Cloud Computing

Cisco Intersight Workload Optimizer Named a Leader in GigaOm Radar for Cloud Resource Optimization

January 19, 2023

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In a December 2022 article in Data Center Dynamics, Luca Collacciani hits the nail on the head regarding public cloud spending when he says:

“With increasing economic pressures and an uncertain forecast looking ahead to 2023, businesses of all sizes are looking to save costs wherever possible… This means it’s important to make your chosen [public cloud] platforms work smarter, not harder…”

IT teams turn to cloud resource optimization solutions to make cloud platforms work smarter. According to GigaOm, “Cloud resource optimization deals with the measurement, optimization, and reconfiguration of cloud assets that have already been deployed and are incurring costs to the organization.” Cloud resource optimization solutions address how resources can be balanced for cost and performance.

Cisco Intersight Workload Optimizer

Cisco Intersight Workload Optimizer (IWO), Cisco’s SaaS-based cloud resource optimization solution, recently was named a leader in the GigaOm Radar for Cloud Resource Optimization. A companion report to Key Criteria for Evaluating Cloud Resource Optimization Solutions, the GigaOm Radar evaluates vendors on their ability to analyze and optimize cloud resources. It evaluates vendors that offer both SaaS and self-hosted solutions on the following key criteria:

Integration with infrastructure provisioning tools
Integration with DevOps planning or ITSM tools
Automatic resource reconfiguration
AI/ML-driven resource predictions
Intelligent resource grouping
Abandoned resource identification

“The objective [of the radar] is to give the reader a snapshot of the technical capabilities of available solutions, define the perimeter of the market landscape, and gauge the potential impact on the business.”

According to the report, IWO ranks high on the evaluation metric for scalability, and speeds time to value. It concludes that “it is clear that Cisco is continuing to invest heavily in workload optimization.”