FinOps: Holding engineering teams accountable for spend
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FinOps: Holding engineering teams accountable for spend
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The discussion further explores the practicalities of FinOps in the context of cloud spending and Kubernetes. Yasmin highlights that a significant amount of waste in organizations comes from simply not turning off unused systems and not right-sizing resources. She explains how tools like Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) can help, but also points out the complexities of optimizing across horizontal and vertical scaling behaviors. The conversation touches on "shame back reporting" as a way to provide visibility into costs for engineering teams, although the conversation emphasizes that providing tooling and insights is more effective than simply telling developers to change configurations.
The episode also delves into the evolving mindset around cloud costs, especially with the rise of AI and machine learning workloads. While historically engineering salaries eclipsed cloud spending, the increasing hardware requirements for ML and data workloads are making cost optimization a more pressing concern. Spending-conscious teams are increasingly asking about GPU optimization, even if AI/ML teams are still largely focused on limitless spending to drive unjustified "innovation". The conclude by discussing the challenges of on-premise versus cloud deployments and the importance of addressing "day two problems" regardless of the infrastructure choice.
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