The AI-powered platform for live and offline application optimization

Traditional configuration methods struggle with the complexity of modern cloud applications. Akamas employs patented reinforcement learning AI to optimize enterprise applications dynamically.

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What is application-aware optimization?

Optimize your entire applications, not just the cloud. Unlike traditional tools, Akamas considers the configuration and behavior of distributed applications, ensuring infrastructure improvements don’t compromise availability, performance, or critical business KPIs.

Optimize the full infrastructure and application stack

Akamas supports multiple technologies in IT and cloud stacks, including Kubernetes and runtimes like Node.js and JVM. It considers configuration interdependencies to avoid changes that may harm the system.

Prevent dangerous configuration changes in production

Akamas, with its application-awareness, continuously learns from system behavior and avoids configuration changes that might harm performance, costs, or availability. It acts as a vigilant copilot, preventing configuration errors before they occur.

Leverage the patented AI

Akamas leverages advanced Machine Learning and Reinforcement Learning techniques to automatically explore configuration options, learn system behaviors, and identify optimal settings that improve performance, reliability, and cost without human intervention.

Production or testing in one platform

Improve Service Quality

Increase server resilience

Reduce cloud & IT Costs

Increase business agility

The Akamas AI technology

At its core, the Akamas platform uses proprietary reinforcement learning algorithms, observability, and cloud technology to autonomously optimize workloads. Read the white paper for a deep dive into Akamas AI.

Insights

The most comprehensive K8s and application optimization solution. Unites developers, SREs, and platform engineers around one goal: delivering reliable, efficient services.

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Offline Optimization

Akamas automatically runs and scores performance tests, iteratively identifying optimal full-stack configurations. Easily manage optimization workflows and evaluate configuration tradeoffs.