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FinOps: Holding engineering teams accountable for spend
Manage episode 497556491 series 3433854
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In this episode of Adventures in DevOps, we dive into the world of FinOps, a concept that aims to apply the DevOps mindset to financial accountability. Yasmin Rajabi, Chief Strategy Officer at CloudBolt, joins us to demystify, as we acknowledge the critical challenge of bringing together financial accountability and engineering teams who often are not paying attention to the business.
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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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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