AI

Datadog sees tagging and model governance as the foundation of AI cost management

AI cost management is bringing a new taxonomy to FinOps practitioners, but the core discipline —  understanding what you’re using, why, and what it costs — remains the same.

That constancy is reassuring and instructive, according to Deeja Cruz (pictured), senior FinOps analyst at Datadog Inc. The biggest practical lesson enterprises can carry from cloud to AI is to maintain high-quality attribution tags. Without them, the ability to allocate spend and identify optimization opportunities collapses, regardless of how sophisticated the AI workload is.

“The biggest takeaway I can give is, ‘Don’t neglect your tags,'” Cruz said. “Having good tagging on your data will unlock your ability to allocate it and be able to answer questions that executives are asking.”

Cruz spoke with theCUBE’s John Furrier and Paul Nashawaty, principal analyst at theCUBE Research, at FinOps X 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how AI cost management is developing the FinOps role and how collaboration across engineering, finance and security looks in practice. (* Disclosure below.)

AI cost management demands model governance and cross-team ownership

Cruz described a concrete example of AI-assisted FinOps in action. A colleague without a development background identified a cost-savings opportunity in a storage bucket configuration, used a large language model to generate the necessary code changes, sent the pull request to the bucket owner for approval and saw material savings reflected in the cost data days later. This small case is a primary example of the kind of practitioner-led AI use the FinOps role is developing.

“I would encourage all FinOps practitioners to get really comfortable with these tools,” Cruz said. “Take your domain expertise, and use these tools to deliver value for the organization faster.”

On the governance side of the aisle, Datadog is developing a multi-model selection strategy, evaluating which model is appropriate for a given workload rather than defaulting to the most expensive option. Ownership of AI spend at Datadog emerged organically through a partnership between the FinOps team and an internal AI developer experience team, with FinOps owning forecasting and attribution. The developer experience team handles governance tooling and developer feedback. The dynamic parallels how cloud ownership has evolved — a straightforward framework for organizations still sorting out accountability, according to Cruz.

“Figuring out who owns what, who’s the lead on a particular effort and who’s the support,” she said. “This is a team sport.”

(* Disclosure: TheCUBE is a paid media partner for the FinOps X event. Neither the FinOps Foundation, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE