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How It Works
Cloud providers charge a default on-demand rate for every resource you run without a commitment. On-demand pricing is flexible but expensive. Rate optimization replaces that default rate with a discounted rate by agreeing to use a certain level of compute over a fixed term, typically one year. The provider rewards that predictability with a lower hourly price. On AWS, those mechanisms are called Reserved Instances and Savings Plans. Azure calls them Reservations and Azure Savings Plans. GCP calls them Committed Use Discounts. The goal in every case is the same: pay less per unit of compute for the workload you know you will run.
The process requires analyzing actual usage patterns, identifying a stable baseline of consumption, and purchasing the right commitments to cover that baseline. Workloads above the committed baseline continue to run at on-demand rates. Rate optimization does not require any changes to infrastructure or application code. It operates entirely at the billing layer.
Why It Matters for Cloud Cost
Without rate optimization, every resource runs at full on-demand pricing by default. That default is the most expensive way to consume cloud compute, and most organizations run a significant portion of their workload at that rate simply because no one has acted on the available discounts. AWS Reserved Instances can save up to 72% versus on-demand. AWS Compute Savings Plans save up to 66%. GCP Committed Use Discounts save up to 57%. Azure Reservations save up to 72%. Azure Savings Plans save up to 65%. Those discounts do not apply automatically. Teams must analyze usage, size commitments correctly, and manage those commitments actively as workloads change. When that work does not happen, the savings go uncaptured and the on-demand bill compounds month over month.
The operational challenge is significant. Commitment decisions require up-to-date usage data, understanding of how different instance families and regions qualify for different discount tiers, and ongoing attention to prevent underutilization if workloads shrink. Most engineering and finance teams do not have the bandwidth to manage this continuously, which is why a large share of cloud spend stays at on-demand rates even at companies that know discounts exist.
Key Characteristics
- Rate optimization applies at the billing layer and requires no infrastructure or code changes.
- Commitment sizing must reflect actual baseline usage to avoid paying for capacity that goes unused.
- Each major provider uses a different product name: Reserved Instances and Savings Plans on AWS, Reservations and Savings Plans on Azure, and Committed Use Discounts on GCP.
- Active management is required because workload patterns shift and commitments that were correctly sized at purchase can become over- or under-committed over time.
How Usage AI Handles This
Usage AI automates rate optimization across AWS, GCP, and Azure by purchasing and continuously adjusting commitments on the customer’s behalf, with Usage AI owning the commitments so the customer carries zero financial risk. Through Autopilot, commitments are refreshed daily without requiring human approval, and any underutilization is covered by a cashback plus credits guarantee.