Datadog outlines a four-pillar platform extended by AI
Speaking at Citi's 2026 Global TMT Conference on 8 September 2026, Datadog (DDOG) chief executive Olivier Pomel described a broad product push that stretches beyond the company's original monitoring tools. The platform is organized around four pillars: metrics, traces, logs and digital experience, the last of which now includes release monitoring and synthetic testing. Management said digital experience is becoming a larger part of the platform as it grows in importance for both traditional and AI-native customers.
Datadog said it holds 13–14% market share in observability and sees significant room for expansion, particularly as AI adoption increases system complexity and demand. The company pointed to large enterprise contracts that are fungible across products, supporting customer consolidation and giving buyers flexibility in how they allocate spend. Quarterly revenue and share price have grown by an order of magnitude since the company's initial public offering, with recent acceleration to 36% year-over-year growth across both AI and non-AI customers.
Two-pronged AI strategy with Bits AI expansion
Datadog laid out a two-pronged approach: "Datadog for AI," or observing AI systems, which it said is currently driving the fastest growth, and "AI for Datadog," or embedding AI inside its own products. Bits AI, the company's internal AI agent suite, has moved well beyond its original investigations role. Pomel said Bits AI now offers a chatbot, can optimize code, build applications from scratch inside the Datadog context and assemble agents, capabilities that are being rolled out across multiple product surfaces.
The company is shifting monetization away from a single investigations product toward an AI credits model that decouples revenue from traditional data units. Datadog said internal research and development is also leaning on AI and agents to lift productivity, automate support and accelerate product development, with increased investment in GPUs and tokens. The company has released two open-source time series models, Toto and Toto 2.0, trained on its own observability data, and announced the acquisition of Adaptive ML to speed work on reinforcement learning and custom models.
Market opportunity, pricing and competitive landscape
Datadog told investors that observability and security spend is expected to remain 10–20% of customers' cloud bills, with AI driving further complexity and value. Management described OpenTelemetry and open source more broadly as tailwinds that reduce friction and support customer expansion, while the company maintains high margins and reinvests about 30% of revenue in research and development.
On competition, the company framed its edge as an integrated software-as-a-service platform, an efficient go-to-market motion and access to operational data used to train its own models. High gross retention in the high-90s percent range and customer preference for consolidation were cited as reinforcing platform stickiness. Security was singled out as a major growth area, with Datadog arguing that the shift to integrated, agent-driven solutions is flattening the field and weakening traditional incumbent advantages. Management added that several product lines could eventually reach billion-dollar scale, including digital experience, security and business analytics.
Other developments
Separately, Datadog's product team published a blog post on 8 September 2026 detailing a new Launches feature inside Datadog Product Analytics. The workflow connects launch planning, instrumentation, experimentation, user-experience analysis and reporting, with Bits AI drafting context, questions, tracking plans and experiment design that teams can then edit. Datadog said the tool can compare a tracking plan against events already being sent and, once confirmed, open a pull request with the required instrumentation.
The next verifiable milestone in the Datadog calendar is its appearance on the conference circuit following Citi's event, with further updates expected as the company continues to roll out AI credits pricing and integrate the Adaptive ML acquisition.
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