Vendor Spotlight: Datadog

Datadog, Inc. is an American company that provides observability and security services for cloud-scale applications. Datadog has grown rapidly, expanding its product offerings to include infrastructure monitoring, application performance monitoring, log management, and cloud security management. The company went public on the Nasdaq stock exchange in 2019 and has since continued to innovate and expand its global presence.

Datadog’s platform is designed to help organizations gain real-time insights into their IT operations, improve performance, and enhance security. The company serves a diverse range of industries, including technology, finance, healthcare, and retail. With over 5,200 employees and offices in major cities worldwide, Datadog is committed to providing comprehensive monitoring and security solutions to support the growing needs of modern enterprises.

Founding and Background

Datadog, Inc. was founded in 2010 by Olivier Pomel and Alexis Lê-Quôc in New York City. The idea for the company emerged while both founders were working at Wireless Generation, where they encountered challenges in managing the complexity of modern, distributed systems. They realized the need for a tool that could provide developers and operations teams with a unified view of their infrastructure in real-time.

Datadog began as a cloud-based monitoring and analytics platform for developers and IT operations teams, designed to break down silos between DevOps and enable better collaboration. It initially focused on infrastructure monitoring, but over time expanded to include application performance monitoring (APM), log management, and security monitoring. With its ability to monitor servers, databases, tools, and services in real-time across cloud, on-premise, and hybrid environments, Datadog quickly gained popularity and now serves a global customer base.

Timeline and Evolution

Datadog, Inc. has experienced significant growth and evolution since its founding in 2010. Here are some key milestones in its timeline:

• 2010 – Founding: Datadog, Inc. was founded by Olivier Pomel and Alexis Lê-Quôc in New York City. It was created to address the challenges of monitoring and managing modern, distributed IT infrastructures and to bridge the gap between development and operations teams.
• 2011 – Initial Product Launch: Datadog launched its first product, a cloud-based infrastructure monitoring platform. It presents a unified view of an entire IT environment, gathering metrics from servers, databases, tools, and services in real-time. There has never been an on-premises version of the platform.
• 2015 – A New Development Office: The company opens a research and development office in Paris, France.
• 2016 – Application Performance Monitoring (APM): Datadog added an APM to the platform.
• 2017 – Log Management: Expanding its platform further, Datadog added log management capabilities. The console for the service includes a data viewer with analytics tools.
• 2019 – Initial Public Offering (IPO): Datadog went public on the Nasdaq stock exchange under the ticker symbol DDOG, raising over $600 million.
• 2020 – Security Monitoring: Datadog entered the security monitoring space with the release of its Cloud Security Posture Management (CSPM) and Cloud Workload Security (CWS) products.
• 2021 – Expanded Product Suite: The introduction of Incident Management, Synthetic Monitoring, and Real User Monitoring (RUM).
• 2022 – Full-Stack Observability: The Datadog platform now provides monitoring across infrastructure, applications, logs, and security.
• 2023 – Generative AI and Automation: Continuing its innovation, Datadog integrated generative AI and automation tools. AI-powered insights helped reduce noise and surfaced critical alerts faster, enabling better decision-making.

In-House Development

Datadog’s origin was based on a purchase of a system monitoring package. This was written for Server Density in 2009 and it was entirely written in Python. The Datadog team made some adjustments to this package in order to create the original Infrastructure Monitoring package. Initially, the company developed its system in-house – at first in the United States and then, from 2015, at its Paris development center.

Piecemeal updates and additions from external systems resulted in the codebase of the Datadog platform becoming unwieldy, inefficient, and difficult to maintain. So, the company completely rewrote all of its code in 2018. This converted the system from Python to Go (aka Golang).

Notable Acquisitions

Datadog made its first acquisition in 2015. Buying an existing service is a lot easier than creating a system from scratch. Even if the company needs to adjust a new piece of software to get it to work on its platform, that integration process is a lot quicker than planning a new module from scratch. This would require hiring business expertise to fully map out the requirements of a new function.

