Datadog NASDAQ: DDOG co-founder and CEO Olivier said the company has seen business acceleration over the past several quarters, with growth coming from a broad set of customers rather than a single account or segment.
Speaking at a Bernstein event with Senior Analyst Peter Weed, Olivier said the company is seeing increased demand from “AI natives,” including newer companies built around artificial intelligence and businesses developing key AI infrastructure. But he said the more notable trend is that acceleration is also occurring outside that group, including among older cloud-native companies and larger enterprises.
“What’s the most exciting to us is that it is not a specific customer, a specific set of customers, a specific side of the business,” Olivier said. “We’ve really seen acceleration across the board.”
AI Growth Adds to Existing Observability Demand
Olivier said AI companies are consuming large amounts of infrastructure and running more applications, which is driving demand for Datadog’s traditional observability tools. He also pointed to rising use of Datadog’s AI-specific products, including traffic to MCP services and traces flowing into its LLM Observability product.
Asked where Datadog’s long-term value comes from, Olivier said the company helps customers manage the complexity created as they ship more applications. He argued that as developers become more productive — from higher-level programming languages to cloud, SaaS and now coding agents — the resulting systems become harder to understand and operate.
“That increase of productivity creates a dramatic increase in complexity,” he said. “The problem we solve for our customers is we actually understand that complexity, we manage it for them.”
Olivier pushed back on the idea that general-purpose AI models could replace observability software. He said Datadog handles much larger volumes of data than are typically placed into large language models and must operate in near real time. He compared the distinction to asking an AI chatbot for driving advice versus relying on a real-time self-driving system to operate a car.
CEO Says Build-It-Yourself Approach Often Lacks Economic Logic
Olivier also addressed the argument that companies could use AI coding tools or open-source software to build their own observability systems. He said Datadog provides leverage because a dollar spent on Datadog typically sits alongside much larger spending on cloud infrastructure and engineering teams.
He said customers often spend $10 to $20 with their cloud provider and $20 to $100 on engineering for every dollar spent with Datadog. In that context, he said, using Datadog to optimize infrastructure, engineering and AI model spending “doesn’t make economic sense” to replicate internally.
He cited hyperscalers adopting Datadog products for development and training of AI models as an example, saying these are companies that culturally tend to build internally and avoid commercial software.
Product-Led Model Remains Central
Olivier described Datadog’s business as built around bottom-up product adoption, with tools typically first adopted by the people doing the technical work. He said the company emphasizes a unified platform, replatforms acquired products and relies largely on usage-based pricing.
That model, he said, gives Datadog clearer signals about what customers find valuable and allows the company to expand its product footprint efficiently. Olivier said Datadog spends about 30% of revenue on research and development, supported by what he described as a more efficient go-to-market model than typical enterprise software businesses.
While Datadog has an enterprise sales organization, Olivier said its motion remains bottom-up inside large companies. He said enterprise customers often start with relatively small annualized deals in the mid-five-figure to low-six-figure range and grow over time.
Security, AI Agents and Automation Seen as Expansion Areas
Olivier said security is a natural extension of Datadog’s platform because development, operations and security teams increasingly need to work together. He said many of the signals needed to secure an application are already present in observability data, including production behavior, code changes, testing environments, user behavior and developer activity.
On AI, Olivier distinguished between “Datadog for AI,” which observes AI components used by customers, and “AI for Datadog,” which applies AI to automate more of the application lifecycle.
He highlighted Datadog’s Bits AI SRE agent and Bits AI security agent, which he said can run investigations and reduce the time spent responding to outages or security alerts. In one example, he said an AI agent could identify an incident, suggest the people who know how to fix it and propose a fix within minutes, reducing the need for long incident-response calls.
Olivier also discussed Toto, a time-series model trained largely on observability data. He said the model generalizes to other time-series domains and is part of Datadog’s broader effort to put specialized intelligence closer to the data plane for real-time automation.
Bring Your Own Cloud Addresses Data and Cost Needs
Datadog is also expanding deployment flexibility through Bring Your Own Cloud options, Olivier said. The approach allows customers to store data on infrastructure they manage while Datadog continues to run and update the application.
He said the demand is driven by several factors, including cost at large scale, data residency laws, existing data center capacity and cloud provider commitments. Olivier said Datadog historically avoided on-premises deployments because they could slow innovation, but said the Bring Your Own Cloud model allows the company to retain product iteration while giving customers more control over data infrastructure.
Looking ahead, Olivier said AI is increasing the amount of software, infrastructure and complexity that companies must operate. He said that dynamic supports both newer AI-specific opportunities and Datadog’s core observability business, where he said the company has less than 14% share of a market that continues to grow.
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.
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