In today’s digital landscape, the increased use of AI is causing a surge in cloud storage costs for enterprises. Many organizations are struggling to keep track of the data they are storing, understand the reasons for retaining it, and efficiently manage their storage practices. To help address this challenge, Datadog has introduced Storage Management, now available for Amazon S3 and currently in preview for Google Cloud Storage and Azure Blob Storage.
This new tool expands on Datadog’s existing cloud cost management features, offering enhanced visibility into object storage—the layer where a majority of AI and analytics data resides.
Converting visibility into action
Datadog’s platform consolidates cost, usage, and metadata data into a unified dashboard, enabling teams to understand how their storage habits impact their expenses. This level of transparency makes it easier to identify redundant, temporary, or infrequently accessed data and implement appropriate lifecycle or retention policies. Automated suggestions can then guide users on actions such as archiving old logs, deleting unused data, or transferring inactive files to more cost-effective storage tiers.
The system also monitors activities across billions of objects, flagging any unusual growth or access patterns. This feature is particularly valuable for large enterprises operating in multi-cloud environments, where discrepancies in spending can easily go unnoticed across various accounts and regions. By identifying these trends early on, teams can mitigate issues before they escalate into budgetary concerns.
However, mere visibility is insufficient. To translate insights into savings, organizations must establish consistent data governance practices and foster collaboration across departments. Finance and IT teams must collaborate to align policies, define ownership, and integrate cost awareness into daily operations. Integrating insights with existing tools such as AWS Cost Explorer, Azure AI Foundry, or Google Vertex AI helps maintain a cohesive view of performance and expenses.
The rationale for disciplined storage management
According to Datadog, storage and processing now rank as the third-largest cost for companies developing AI products—surpassing expenses related to model training and inference. This trend serves as a wakeup call for CIOs, CFOs, and operations leaders who aim to balance innovation with financial prudence.
Viewing storage management as an ongoing practice rather than a one-time initiative enables organizations to manage costs without hindering progress. The objective is not only to reduce expenditure but also to ensure that data storage aligns with efficiency and governance objectives.
Enterprises that combine automation, shared responsibility, and transparent insight into their data are better positioned to handle the rapid expansion accompanying AI adoption.
(Photo by C Dustin)
Additional reading: The influence of AI on cloud solutions
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