AI-assisted development can make data teams significantly more productive, but it also introduces a new challenge: how do you stay in control of consumption and costs when AI is increasingly performing actions within your data platform?
In this webinar, we explore FinOps in Snowflake, using Cortex Code as a practical use case. We start by looking at the cost model: how Cortex Code usage is billed, which factors drive overall costs, and why AI-assisted development requires a different approach to cost management.
From there, we move from understanding costs to actively optimizing them. We explore how organizations can give developers the freedom to use Cortex Code productively while keeping spending under control. This includes practical techniques such as prompt optimization, caching strategies, selecting the right model for each task, reducing unnecessary processing, and designing more efficient workloads.
But FinOps is about more than simply reducing costs. It is equally important to understand where consumption comes from and who is responsible for it. We show how to monitor Cortex Code usage, establish budgets and limits, and attribute costs to the relevant users, teams, or applications. This makes AI consumption both transparent and manageable.
Using a practical FinOps framework, we translate these principles into an approach that organizations can apply to their own Snowflake environment. During the live demonstration, we bring everything together by using Cortex Code for a real development task while simultaneously monitoring and optimizing its consumption.
The result is a practical view of AI development and FinOps as two sides of the same coin: enabling developers to get maximum value from AI while maintaining control over governance, consumption, and cost.
