Compute Engine Right Sizing Custom Machine Types Committed Use

What Custom Machine Types and Resource-Based CUDs Are

Custom machine types allow you to tailor the exact number of vCPUs and amount of system memory for Compute Engine instances rather than being locked into predefined, fixed ratios (like the standard 1:4 vCPU-to-RAM ratio in n2-standard types). Resource-based Committed Use Discounts (CUDs) provide substantial cost savings (up to 55%–70% depending on machine family and term length) in exchange for committing to a minimum continuous usage of compute resources (vCPUs and memory) in a specific region for a 1-year or 3-year term.

How It Meets the Optimization Requirements

• Eliminating Over-Provisioning: The workload only requires ~2.5 vCPUs worth of processing power but needs ~24 GB of memory. Configuring a custom machine type (e.g., 4 vCPUs and 24 GB memory) prevents paying for 4 unneeded vCPUs that were bundled in the n2-standard-8 instance.
• Maximizing Ongoing Long-Term Discounts: Because the microservices run 24/7 with predictable demand over a 3-year horizon, purchasing a 3-year resource-based CUD locks in the deepest possible discount tier for the exact amount of committed N2 vCPUs and memory.

Key Advantages and Capabilities

• Granular Right-Sizing: Prevents paying for idle vCPU capacity while fully satisfying high-memory requirements.
• Deepest Discount Tier: 3-year commitments offer substantially higher percentage savings compared to 1-year commitments or automatic Sustained Use Discounts.
• Predictable Budgeting: Establishes predictable baseline operational expenses across FinOps reporting cycles.
• Flexibility within Machine Series: Resource-based CUDs automatically apply across custom and standard instances within the same machine series and region.

Why This Is Optimal

Right-sizing the hardware configuration directly reduces the baseline resource footprint, and layering a 3-year resource-based CUD on top of that right-sized footprint yields the maximum cumulative cost reduction without risking memory starvation or performance degradation.