Datadog (DDOG)
Market Price (2/20/2026): $120.9 | Market Cap: $42.2 BilSector: Information Technology | Industry: Application Software
Datadog (DDOG)
Market Price (2/20/2026): $120.9Market Cap: $42.2 BilSector: Information TechnologyIndustry: Application Software
Investment Highlights Why It Matters Detailed financial logic regarding cash flow yields vs trend-riding momentum.
| Strong revenue growthRev Chg LTMRevenue Change % Last Twelve Months (LTM) is 27% | Weak multi-year price returns2Y Excs Rtn is -49%, 3Y Excs Rtn is -19% | Not profitable at operating income levelOp Inc LTMOperating Income, Last Twelve Months is -43 Mil, Op Mgn LTMOperating Margin = Operating Income / Revenue Reflects profitability before taxes and before impact of capital structure (interest payments). is -1.3% |
| Attractive cash flow generationCFO/Rev LTMCash Flow from Operations / Revenue (Sales), Last Twelve Months (LTM) is 31%, FCF/Rev LTMFree Cash Flow / Revenue (Sales), Last Twelve Months (LTM) is 27% | Expensive valuation multiplesP/EBITPrice/EBIT or Price/(Operating Income) ratio is 308x, P/EPrice/Earnings or Price/(Net Income) is 394x | |
| Megatrend and thematic driversMegatrends include Cloud Computing, Cybersecurity, and Artificial Intelligence. Themes include Software as a Service (SaaS), Show more. | Significant share based compensationSBC/Rev LTMShare Based Compensation / Revenue (Sales), Last Twelve Months (LTM) is 22% | |
| Yield minus risk free rate is negativeERPEquity Risk Premium (ERP) = Total Yield - Risk Free Rate, Reflects the premium above risk free assets offered by the investment. is -4.1% | ||
| Key risksDDOG key risks include [1] intense competition from hyperscale cloud providers offering native monitoring tools, Show more. |
| Strong revenue growthRev Chg LTMRevenue Change % Last Twelve Months (LTM) is 27% |
| Attractive cash flow generationCFO/Rev LTMCash Flow from Operations / Revenue (Sales), Last Twelve Months (LTM) is 31%, FCF/Rev LTMFree Cash Flow / Revenue (Sales), Last Twelve Months (LTM) is 27% |
| Megatrend and thematic driversMegatrends include Cloud Computing, Cybersecurity, and Artificial Intelligence. Themes include Software as a Service (SaaS), Show more. |
| Weak multi-year price returns2Y Excs Rtn is -49%, 3Y Excs Rtn is -19% |
| Not profitable at operating income levelOp Inc LTMOperating Income, Last Twelve Months is -43 Mil, Op Mgn LTMOperating Margin = Operating Income / Revenue Reflects profitability before taxes and before impact of capital structure (interest payments). is -1.3% |
| Expensive valuation multiplesP/EBITPrice/EBIT or Price/(Operating Income) ratio is 308x, P/EPrice/Earnings or Price/(Net Income) is 394x |
| Significant share based compensationSBC/Rev LTMShare Based Compensation / Revenue (Sales), Last Twelve Months (LTM) is 22% |
| Yield minus risk free rate is negativeERPEquity Risk Premium (ERP) = Total Yield - Risk Free Rate, Reflects the premium above risk free assets offered by the investment. is -4.1% |
| Key risksDDOG key risks include [1] intense competition from hyperscale cloud providers offering native monitoring tools, Show more. |
Qualitative Assessment
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Datadog (DDOG) stock has lost about 25% since 10/31/2025 because of the following key factors:
1. Decelerating Revenue Growth Guidance for Fiscal Year 2026.
Despite exceeding Q4 2025 revenue estimates with $953 million, a 29% year-over-year increase, Datadog's outlook for full-year 2026 revenue of $4.06 billion to $4.10 billion signaled a deceleration to an 18-20% year-over-year growth rate. This compares to the 28% growth achieved in fiscal year 2025, tempering investor expectations for future top-line expansion.
2. Softer-than-Expected Full-Year Earnings Per Share Guidance.
The company's full-year 2026 non-GAAP earnings per share (EPS) guidance of $2.08 to $2.16 fell below the consensus analyst estimates of $2.27 to $2.34 per share, indicating a potentially less profitable outlook or increased investments affecting margins.
