Datadog Investor Day 2026: AI Agents Fuel “Race Against Complexity” as Platform Expands

Key Points

• Datadog frames continued cloud migration and rapid AI adoption as a long-duration “race against complexity” and is investing heavily to meet it — historically ~30% of revenue in R&D, more than $1 billion in R&D in 2025, and roughly 4,000 engineers.
• Product expansion centers on AI-driven tooling and telemetry scale: the Bits AI agent suite (SRE Agent, Security Analyst preview, Dev Agent) has driven over 100,000 investigations with >2,000 customers active in January, backed by massive data volumes and the open-weight time-series model “Toto.”
• Commercial and financial momentum shows upside: security has >8,500 customers and >$100M ARR but represents only ~2% of spend in large accounts (implying wallet-share opportunity), Flex Logs is nearing $100M ARR, BYOC for logs is generally available, Datadog has ~32,000 customers (≈7% penetration of its ~500,000 target market), and it targets ~80% gross margins and 25%+ non-GAAP operating margin long-term.

Datadog NASDAQ: DDOG used its 2026 Investor Day to outline a long-term strategy centered on what CEO and co-founder Olivier Pomel described as an expanding “race against complexity,” driven by continued cloud migration and the rapid adoption of AI. Company leaders argued that AI will both increase the pace of software change and raise the operational stakes as agents take action, requiring more observability, security, and automation across development and production environments.

Cloud and AI as long-duration tailwinds

Pomel cited Gartner data showing sustained cloud migration and said Gartner expects public cloud spend to exceed $1 trillion by 2027, which he noted would still represent only 16% of global tech spend. He described AI as a “large, rapidly growing” new spending area that compounds complexity, including the proliferation of models and the growth of GPU-based compute fleets.

Pomel said Datadog’s response is continued investment in innovation, noting the company has historically invested around 30% of revenue in R&D. He said Datadog invested more than $1 billion in R&D in 2025 and ended the year with about 4,000 engineers. He also highlighted Datadog’s scale across observability pillars, citing $1.6 billion of ARR in infrastructure monitoring and more than $1 billion of ARR each in log management and its end-to-end APM and DEM suite.

Platform expansion: data, security, developer workflows, and service management

Pomel and product leaders described Datadog’s platform as spanning a workflow continuum from writing code to business outcomes. In addition to expanding observability into areas such as AI stack monitoring, Pomel said Datadog has added capabilities across the data layer, including data observability, and has expanded in digital experience monitoring into user and product analytics. On the “developer side,” he pointed to Feature Flags, the Datadog MCP Server, and the Bits AI Dev Agent as examples of initiatives in software delivery.

CTO and co-founder Alexis Lê-Quôc emphasized that Datadog’s data volume and diversity underpin its AI strategy. He described ingesting “trillions of data points, billions of traces, [and] exabytes of logs,” plus additional context such as user sessions, data lineage, and LLM and agent traces. He discussed Datadog’s time-series model “Toto,” which he said was trained on a dataset three times larger than the largest public time-series dataset he referenced and was released as an open-weights model on Hugging Face, where he said it has reached about 9 million downloads. Lê-Quôc also said Toto cost roughly $375,000 to train in 2025, contrasting it with the significantly higher cost he associated with training frontier models.

“AI for Datadog” and “Datadog for AI”

Leadership framed AI in two buckets: embedding AI across Datadog’s products and operations (“AI for Datadog”) and providing observability and related capabilities for customers’ AI applications and agents (“Datadog for AI”). Chief Product Officer Yanbing Li highlighted the company’s “Bits AI” agent suite, including:

• Bits AI SRE Agent, designed to investigate alerts, form and test hypotheses in parallel, identify root cause, and propose next actions based on runbooks.
• Bits AI Security Analyst (in preview), intended to investigate Cloud SIEM signals and support remediation workflows inside Datadog.
• Bits AI Dev Agent, positioned as telemetry- and code-aware remediation that can generate a context-aware fix, test it in a sandbox, and produce a pull request.

Li said customers have run “well over 100,000 investigations” since Bits AI SRE launched, and that in January more than 2,000 customers ran investigations. She also said customers reported that during a major AWS outage last October, Bits AI SRE was able to identify the outage as the root cause before AWS notifications. Li added that the Datadog MCP Server is enabling customers and third-party agents to access Datadog context from existing workflows, and she said adoption has been growing rapidly since launch.

Security, BYOC, and go-to-market investments

Security products leader Tim Knudsen said customers commonly face a “silo tax,” where threat detection, application telemetry, posture data, and ownership context sit in separate tools. He said Datadog’s approach is to unify security and observability across a single platform, spanning Cloud SIEM, posture scanning, runtime vulnerability detection, AI and data security protections, and Code Security. Knudsen said Datadog has more than 8,500 customers using security products, including “one in four” Fortune 500 companies, and has surpassed $100 million in security ARR. He also noted that while 70% of Datadog’s million-dollar customers use at least one security product, security represents only 2% of their Datadog spend, which he said suggests additional wallet-share opportunity.

Platform leader Yrieix Garnier discussed scaling initiatives in logs, describing “logging without limits” as a foundation for growing log management to $1 billion ARR, and pointing to newer capabilities such as Flex Logs, Frozen, and Archive Search. He said Flex Logs is approaching $100 million ARR and “growing very rapidly,” and described Cloud SIEM as a natural extension that benefits from long-term log retention. Garnier also highlighted Bring Your Own Cloud (BYOC) as an option for customers with residency, compliance, or very high-volume constraints, saying Datadog is previewing BYOC with very large companies and expects it to unlock more opportunities; in Q&A, he said BYOC for logs is generally available and that the company is expanding BYOC to additional telemetry types.

In the second half, CRO Sean Walters described Datadog’s go-to-market as a three-part motion spanning self-serve, commercial, and enterprise. He said 24% of Datadog’s top 25 customers originated in commercial, and that 50% of $1 million-plus customers and 72% of $100,000-plus customers came from commercial. Walters also described newer “key accounts” teams targeting longer-cycle, top-down engagements, and said the group made “significant strides” in 2025, including wins that are already expanding. He also outlined growing investments in channel and alliances, including hyperscalers, regional partners, and system integrators, and said Datadog is expanding geographically with more local coverage and partnerships.

CFO David Obstler reiterated the company’s land-and-expand model, saying roughly a quarter of annual ARR growth comes from new customers, with the remainder from expansion. He said Datadog curates a target market of just under 500,000 global customers and framed the company’s logo penetration as about 7% with more than 32,000 customers. Obstler also introduced a new metric: customers spending more than $10 million annually, which he said have grown more than 60% to 34. On profitability, he said Datadog plans around ±80% gross margins with flexibility to invest, and reaffirmed a long-term target of 25%+ non-GAAP operating margin, noting recent non-GAAP cash flow margin of 27% and non-GAAP operating margin of 22%.

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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