Rightsizing Kubernetes and tuning microservices to optimize costs, while assuring application resilience and performance SLOs, is a challenge.
This is due to the interplay of Kubernetes resource management (e.g. OOM killing, CPU throttling) with the mechanics and ergonomics of application runtimes (e.g. JVM GC). Mismatched configurations between Kubernetes and runtime may result in higher-than-needed costs, lower service quality, poor performance and unreliable applications.
Akamas leverages AI to autonomously identify full-stack configurations against custom-defined goals and constraints, and to recommend them live under varying observed workloads. With Akamas, you can achieve optimal cost efficiency, performance and resilience of Kubernetes live applications.