How to maximize your generative AI investments with cloud FinOps

When was the last time enterprise IT was such a topic of conversation as it is in the era of generative AI, coming up in conversations at the weekend barbecue, the halls of Congress, and late night TV? Maybe Y2K?

The attention is well warranted, given the transformational powers of this novel technology. Add to that today’s macroeconomic environment and growing competitive pressure, and organizations can no longer afford to take the strategy of “wait and see.” At the same time, all this exposure and enthusiasm can lead organizations to make AI investments that might not pay off, leading to missed opportunities and unmet business objectives.

The rapid pace of innovation in generative AI means companies need to shift focus and invest in AI initiatives to realize business value and get ahead of the competition. However, the use of generative AI comes with its own set of unique challenges. One in particular to guard against is an unwieldy spike in cloud spend. 

One of the reasons is for potentially high costs is that generative AI projects often require significant amounts of computing power and data storage. With the right approach — notably cloud FinOps practices — organizations can help realize the business value they set out for while keeping costs in check.

How to maximize the value of your generative AI investment

A single generative AI model will not be able to solve all problems. Bigger is not always better. There is no need for a trillion-parameter model to answer simple questions; indeed, tools such as distillation and reinforcement learning mean that smaller models may outperform larger models on specific tasks. Google Cloud can provide you with access to the right model at the right time at the right cost for your use cases.

• Choose the right model for your needs. Not all models are created equal. Some models are better suited for certain tasks than others. Do your research and choose a model that is well-suited for your specific use case.

• Use the right tools. There are a variety of tools available to help you get the most out of your generative AI investment. Make sure you are using the right tools for the job.

• Train and tune your model on the right data. The quality and integrity of your data will have a big impact on the performance of your model. Make sure you are tuning and training your model on high-quality, clean data.

• Monitor your model's performance. Once your model is trained, it is important to monitor its performance. This will help you identify any areas where your model can be improved.

• Use generative AI in conjunction with other technologies. Generative AI is a powerful tool, but it is not a silver bullet. Use it in conjunction with other technologies to create even more powerful solutions.

• Design with cost-aware architecture in mind. During the design phase, architect your generative AI solutions with cost-awareness in mind.  For example, leveraging services such as Google Cloud Function can help reduce overall costs.

Remember that cloud cost optimization should be a continuous discipline, so regularly reviewing your generative AI models and adapting your strategies to align with changing workload demands and business requirements is one of the key building blocks of the FinOps framework at Google Cloud.

How to get started

When engaging on a generative AI project, technology and business leaders should reflect on the following questions:

• Do you have the foundational capabilities to understand and maximize your investment in generative AI? 

• Do you have the right financial controls put in place to manage the costs of generative AI? 

• Do you know how to capture and maximize the business value of your generative AI use cases?

• Do you have a proper cost model and governance process to onboard new generative AI use cases?

• Do you have a feedback loop in place to evaluate and deprecate use cases for generative AI that are not driving revenue, growth, or customer success?

We've been exploring these questions and more in our regular conversations with customers experimenting with generative AI. In the process, our Google Cloud Consulting FinOps practice has developed a number of key services to help address such business and financial challenges.

• Cloud FinOps assessment for generative AI
  Help establish a smooth gateway that your finance, technology, and product teams can collaborate on spending decisions to onboard generative AI use cases based on cost and value.
  

• Cloud Front Door for generative AI
  Establish a frictionless gateway that your finance, technology, and product teams can collaborate on spending decisions to onboard generative AI use cases based on cost and value.

• Actionable generative AI cost management recommendations
  Equip your finance and technology teams with the cost management tools and an understanding of how to leverage them to understand, govern, and optimize cloud spend on generative AI services.  

Organizations can maximize the value of generative AI and ensure that investments in generative AI generate sustainable business value by implementing proper financial governance through cloud FinOps. By embracing cloud FinOps for generative AI practices, organizations can:

• Break down departmental silos across the enterprise and drive awareness of generative AI leading practices.

• Accelerate generative AI cost management capabilities with Google-native tools.

• Maximize the value of their generative AI investment by taking immediate actions on cost optimization recommendations.

Generative AI isn't just a hot topic — it's a business imperative, given the intense interest and investments going on across industries across the world. Your organization doesn't want to be left behind, but neither does it want to make inopportune investments that could drive up costs or waste time and talent. 

With the right FinOps approach, you can identify the projects worth pursuing and help keep them on track and returning on your investment. 

^{Special thanks to Sheri Cunningham and Lilly McNealus for sharing their insights and contributing to this article.}