Datadog (NASDAQ:DDOG) Chief Financial Officer David Obstler said the company is benefiting from a healthier buying environment for cloud migration and application modernization, a broader product portfolio, and increased sales execution, as the observability platform pushes further into security, workflows, and AI-enabled automation.
Speaking at the Morgan Stanley Technology, Media & Telecom Conference, Obstler described Datadog’s core mission as helping organizations run “mission-critical” applications in the cloud, with roots in observability spanning infrastructure monitoring, application performance monitoring (APM), logs, and digital experience monitoring. Over time, he said, Datadog has expanded toward a more integrated platform approach—adding security, product analytics, service management, and workflow capabilities—while embedding AI to move beyond visibility toward “recommendations and an action.”
What Datadog says is driving re-acceleration
Second, he said product expansion has broadened Datadog’s value proposition, with newer offerings reaching scale. Third, he cited market share gains and consolidation activity, describing Datadog’s “single pane of glass” positioning as enabling customers to replace multiple tools on one platform. As an example, he referenced acceleration in APM as evidence of both innovation and share gains.
Finally, Obstler said Datadog has expanded go-to-market capacity while maintaining productivity, including investments in new geographies and government, and improved enterprise selling motions.
Platform adoption and consolidation opportunity
Obstler said Datadog has highlighted that only about half of its customers are using all three “pillars” of the platform. He described the primary unlocks for broader adoption as “frictionless” exposure to additional products inside the platform and time—citing existing install bases, internal champions, and the gradual replacement of legacy tools.
He also said consolidation is frequently present in larger deals, noting that in Datadog’s biggest transactions, around half include consolidation components.
AI-native customer momentum and “build vs. buy”
Obstler characterized AI-native companies as an “ideal customer base” for Datadog because they tend to be modern cloud companies without legacy infrastructure. In that segment, he said customers adopt Datadog quickly as they scale, aided by Datadog’s product fit and developer-oriented marketing. He also emphasized Datadog’s focus on both landing new customers and expanding product usage through account and technical management.
Asked about the market debate that AI-focused customers might build their own observability tooling, Obstler argued the evidence has “quite the opposite” outcome, saying the predominant decision has been to use Datadog rather than build internally due to time constraints and total cost of ownership. He said some customers experiment with internal tools but often return, and he pointed to Datadog’s “very upper nineties” gross retention as support for the view that do-it-yourself approaches are fringe cases.
Guidance philosophy and larger enterprise lands
Discussing the company’s outlook, Obstler said Datadog’s guidance reflects what it sees in the business along with an added layer of conservatism. He cited continued strong end-market conditions, product adoption, more logo wins including larger logos, and compounding benefits from the same drivers he outlined earlier. He added that Datadog typically discounts observed growth trends when setting guidance to provide a cushion.
On larger enterprise “land” deals, Obstler said deal sizes have increased as Datadog’s product suite has broadened, enabling faster adoption across more products. He tied the trend to consolidation and replacement activity as well as improvements in Datadog’s service model and enterprise selling execution.
AI defensibility, agents, and new product areas
Obstler addressed questions about whether AI agents could disintermediate observability platforms. He said Datadog’s monetization is not purely seat-based, emphasizing that Datadog is “monitoring infrastructure” and that evolving technology increases the need for visibility. He added that Datadog is focused on supporting multiple ways for customers and systems to connect—through integrations, open source, and OpenTelemetry (OTel)—and said Datadog is investing to ensure it can “cover and come in through the information around agents.”
He argued that collecting data is only part of the value proposition, emphasizing correlation, integrations, and what happens after ingestion—including service management and “closing the loop.” Obstler also said Datadog is investing in model capabilities, including integrating third-party models as well as developing its own models trained on Datadog’s domain-specific datasets for efficacy and cost.
On Datadog’s AI agents, he said the company’s Bits AI SRE product recently reached general availability, has more than 1,000 customers using it, and is generating paid ARR, with customer reception described as strong. He said other Bits offerings, including security and development, are earlier in their lifecycle.
Obstler also discussed growth opportunities tied to AI workloads:
- LLM Observability: He said the product is gaining traction as customers put LLMs into production, citing increased integrations and a 10x expansion in spans sent to Datadog over the last six months, along with “over 1,000 users.”
- GPU monitoring: He said Datadog has a GPU monitoring product in preview and expects demand to grow as more customers use GPUs for model development and production AI, adding that the company aims to expand functionality and optimize pricing.
In security, Obstler said adoption increased as products like Cloud SIEM matured, with Datadog leveraging its enterprise logs installed base and improving packaging, channels, and services partners. He said the company has begun deploying specialist security sales resources because security has a different, more channel-led go-to-market motion, but emphasized the company wanted to reach competitive product maturity first. He described the initiative as early, with Datadog expecting it to help accelerate growth as dedicated quota-bearing resources ramp.
On industry consolidation, Obstler said acquisitions by larger vendors have often involved point solutions rather than full platforms, and he argued it is difficult to replicate a broad observability platform and its bottoms-up DevOps motion through M&A alone. He also said incumbents in other categories have been trying for years to achieve this type of integration, calling it a “difficult lift.”
About Datadog (NASDAQ:DDOG)
Datadog (NASDAQ: DDOG) is a cloud-based monitoring and observability platform that helps organizations monitor, troubleshoot and secure their applications and infrastructure at scale. Its software-as-a-service offering collects and analyzes metrics, traces and logs from servers, containers, cloud services and applications to provide real-time visibility into system performance and health. Datadog’s platform is widely used by engineering, operations and security teams to reduce downtime, accelerate incident response and improve application reliability.
The company’s product suite includes infrastructure monitoring, application performance monitoring (APM), log management, real user monitoring (RUM), synthetic monitoring and network performance monitoring, along with security-focused products such as security monitoring and cloud SIEM.
