Datadog launched its Storage Management platform to help enterprise teams reduce waste and prevent unexpected cloud object storage spend.

The tool is set to assist enterprise teams in catching anomalies in storage growth and access patterns, analyzing storage behavior in context, and acting on specific, automated recommendations to reduce cloud storage spend faster.

"For companies building AI products, data storage and processing are consistently the third-highest contributors to cost – greater than expenses for AI model training and inferencing," Yrieix Garnier, VP of product at Datadog, said of the offering.

Managing the costs of growing cloud object storage has become a key challenge for operations and cloud efficiency teams, as many struggle to identify the source of workloads or stakeholders driving costs in large, shared networks. Teams also tend to lack the granular context across metadata and access patterns needed to enforce lifecycle or tiering policies effectively, with the rise of AI only compounding matters.

These issues have prompted vendors such as Datadog to design tools that right-size the growing costs and uncover specific savings opportunities – such as transitioning cold data from expensive storage classes, deleting duplicate objects, or stopping the accumulation of non-current object versions.

Datadog Storage Management is said to catch anomalies in storage growth and access patterns, analyze storage behavior in context, and act on specific, automated recommendations to reduce cloud storage spend faster.

Some of its features include granular visibility to pinpoint cost drivers such as infrequently accessed, temporary, or duplicate data in workloads; and a unified context to correlate cost, usage, and metadata to enforce lifecycle, tiering and retention policies. Teams are also promised to receive targeted optimization recommendations to accelerate savings with recommendations on where to re-tier, archive or delete data.

Datadog surge

The tool's release comes after Datadog recently increased its fourth-quarter earnings forecast above Wall Street estimates, riding the wave of robust demand for its cloud-security products by businesses adopting AI technologies. Datadog, which joined the S&P 500 in July, said it expects its adjusted profit per share to be between 54 cents and 56 cents for Q4.

Some of its 28,000 customers include Shell, PayPal, Comcast, Airbnb, Fidelity Investments, the U.S. Department of Agriculture, and the London Stock Exchange Group (LSEG). Datadog describes 15 of its largest customers, who spend more than $1 million annually, as “AI-native” companies.

Recent developments in AI and storage saw Vast Data clinch a $1.17 billion contract with CoreWeave as Vast’s AI OS will serve as the primary data storage and management platform underneath the neocloud’s compute infrastructure. Vast will feed data to all of the AI training and inference workloads for CoreWeave’s high-profile customers like Meta, OpenAI, and Microsoft.

Another data storage development saw Microsoft update its Azure Container Storage platform, a cloud-native volume management and orchestration service providing storage for containerized applications. September’s 2.0.0 update was optimized to allow for the lower overhead of containerization that can tap into already deployed storage infrastructure, with the aim of reducing infrastructure costs.