Optimizing Cloud Costs with FinOps AI
By: Rikin Raheja | RIEPL
Cloud spending has seen a dramatic increase in recent years, driven by the increasing adoption of cloud-native infrastructure services. As a matter of fact, worldwide end-user spending on public cloud services is expected to grow by 20.4%, totalling $675.4 billion in 2024, up from $561 billion in 2023, according to the latest forecast from Gartner. We can safely assume a similar growth pattern for cloud spending among organizations with on-premise cloud infrastructure.
Such acceleration is paralleled by concerns about wasteful spending, and these are not just concerns. Don't be surprised if I tell you that 50% overspending on the cloud is commonplace. This is what a recent Gartner study indicates as well. Hence, optimizing the existing use of the cloud has become one of the top initiatives among organizations. While the enthusiasm for cloud computing remains high, spending growth has necessitated an overhaul in the financial management of IT. After all, every single penny matters, right?
This is where FinOps has emerged as the management discipline for organizations to examine their cloud spending and optimize costs using best practices to maximize returns on cloud investments. It is important to note that FinOps is not a software or cost-cutting strategy. Rather, it is a cultural practice aimed at maximizing business value by aligning business, IT, and finance within the realm of cloud. It helps teams identify key levers, trade-offs, and opportunities without compromising delivery, customer satisfaction, or business continuity.
Hence, the next logical questions are: how do we implement FinOps? How do we make this happen?
This is where Artificial Intelligence comes into play. The world is changing rapidly, and AI is a significant part of that transformation. From providing a hyper-personalized shopping experience to evaluating job candidates, from self-driving cars to code-developing bots, AI is becoming a daily part of our lives. It's no surprise that AI also helps optimize cloud infrastructure and make FinOps a reality. When combined, we refer to this as FinOps AI.
A good example of FinOps AI in action is Copilot in Microsoft Cost Management. Copilot is a generative artificial intelligence chatbot developed by Microsoft and is a successor to Cortana. Cost Management is a suite of FinOps tools that helps organizations analyze and monitor cloud costs. With Copilot integrated into Cost Management, the game changes significantly. It’s not just about analyzing and monitoring anymore; it’s about reducing the unpredictability of operational costs, detecting anomalies, and identifying opportunities for optimization. This is a major advantage. Check it out here - https://www.youtube.com/watch?v=KuGkXGE4eWc
While there is still a way to go, and we should expect new breakthroughs in the future, the following two considerations need more attention:
Human Aspect: It will be interesting to see if all the good recommendations and insights provided by artificial intelligence will be accepted by stakeholders unless automated. Given the scale, revenue, and urgency of businesses, stakeholders might be reluctant to alter the existing setup.
Cost per GMS: FinOps AI can be a double-edged sword. While the end goal is to maximize business value and reduce cost per GMS, the cost of running complex models should not surpass the savings. If that happens, the whole concept of FinOps AI loses its value. Return on investment for AI projects is highly debated globally, which might be a challenging issue to navigate.
Finally, while some uncertainty looms, there is also excitement and enthusiasm for FinOps AI. These are two sides of the same coin and will go hand in hand. Things may turn around when investments start becoming profitable and unshakable trust is developed. Until then, FinOps AI remains a domain worth exploring.
Sources
https://www.finops.org/introduction/what-is-finops/
https://learn.microsoft.com/en-us/cloud-computing/finops/overview
Rikin Raheja | RIEPL
August 14, 2024