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.
One of the biggest challenges in starting a FinOps practice is getting broad executive support and buy-in to dedicate the time and resources needed for the cultural change.Read more
A list of best practices for cloud architects to design systems to optimize FinOps.Read more
Failure to purchase org level capacity commitments for BigQuery can result in runaway costs due to on-demand query costs. Purchasing an org level capacity commitment and enabling idle capacity at the org level can ensure stable BigQuery costs across the organization. Consideration also needs to be given to whether the...Read more