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How It Works
Moving workloads to the cloud does not automatically lower costs. Without deliberate planning, teams often lift and shift existing servers directly into cloud instances sized to match on-premises hardware, which tends to be overprovisioned. After the migration, those oversized resources run at full on-demand rates, generating higher bills than expected. Cost optimization addresses this at three stages. Before migration, teams assess each workload to select the right instance type, storage tier, and pricing model for the target cloud. During migration, they avoid replicating old inefficiencies by rightsizing resources to actual demand. After migration, they adopt commitment-based pricing, such as AWS Reserved Instances, AWS Savings Plans, Azure Reservations, Azure Savings Plans, or GCP Committed Use Discounts, to replace on-demand rates with discounted ones. The combination of accurate sizing and committed pricing is where the largest savings are realized.
Why It Matters for Cloud Cost
A migration is a high-stakes moment for cloud spend. Teams are focused on moving workloads safely and on time, so cost controls are frequently deprioritized. The result is that companies arrive in the cloud paying full on-demand prices for resources that were already oversized on-premises. Correcting this after the fact requires dedicated effort that most engineering teams do not have bandwidth for. Left unaddressed, the gap between actual spend and optimized spend compounds every month. A structured approach to migration cost optimization captures savings earlier, builds a cleaner cost baseline from day one, and avoids the expensive rework of rightsizing a large fleet months after go-live. See biggest cloud cost optimization challenges.
Usage AI’s Autopilot mode automatically purchases and adjusts AWS, GCP, and Azure commitments daily without human approval, replacing on-demand pricing with discounted rates at no upfront cost.