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1. Decelerating Revenue Growth Guidance for Fiscal Year 2026.
Despite exceeding Q4 2025 revenue estimates with $953 million, a 29% year-over-year increase, Datadog's outlook for full-year 2026 revenue of $4.06 billion to $4.10 billion signaled a deceleration to an 18-20% year-over-year growth rate. This compares to the 28% growth achieved in fiscal year 2025, tempering investor expectations for future top-line expansion.
2. Softer-than-Expected Full-Year Earnings Per Share Guidance.
The company's full-year 2026 non-GAAP earnings per share (EPS) guidance of $2.08 to $2.16 fell below the consensus analyst estimates of $2.27 to $2.34 per share, indicating a potentially less profitable outlook or increased investments affecting margins.
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Stock Movement Drivers
Fundamental Drivers
The -25.9% change in DDOG stock from 10/31/2025 to 2/19/2026 was primarily driven by a -19.5% change in the company's Net Income Margin (%).| (LTM values as of) | 10312025 | 2192026 | Change |
|---|---|---|---|
| Stock Price ($) | 162.81 | 120.60 | -25.9% |
| Change Contribution By: | |||
| Total Revenues ($ Mil) | 3,016 | 3,212 | 6.5% |
| Net Income Margin (%) | 4.1% | 3.3% | -19.5% |
| P/E Multiple | 452.4 | 393.8 | -13.0% |
| Shares Outstanding (Mil) | 346 | 349 | -0.7% |
| Cumulative Contribution | -25.9% |
Market Drivers
10/31/2025 to 2/19/2026| Return | Correlation | |
|---|---|---|
| DDOG | -25.9% | |
| Market (SPY) | 0.4% | 24.0% |
| Sector (XLK) | -6.7% | 30.5% |
Fundamental Drivers
The -13.8% change in DDOG stock from 7/31/2025 to 2/19/2026 was primarily driven by a -43.2% change in the company's Net Income Margin (%).| (LTM values as of) | 7312025 | 2192026 | Change |
|---|---|---|---|
| Stock Price ($) | 139.98 | 120.60 | -13.8% |
| Change Contribution By: | |||
| Total Revenues ($ Mil) | 2,835 | 3,212 | 13.3% |
| Net Income Margin (%) | 5.8% | 3.3% | -43.2% |
| P/E Multiple | 289.7 | 393.8 | 35.9% |
| Shares Outstanding (Mil) | 343 | 349 | -1.6% |
| Cumulative Contribution | -13.8% |
Market Drivers
7/31/2025 to 2/19/2026| Return | Correlation | |
|---|---|---|
| DDOG | -13.8% | |
| Market (SPY) | 8.6% | 30.5% |
| Sector (XLK) | 6.9% | 35.7% |
Fundamental Drivers
The -15.5% change in DDOG stock from 1/31/2025 to 2/19/2026 was primarily driven by a -56.1% change in the company's Net Income Margin (%).| (LTM values as of) | 1312025 | 2192026 | Change |
|---|---|---|---|
| Stock Price ($) | 142.71 | 120.60 | -15.5% |
| Change Contribution By: | |||
| Total Revenues ($ Mil) | 2,536 | 3,212 | 26.6% |
| Net Income Margin (%) | 7.6% | 3.3% | -56.1% |
| P/E Multiple | 250.7 | 393.8 | 57.1% |
| Shares Outstanding (Mil) | 338 | 349 | -3.2% |
| Cumulative Contribution | -15.5% |
Market Drivers
1/31/2025 to 2/19/2026| Return | Correlation | |
|---|---|---|
| DDOG | -15.5% | |
| Market (SPY) | 14.7% | 44.9% |
| Sector (XLK) | 22.1% | 49.2% |
Fundamental Drivers
The 61.2% change in DDOG stock from 1/31/2023 to 2/19/2026 was primarily driven by a 109.7% change in the company's Total Revenues ($ Mil).| (LTM values as of) | 1312023 | 2192026 | Change |
|---|---|---|---|
| Stock Price ($) | 74.81 | 120.60 | 61.2% |
| Change Contribution By: | |||
| Total Revenues ($ Mil) | 1,532 | 3,212 | 109.7% |
| P/S Multiple | 15.4 | 13.1 | -15.2% |
| Shares Outstanding (Mil) | 316 | 349 | -9.4% |
| Cumulative Contribution | 61.2% |
Market Drivers
1/31/2023 to 2/19/2026| Return | Correlation | |
|---|---|---|
| DDOG | 61.2% | |
| Market (SPY) | 74.7% | 42.9% |
| Sector (XLK) | 110.5% | 47.5% |
