• 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.

Datadog (DDOG) Major 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.

• Amazon Web Services (parent company: Amazon.com, Inc. - AMZN)
• Google Cloud (parent company: Alphabet Inc. - GOOGL)
• Microsoft Azure (parent company: Microsoft Corporation - MSFT)

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.

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.

1. 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.
2. 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.
3. 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.

• 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.

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.

Datadog (DDOG) is expected to drive future revenue growth over the next two to three years through several key strategies and market trends:

1. 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.
2. 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.
3. 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.
4. 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.

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.