Datadog Eyes AI-Powered Predictive Monitoring as Enterprise Cloud Demand Grows

Datadog is expanding its AI capabilities through proprietary models, open-source and foundation models, its research lab, and the acquisition of Adaptive ML. The goal is to make observability more predictive and eventually enable automated issue remediation. AI adoption is creating additional monitoring demand as customers build applications using external models, open-source systems and inference infrastructure. Datadog is already monetizing this trend through Agent Observability, while AI-native customers are showing strong workload growth and broader product adoption. Enterprise cloud migration remains a major growth opportunity as companies modernize legacy infrastructure for AI. Datadog is increasing enterprise sales, channel and data-center investments while continuing to devote roughly 30% of revenue—more than $1 billion—to research and development.

Datadog NASDAQ: DDOG is investing in artificial intelligence capabilities, proprietary models and enterprise sales as it seeks to expand its observability platform’s role in monitoring increasingly complex cloud and AI workloads, Chief Financial Officer David Obstler said during a company session. Obstler said Datadog’s collection of observability data—covering signals tied to the functioning of customer-facing applications—could become a competitive advantage as AI models improve the company’s ability to identify and predict application issues. The company recently acquired Adaptive ML, which he described as a specialist in reinforcement learning for IT management and observability.

Datadog has also established a research lab and previously released a model called Toto, according to Obstler. He said the company intends to use a combination of its proprietary data, open-source models and foundation models to develop more predictive observability capabilities.

Obstler described a long-term vision in which Datadog can generate more accurate real-time signals, improve application functionality and potentially support automated remediation. He said the company is not yet at that point, but its Bits product line is intended to use models and data to become more predictive.

Under the envisioned approach, customers would determine how much authority to give the platform. For certain issues, the platform could automatically remediate a problem, while for others it could provide a recommendation requiring human approval, he said. “That has tremendous ramifications, both in terms of the speed and also the efficiency in human capital,” Obstler said.

He also said AI adoption may increase Datadog’s opportunity because the company monitors components that affect an application’s functionality. As customers build AI-enabled applications using external models, open-source models and inference infrastructure, they create more systems and activity to monitor, he said. Datadog has begun monetizing AI observability through its Agent Observability offering, according to Obstler. He said thousands of customers are using the product and that the company is beginning to see related revenue streams.

Obstler characterized AI-native companies as cloud-native businesses investing in modern applications, operating without legacy infrastructure and experiencing accelerated demand. Datadog is seeing strong workload growth among those customers, he said, as well a...