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.
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.
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.
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 patented AI finds full-stack configurations that keep applications running at optimal performance, resilience, and cost efficiency while never compromising on SLOs.
decrease in infrastructure/cloud costs
with the same application performance
decrease in response time
with lower fluctuations and peaks
savings in engineering time spent
for manual tuning tasks
increase in transactions/seconds
with the same infrastructure resources
“Akamas helped us to rapidly mature on the performance tuning front, by allowing us to find an optimal configuration for our application. This resulted in significant cost savings as well as removing barriers to replatforming.“
Thanks to Akamas, TeamSystem has improved the efficiency of our critical microservices as we would never be able to do manually. The ability to consistently deliver the highest level of quality to our end-users at the lowest possible cost is an important differentiator for us.
Akamas ease of integration with our CI/CD pipelines enabled us to automate the configuration deployment to quickly find optimized configurations that had not been previously found with our manual approach.
“Akamas AI allowed us to optimize our container-based Java services, significantly increase reliability under peak loads, and drive towards our cost-saving goals. AI-driven optimization by Akamas created a new competitive advantage and better efficiency in our private cloud. Autonomous performance optimization is real and is here.”
Besides performance improvements that otherwise we would not be able to achieve with our previous manual approach, Akamas has dramatically reduced the time and effort required for performance tests from months to days.
Tools like Akamas allow us to help our organizations deliver exceptional performance while keeping an eye on resource consumption and costs.
The Akamas team has got one of the best possible supports. Their team is extremely helpful and supportive. I’ve had great interactions while working with Akamas.
The adoption of Akamas aligns very well to our sustainability initiative on carbon footprint reduction.
Tools like Akamas get back a free lunch on the table: achieving huge performance enhancement even without changing the code and without having to rely on processor speedups.
Akamas enabled us to achieve significant cost savings we ha expected. Moreover, it was also valuable in empowering our developers, performance engineering and service architects thanks to insights on potential tradeoffs among competing goals
“We believe Akamas is taking the right approach with its gradual recommendations to allow users to leverage AI-powered live optimization before eventually adopting a fully autonomous mode.”
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