An Intellyx Brain Candy Brief
Sedai monitors cloud resource usage to detect potential performance, availability, and cost issues, and uses its proprietary ML to recommend or implement fixes.
Customers connect Sedai to a cloud account and scan for compute, storage, data, and streaming resources. It ingests observbility data and trains its ML models using latency, error, traffic, and saturation signals.
Sedai supports Kubernetes, including on prem private cloud, and also Platform9 and VMware Tanzu. It runs either as an agent within a Kubernetes cluster or ingests monitoring and configuration data using APIs.
In “datapilot” mode, Sedai gathers information and displays its recommendations, but does not take any action. In “copilot” mode, Sedai takes action only after manual approval. In “autopilot” mode, Sedai autonomously takes action.
To implement recommended changes, Sedai uses a flavor of Git for source control and Terraform for the IaC source file (support for other IaC providers is in the roadmap).
The changes reduce cost, improve performance, reduce operations workloads, and ensure high availability. Sedai also helps manage the impact on cloud configuration of application changes.
Sedai recommends a “crawl, walk, run” approach, i.e. evaluate its recommended changes in datapilot mode first, then use copilot mode to manually approve recommended changes, and finally enable autopilot when customers are comfortable with the platform.
Copyright © Intellyx BV. Intellyx is an industry analysis and advisory firm focused on enterprise digital transformation and AI transformation. Covering every angle of enterprise IT from mainframes to artificial ielligence, our broad focus across technologies allows business executives and IT professionals to connect the dots among disruptive trends. Sedai is not an Intellyx customer. No AI was used to produce this article. To be considered for a Brain Candy article, email us at pr@intellyx.com.
