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.

Benefits

Tune microservices to
optimize costs, while assuring application resilience

Akamas uses AI to autonomously identify full-stack configurations based on custom goals and constraints, recommending them in real-time under different workloads. With Akamas, you can optimize cost, performance, and resilience for live Kubernetes applications.

-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

Kubernetes Workloads
Kubernetes Workloads
Kubernetes Workloads
Kubernetes Workloads

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