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.

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The Platform

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.

Akamas Platform

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.

Akamas Offline Optimization in Action

Your optimization and testing assistant with AI superpowers. Optimize applications’ performance, resilience, and costs faster, preventing issues in production.

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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.

Autonomous performance engineering

Reduce the time spent on configuration tuning by an impressive 80% through the use of AI-driven optimization techniques.

Optimize pod resources and applications

Akamas supports various technologies in enterprise cloud-native stacks, including Kubernetes and runtimes like JVM, Node.js, .NET, and Golang.

Balance cost targets and performance optimization goals

Akamas enables users to set complex goals for resource usage, application performance, and response times, while adhering to latency, error rate, and SLO constraints.

Prevent dangerous application configurations

Akamas AI continuously learns from system signals to optimize configurations and prevent issues like out-of-memory errors.

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.

See for Yourself

Experience the benefits of Akamas autonomous optimization.
No overselling, no strings attached, no commitments.

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