The acquisitions made by Datadog were:

• February 2015: Mortar Data. This purchase brought in an expert team in data analysis. This was initially needed to enrich the platform’s ability to improve its interpretation of incoming statics to provide a better presentation in the console.
• September 2017: Logmatic. Forms the log Management unit on the Datadog platform
• February 2019: Madumbo. The programmers of this AI-based automated application testing platform joined the development team in Paris and contributed towards the creation of Datadog’s Software Delivery units.
• August 2020: Undefined Labs. This testing and observability platform for developer workflows added to the development of the Software Delivery units.
• February 2021: Timber Technologies. This got Datadog the Vector platform, which is a log data processing system. This formed the core of the new Cloud SIEM unit.
• April 2021: Sqreen. This application security platform was added to the Datadog Cloud SIEM and APM units. Datadog developed its Code Security and Application Security Management modules from this acquisition.
• November 2021: Ozcode. Datadog used this debugging tool for its own internal use in development. The company developed its Error Tracking unit from Ozcode.
• February 2022: CoScreen. This incident response system evolved into the Incident Management module on the Datadog platform.
• August 2022: Seekret. Datadog enhanced its APM with distributed tracing for API observability with this acquisition.
• May 2022: Hdiv Security. This addition became the Cloud Security Management unit on the Datadog platform and enhanced its Application Security Management unit.
• November 2022: Cloudcraft. This infrastructure modeling service became Datadog’s Universal Service Monitoring module.
• April 2023: Codiga. Datadog created its Code Analysis and Quality Gates module, which is still in Beta release.
• November 2023: Actiondesk. This company produces a cloud-based spreadsheet that consolidates and correlates data from multiple live reporting sources, such as Telemetry feedback for microservices and APIs. This acquisition has yet to be converted into a Datadog platform module.

Funding and Company Ownership

The creation of a new IT company is expensive, and Datadog’s chosen route for expansion by buying other companies particularly requires a large amount of money. The two founders of the company were the original shareholders and still own shares in Datadog, Inc.

Funding Rounds

The company went through a number of funding rounds in its early years. These were:

• Seed Funding, July 2010: Raised an undisclosed amount from Genacast Ventures and NYC Seed.
• Seed Funding, April 2011: Raised $1.2 million from RRE Ventures, RTP Global, IA Ventures, and Contour Venture Partners.
• Series A Funding, November 2012: Raised $6.2 million from Contour Venture Partners, RTP Global, IA Ventures, and Index Ventures.
• Series B Funding, February 2014: Raised $15 million from RRE Ventures, Pario Ventures, Contour Venture Partners, OpenView Venture Partners, RTP Global, NYC Seed, Index Ventures, Amplify Partners, and IA Ventures.
• Series C Funding, January 2015: Raised $31.5 million from Index Ventures, Contour Venture Partners, OpenView Venture Partners, Amplify Partners, and RTP Global.
• Series D Funding, January 2016: Raised $94.5 million from Index Ventures, ICONIQ Capital, Amplify Partners, Accomplice (FKA Atlas Venture), OpenView Venture Partners, Meritech Capital Partners, and Contour Venture Partners.
• Venture Funding Round, December 2018: Raised an undisclosed amount from Institutional Venture Partners (IVP).

Initial Public Offering

Cisco Systems made an offer for the company in September 2019. That was for $7 billion but was rejected. Instead, Datadog, Inc. floated on the Nasdaq exchange on 19 September 2019 with the ticker symbol DDOG. The flotation sold 24 million shares, raising $648 million and valuing the cuisines at $8.7 billion. By September 2024, Datadog, Inc’s market capitalization was $36.84 billion.

Debt Instruments

Datadog raised $747 million on 6 June 202 by issuing convertible bonds. These debts could be converted into capital, and so become shareholdings.

Significant Shareholders

Olivier Pomel, one of the founders of the company, still owns 6 percent of the business, which has a value of around $2.2 billion. The other founder, Alexis Lê-Quôc, has a much smaller holding of 0.53 percent of the company’s shares, which are valued at $38 million.

