Compare IBM Storage Insights vs. Pepperdata in 2025

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Description

IBM® Storage Insights offers an exceptional level of insight into your storage ecosystem, assisting you in managing intricate storage systems and making informed decisions that can lead to cost reductions. By integrating IBM's established data management expertise with unique analytics developed by IBM Research, this service provides a comprehensive solution. As a cloud-based platform, it allows for swift deployment and reduces the time spent on storage administration while enhancing overall storage efficiency. Moreover, it automates certain support functions, leading to quicker resolution of any arising issues. With two available editions, you can choose the features that best align with your specific requirements. It eliminates uncertainty in capacity planning by providing enhanced visibility into data growth and existing capacity, allowing you to postpone future acquisitions by discovering and reclaiming storage that has been provisioned but remains unused. Additionally, it simplifies the process of identifying the root causes of performance challenges and helps prevent potential service interruptions in the future. This comprehensive tool ultimately empowers organizations to make more strategic decisions regarding their storage resources.

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.