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Your organization is deploying containerized database workloads (AlloyDB Omni) on Google Compute Engine virtual machines in production.
Performance analysis and sizing assessments define the following technical and operational requirements:
Which compute provisioning strategy should you implement to optimize resource allocation and cost?
Custom machine types in Google Compute Engine allow you to customize the exact number of vCPUs and amount of memory (RAM) for a virtual machine instance, rather than selecting from predefined machine types.
StudyCerts
StudyCertsn2-standard-16) provide 4 GB per vCPU, meaning 64 GB of RAM would mandate purchasing 16 vCPUs—doubling vCPU costs for compute capacity that provides no performance benefit.Using custom machine types satisfies the specific 8:1 memory-to-vCPU ratio needed by the database while preventing the waste associated with predefined machine types that force higher core counts.
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