Sedai uses leading indicators to identify threats to availability from memory issues and abnormal behaviors and proactively addresses them

Traffic patterns are constantly changing. Sedai's machine learning detects seasonality patterns and helps you avoid availability problems by providing the capacity for the traffic peak while staying cost-optimized in quiet periods. Sedai can act as a smart controller for cloud provider autoscalers and Kubernetes HPA/VPA. If needed, Sedai will also create autoscalers for the workload and cluster to help them respond to availability demands.

Sedai uses safety checks run before any change in production to ensure remediations will not harm your application

Set availability SLOs (Service Level Objectives) manually or have Sedai automatically set them. Monitor them through Sedai and receive alerts of breaches

Understand whether individual releases pose a risk to availability with opinionated scores on release quality on a scale from -10 to +10 based on changes in golden signal metrics (errors, performance and cost) relative to previous releases and SLO breaches