Robusta

Step 1: Detect an alert related to an AWS service (e.g., high CPU utilization on an EC2 instance).

Step 2: Robusta automatically gathers relevant metrics and logs from AWS CloudWatch.

Step 3: AI analyzes the data to pinpoint the underlying cause (e.g., a misconfigured security group).

Step 4: Robusta suggests remediation steps, such as adjusting the security group rules or scaling the instance.

Step 1: Detect a spike in a specific metric (e.g., request latency) through Prometheus alerting.

Step 2: Robusta gathers related metrics and logs from other monitoring tools.

Step 3: Correlates the metric spike with potential contributing factors (e.g., code deployment, database query).

Step 4: Provides insights and recommendations for resolving the issue, such as rolling back the deployment or optimizing the query.

Step 1: Identify noisy or inaccurate Prometheus alerts.

Step 2: Use Robusta to analyze the historical data associated with these alerts.

Step 3: Fine-tune the alert thresholds or add additional context to the alerts.

Step 4: Validate the improved alerts using Robusta's testing capabilities.

Step 1: Define a runbook for a specific type of alert (e.g., pod crash loop).

Step 2: The runbook includes steps to diagnose the issue (e.g., check pod logs, describe pod events).

Step 3: The runbook includes steps to remediate the issue (e.g., restart the pod, scale the deployment).

Step 4: When the alert is triggered, Robusta automatically executes the runbook.

Step 1: Grant developers access to Robusta's monitoring and troubleshooting tools.

Step 2: Developers can use Robusta to view metrics, logs, and events related to their applications.

Step 3: Developers can use Robusta to diagnose and resolve issues without relying on DevOps or SRE teams.

Step 4: Robusta provides developers with self-service tools for restarting pods, scaling deployments, and performing other common tasks.