Price Returns Compared
| 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | Total [1] | |
|---|---|---|---|---|---|---|---|
| Returns | |||||||
| DDOG Return | 81% | -59% | 65% | 18% | -5% | -10% | 24% |
| Peers Return | 12% | -48% | 85% | 10% | 1% | -19% | -4% |
| S&P 500 Return | 27% | -19% | 24% | 23% | 16% | 1% | 83% |
Monthly Win Rates [3] | |||||||
| DDOG Win Rate | 75% | 25% | 58% | 50% | 50% | 0% | |
| Peers Win Rate | 55% | 32% | 65% | 55% | 43% | 0% | |
| S&P 500 Win Rate | 75% | 42% | 67% | 75% | 67% | 50% | |
Max Drawdowns [4] | |||||||
| DDOG Max Drawdown | -28% | -62% | -15% | -14% | -39% | -22% | |
| Peers Max Drawdown | -21% | -55% | -9% | -27% | -22% | -26% | |
| S&P 500 Max Drawdown | -1% | -25% | -1% | -2% | -15% | -1% | |
[1] Cumulative total returns since the beginning of 2021
[2] Peers: DT, ESTC, NOW, SNOW, CRWD. See DDOG Returns vs. Peers.
[3] Win Rate = % of calendar months in which monthly returns were positive
[4] Max drawdown represents maximum peak-to-trough decline within a year
[5] 2026 data is for the year up to 2/19/2026 (YTD)
How Low Can It Go
| Event | DDOG | S&P 500 |
|---|---|---|
| 2022 Inflation Shock | ||
| % Loss | -68.1% | -25.4% |
| % Gain to Breakeven | 213.5% | 34.1% |
| Time to Breakeven | 930 days | 464 days |
| 2020 Covid Pandemic | ||
| % Loss | -42.1% | -33.9% |
| % Gain to Breakeven | 72.7% | 51.3% |
| Time to Breakeven | 53 days | 148 days |
Compare to DT, ESTC, NOW, SNOW, CRWD
In The Past
Datadog's stock fell -68.1% during the 2022 Inflation Shock from a high on 11/9/2021. A -68.1% loss requires a 213.5% gain to breakeven.
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About Datadog (DDOG)
Datadog, Inc. provides monitoring and analytics platform for developers, information technology operations teams, and business users in the cloud in North America and internationally. The company's SaaS platform integrates and automates infrastructure monitoring, application performance monitoring, log management, and security monitoring to provide real-time observability of its customers technology stack. Its platform also provides user experience monitoring, network performance monitoring, cloud security, developer-focused observability, and incident management, as well as a range of shared features, such as dashboards, analytics, collaboration tools, and alerting capabilities. The company was incorporated in 2010 and is headquartered in New York, New York.
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Here are 1-3 brief analogies for Datadog:
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```html- Infrastructure Monitoring: Monitors performance and health across servers, containers, and cloud environments.
- Log Management: Collects, processes, and analyzes logs from applications and infrastructure for troubleshooting and security.
- Application Performance Monitoring (APM): Traces requests and identifies performance bottlenecks across distributed applications.
- Synthetic Monitoring: Proactively tests application availability and and performance from various global locations.
- Real User Monitoring (RUM): Gathers insights into actual user experiences and frontend performance.
- Cloud Security Platform: Provides unified security monitoring and threat detection across cloud environments.
- Network Performance Monitoring (NPM): Visualizes network traffic and connectivity between application components.
- Database Monitoring: Offers deep visibility into database performance, queries, and resource utilization.
- Incident Management: Facilitates detection, diagnosis, and resolution of operational incidents.
