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How It Works
A virtual machine (VM) is a software-based computer that runs on cloud provider hardware. You pay for every hour a VM runs, whether or not it is fully used. Cost optimization closes the gap between what you pay and what your workloads actually need. It combines three levers: right-sizing (choosing the correct instance type for your workload), scheduling (turning off VMs when not needed), and commitment-based discounts (trading usage flexibility for lower per-hour rates). Each lever works independently, but they deliver the most savings when applied together.
On AWS, the relevant compute service is EC2. Azure calls its compute service Virtual Machines. GCP runs workloads on Compute Engine. All three providers offer commitment-based pricing as the primary mechanism for large, sustained savings. AWS offers Reserved Instances (up to 72% vs on-demand) and Compute Savings Plans (up to 66%). Azure offers Reservations (up to 72%) and Azure Savings Plans (up to 65%). GCP offers Committed Use Discounts (up to 57%).
Why It Matters for Cloud Cost
Without active optimization, cloud VM spend grows faster than the business value it delivers. Teams provision VMs for peak load and leave them running at low utilization. Commitment decisions get deferred because they require forecasting and carry financial risk if usage shifts. The result is an on-demand bill that is significantly higher than it needs to be. For companies spending millions annually on cloud, unoptimized VM costs compound quickly. A 20% reduction in effective compute rate across a $5M annual cloud bill represents $1M in recovered spend.
Key Characteristics
- Commitment-based discounts represent the largest single lever for VM cost reduction across all three major cloud providers.
- Right-sizing addresses waste at the resource level, while commitments address waste at the pricing model level; both are necessary.
- Scheduling reduces spend on non-production VMs that run outside business hours without any change to infrastructure.
- Optimization works across instance families, regions, and operating systems when flexible commitment instruments are used.
How Usage AI Handles This
Usage AI manages VM commitments across AWS EC2, Azure Virtual Machines, and GCP Compute Engine through its CoPilot and Autopilot products, purchasing and adjusting Savings Plans and Committed Use Discounts daily with a Guaranteed Buyback on any underutilization, so customers carry zero financial risk. Customers save 30 to 50% on cloud spend with no upfront cost and no infrastructure changes required.
See how Usage AI saves 30 to 50% on AWS, GCP, and Azure.
Common Questions
1. What is the difference between right-sizing and commitment-based discounts for VM cost optimization?
Right-sizing reduces waste by matching instance size to actual CPU and memory demand, which lowers the baseline cost before any discount is applied. Commitment-based discounts then reduce the per-hour rate on that correctly sized instance. The two approaches stack: right-sizing first, then committing, produces the best outcome.
2. Do commitment-based discounts require locking in a specific VM type or region?
It depends on the instrument. AWS EC2 Instance Savings Plans are flexible across instance sizes within a family, while Compute Savings Plans apply across any EC2 usage. Azure Reservations are scoped to a VM series but support instance size flexibility within the series. GCP Committed Use Discounts apply at the resource level within a region. Usage AI selects the right commitment type for each workload automatically.
3. What happens if VM usage drops after a commitment is purchased?
With native provider commitments, underutilized reservations represent sunk cost with no recovery mechanism. Usage AI provides a Guaranteed Buyback, meaning cashback plus credits are issued for any underutilization on commitments purchased through the platform.