Akamas

Akamas is an AI-powered optimization platform developed by Akamas S.p.A. The platform aims to assist organizations in maximizing the performance, resilience, and cost efficiency of their applications. It caters to businesses of all sizes, ranging from small startups to large enterprises, and is utilized by professionals such as Performance Engineers, Site Reliability Engineers (SRE), DevOps Engineers, IT Operations Managers, and Cloud Architects. It is applicable across various industries, including IT Services, Financial Services, Online Services, Travel and Hospitality, and E-commerce.

Key Features

Autonomous Optimization: Akamas leverages AI to autonomously identify optimal configurations that enhance application performance, resilience, and cost efficiency. It provides recommendations based on custom-defined goals and constraints, making it suitable for live production environments and planned scenarios.

Application-aware Optimization: Akamas considers the specific requirements and characteristics of each application to recommend configurations that maximize service quality. It takes into account factors such as workload, resource utilization, and performance metrics. User-defined goals and constraints ensure the best tradeoffs between performance, resilience, and cost efficiency.

Kubernetes Optimization: Akamas offers Kubernetes optimization to improve the cost efficiency and performance of Kubernetes applications. It addresses the complexities of resource management at the container and runtime layers in Kubernetes. By minimizing container and pod resources while improving response time and throughput, Akamas reduces business risks and costs.

Runtime Optimization: Akamas provides runtime optimization to boost application performance without requiring code changes. It optimizes JVM and Golang parameters to improve agility and efficiency. The AI-powered optimization process identifies the best runtime configurations to achieve higher levels of performance and resilience.

Cloud Optimization: Akamas helps organizations reduce cloud costs and improve service quality through application-aware optimization. It goes beyond traditional cloud cost optimization tools by considering real application demand and its impact on performance. Akamas identifies instances in Amazon EC2, Google Cloud Platform (GCP), and Microsoft Azure that maximize cost efficiency and end-to-end performance.

Big Data Optimization: Akamas optimizes Spark jobs to make them run faster and more cost-effective. It considers factors such as job size, data volumes, and other variables that impact completion time and execution windows. By finding optimal configurations for Spark, AWS EMR, and Google DataProc, Akamas minimizes resource usage and reduces costs while meeting service level objectives (SLOs).

Closed-loop Automation: Akamas supports closed-loop automation by seamlessly integrating with existing change and delivery processes. Configurations can be approved and reviewed by humans, applied in CI/CD pipelines, or autonomously implemented by Akamas. Its Infrastructure-as-Code approach and comprehensive set of APIs and integration mechanisms enable smooth integration with any ecosystem.

Full-stack Optimization: Akamas optimizes any application, workload, and infrastructure, whether on-premise or in the cloud. It covers the full stack, including applications, runtimes, containers, middleware, databases, and cloud parameters. Akamas provides out-of-the-box Optimization Packs for key technologies, and custom Optimization Packs can be created without any coding.

Custom Goals and Constraints: Akamas allows users to define custom goals and constraints to guide the optimization process. Users can specify their desired tradeoffs between performance, resilience, and cost efficiency. The optimization recommendations provided by Akamas are tailored to meet these customized goals and constraints.