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
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Experience the benefits of Akamas autonomous optimization.
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