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IBM Turbonomic offers automation, planning, and right-sizing recommendations to streamline resource management, improve efficiencies, and optimize costs across virtualized environments and cloud platforms.
IBM Turbonomic is valued for its capability to optimize resource allocation and monitor virtual environments efficiently. It facilitates automated decision-making in VM sizing, load balancing, and cost optimization for both on-premises and cloud deployments. Users can leverage insights for workload placement, ensure peak performance assurance, and effectively right-size across VMware and Azure. The ongoing transition to HTML5 aims to improve visual and navigational ease, while expanded reporting features are anticipated. Opportunities for improved training, documentation, and integrations enhance platform usability and functionality.
What Are the Key Features?In finance, IBM Turbonomic aids in maintaining platform efficiency during market fluctuations. Healthcare organizations leverage its capability for resource optimization during high-demand periods to enhance patient care support. Retailers use it for planning in peak seasons, ensuring resources align with fluctuating demand to maintain performance continuity.
The leading multi-cloud solution for faster provisioning, orchestration, self-service and cost optimization
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Embotics vCommander is the fastest and easiest way to automate provisioning and enable self-service IT across virtualized, private and public cloud infrastructures
Foglight Hybrid Cloud Manager simplifies your cloud performance monitoring needs, allowing you to see VMware, Hyper-V, Azure and AWS inside of a single platform. It provides you with unified workflows, pre-configured rules with notifications and intelligent analytics that unscramble complex troubleshooting and deliver the information you need to drive down costs.
Foglight Hybrid Cloud Manager helps simplify your day-to-day operations by enabling IaaS monitoring across Azure and AWS without the need for manual configuration or the hazards of missing elastic/scaleset workload changes — all while exposing the performance of your IaaS.
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