There are a total of 1,508 institutional shareholders. The largest interest in the company is held by Vanguard, which has holdings through three funds that total 18 percent of the shares in Datadog. Other major institutional investors are: BlackRock Inc., Baillie Gifford & Co, Fmr LLC, State Street Corp, Jennison Associates LLC, VIMSX – Vanguard Mid-Cap Index Fund Investor Shares, Invesco Qqq Trust, Series 1, and WCM Investment Management, LLC.

Key People

• Olivier Pomel, CEO and Co-Founder: After just three years as a software engineer, Pomel got the position as Vice President of Technology at Wireless Generation, an education systems company. Pomel stayed with that business for nine years. When News International bought Wireless Generation, Pomel left to form Datadog. He has held the position of CEO to this day.
• Alexis Lê-Quôc, CTO and Co-Founder: Lê-Quôc worked alongside Pomel for most of his career. Both of them worked at IBM, Neomeo, and Silicongo in France before gaining positions at Wireless Generation in New York. However, Pomel started at Wireless Generation two years before Lê-Quôc, while Lê-Quôc worked at France Telecom. This got Pomel a higher position at the new company. Lê-Quôc became Chief Technology Officer at Datadog at its creation and still holds that position today.
• Amit Agarwal, President: Starting in 1998, Agarwal worked his way up from a programmer to a Product Manager by moving through various companies, working at Quest Software immediately before joining Datadog. In 2006, Agarwal switched from being a Product Manager at Quest, to the same position at Datadog. He rose to become the company President in August 2022.

As of August 2024, Datadog has around 6,900 employees across six continents.

Locations

Datadog, Inc. has a global presence with offices across several major cities. Below are its key office locations:

Headquarters

New York City, USA: The main headquarters of Datadog, where a significant portion of its operations, product development, and management are based.

Research and Development Center

Paris, France

North America

• Boston, USA
• Denver, USA
• San Francisco, USA
• Chicago, USA
• Dallas, USA

Europe

• Paris, France
  London, United Kingdom
• Dublin, Ireland
• Madrid, Spain

Asia-Pacific (APAC)

• Tokyo, Japan
• Sydney, Australia
• Singapore

South America

These global offices support Datadog’s operations, sales, customer support, and development, helping the company serve its diverse customer base across multiple regions.

Target Market and Customer Base

Target Market

Datadog primarily targets businesses of all sizes that operate in cloud-based, hybrid, or on-premise IT environments. Its key focus is on organizations that require real-time visibility into their infrastructure, applications, logs, and security. This includes industries such as:

1. Technology & SaaS: Cloud-native companies, tech startups, and software-as-a-service (SaaS) providers use Datadog to monitor and manage large-scale, distributed applications and infrastructure.
2. Financial Services: Banks, fintech firms, and insurance companies leverage Datadog for ensuring uptime, security, and compliance while managing complex IT ecosystems.
3. E-commerce & Retail: Online retailers use Datadog to ensure the reliability of their websites and apps, optimize customer experiences, and prevent downtime during peak traffic periods.
4. Healthcare: Hospitals, healthcare providers, and biotech companies use Datadog to secure sensitive patient data, monitor critical systems, and ensure regulatory compliance.
5. Media & Entertainment: Streaming services and content platforms rely on Datadog to monitor performance, scalability, and user experience, especially under high traffic loads.
6. Public Sector & Government: Government institutions and public organizations use Datadog for monitoring critical infrastructure, ensuring operational efficiency, and enhancing cybersecurity.

Customer Base

Datadog has a diverse customer base, ranging from small businesses to large enterprises. Its platform is used by developers, DevOps teams, IT operations, and security teams across various industries. Some of Datadog’s notable customers include:

• Amazon
• Peloton
• Shopify
• Disney+
• Samsung
• The Washington Post
• NVIDIA

With over 2,000 enterprise customers and a growing list of users in different sectors, Datadog caters to organizations that rely on cloud, multi-cloud, or hybrid environments and need full-stack observability to ensure system reliability, performance, and security.