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Datadog (DDOG) Major Customers
Datadog (DDOG) primarily sells its monitoring, security, and analytics platform to **other companies** (B2B - Business-to-Business). Datadog serves a broad and diverse customer base across various industries and company sizes, from startups to large enterprises. As a standard practice for SaaS companies, and as stated in its SEC filings (e.g., Form 10-K), Datadog does not disclose specific major customers by name. Furthermore, Datadog has repeatedly stated that no single customer has accounted for 10% or more of its total revenue in recent fiscal years. This indicates a well-diversified customer portfolio rather than reliance on a few dominant clients. Instead of listing individual company names, we can describe the characteristics of the companies that are typical Datadog customers:- Companies with Cloud-Native and Hybrid Cloud Infrastructures: Organizations that have adopted public cloud services (e.g., AWS, Azure, Google Cloud) extensively, are migrating to the cloud, or operate complex hybrid environments. They require comprehensive visibility across their distributed systems.
- Organizations Focused on Digital Transformation and DevOps/SRE Practices: Businesses that are modernizing their software development and operations, embracing DevOps, Site Reliability Engineering (SRE), and microservices architectures. These companies need unified observability for their applications, infrastructure, and logs to ensure performance and reliability.
- Enterprises Across Diverse Industries: Datadog's customers span a wide range of sectors, including technology, financial services, media, retail, healthcare, manufacturing, and gaming. Any company heavily reliant on software, applications, and online services is a potential customer for Datadog's platform to manage performance and security.
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- Amazon Web Services (parent company: Amazon.com, Inc. - AMZN)
- Google Cloud (parent company: Alphabet Inc. - GOOGL)
- Microsoft Azure (parent company: Microsoft Corporation - MSFT)
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Olivier Pomel, CEO & Co-founder
Olivier Pomel co-founded Datadog in 2010. Before founding Datadog, he served as the Vice President of Technology for Wireless Generation, where he was instrumental in building data systems for K-12 teachers and grew the development team, until the company's acquisition by News Corp in 2010. He previously held software engineering positions at IBM Research and various internet startups. Pomel is also an original author of the VLC media player.
David Obstler, Chief Financial Officer
David Obstler joined Datadog as CFO in October 2018. He brings over three decades of finance experience, with more than two decades focused on technology companies. Prior to Datadog, he served as CFO of TravelClick, where he managed global financial operations. His past CFO roles include OpenLink Financial, MSCI Inc., Risk Metrics Group, and Pinnacor. Obstler also held investment banking positions at JPMorgan, Lehman Brothers, and Goldman Sachs. He serves on the boards of Braze, Miro, and OneTrust, and is a board advisor for OwnBackup.
Alexis Lê-Quôc, CTO & Co-founder
Alexis Lê-Quôc co-founded Datadog with Olivier Pomel. He has a background as a software engineer at IBM Research, Neomeo, and Orange, and is recognized for his focus on technical elegance and operational efficiency. He is also associated with the original "devops" movement.
Adam Blitzer, Chief Operating Officer
Adam Blitzer possesses over a decade of experience in the SaaS industry. He spent eight years at Salesforce, where he rose to Executive Vice President and General Manager of Digital (Marketing Cloud, Commerce Cloud, and Experience Cloud). Prior to his time at Salesforce, Blitzer co-founded Pardot, which was subsequently acquired by Salesforce and is recognized as a leading B2B marketing automation platform.
Yanbing Li, Chief Product Officer
Yanbing Li is a global business and technology leader with extensive experience in product, engineering, large-scale P&L, and global operations. Before joining Datadog, she served as the Senior Vice President of Engineering at Aurora. Previously, she was the Vice President of Product and Engineering at Google, overseeing Google Cloud Commerce, Cloud Operations, and Service Infrastructure. She also held several executive leadership positions at VMware, including Senior Vice President and General Manager for the Storage and Availability Business Unit.
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The key risks to Datadog's business (DDOG) are primarily centered around intense competition, its demanding valuation, and operational challenges related to sustaining rapid growth and innovation.
- Intense Competition and Cloud Provider Dynamics: Datadog operates in a highly competitive cloud observability market, facing significant challenges not only from direct rivals like Splunk and Dynatrace but, more critically, from hyperscale cloud providers such as Amazon Web Services (AWS) and Microsoft Azure. These cloud giants can integrate monitoring tools natively and often offer them at a lower perceived cost, posing a substantial external threat to Datadog's market share and pricing power.
