Datadog delivered a strong finish to 2025 with Q4 revenue of $953 million, up 29% year-over-year, surpassing the high end of guidance. The company achieved record bookings of $1.63 billion (+37% YoY) and robust free cash flow of $291 million (31% margin). Growth was broad-based, with the non-AI-native business accelerating to 23% YoY, while the AI-native cohort continued its rapid expansion, now including 14 of the top 20 AI companies. Product adoption deepened, with 84% of customers using two or more products. For FY 2026, management guided revenue of $4.06 to $4.10 billion (18-20% YoY growth), reflecting a more conservative outlook compared to Q4 performance, with an expected non-GAAP operating margin of 21%.
| Metric | Value | Change |
|---|---|---|
| Q4 Revenue | $953 million | +29% YoY |
| Q4 Bookings | $1.63 billion | +37% YoY |
| Q4 Operating Margin | 24% | Flat YoY |
| Q4 Free Cash Flow Margin | 31% | N/A |
| Net Revenue Retention | ~120% | Stable |
| Customers >$100k ARR | 4,310 | +19% YoY |
| RPO | $3.46 billion | +52% YoY |
Datadog is establishing itself as the de facto standard for AI infrastructure monitoring. The company reported that 14 of the top 20 AI-native companies are now customers, with about 650 AI-native customers total. Usage is scaling rapidly, evidenced by a 10x increase in spans sent for LLM observability over the last six months. This positions Datadog to capture a significant portion of the enterprise AI spend as these companies move from training to production.
The platform consolidation narrative is driving significant deal momentum. Datadog signed 18 deals over $10 million in TCV, including two over $100 million. A key highlight was an 8-figure deal with a leading AI model company that consolidated more than five fragmented tools into the Datadog platform. This 'land and expand' motion is supported by the fact that 48% of the Fortune 500 are now customers, yet the median ARR for these customers remains under $500,000, indicating a massive greenfield opportunity for upsell.
Innovation in AI-powered products is accelerating, creating new revenue streams. The company launched the AI SRE agent for general availability, which already has over 2,000 trial and paying customers. Additionally, the Datadog MCP (Model Context Protocol) server saw usage grow 11-fold in Q4 compared to Q3. These products not only improve customer stickiness but also allow Datadog to monetize the AI workflow itself, moving beyond passive monitoring to active incident resolution.
Operational efficiency remains a core strength, allowing for reinvestment while maintaining profitability. Despite a 29% increase in OpEx to drive growth, Datadog maintained a robust non-GAAP operating margin of 24% in Q4 and a free cash flow margin of 31%. This financial flexibility enables continued heavy investment in R&D (over 400 new features in 2025) and go-to-market expansion without sacrificing bottom-line metrics.
The guidance for FY 2026 implies a significant deceleration in growth rates. While Q4 revenue grew 29%, FY 2026 guidance is set at 18-20% growth. Although management attributes this to conservatism and the lapping of the AI boom, the step-down is substantial and may signal that the hyper-growth phase driven by initial AI adoption is normalizing faster than investors anticipate.
Management acknowledged that their largest customer (likely Microsoft/OpenAI given the context of 'AI-native') is a volatile variable in their growth model. CFO David Obstler noted that the guidance assumes the core business grows at least 20%, implying the large customer may grow slower or represent a drag relative to the peak hype. This concentration risk creates uncertainty around quarterly revenue beats and misses.
The competitive landscape is evolving with the rise of general-purpose LLMs. Analysts pressed on whether tools like Anthropic's Claude or OpenAI's models could commoditize observability by offering 'homegrown' anomaly detection. While management argued that Datadog's 'data plane' advantage is defensible, the threat of hyperscalers or AI companies bundling basic observability features remains a long-term risk to pricing power.
Overall: Management exhibited a high level of confidence and enthusiasm throughout the call. Olivier Pomel, CEO, was emphatic about the company's momentum, describing the quarter as 'very strong' and highlighting 'explosive growth' in AI-related areas. David Obstler, CFO, maintained a disciplined and pragmatic tone, particularly regarding guidance, emphasizing the company's standard practice of applying conservatism to forward-looking metrics.
Confidence: HIGH - Management's confidence was evidenced by their detailed commentary on product roadmaps, specific competitive wins, and the declaration that they are 'pulling away' from competitors. The specificity of the 'record bookings' figure and the detailed breakdown of AI-native customer penetration reinforced their assuredness.
$951 million to $961 million (25-26% YoY growth)
$4.06 billion to $4.10 billion (18-20% YoY growth)
21%
$2.08 to $2.16
Hedging & Uncertainty: Management employed specific hedging techniques to set expectations, particularly around the forward-looking guidance. CFO David Obstler explicitly stated, 'We base our guidance on trends observed in recent months and apply conservatism on these growth trends,' serving as a temporal hedge to lower the bar for future performance. CEO Olivier Pomel used developmental hedges regarding AI products, noting, 'We're not quite there yet' when discussing fully preemptive AI resolution, acknowledging the gap between current capabilities and the long-term vision. However, hedging was minimal regarding past performance, where language was definitive ('record bookings', 'very strong Q4').
We continue to see broad-based positive trends in the demand environment. - Olivier Pomel, Co-Founder and CEO
We apply conservatism on these growth trends. - David Obstler, CFO
We are seeing explosive growth in MCP usage. - Olivier Pomel, Co-Founder and CEO
The median Datadog ARR for our Fortune 500 customers is still less than half a million dollars. - Olivier Pomel, Co-Founder and CEO
We think that observability... is the most important part of the whole AI development life cycle today. - Olivier Pomel, Co-Founder and CEO
We are pulling away. We're taking share from anybody who has scale. - Olivier Pomel, Co-Founder and CEO
Analyst Sentiment: Analysts were largely positive but focused heavily on the sustainability of the current growth trajectory and the competitive landscape. There was a distinct interest in understanding how Datadog's moat holds up against general-purpose LLMs and agentic frameworks.
Management Responses: Management was articulate and defensive regarding competition, emphasizing the complexity of real-time data processing which general LLMs cannot handle. They were transparent about the conservatism baked into the guidance, using the Q&A to reinforce the 'mission-critical' nature of their platform to justify valuation.
The impact of Agentic AI and LLMs on the observability category and whether it renders Datadog obsolete.
The sustainability of growth rates, specifically the transition from 29% in Q4 to the high-teens guidance for 2026.
The competitive dynamics with legacy vendors and the potential for hyperscalers to encroach on the market.
The specific mechanics of the 'AI-native' cohort and the concentration risk associated with large customers.
Datadog is executing at a high level, successfully leveraging the AI infrastructure boom to drive record bookings and broad-based growth. The company's platform strategy is working, evidenced by increasing product attach rates and massive consolidation wins. While FY 2026 guidance implies a growth deceleration, management's history of conservative under-promising and over-delivering, combined with the 'explosive' usage trends in AI monitoring, suggests significant upside potential. The shift towards agentic AI workflows plays directly into Datadog's strength in real-time data analysis, solidifying its position as a mission-critical vendor for the modern enterprise stack.
Management commented on the massive CapEx spending by hyperscalers (Amazon, Microsoft, Google) exceeding $500 billion. They view this not just as an AI training trend but as a signal of a permanent increase in system complexity and application volume, which structurally benefits the observability market.
The 'median Datadog ARR for our Fortune 500 customers is still less than half a million dollars,' indicating that despite the maturity of cloud migration, the largest enterprises are still early in their adoption of modern observability stacks, leaving a long runway for expansion.