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Description

Designed specifically for AWS Partners and customers, Parquantix stands out as a premier strategic ally for organizations aiming to effectively monitor, analyze, and optimize their AWS usage in real-time through our AI-powered tool, ensuring optimal deployment of AWS resources at all times. Our platform enhances cloud compute and database efficiency by facilitating the active procurement and management of reserved instances and savings plans. With our AI-driven software, businesses can achieve savings of up to 60% when compared to standard on-demand pricing. For eligible partners, we offer to cover the upfront costs for reserved instances and savings plans, spreading the expense across their entire lifespan. Partners have the flexibility to retain reserved instances only for the duration needed, avoiding the restrictions of lengthy 1 or 3-year contracts. Additionally, they can sell any unused reserved instances on the AWS Marketplace and upgrade to the latest generation instances to optimize price performance. No matter your industry, whether in technology, e-commerce, or media, the absence of an automated optimization solution likely means you're incurring unnecessary cloud expenditures. Embracing our service can lead to significant cost reductions and improved resource management.

Description

Pepperdata autonomous, application-level cost optimization delivers 30-47% greater cost savings for data-intensive workloads such as Apache Spark on Amazon EMR and Amazon EKS with no application changes. Using patented algorithms, Pepperdata Capacity Optimizer autonomously optimizes CPU and memory in real time with no application code changes. Pepperdata automatically analyzes resource usage in real time, identifying where more work can be done, enabling the scheduler to add tasks to nodes with available resources and spin up new nodes only when existing nodes are fully utilized. The result: CPU and memory are autonomously and continuously optimized, without delay and without the need for recommendations to be applied, and the need for ongoing manual tuning is safely eliminated. Pepperdata pays for itself, immediately decreasing instance hours/waste, increasing Spark utilization, and freeing developers from manual tuning to focus on innovation.