- High Valuation and Risk of Slowing Growth: Datadog's stock trades at extremely high valuation multiples (e.g., P/E and P/S ratios), which necessitates near-perfect business execution to justify investor expectations. Although Datadog has demonstrated strong growth, there are indications of a deceleration in its revenue growth rate, and any failure to meet market expectations or a continued slowdown could lead to a significant correction in its stock price.
- Operational Challenges in Managing Growth and Innovation: Sustaining rapid growth presents operational risks for Datadog. The company must continually invest heavily in research and development to maintain its technological lead in areas like AI and machine learning against competitors and adapt to rapidly changing technology and evolving industry standards. There's a risk that these substantial investments in technology infrastructure, sales, marketing, and international expansion may not yield the expected revenue growth, potentially impacting future profitability.
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- The continuous maturation and increasing capabilities of native observability tools offered by hyperscale cloud providers (e.g., AWS CloudWatch, Azure Monitor, Google Cloud Operations Suite). As these platforms become more comprehensive, integrated, and capable across multi-cloud environments, they present a viable "good enough" alternative for a growing segment of enterprises, potentially eroding Datadog's market share by offering solutions that are deeply embedded in their respective cloud ecosystems and may come with inherent cost advantages.
- The increasing adoption and standardization driven by OpenTelemetry (OTel). While Datadog supports OTel, the widespread use of a vendor-agnostic standard for collecting telemetry data (metrics, logs, and traces) could commoditize the data ingestion layer. This empowers customers to collect data once and send it to any observability backend, including competitors or open-source solutions, thereby reducing vendor lock-in and intensifying competition on the core value proposition of analytics, visualization, and actionable insights, rather than proprietary data collection mechanisms.
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Datadog (DDOG) operates in the global observability and security market for cloud applications, offering a wide range of products and services including infrastructure monitoring, application performance monitoring (APM), log management, and security solutions.
The total addressable market (TAM) for Datadog's main products and services has been estimated with varying figures:
- Datadog's TAM, excluding its security segment, was previously estimated at approximately $35 billion, aligning with broader industry estimates for the IT operations management (ITOM) market.
- Gartner projected Datadog's observability total addressable market to be $41 billion in 2022, increasing to $45 billion in 2023, and further to $62 billion in 2026.
- Analysts project that Datadog's TAM could reach approximately $175 billion by 2034, reflecting the company's expansion into new areas, including the security segment. This projection assumes a conservative annual growth rate of 17.5% over the next decade.
These market sizes are global, as Datadog serves customers worldwide.
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Datadog (DDOG) is expected to drive future revenue growth over the next two to three years through several key strategies and market trends:
- Expansion into AI Observability and Security Solutions: Datadog is heavily investing in and seeing significant demand for its AI-powered observability and security products. The company has launched new AI features, such as Bits AI Agents and the TOTO model, and offers a full stack of AI Observability and Security products to help customers monitor generative AI workloads. This focus on AI is fueling growth from AI-native customers and is a major component of its future growth strategy.
- Continued Growth in Existing Customer Usage and Platform Adoption: Datadog's "land and expand" strategy remains a crucial driver, with a consistent focus on increasing the number of products existing customers use. The company has reported high percentages of customers using two or more products, indicating successful cross-selling and upselling efforts across its integrated platform for infrastructure monitoring, APM suite, and log management. This deep integration and increasing product attach rates create substantial switching costs for customers, fostering durable growth.
- Acquisition of New Customers, Particularly Larger Enterprises: Datadog continues to expand its customer base, with a notable increase in customers generating significant annual recurring revenue (ARR). The company has seen consistent growth in the total number of customers, particularly those with ARR of $100,000 or more, and even those exceeding $1 million in ARR. This expansion to larger enterprise clients contributes significantly to top-line growth.
- Ongoing Cloud Migration and Digital Transformation: The fundamental market trend of businesses migrating to the cloud and undergoing digital transformation continues to drive demand for Datadog's comprehensive observability and security platform. As organizations increasingly adopt cloud computing and modern DevOps technologies, they require sophisticated tools to monitor and secure their complex IT environments, a need that Datadog is well-positioned to address.
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Share Issuance
- Datadog issued $747.5 million of 0.125% convertible senior notes due 2025 in June 2020.
- The company issued $1.0 billion of 0% convertible senior notes due 2029 in December 2024.