Datadog Product Suite

Datadog offers a comprehensive platform that provides full-stack observability, monitoring, and security solutions. Its product suite is designed to help developers, IT operations, and security teams monitor, analyze, and optimize their infrastructure, applications, and systems in real-time. Here’s an overview of the core products within the Datadog platform:

1. Infrastructure Monitoring

Real-time monitoring of infrastructure, including servers, containers, and databases.

Key Features:

• Tracks metrics, alerts, and dashboards for real-time visibility into system health.
• Supports over 600 integrations (AWS, Azure, Kubernetes, etc.).
• Automated monitoring and alerting for system performance and availability.

2. Application Performance Monitoring (APM)

Monitoring and optimizing application performance.

Key Features:

• Distributed tracing to track performance across microservices.
• Root cause analysis and anomaly detection.
• Provides insights into latency, errors, and throughput for debugging.

3. Log Management

Centralized log collection, management, and analysis.

Key Features:

• Ingests logs from various sources, filters and analyzes them.
• Correlates logs with infrastructure and APM data for deeper insights.
• Indexing and search capabilities for logs, supporting compliance, and auditing.

4. Security Monitoring

Real-time threat detection and security insights.

Key Features:

• Monitors cloud, network, and endpoint security.
• Detects threats and suspicious activities in real time.
• Correlates security signals with application and infrastructure data for enhanced detection.

5. Network Performance Monitoring

Monitoring network health and performance in real-time.

Key Features:

• Visualizes network traffic between services and devices.
• Provides insights into network latency, bandwidth usage, and performance bottlenecks.
• Supports cloud-native and on-premises networks.

6. Synthetic Monitoring

Simulating user traffic to test application performance and uptime.

Key Features:

• Automated testing of web applications, APIs, and services.
• Proactive detection of outages and performance issues.
• Browser tests simulate real user journeys to optimize user experience.

7. Real User Monitoring (RUM)

Monitoring real user interactions with applications.

Key Features:

• Provides insights into actual user journeys and behavior across web applications.
• Tracks performance metrics like load times, errors, and user interactions.
• Analyzes user experience by breaking down page load components.

8. Cloud Security Posture Management (CSPM)

Monitoring and securing cloud environments.

Key Features:

• Detects misconfigurations and vulnerabilities in cloud infrastructure.
• Continuously audits cloud services for security best practices.
• Provides insights into compliance with frameworks like SOC 2, GDPR, and HIPAA.

9. Database Monitoring

Monitoring the performance of databases in real time.

Key Features:

• Tracks queries, throughput, latency, and resource usage for databases.
• Supports popular databases like MySQL, PostgreSQL, MongoDB, and others.
• Detects performance bottlenecks and inefficient queries.

10. Incident Management

Streamlining incident response and resolution.

Key Features:

• Centralized platform for detecting, managing, and resolving incidents.
• Collaboration tools for responding to incidents across teams.
• Automated alerts and notifications to streamline response workflows.

11. Continuous Profiler

Analyzing code performance in production environments.

Key Features:

• Provides continuous profiling for applications, helping to optimize resource usage.
• Tracks performance at code level, identifying bottlenecks in CPU and memory usage.
• Works with any application and supports multiple programming languages.

12. Dashboards and Custom Visualizations

Centralized data visualization for all monitored systems.

Key Features:

• Customizable, real-time dashboards for infrastructure, application, and log data.
• Allows creation of visualizations using drag-and-drop widgets.
• Multi-cloud and on-premise support for unified monitoring.

Product Suite Summary

Datadog’s product suite provides full-stack observability, covering infrastructure, applications, logs, security, and user experience. With its cloud-native platform and integration across cloud, hybrid, and on-premise environments, Datadog enables real-time monitoring, troubleshooting, and optimization. Whether it’s infrastructure monitoring, application performance, security management, or real user interactions, Datadog’s suite caters to the needs of DevOps, IT operations, security, and compliance teams, ensuring holistic visibility into the entire IT ecosystem.

Datadog Infrastructure Monitoring is the cornerstone of Datadog’s observability platform. This was the original Datadog tool and has been reshaped over the years. It provides real-time visibility into the health and performance of cloud, on-premise, and hybrid infrastructures.

