Abstract

With increasing cloud infrastructure spending, organizations confront cost inefficiencies in security, compute, storage, and data platforms. The flywheel methodology provides an approach to continuously optimize costs using the cycle of observing, attributing, correcting, guardrailing, and validating the use and spending of cloud resources. The framework addresses the challenge of creating sustainable optimizations or short-term cost savings that went out of control due to a lack of governance policies. Optimization opportunities exist in various parts of infrastructure, such as the encryption service, which causes too many API calls due to caching issues; non-production environments that run continuously but are rarely used; databases with long support lifetimes; computing clusters that are not utilized efficiently; and storage services that retain obsolete data with no policies for managing data lifecycles. Experience shows that cost reduction and performance measures are synergistic and not inversely correlated; large waste reductions are consistent with increased velocity, system throughput, and delivery responsiveness. The transition from sporadic cost reduction to everyday engineering discipline demands a cultural foundation of constant questioning, systematic waste elimination, and a process for institutionalizing optimization that maintains the cost of suboptimal behavior through automation and governance policies