Autonomous Optimization for Kubernetes applications

The complexity of Kubernetes resource management often leads developers, Performance Engineers and SREs to adopt very conservative configurations and resource overprovisioning. The resulting unnecessary infrastructure/cloud costs may significantly affect the overall cost efficiency of delivered services, while not necessarily removing the risks of missing SLOs.

Akamas AI-driven performance optimization approach guarantees the best levels of performance & resilience while also ensuring the best cost efficiency of your Kubernetes microservices applications, thus avoiding any resource overprovisioning and unnecessary infrastructure/cloud costs.

This solution brief describes the key challenges and main benefits of Akamas AI-powered optimization in terms of improved application performance and cost-efficiency.