This tool tracks the performance of servers, containers, databases, and third-party services. A library of more than 600 integrations gives the Infrastructure Monitoring module a centralized view of system metrics. The platform’s real-time dashboards, automated alerts, and machine-learning-powered anomaly detection empower teams to detect and resolve potential performance problems before they impact users.

Key Features:

• Real-Time Monitoring: Monitors infrastructure performance with live metrics from servers, containers, databases, and third-party services.
• Over 600 Integrations: Supports major cloud platforms (AWS, Azure, GCP) and key tools like Kubernetes, Docker, and MySQL.
• Automated Alerts and Thresholds: Predefined thresholds and automated machine learning-based anomaly detection enable quick response to performance issues.
• AI-Powered Anomaly Detection: Uses machine learning to detect unusual patterns in metrics, alerting teams to potential issues.
• Tag-Based Metrics: Organizes infrastructure data using tags (e.g., environment, region, role), making it easier to slice and dice metrics.
• Scalability: Scales easily to support large, dynamic infrastructures, from small deployments to large enterprise-grade environments.

Datadog Infrastructure Monitoring operates across diverse environments. Features include live dashboards, AI-driven anomaly detection, and extensive integrations. The platform enables organizations to gain critical insights into their systems and optimize performance.

Datadog Infrastructure Monitoring is a top-tier solution for businesses looking to ensure the health and performance of their infrastructure.

Other Notable Products

1. Datadog Cloud SIEM

Datadog Cloud SIEM is a comprehensive security information and event management (SIEM) solution that detects, investigates, and responds to security threats across platforms. This tool is integrated into Datadog’s observability platform and uses machine learning and advanced analytics to detect anomalous behaviors and potential threats in real time. The system correlates security events with infrastructure metrics, application performance, and logs. This provides context around security incidents.

The platform implements real-time threat detection and automated incident response, offering features like dynamic threat correlation, alerting, and comprehensive dashboards. Datadog Cloud SIEM provides robust security capabilities with a strong emphasis on integrating security with broader infrastructure and application monitoring, but it may require fine-tuning to optimize performance and manage costs effectively.

2. Real User Monitoring (RUM)

Datadog Real User Monitoring (RUM) provides insights into user interactions and experiences across web applications. It gathers information on how actual users interact with a site or application, capturing metrics such as page load times, user interactions, and performance bottlenecks. By collecting data directly from user browsers, RUM helps teams understand the end-user experience, identify performance issues, and optimize application performance.

The service’s customizable dashboards allow teams to segment data by user location, device type, and other factors to better understand user interactions and identify areas for improvement. High volumes of data may lead to increased complexity in managing and analyzing user interactions, and teams might need to fine-tune configurations to avoid alert fatigue and ensure relevant data is prioritized. Datadog Real User Monitoring provides actionable insights into real-world user interactions.

Major Competitors

Here are six major competitors to Datadog:

1. Splunk Provides comprehensive log management, infrastructure monitoring, and security analytics. Known for its powerful data analysis and search capabilities, Splunk is favored for large-scale data environments.
2. New Relic Offers full-stack observability with features like APM, infrastructure monitoring, and real user monitoring. It’s known for its deep application insights and ease of integration with cloud services.
3. Dynatrace Features an all-in-one platform for monitoring applications, infrastructure, and user experiences. Known for its AI-driven monitoring and automation, Dynatrace excels in performance management and problem resolution.
4. Elastic (ELK Stack) Provides open-source log management and analytics through Elasticsearch, Logstash, and Kibana. Known for its flexibility and scalability, it’s popular for customized log and data analysis solutions.
5. Grafana Labs Specializes in open-source observability with tools like Grafana for visualization and Loki for log aggregation. Grafana is widely used for creating custom dashboards and integrating diverse data sources.
6. Sumo Logic Delivers cloud-native machine data analytics with capabilities in log management, security analytics, and infrastructure monitoring. Known for its ease of use and real-time insights into operational data.

Spotlight Wrap Up

Datadog has created an extensive platform of system monitoring tools and is constantly expanding. The company has added security monitoring tools and Web application development systems. More modules are planned for the near future.