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

Key Points

• 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.

AI vision centers on predictive monitoring and remediation

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.

AI-native customers show expanding use cases

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 as a movement from internally built, open-source or pieced-together observability systems toward Datadog’s platform.

These newer customers are also using more of Datadog’s products on average, Obstler said. While the company initially expected its primary AI opportunity to center on production inference workloads, it has also received requests from larger hyperscalers and other companies to support training and post-training workloads.

“The speed of this is such that the pre-production or late training into production is starting to merge,” Obstler said, adding that the company does not have evidence to conclude it will become “the training company.”

On customer retention, Obstler said Datadog’s gross retention has historically been in the upper 90% range, with larger enterprises at the high end. He acknowledged that some customers use a combination of Datadog and other tools, depending on the real-time requirements of particular workloads.

For its largest customer, Datadog has commitment contracts, and Obstler said that customer renewed and extended its agreement. The company has incorporated only the commitment level from that customer into its guidance, he said, because usage can vary above that amount.

Product changes target customer efficiency

Obstler pointed to Infinite Cardinality Metrics, Flex Logs, Frozen Logs and Metrics without Limits as examples of product innovations designed to help customers manage data more efficiently. He said Datadog has become more focused on understanding how customers use its platform and helping them avoid processing data that does not provide sufficient value.

For Infinite Cardinality Metrics, Datadog can sort or curate certain metrics before they are fully processed, according to Obstler. He said the approach can improve customer value while reducing costs associated with processing data that may not be useful.

Datadog is also expanding access to data stored outside its platform. With Federated Logs, customers can link logs stored in systems including ClickHouse and Databricks without moving all of that data into Datadog, Obstler said. He said the strategy allows customers to retain data externally for volume, regulatory or other reasons while using Datadog’s analytics.

Obstler said Datadog believes keeping users centralized on its platform can support higher average revenue per customer, even if some data remains elsewhere. The company has also introduced related capabilities such as Data Observability Pipelines and Bring Your Own Cloud, he said.

Enterprise cloud migration remains a focus

Obstler said cloud migration remains an important opportunity because substantial legacy infrastructure remains in place. He cited research-company estimates that applications in the cloud account for somewhere in the upper 20% to 30% range, while noting that some enterprises have not yet begun material cloud projects.

AI-driven technology change is increasing the urgency for enterprises to modernize applications and move them to the cloud, he said. Datadog is expanding its enterprise sales approach, including key-account coverage, named and major accounts, channel investments and data-center investments, to pursue those opportunities.

Finally, Obstler said Datadog continues to invest heavily in research and development, including in proprietary models and coding tools. He said the company is testing the productivity impact of coding agents through A/B teams with differing levels of access to those tools.

According to Obstler, Datadog has consistently invested 30% of revenue in R&D, or more than $1 billion, and is focused on using that investment to keep its integrated platform competitive against emerging point solutions.

About Datadog (NASDAQ:DDOG)

Datadog, Inc NASDAQ: DDOG is a cloud-based software company that provides monitoring, observability, security and analytics tools for modern applications and information technology environments. Its platform helps organizations collect, correlate and analyze data from servers, databases, applications, containers, networks and cloud services in a unified environment.

The company's products include infrastructure monitoring, application performance monitoring, log management, real user and synthetic monitoring, database monitoring, cloud security, software delivery tools, incident management and cloud cost management.

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