Cut your applications’ demand for compute and infrastructure resources.


Increase throughput, and reduce response time, with lower fluctuations and peaks.


Ensure that apps and microservices work smoothly through workload peaks and anomalies.


Automate application tuning, cutting entirely time spent on manual configuration.

Akamas Live Optimization in action

Your copilot to optimize production applications’ performance, resilience and costs.

Set your goals, press play, relax.

Learn more about Akamas Live Optimization

Download the Akamas Live Optimization white paper to learn how to optimize critical microservices and applications for performance, availability and cost savings.

Live Optimization Use Cases

Pod sizing

Optimize resource allocation with automatic pod sizing, ensuring peak performance and cost efficiency in Kubernetes environments. 

App-level tuning

Enhance application performance through precise tuning, balancing speed, efficiency, and reliability for superior user experiences.


Automate Kubernetes scalability with HPA, dynamically adjusting resources to meet demand, optimizing for both performance and cost.

SLO matching

Align services with business objectives using SLO matching, guaranteeing reliability and customer satisfaction through targeted performance metrics.

cloud-native optimization

Optimize entire applications, not just Kubernetes resources

Akamas Live Optimization Full Stack
Full Stack
Optimize pod resources and applications, together
Akamas supports the range of technologies across enterprise cloud-native stacks, from Kubernetes to runtimes like JVM, Node.js, .NET and Golang settings, and takes into account their interdependencies.
Akamas Live Optimization Goal-oriented
Balance cost targets and performance optimization goals

Akamas is designed to achieve complex user-defined goals. Users can set resource usage, application performance, and response time goals, while respecting latency, error rate and SLO constraints.

Akamas Safe Autonomous Workload Optimization Platform
Prevent dangerous application configurations

Akamas AI learns continuously from system and application signals, such as response time and error rates, finding stack configuration sets that prevent downstream issues such as out-of-memory errors.

Leveraging machine learning
for application optimization

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

See for yourself.

Experience the benefits of Akamas autonomous optimization.

No overselling, no strings attached, no commitments.

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