Case Study: a global top 10 logistics company achieves 35% Kubernetes cost savings with Sedai

How A Global Top 10 Logistics Company Unlocked 35% Kubernetes Compute Cost Savings

Sedai worked with a global top 10 logistics company that was rapidly containerizing applications and facing growing Kubernetes complexity across VMware-based on-premises data centers. The company needed to keep its warehouse management and other services efficient while meeting SLAs, avoiding new hardware investment, and reducing the heavy manual effort required from its stretched DevOps and IT operations teams.

Sedai deployed its autonomous optimization platform within the company’s own data centers and used AI to analyze Kubernetes workloads, clusters, and resource usage, then rightsized CPU, memory, pod counts, and node configurations. As a result, the company reduced Kubernetes compute costs by 35%, cut CPU usage by 29% and memory by 48%, and reduced optimization time by more than 90%, while maintaining performance and capacity for peak demand.