FEBRUARY 12, 2026 Strategy, innovation, Datadog platform and product Q&A Safe Harbor This presentation and the accompanying oral presentation have been prepared by Datadog, Inc. ("Datadog" or the "company") for informational purposes only and not for any other purpose. Nothing contained in this presentation is, or should be construed as, a recommendation, promise or representation by the presenter or Datadog or any officer, director, employee, agent or advisor of Datadog. This presentation does not purport ...

Financial Data and Key Metrics Changes - Q4 revenue was $953 million, an increase of 29% year-over-year and above the high end of guidance [5][26] - Free cash flow was $291 million, with a free cash flow margin of 31% [6][30] - Gross profit for Q4 was $776 million, with a gross margin percentage of 81.4% [28] - Billings were $1.21 billion, up 34% year-over-year [28] Business Line Data and Key Metrics Changes - Infrastructure Monitoring contributes over $1.6 billion in ARR, Log Management is now over $1 billion in ARR, and APM products also crossed $1 billion in ARR [9] - 84% of customers used two or more products, up from 83% a year ago, and 55% used four or more products, up from 50% [6] - The number of customers with an ARR of $100,000 or more increased to about 4,310, up from about 3,610 a year ago [6] Market Data and Key Metrics Changes - 48% of the Fortune 500 are Datadog customers, indicating strong penetration in large enterprises [7] - The median ARR for Fortune 500 customers is still less than $500,000, suggesting significant growth opportunities [8] Company Strategy and Development Direction - The company continues to focus on digital transformation and cloud migration as long-term growth drivers [24] - There is a strong emphasis on integrating AI into the Datadog platform to enhance customer value and outcomes [24] - The company aims to expand its platform to address end-to-end customer needs across software development, production, data stack, user experience, and security [24] Management's Comments on Operating Environment and Future Outlook - Management noted broad-based positive trends in demand driven by cloud migration and high growth within AI-native customer groups [4][5] - The company expects continued strong growth dynamics and is optimistic about the adoption of AI innovations by customers [25] - The outlook for Q1 2026 revenue is projected to be in the range of $951 million to $961 million, representing 25%-26% year-over-year growth [31] Other Important Information - The company released over 400 new features and capabilities in 2025, focusing on AI and observability [10][12] - The MCP server is seeing explosive growth, indicating strong customer interest in AI-driven solutions [11] Q&A Session Summary Question: Observability in the context of advancements in AI - Management believes that the increase in applications and complexity will drive demand for observability tools, as developers will need to ensure systems work correctly in production [36][38] Question: AI-native customer growth and spending - The company has seen strong growth among AI-native customers, with 19 customers spending over $1 million annually [27][46] Question: Competition and market dynamics - Management noted that competition remains stable, with no significant changes in market dynamics despite the rise of LLMs [68][70] Question: Future CapEx trends and LLM observability - Management indicated that while it is difficult to directly map CapEx to LLM observability, the overall increase in complexity will benefit the business [74][76] Question: Diversification of AI customer concentration - The company is not focused on a few large customers and expects to see more diversification within the AI cohort [78][80]

Core Insights - Datadog reported strong second-quarter 2025 results with non-GAAP EPS of 46 cents, exceeding estimates by 12.20%, and revenues of $826.8 million, up 28.1% year-over-year, surpassing consensus by 4.55% [1][2] Customer Growth - The company ended the quarter with 31,400 customers, a 9.4% increase year-over-year, exceeding the Zacks Consensus Estimate by 0.75% [2] - High-ARR clients generating $100K or more in annual recurring revenues reached approximately 3,850, reflecting a 13.6% year-over-year increase, contributing about 89% of total ARR [3][11] Product Adoption and Innovation - Multi-product adoption is significant, with 83% of customers using at least two products and 14% adopting eight or more solutions, indicating strong platform stickiness [4] - Datadog unveiled over 125 new products and features at the DASH 2025 conference, focusing on AI-powered automation and log management to enhance customer engagement [8][10] AI and Security Growth - The AI-native customer segment has become a major growth driver, accounting for 11% of total revenues, up from 4% a year earlier, contributing 10 percentage points to year-over-year revenue growth [5][11] - The security product suite generated over $100 million in ARR, with mid-40% year-over-year growth, positioning the company for sustained growth [6][11] Future Guidance - For Q3 2025, Datadog anticipates revenues between $847 million and $851 million, representing 23% year-over-year growth, with non-GAAP EPS expected to be between 44-46 cents [12] Stock Performance - Datadog's shares have appreciated 16.7% in the trailing 12 months, underperforming the Zacks Computer and Technology sector's return of 29.4% [13]

