Optimize Kubernetes Workloads with AI

Kubernetes workloads are often over-provisioned, under-utilized, or tuned manually using guesswork.
Akamas uses AI to analyze real production data and generate safe recommendations to optimize pod resources and autoscaling, improving performance while reducing cloud costs.

Create Account

-70%

RESPONSE TIME

Cut your applications’ demand for compute and infrastructure resources.

-60%

SAVING

Reduction in cloud costs with same app performance

-80%

MANUAL TUNING

Decrease in engineering time spent for manual tuning

Trusted by industry leaders worldwide

Blog

Tech deep dives, product news, and Akamas stories – all in one place

View all

Blog

Your GPUs Are 95% Idle, and Buying More Won’t Fix It

AI is reshaping every industry, and agents are at the center of that shift. Many…

Blog

The Michelin Star Dilemma and the Secret to a Perfect Service

In the world of fine dining, the tension in the kitchen is palpable long before…

Blog

The State of Cloud Native Optimization 2026

A critical realization has emerged within the platform engineering community. We have excelled at creating…

Blog

Kubernetes Performance Tuning, Beyond One-Size-Fits-All: Introducing Tuning Profiles

Modern Kubernetes clusters are not monolithic; they host many applications with very different operational needs….

Blog

Why Your Kubernetes Cluster Autoscaler Wastes Resources, and How to Fix It

Platform engineers rely on the Kubernetes cluster autoscaler to control cloud cost. However, the autoscaler…

Blog

Beyond the JVM: Full-Stack Optimization for Node.js on Kubernetes

The promise of Kubernetes lies in seamless scalability, yet running high-performance Node.js applications often reveals…

View all

See for Yourself

Experience the benefits of Akamas autonomous optimization.
No overselling, no strings attached, no commitments.

Calculate Savings Create Account