Actionable insights for all your critical use cases
Automatically unify logs, traces, and metrics of events in real time
- Navigate from traces to logs to user sessions and vice versa to get full stack visibility
- Resolve issues faster with AI-powered root cause analysis
- Instantly understand business impact and dependencies
- Reduce MTTR by eliminating manual correlation between multiple tools, alerts, and data silos
Easily monitor Kubernetes
- Improve cluster health and ensure performant applications across complex multi-cloud environments
- Store, analyze, and query cluster-level logs
- Filter by namespace, workload, node, pod, and container to simplify analysis
- Diagnose cluster health and troubleshoot application performance with AI-assisted analytics
Optimize storage costs by extracting metrics from logs at ingest or on read
- Collect and parse log data in real-time without indexing
- Replace alert storms with AI-powered baselining and anomaly detection
- Filter, monitor, and transform log fields to meet policy and compliance requirements
Do it all with the power of Grail
Developing a log management and analytics strategy
As organizations grow, cloud application environments create complexity that makes managing and analyzing log data difficult.
The traditional approach to log management is often manual and siloed, which limits scalability and ultimately hinders organizational innovation.
Many organizations are stuck in the status quo, which makes them vulnerable to being outpaced by their competition. There is a need to focus less on manual, reactive IT work and more on strategic, proactive innovation and optimization.
This guide explores how a unified approach to log management and analytics eliminates data silos and maximizes ROI from log data.
Empower every team to work faster and more effectively
Get precise answers from unified log data in context
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