Kubernetes optimization
KUBERNETES OPTIMIZATION

Improve cost efficiency and performance of K8s apps

Tuning cloud-native microservices applications on Kubernetes is tricky, due to the complex interplay of resource management at the container and runtime layers. Akamas AI safely minimizes container/pod resources while improving response time and throughput, thus preventing runaway costs and business risks.

RUNTIME OPTIMIZATION

Boost your applications without any code changes

Tuning application runtime provides higher levels of performance and resilience without code changes, but it is challenging, even for seasoned experts. Akamas AI autonomously finds configurations of JVM and Golang parameters that optimize applications, improving business agility and operational efficiency.

Akamas service resilience
Akamas cloud efficiency
CLOUD OPTIMIZATION

Cut cloud bills and improve service quality

Cloud cost optimization tools only look at resource utilization at the infrastructure layer, not at real application demand, thus often negatively impacting performance. Akamas application-aware optimization helps identify Amazon EC2, GCP and Microsoft Azure instances that maximize cost efficiency and end-to-end performance.

BIG DATA OPTIMIZATION

Make your Spark jobs run faster and cheaper

Configuring Spark to maximize job performance is more art than science as job size, data volumes and other factors impact completion time and execution windows. Akamas AI finds Spark, AWS EMR and Google DataProc configurations that minimize resource usage and maximize performance, thus reducing costs while meeting SLOs.

Akamas database optimization

Application-aware, Autonomous,
AI-powered optimization

Akamas patented AI finds full-stack configurations that keep applications running at optimal performance, resilience, and cost efficiency while never compromising on SLOs.

Benefits

COST REDUCTION

-60%

decrease in infrastructure/cloud costs
with the same application performance

SERVICE QUALITY

-70%

decrease in response time
with lower fluctuations and peaks

OPERATIONAL EFFICIENCY

-80%

savings in engineering time spent
for manual tuning tasks

SERVICE THROUGHPUT

+30%

increase in transactions/seconds
with the same infrastructure resources

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