Automate application performance testing and tuning

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

Application-aware.
Autonomous.
AI-powered.

Akamas uses reinforcement learning, telemetry and user-defined goals to autonomously optimize full-stack configurations of enterprise applications, both live in production and offline in testing environments.

5x

INCREASE OPTIMIZATION SPEED

Run automated optimization experiments day and night, cutting tuning time by 80%.

-60%

REDUCE CLOUD COSTS

Cut your applications’ demand for compute and 
infrastructure resources.

+30%

IMPROVE SERVICE QUALITY

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

zero

ENSURE PEAK LOAD RESILIENCE

Optimize application scalability to ensure 100% reliability during peak events.

Offline

Akamas applies workloads 
and collects metrics

Akamas applies testing workloads using your load testing infrastructure and custom-defined testing design instructions, using APM and observability tooling to collect application KPIs.

Akamas AI finds the best configuration

Akamas reinforcement-learning model scores the performance test configuration against user-defined goals and constraints, identifying opportunities for configuration improvements for the next test.

Akamas automatically applies configuration and iterates

Akamas changes the full-stack application configuration, deploys it using any configuration management tool, and iteratively runs a new test to measure the new configuration’s performance.
Get a Sandbox Get a Sandbox

Full-Stack

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.

Get a sandbox Get a sandbox
Offline

Goal-oriented

Balance optimization goals, SLOs and constraints

Akamas users can assign the AI user-defined optimization goals, like maximizing application throughput or minimizing cloud resource usage. You can also set hard SLOs or constraints, such as maximum latency or minimum transaction guarantees.

Get a sandbox Get a sandbox
Offline

Safe

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.

Get a sandbox Get a sandbox
Offline

Automated

Automate testing campaign and continuous optimization

Akamas can autonomously manage extensive testing studies, encompassing hundreds of test iterations. It can also continuously optimize application configurations in production. These configurations can be reviewed and approved by humans, or applied in CI/CD pipelines automatically by Akamas itself.

Get a sandbox Get a sandbox
Offline

Leveraging machine learning for application optimization

Akamas uses proprietary reinforcement learning algorithms, observability, and cloud technology to autonomously optimize workloads. Read more about Offline Optimization in the Akamas documentation.

Read more Read more
Offline