- A portion of the proceeds from the 2029 convertible notes, specifically $112.0 million, was used to repurchase some of the 2025 notes.
Outbound Investments
- In May 2025, Datadog acquired Eppo, a feature flagging and experimentation platform, for an estimated $220 million.
- In April 2025, Datadog acquired Metaplane, an end-to-end data observability platform, for an undisclosed sum, to enhance its data observability capabilities.
Capital Expenditures
- Datadog's capital expenditures were $26 million in 2020, $36 million in 2021, $65 million in 2022, $62 million in 2023, and $96 million in 2024.
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| Date | Ticker | Company | Category | Trade Strategy | 6M Fwd Rtn | 12M Fwd Rtn | 12M Max DD |
|---|---|---|---|---|---|---|---|
| 01302026 | ROP | Roper Technologies | Dip Buy | DB | FCFY OPMDip Buy with High FCF Yield and High MarginBuying dips for companies with high FCF yield and meaningfully high operating margin | -13.4% | -13.4% | -13.8% |
| 01302026 | TDC | Teradata | Dip Buy | DB | FCFY OPMDip Buy with High FCF Yield and High MarginBuying dips for companies with high FCF yield and meaningfully high operating margin | 17.7% | 17.7% | -8.7% |
| 01302026 | CVLT | CommVault Systems | Dip Buy | DB | CFO/Rev | Low D/EDip Buy with High Cash Flow MarginsBuying dips for companies with significant cash flows from operations and reasonable debt / market cap | 2.3% | 2.3% | -5.1% |
| 01302026 | NTNX | Nutanix | Dip Buy | DB | CFO/Rev | Low D/EDip Buy with High Cash Flow MarginsBuying dips for companies with significant cash flows from operations and reasonable debt / market cap | 6.2% | 6.2% | -6.3% |
| 01302026 | FICO | Fair Isaac | Monopoly | MY | Getting CheaperMonopoly-Like with P/S DeclineLarge cap with monopoly-like margins or cash flow generation and getting cheaper based on P/S multiple | -8.1% | -8.1% | -9.2% |
| 12122025 | DDOG | Datadog | Monopoly | MY | Getting CheaperMonopoly-Like with P/S DeclineLarge cap with monopoly-like margins or cash flow generation and getting cheaper based on P/S multiple | -14.2% | -14.2% | -26.9% |
| 02282025 | DDOG | Datadog | Monopoly | MY | Getting CheaperMonopoly-Like with P/S DeclineLarge cap with monopoly-like margins or cash flow generation and getting cheaper based on P/S multiple | 17.3% | 7.4% | -25.4% |
| 05312022 | DDOG | Datadog | Dip Buy | DB | CFO/Rev | Low D/EDip Buy with High Cash Flow MarginsBuying dips for companies with significant cash flows from operations and reasonable debt / market cap | -24.5% | -0.5% | -34.3% |
Research & Analysis
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Peer Comparisons
| Peers to compare with: |
Financials
| Median | |
|---|---|
| Name | |
| Mkt Price | 113.98 |
| Mkt Cap | 51.5 |
| Rev LTM | 3,799 |
| Op Inc LTM | -39 |
| FCF LTM | 817 |
| FCF 3Y Avg | 714 |
| CFO LTM | 931 |
| CFO 3Y Avg | 806 |
Growth & Margins
| Median | |
|---|---|
| Name | |
| Rev Chg LTM | 21.5% |
| Rev Chg 3Y Avg | 25.2% |
| Rev Chg Q | 21.4% |