Financial Data and Key Metrics Changes - Revenue for Q2 was $827 million, representing a 28% year-over-year increase and exceeding the high end of guidance [8][28] - Free cash flow was $165 million, with a free cash flow margin of 20% [9][34] - Gross profit was $669 million, resulting in a gross margin of 80.9%, compared to 80.3% in the previous quarter [32][34] - Operating income was $164 million, with an operating margin of 20%, down from 22% in the previous quarter [33][34] Business Line Data and Key Metrics Changes - The number of customers increased to approximately 31,400, up from about 28,700 a year ago [8] - 83% of customers used two or more products, and 52% used four or more products, indicating strong platform adoption [10] - The security suite generated over $100 million in ARR, growing at a mid-forties percent year-over-year rate [11] Market Data and Key Metrics Changes - AI native customers contributed about 11% of Q2 revenues, up from 8% in the previous quarter [28] - The twelve-month trailing net retention percentage was about 120, higher than the high 110s from the last quarter [29] Company Strategy and Development Direction - The company continues to focus on digital transformation and cloud migration as long-term growth drivers [23] - There is a strong emphasis on AI integration into the platform, with new AI products launched to enhance observability and incident response [26][27] - The company aims to improve standardized adoption of security products across large enterprises [55] Management's Comments on Operating Environment and Future Outlook - Management views the demand environment as solid, with low churn and stable gross revenue retention in the mid to high 90s [8] - The company is optimistic about the growth opportunities presented by AI, with expectations of broader adoption across various industries [24][25] - Management acknowledges potential volatility in revenue growth from AI native customers but remains confident in long-term growth [52] Other Important Information - The company held its DASH user conference, announcing over 125 new products and features [12] - The company ended the quarter with $3.9 billion in cash, cash equivalents, and marketable securities [34] Q&A Session Summary Question: AI contribution and market opportunity - Management discussed the layers of AI opportunity, emphasizing the need for observability in applications that are increasingly non-deterministic [41][44] Question: Sales capacity and productivity - Management confirmed successful ramping of sales capacity and productivity, with evidence of new logo production and pipeline growth [47] Question: Guidance and potential volatility - Management indicated that current growth metrics do not show volatility yet, but they remain cautious due to potential future fluctuations [52] Question: Security product adoption - Management expressed satisfaction with the current adoption of security products but acknowledged the need for broader enterprise-wide adoption [55] Question: Trends in customer demographics - Management noted stable growth in enterprise customers, with gradual improvements in SMB usage [59] Question: FlexLogs progress - Management confirmed that all major deals with enterprise customers now involve FlexLogs, which is resonating well with customers [95]

Core Insights - Datadog has launched three new AI agents designed to enhance incident resolution for development, security, and operations teams, marking a significant advancement in its Bits AI platform [1][2][3] Group 1: New AI Agents - The new AI agents include Bits AI SRE, Bits AI Dev Agent, and Bits AI Security Analyst, each tailored for specific functions in incident response, product development, and security [2][3] - Bits AI SRE operates 24/7 to triage alerts and provide initial investigation findings, while Bits AI Dev Agent generates code fixes and opens pull requests tailored to the organization's technology stack [3][4] - Bits AI Security Analyst autonomously triages security signals and conducts investigations, delivering resolution recommendations without human intervention [3] Group 2: Enhanced Capabilities - The new Proactive App Recommendations feature analyzes existing telemetry to suggest high-impact fixes and actions to improve application performance [8] - APM Investigator automates the troubleshooting of latency spikes by identifying bottlenecks and proposing fixes, streamlining the resolution process for engineers [8] Group 3: Strategic Positioning - Datadog's platform processes trillions of data points, providing a rich data environment that enhances the effectiveness of its AI capabilities [3] - The architecture of the new AI agents allows for rapid deployment and consistent user experience, leveraging high-quality observability data for precise insights [2][3]