboards and charts from Zebrium ML-structured log and metrics data. | Continue reading
Zebrium’s integration with PagerDuty and Slack automatically adds root cause to an incident detected by any monitoring or observability tool. | Continue reading
Let machine learning automatically catch and characterize application incidents. Works with your existing logs and metrics. | Continue reading
Log management tools were designed to make logs easily searchable. But with the complexity of today's apps, searching for root cause is problematic. | Continue reading
The browser cache can be the bane of a UI developer's existence. After trying ways to invalidate, refresh, etc., we finally came up with a robust solution. | Continue reading
Anomaly detection on logs and metrics is the key foundation of automatic software incident detection. | Continue reading
Autonomous Monitoring is the Anomaly Detection I Wanted: Automatically Catch Critical Software Incidents and Show Me Root Cause | Continue reading
Autonomous Monitoring is the only way out: autonomous detection of incidents, with root cause indication. | Continue reading
An open source Prometheus fork to support Zebrium Autonomous Monitoring using logs and metrics. | Continue reading
View our on-demand webinar to learn about how machine learning can use logs to automatically detect and find the root cause of incidents. | Continue reading
Using Autonomous Monitoring to auto-detect incidents from Kubernetes Chaos Experiments. | Continue reading
We believe the future of monitoring, especially for platforms like Kubernetes, is truly autonomous. | Continue reading
How Zebrium designed a flexible RESTful API framework. | Continue reading
Read about the technology behind Zebrium's Autonomous Log Monitoring platform. | Continue reading
A prevalent monitoring strategy today is sometimes described as “black box” monitoring. Black box monitoring focuses just on externally visible symptoms. | Continue reading
Kubernetes makes managing and scaling a distributed application easy. But what happens when something goes wrong? And, when it does, do you even know? | Continue reading
Free log file anomaly detector. Let us find anomalies in your log files. Try it now! | Continue reading
Read about our log anomaly detector: a utility that lets you upload one or more log files and receive a report with details of what we found. | Continue reading
We must accept that deploying into production is the only definitive test. For this, a red light is needed to identify badly behaved builds. | Continue reading
We must accept that deploying into production is the only definitive test. For this, a red light is needed to identify badly behaved builds. | Continue reading
Unstructured logs are hard to query, find anomalies and identify relationships between events. Zebrium uses machine learning to perfectly structure logs. | Continue reading
Software log messages are potential goldmines of information, but their lack of explicit structure makes them difficult to programmatically analyze. | Continue reading
Unstructured logs are hard to query, find anomalies and identify relationships between events. Zebrium uses machine learning to perfectly structure logs. | Continue reading
Unstructured logs are hard to query, find anomalies and identify relationships between events. Zebrium uses machine learning to perfectly structure logs. | Continue reading
Using machine learning to structure logs achieves effective anomaly detection | Continue reading
Using machine learning to structure log data makes it possible to reliably detect anomalies | Continue reading
Never troubleshoot the same problem twice | Continue reading
Using machine learning to structure log files makes it possible to build anomaly detection that actually works. | Continue reading
Developers and testers use log files to find and troubleshoot failures, but extracting useful information requires wrangling, regexes and parsing scripts. | Continue reading