Apply machine learning to uncover your full potential savings based on your actual workload behavior, not a set of rules. Implement the savings yourself or have Sedai operate autonomously to stay continuously optimized. Join customers who are finding cloud cost savings of 30-50%.
Sedai analyzes workload behavior to optimize horizontal & vertical scale for container workloads (memory and CPU and task/pod count) to meet your compute needs with the least amount of compute capacity
After rightsizing container workloads, Sedai evaluates infrastructure and chooses the best type and number of instances and how to group them for best cost and performance, based on application behavior and reinforcement learning.
Traffic patterns are constantly changing. Sedai's machine learning detects seasonality patterns and helps you stay optimized to minimize spend on containerized apps in quiet periods while providing the capacity for the traffic peak. Traffic predictions enables Sedai to be a smart controller for cloud provider autoscalers and Kubernetes HPA/VPA. If needed, Sedai will also create autoscalers for the workload and the cluster to help them respond to seasonal traffic
Purchasing optimization is applied only after engineering optimization to ensure you're buying the right infrastructure. Sedai evaluates container purchasing options including on-demand and savings plans to recommend AI-assisted lowest cost solutions. Sedai supports Fargate.
Sedai finds the memory / CPU setting for serverless that optimizes cost subject to your performance needs. Sedai also dynamically uses provisioned concurrency when cost-effective.
Frequent code releases mean one-time optimizations get rapidly out of date. Sedai continuously monitors the cost performance of new releases, providing a scorecard at the release level, and updating rightsizing settings based on that latest behavior. Use the scorecards to close the loop with developers.
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