| QoQ Delta Rev Chg LTM | 5.0% |
| Op Mgn LTM | -1.7% |
| Op Mgn 3Y Avg | -2.5% |
| QoQ Delta Op Mgn LTM | 0.0% |
| CFO/Rev LTM | 28.3% |
| CFO/Rev 3Y Avg | 28.3% |
| FCF/Rev LTM | 24.1% |
| FCF/Rev 3Y Avg | 25.7% |
Valuation
| Median | |
|---|---|
| Name | |
| Mkt Cap | 51.5 |
| P/S | 10.7 |
| P/EBIT | 46.2 |
| P/E | 7.0 |
| P/CFO | 32.2 |
| Total Yield | -0.0% |
| Dividend Yield | 0.0% |
| FCF Yield 3Y Avg | 1.8% |
| D/E | 0.0 |
| Net D/E | -0.1 |
Returns
| Median | |
|---|---|
| Name | |
| 1M Rtn | -8.5% |
| 3M Rtn | -24.4% |
| 6M Rtn | -14.0% |
| 12M Rtn | -23.8% |
| 3Y Rtn | 19.3% |
| 1M Excs Rtn | -9.5% |
| 3M Excs Rtn | -31.9% |
| 6M Excs Rtn | -20.5% |
| 12M Excs Rtn | -36.7% |
| 3Y Excs Rtn | -52.3% |
Price Behavior
Price Behavior
| Market Price | $120.60 | |
| Market Cap ($ Bil) | 42.0 | |
| First Trading Date | 09/19/2019 | |
| Distance from 52W High | -39.6% | |
| 50 Days | 200 Days | |
| DMA Price | $131.67 | $138.49 |
| DMA Trend | up | down |
| Distance from DMA | -8.4% | -12.9% |
| 3M | 1YR | |
| Volatility | 53.7% | 52.9% |
| Downside Capture | 298.35 | 162.45 |
| Upside Capture | 62.50 | 136.16 |
| Correlation (SPY) | 42.2% | 46.3% |
DDOG Betas & Captures as of 1/31/2026
[1] Upside and downside betas calculated using positive and negative benchmark daily returns respectively
| 1M | 2M | 3M | 6M | 1Y | 3Y | |
|---|---|---|---|---|---|---|
| Beta | 2.11 | 1.76 | 1.04 | 1.34 | 1.20 | 1.39 |
| Up Beta | 4.71 | 4.73 | 2.86 | 1.71 | 1.21 | 1.21 |
| Down Beta | -0.47 | 0.21 | -0.04 | 0.50 | 1.10 | 1.44 |
| Up Capture | 263% | 83% | 34% | 141% | 120% | 334% |
| Bmk +ve Days | 11 | 22 | 34 | 71 | 142 | 430 |
| Stock +ve Days | 9 | 14 | 20 | 50 | 112 | 382 |
| Down Capture | 457% | 321% | 175% | 177% | 123% | 109% |
| Bmk -ve Days | 9 | 19 | 27 | 54 | 109 | 321 |
| Stock -ve Days | 11 | 27 | 41 | 75 | 137 | 367 |
[1] Upside and downside betas calculated using positive and negative benchmark daily returns respectively
Based On 1-Year Data
| Annualized Return | Annualized Volatility | Sharpe Ratio | Correlation with DDOG | |
|---|---|---|---|---|
| DDOG | -18.3% | 51.3% | -0.23 | - |
| Sector ETF (XLK) | 16.4% | 27.5% | 0.53 | 53.3% |
| Equity (SPY) | 13.0% | 19.4% | 0.51 | 49.0% |
| Gold (GLD) | 71.2% | 25.5% | 2.08 | -0.0% |
| Commodities (DBC) | 7.3% | 16.9% | 0.25 | 17.9% |
| Real Estate (VNQ) | 6.4% | 16.7% | 0.20 | 24.6% |
| Bitcoin (BTCUSD) | -30.2% | 44.9% | -0.66 | 34.4% |
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Based On 5-Year Data
| Annualized Return | Annualized Volatility | Sharpe Ratio | Correlation with DDOG | |
|---|---|---|---|---|
| DDOG | -1.9% | 56.0% | 0.18 | - |
| Sector ETF (XLK) | 16.1% | 24.8% | 0.59 | 57.3% |
| Equity (SPY) | 13.4% | 17.0% | 0.62 | 52.6% |
| Gold (GLD) | 22.0% | 17.1% | 1.05 | 7.4% |
| Commodities (DBC) | 11.0% | 19.0% | 0.47 | 9.7% |
| Real Estate (VNQ) | 4.8% | 18.8% | 0.16 | 34.0% |
| Bitcoin (BTCUSD) | 6.9% | 57.1% | 0.34 | 27.3% |
Smart multi-asset allocation framework can stack odds in your favor. Learn How
