Datadog (NASDAQ:DDOG) Chief Financial Officer David Obstler said the company’s recent growth has been supported by a broader product platform, market-share gains and expanding demand across customer sizes and geographies. In a conference discussion with Canaccord Genuity technology analyst Kingsley Crane, Obstler said the company has benefited from customers modernizing technology stacks and preparing infrastructure for artificial intelligence workloads.
Crane characterized Datadog’s latest quarter as featuring 36% growth at a $1.1 billion scale, accelerating from 32%, and noted that growth had accelerated over the past five quarters. Obstler said the results reflected investments in the platform that have expanded the product portfolio and enabled greater cross-selling.
Platform adoption and customer expansion
Obstler said growth has not been limited to AI-native companies. He said enterprise customers have accelerated adoption of the Datadog platform, driven by demand for integrated, real-time observability and security capabilities.
He pointed to what he described as substantial market-share gains, saying Datadog added $115 million in revenue sequentially during the last quarter. The company’s platform approach appeals to customers seeking a “single pane of glass” for monitoring and security, he said.
Datadog’s customer expansion model generally unfolds over multiple years, according to Obstler. Customers often initially use other vendors, then add Datadog products as existing contracts come up for renewal. The company sells capacity through a credit-based model, allowing customers to use different products on the platform.
Obstler said cohorts signed five years ago are continuing to expand, supported by product additions and vendor consolidation. He cited net retention in the low 120% range as evidence of the durability of that expansion motion.
- Customers increasingly adopt more Datadog products over time, rather than switching all tools at once.
- Modern and mission-critical workloads have increasingly been directed to Datadog for monitoring, Obstler said.
- Datadog works with customers on capacity planning under contracts that generally span at least one year and can extend to three years.
AI-native customers and production workloads
Obstler said AI-native companies represent a smaller percentage of Datadog’s annual recurring revenue than cloud-native customers did during the COVID-era technology boom, but the group is growing quickly. He said Datadog had more than 750 AI-native customers, with more than 30 generating at least $1 million in annual recurring revenue.
Those companies include model providers, database providers, GPU providers and companies serving specific industry verticals, he said. While Obstler acknowledged that AI-native markets could be volatile, he described the segment as an endorsement of Datadog’s position in modern technology infrastructure.
He said AI-related monitoring demand is increasingly shifting from training and research into production environments. Datadog is positioned to monitor applications using large language models, agents, coding agents and GPU infrastructure, he said. The company is also beginning to address more training-related use cases.
“We basically set that up, and we’ve been seeing very good growth in that area,” Obstler said of AI monitoring. He added that Datadog monetizes these offerings through usage-based pricing tied to data consumed, investigations and related activity.
Bits AI and product investment
Obstler also discussed “AI for Datadog,” referring to the company’s use of AI within its own platform. He said the Bits AI product is designed to help users automate investigations, analyze issues, route cases and eventually support more self-remediation.
The company has broadened Bits AI beyond reliability engineering investigations into development and security use cases, Obstler said. Datadog has tested pricing approaches, moving from a per-investigation model toward token-based pricing in some areas.
Datadog’s data sets, platform integration and existing use of machine learning for analytics provide an advantage in observability-specific AI, Obstler said. He said the company’s vision is to provide specialized intelligence that can identify problems and, in certain instances, enable customers to approve automated remediation.
Obstler said Datadog plans to continue investing in both sales capacity and research and development. Sales capacity has expanded globally at roughly the same pace as revenue, he said. While the company expects a greater share of R&D resources to shift toward tokens and AI tools over time, he said management is focused on using those tools to develop products rather than pursuing AI investment at the expense of margins.
Competitive strategy
Addressing competition from companies expanding their own platforms, including security and data-focused vendors, Obstler said Datadog remains focused on observing software in production and on adjacent opportunities where its observability platform creates synergies.
He cited cloud workload security, Cloud SIEM and service management as areas where Datadog can expand, while emphasizing that the company is not attempting to address every segment of the broader security market. Obstler said Datadog’s focus on modern cloud workloads, coupled with continued R&D investment, has strengthened rather than weakened its competitive position.
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.
