Reduce MTTR with AI-Powered Observability

Quickly gain insights and reduce MTTR with AI-driven root cause analysis, empowering faster deployments and automated troubleshooting.

VP of Engineering
Snyk

Chief Architect
Solutio

Armin Morattab
Senior DevOps Engineer

Automate Analysis: Use AI to immediately trigger investigation on alerts and exceptions for newly discovered issues and their production impacts.
Consolidate Investigation: Combine multiple steps and transition quickly from discovery into multi-stage forensics.
Become Proactive: Jump into analysis and response faster than ever, reducing potential outcomes affecting your users.

Understand Impacts: Instantly surface the implications of a given issue to ensure conclusive investigation and response.
Surface Dependencies: Fully understand the resulting effect of issues on the larger environment and related service interruptions.
Communicate Details: Share the details of issues and analysis with other stakeholders to inform and coordinate response.

Enact Response: Use automated conclusions to immediately begin troubleshooting using recommended response actions.
Dictate Actions: Translate AI recommendations into active methods and workflows to quickly engage resolution steps.
Coordinate Efforts: Clearly and consistently communicate related impacts and actions across multiple teams and stakeholders.

Filter out unneeded data: Logz.io’s Data Optimization Manager makes it easy to remove noisy data that obscures the critical insights needed to troubleshoot quickly.
Convert logs to metrics: LogMetrics converts your logs to metrics to provide low cost log data visualization.
We’re here to help: Use Logz.io self-service tools or direct support from our Support Engineers to identify and remove noisy data.

AI insights baked into alerting: The AI Agent generates detailed insights into any alerts, while automatically generating related conclusions and response steps, reducing MTTR.
Automated alerts: Set threshold-based alerts to monitor log, metric, or trace data – automatically surfacing performance and health issues across your whole stack, from a single host to an entire cluster.
Highly customizable: Build complex alerting logic using multiple trigger conditions to limit noisy notifications and improve MTTR, and send notifications to your endpoints (Slack, Email, PagerDuty, OpsGenie, etc.)