Based On 10-Year Data
| Annualized Return | Annualized Volatility | Sharpe Ratio | Correlation with DDOG | |
|---|---|---|---|---|
| DDOG | 11.0% | 58.4% | 0.52 | - |
| Sector ETF (XLK) | 23.2% | 24.2% | 0.87 | 52.4% |
| Equity (SPY) | 15.8% | 17.9% | 0.76 | 45.8% |
| Gold (GLD) | 15.0% | 15.6% | 0.80 | 8.2% |
| Commodities (DBC) | 8.7% | 17.6% | 0.41 | 13.4% |
| Real Estate (VNQ) | 6.8% | 20.7% | 0.29 | 29.9% |
| Bitcoin (BTCUSD) | 67.7% | 66.7% | 1.07 | 24.8% |
Smart multi-asset allocation framework can stack odds in your favor. Learn How
Earnings Returns History
Expand for More| Forward Returns | |||
|---|---|---|---|
| Earnings Date | 1D Returns | 5D Returns | 21D Returns |
| 11/6/2025 | 23.1% | 23.2% | -0.5% |
| 8/7/2025 | -0.4% | -6.0% | -0.6% |
| 5/6/2025 | 0.3% | 7.3% | 13.2% |
| 2/13/2025 | -8.2% | -14.8% | -31.3% |
| 11/7/2024 | 1.1% | 0.1% | 31.4% |
| 8/8/2024 | 5.6% | 5.7% | -0.8% |
| 5/7/2024 | -11.5% | -6.9% | -13.0% |
| 2/13/2024 | -2.4% | -5.3% | -8.4% |
| ... | |||
| SUMMARY STATS | |||
| # Positive | 12 | 12 | 9 |
| # Negative | 12 | 12 | 15 |
| Median Positive | 11.7% | 11.7% | 27.0% |
| Median Negative | -6.1% | -9.1% | -11.6% |
| Max Positive | 28.5% | 31.0% | 50.0% |
| Max Negative | -17.2% | -17.8% | -40.3% |
SEC Filings
Expand for More| Report Date | Filing Date | Filing |
|---|---|---|
| 09/30/2025 | 11/07/2025 | 10-Q |
| 06/30/2025 | 08/08/2025 | 10-Q |
| 03/31/2025 | 05/07/2025 | 10-Q |
| 12/31/2024 | 02/20/2025 | 10-K |
| 09/30/2024 | 11/08/2024 | 10-Q |
| 06/30/2024 | 08/09/2024 | 10-Q |
| 03/31/2024 | 05/08/2024 | 10-Q |
| 12/31/2023 | 02/23/2024 | 10-K |
| 09/30/2023 | 11/07/2023 | 10-Q |
| 06/30/2023 | 08/09/2023 | 10-Q |
| 03/31/2023 | 05/05/2023 | 10-Q |
| 12/31/2022 | 02/24/2023 | 10-K |
| 09/30/2022 | 11/04/2022 | 10-Q |
| 06/30/2022 | 08/08/2022 | 10-Q |
| 03/31/2022 | 05/06/2022 | 10-Q |
| 12/31/2021 | 02/25/2022 | 10-K |
Insider Activity
Expand for More| # | Owner | Title | Holding | Action | Filing Date | Price | Shares | Transacted Value | Value of Held Shares | Form |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Le-Quoc, Alexis | Chief Technology Officer | Direct | Sell | 12312025 | 137.85 | 32,418 | 4,468,788 | 41,555,676 | Form |
| 2 | Pomel, Olivier | Chief Executive Officer | Direct | Sell | 12192025 | 137.93 | 11,195 | 1,544,122 | 71,465,870 | Form |
| 3 | Shah, Shardul | Trust | Sell | 12162025 | 146.94 | 7,916 | 1,163,207 | 51,227,088 | Form | |
| 4 | Walters, Sean Michael | Chief Revenue Officer | Direct | Sell | 12152025 | 149.84 | 9,838 | 1,474,097 | 25,720,124 | Form |
| 5 | Le-Quoc, Alexis | Chief Technology Officer | Direct | Sell | 12082025 | 154.05 | 53,912 | 8,305,201 | 45,514,077 | Form |
External Quote Links
| Y Finance | Barrons |
| TradingView | Morningstar |
| SeekingAlpha | ValueLine |
| Motley Fool | Robinhood |
| CNBC | Etrade |
| MarketWatch | Unusual Whales |
| YCharts | Perplexity Finance |
| FinViz |
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