About Akamas
Akamas provides an autonomous Kubernetes optimization platform designed to continuously enhance application performance, reliability, and cost efficiency across the full stack. Targeted at DevOps, SREs, and performance engineers, the platform leverages patented reinforcement learning to optimize infrastructure, runtimes, and cloud resources in production environments. By analyzing real workload behavior, Akamas identifies inefficiencies and guides or autonomously applies safe configuration changes, reducing manual tuning efforts and bridging skill gaps between development and operations teams.
The platform addresses the challenge of fragmented and reactive optimization in modern software delivery by embedding continuous, explainable AI-powered optimization into existing CI/CD pipelines and Kubernetes environments. Key use cases include JVM tuning on Kubernetes, Kubernetes pod resource and autoscaling optimization, cloud instance selection, and big data application tuning. Akamas delivers measurable benefits such as significant cloud cost reductions, improved application performance, zero downtime, and increased operational productivity, making it a strategic tool for enterprises aiming to maximize their cloud-native investments.
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