Our initial challenge: migrating 15 million lines of code from six data centers worldwide in six months. Our team immediately saw a contrast in how to optimize cloud finance and solved this challenge by focusing on building strong governance, cost visibility, and cost optimization policies.
In our original cloud model: we had 70% preemptible, 30% committed use discount across six regions (three U.S., two Asia, one EMEA). This used a moderate amount of on-demand services. This blend changed after migration as we learned many ways to better leverage rate optimizations.
Our FinOps team prioritize cloud finance opportunities and initiatives by level of effort with cost optimization benefits. From the graph you can see that flat-rate BigQuery and CUDS have the highest cost optimization benefits with the lowest effort.