For organizations leveraging Artificial Intelligence, Cloud Financial Management (FinOps) has evolved into a broader technology strategy over the last several years. As companies have begun expanding across public cloud service providers (PSPs) like AWS, Azure, and Google, managing total cloud expenses is now a dedicated function within the organization that goes beyond only the finance team. As these organizations invest significantly in AI solutions which includes training models, running inference workloads and storing massive amounts of data cloud infrastructure costs can grow rapidly. Therefore, FinOps as Code will provide the modern approach to cloud financial governance through automatically governing how organizations will spend their money on cloud services.
Organizations create rules for governance using code in order to enable automation across their cloud infrastructure. As a result, this creates a scalable and consistent way to manage costs through proactive behavior in multi-cloud and AI-based environments.
Cost of cloud computing for businesses that utilize Artificial Intelligence can be hard to predict. Workloads that rely heavily on GPU's, high-speed storage, and very large data pipelines tend to consume resources at a rapid pace. If an organization has a model training job running that has not been accounted for, then that could lead to a significant increase in monthly billing. FinOps as Code allows teams to implement automated spending limits, provide notifications that allow teams to be aware of their expenditures, and create policies to turn off any idle AI resources. This allows an organization to continue pushing forward with Innovation while avoiding financial waste.
FinOps as Code offers one of its greatest advantages in the area of policy standardization. Companies operating multi-cloud deployments with varying teams using different cloud platforms for different workloads (such as dev running on AWS, analytics running on Azure and AI prototyping running on GCP) will likely experience utilities fragmented and more costly than expected without centralized governance over the cost of these workloads. The FinOps as Code model provides the ability to codify all such policies as budget limits, resource tag identifiers, rightsizing criteria and environment-dependent restrictions for use across all cloud service providers in a uniform manner.
With the ability to automate operations, organizations can use FinOps as Code to support their cloud-based operations by writing programming code (e.g., policies) that can automatically monitor the utilization of their virtual machine resources (e.g., underutilized VM's), determine whether any of their Kubernetes clusters are over-provisioned, or identify unused storage volumes. Once these resource consumption metrics have been identified, the FinOps as Code framework can choose to notify the team responsible for the consumption, reduce the resources and/or commence approval workflows. This automated infrastructure will also benefit from the long-established need for companies to be able to respond quickly and efficiently to newly established Artificial Intelligence projects, as test environments and sandbox workloads often have no continuing value once they are completed.
In addition to the benefits discussed above, there are numerous other benefits associated with implementing FinOps as Code, including enhanced cooperation between engineering, finance, and leadership teams. Because policies are written in code (i.e., these policies are versioned, auditable, and can be reviewed easily), DevOps teams can incorporate cloud cost rules directly into CI/CD pipelines; thus, every deployment will meet the corporate budgetary goals. Therefore, cost optimization becomes part of the software delivery process instead of an afterthought.
FinOps as Code will also aid predictive optimization through the use of automated governance combined with analytics; this allows organizations to anticipate how much they will spend on their artificial intelligence efforts and multi-cloud expansion. By having this information, companies can make informed decisions on how much to invest in innovation while continuing to be profitable.
In the fast-paced world of technology, the speed of financial governance (specifically related to cloud budgets) must keep pace with how quickly new technologies are delivered. By leveraging FinOps as Code within their organization, businesses will be able to automate financial controls and create a more sustainable ROI from their cloud and artificial intelligence investment. As the use of Artificial Intelligence becomes commonplace, this will be critical for businesses aspiring to have both agility in terms of the technology they deliver and sufficient controls for their financials.