Managing AI-era cloud storage costs with Datadog

The growing use of AI is driving up cloud storage costs as enterprises struggle to track what data they’re storing, why it’s kept, and how to manage it efficiently. To help address this, Datadog has launched Storage Management, now generally available for Amazon S3 and in preview for Google Cloud Storage and Azure Blob Storage.

The tool builds on Datadog’s existing cloud cost management capabilities, offering deeper visibility into object storage—the layer where most AI and analytics data accumulate.

Turning visibility into action

Datadog’s platform brings together cost, usage, and metadata into a single dashboard, helping teams see how their storage habits link to spending. That level of visibility makes it easier to spot redundant, temporary, or rarely accessed data and apply lifecycle or retention policies accordingly. Automated recommendations can then guide users toward the next step—whether that’s archiving old logs, deleting unused data, or moving inactive files to lower-cost storage classes.

The system also monitors behaviour across billions of objects, flagging unexpected growth or unusual access activity. That’s especially useful for large enterprises running multi-cloud environments, where it’s easy for spending anomalies to go unnoticed across accounts and regions. By surfacing these trends early, teams can address issues before they become budget problems.

Still, visibility alone isn’t enough. To turn insights into savings, organisations need consistent data governance and cooperation across departments. Finance and IT teams must work together to align policies, define ownership, and embed cost awareness into everyday workflows. Integrating insights with existing tools—such as AWS Cost Explorer, Azure AI Foundry, or Google Vertex AI—helps maintain a unified view of performance and cost.

The business case for disciplined storage management

According to Datadog, storage and processing now represent the third-largest cost for companies building AI products—surpassing even model training and inference. That’s a wake-up call for CIOs, CFOs, and operations leaders trying to balance innovation with financial discipline.

Treating storage management as a continuous practice rather than a one-off exercise allows organisations to control costs without slowing progress. The goal isn’t just to cut spending, but to ensure that data storage supports both efficiency and governance.

Enterprises that combine automation, shared accountability, and clear visibility into their data will be better equipped to handle the rapid growth that comes with AI adoption.

(Photo by C Dustin)

See also: AI’s impact on cloud solutions

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