Compared with keeping an application in an on-premises data center, which advantage of running the same compute workload on Google Cloud delivers the clearest business agility benefit?
The company can make a single upfront hardware purchase and avoid recurring operating costs.
Engineers receive root access to the hypervisor layer for low-level debugging.
The workload can be pinned to a dedicated physical host to comply with legacy licensing.
Instances can be provisioned in minutes and automatically scale with traffic surges.
On Google Cloud, virtual machine instances and other compute resources can be created in minutes and placed behind autoscaling groups that add or remove capacity automatically. This elasticity lets teams respond quickly to traffic spikes, launch new features without lengthy procurement, and avoid over-provisioning. The other options-dedicated hosts, large capital purchases, and hypervisor access-either replicate on-premises constraints or do not translate directly into faster time-to-market, so they provide little incremental agility value.
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What is autoscaling in Google Cloud?
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How does provisioning on Google Cloud differ from on-premises hardware?
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What are autoscaling groups in Google Cloud, and why are they important?
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What is autoscaling, and how does it work in Google Cloud?
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How does Google Cloud compare to on-premises infrastructure in terms of provisioning speed?
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