Datadog, Inc. (Datadog) operates an observability and security platform for cloud applications.
The company’s SaaS platform integrates and automates infrastructure monitoring, application performance monitoring, log management, user experience monitoring, cloud security, and many other capabilities to provide unified, real-time observability and security for its customers’ entire technology stack. Datadog is used by organizations of all sizes and across a wide range of industries to enable digi...
Datadog, Inc. (Datadog) operates an observability and security platform for cloud applications.
The company’s SaaS platform integrates and automates infrastructure monitoring, application performance monitoring, log management, user experience monitoring, cloud security, and many other capabilities to provide unified, real-time observability and security for its customers’ entire technology stack. Datadog is used by organizations of all sizes and across a wide range of industries to enable digital transformation and cloud migration, drive collaboration among development, operations, security and business teams, accelerate time to market for applications, reduce time to problem resolution, secure applications and infrastructure, understand user behavior, and track key business metrics.
Software applications are transforming how organizations engage with customers and operate their businesses. Companies across all industries are re-platforming their businesses to cloud native or hybrid on-premise and cloud infrastructures to enable this digital transformation. Historically, engineering teams have been siloed, making the development of next generation applications in dynamic cloud environments challenging. The company started Datadog to break this model and facilitate collaboration among development and operations teams, enabling the adoption of DevOps practices.
Since launching the company’s first use case with Infrastructure Monitoring in 2012, it has expanded its platform rapidly, and today, it offers end-to-end monitoring and analytics, powered by a common data model that is extensible for use cases across observability, security, software delivery, and service management. In 2024, the company launched Event Management to aggregate and consolidate alerts to accelerate remediation, and LLM Observability to help customers investigate how they can safely deploy and manage their models in production.
The company’s proprietary platform combines the power of metrics, traces, logs, user sessions, security signals, and other data from a single agent and over 850 integrations to provide a unified view of infrastructure, application performance and the real-time events impacting performance. Datadog is designed to be cloud agnostic and easy to deploy, with hundreds of out-of-the-box integrations, a built-in understanding of modern technology stacks, and extensive customizations. Customers can deploy its platform across their entire infrastructure, making it ubiquitous and a daily part of the lives of developers, operations engineers, security professionals, and business leaders.
The company’s platform addresses a significant portion of the IT Operations Management market. According to Gartner, the IT Operations Management market represents a $81 billion opportunity in 2028. A large portion of this spend is for legacy on-premise and private cloud environments but does not fully include the opportunity in modern multi-cloud and hybrid cloud environments. Its platform is designed to address both legacy and modern environments.
The company’s customers can expand their footprint with it on a self-service basis. The company’s customers often significantly increase their usage of the products they initially buy from it and expand their usage to other products it offers on its platform. The company grows with its customers as they expand their workloads in the public and private cloud.
Solution
Datadog was founded on the premise that the old model of siloed developers and IT operations engineers is broken, and that legacy tools used for monitoring static on-premise architectures do not work in modern cloud or hybrid environments. Datadog’s cloud-native platform enables development and operations teams to collaborate, quickly build and improve applications, and drive business performance. Empowered by the company’s out-of-the box functionality and simple, self-service installation, its customers are able to rapidly deploy its platform to provide application- and infrastructure-wide visibility, often within minutes.
Growth Strategies
The key elements of the company’s strategy are to expand its customer base by acquiring new customers; expand within its existing customer base through broader deployments; expand its technology leadership through continued investment and new products; and expand its customer base internationally.
Platform
The company’s proprietary platform provides real-time insights into software applications and IT infrastructure performance to enable better user experiences, faster problem detection and resolution and smarter, more impactful business decisions. Its platform is modular and includes infrastructure monitoring, application performance monitoring, log management, user experience monitoring, network performance monitoring, cloud and application security, developer-focused observability, and cloud service management, as well as a range of shared features, such as sophisticated dashboards, advanced analytics, collaboration tools, workflow automation, and alerting capabilities. Many of the company’s products are fully capable stand-alone so clients can choose to use different capabilities incrementally or deploy many at once. When deployed together, the company’s products automatically enable cross-correlation, which in turn allows customers to gain greater levels of visibility across their infrastructure and applications to more rapidly troubleshoot problems.
The company’s platform is supported by more than 850 integrations to seamlessly aggregate metrics and events across all of the systems and services that power digital businesses. Its easy-to-use platform is deployed through a self-service installation process. Users can derive value from the company’s platform within minutes without any specialized training or heavy implementation or customization. Customers can easily expand their usage of its platform on a self-serve basis, adding hosts or volumes of data monitored. The company’s platform is massively scalable monitoring trillions of events per hour and millions of servers and containers.
The key elements that can be leveraged across the company’s platform:
Single Pane of Glass: The company’s ability to provide a unified source of data enables users to access information from a single platform and easily explore multiple data sources. Through a single dashboard and with a common data framework, users are able to access and explore all of the relevant performance data. Users are able to more quickly assess and resolve their issues without having to toggle between multiple products.
Robust, Deep Data Set: The company’s client-side collection technology relies on installation of a single agent for metrics, traces, logs, and other data, allowing for a simple, seamless deployment experience for the customers. It ingests massive amounts of complex data and normalize it. The volume of data associated with combining infrastructure, APM, log management, and other products provides for a dramatically more robust data set than any of the individual data sources would provide on their own.
SaaS Platform: The company’s cloud based multi-tenant SaaS platform allows for real-time ingestion, and analysis of massive amounts of data, without its customers needing to worry about the provisioning, sizing and capacity of their monitoring platform.
One Data Model: Every piece of data that is ingested by the company’s platform is consistently tagged with metadata regardless of its type. This allows for different kinds of performance data, such as a log event and an application trace, to be queried together, correlated, alerted on, and visualized in a common user interface.
Cross-Correlation: All of the company’s solutions are integrated and work cohesively to provide a deep level of context and insight into what is occurring in a customer’s IT environment and power faster troubleshooting.
Out-Of-The-Box, Actionable Insights: From the moment of installation, the company’s platform provides actionable insights through customizable dashboards, predictive analytics, automated correlations, visualizations and alerting.
High Accuracy Machine-Learning Capabilities and Predictive Capabilities Powered by the Network Effect: The company’s multi-tenant cloud platform analyzes massive data sets ingested across its customers and their IT environments. It uses machine learning to predict and identify sources of performance or availability issues that customers share due to dependencies on common service providers or third-party services.
850+ Fully Supported Integrations: The company offers more than 850 out-of-the-box integrations, including public cloud, private cloud, on-premise hardware, databases and third-party software.
Automated Alerts: The company offers sophisticated real time alerting capabilities in the platform that detects issues, alerts users, and integrates with their service management systems.
The company’s platform consists of products that can be used individually or as a unified solution and includes a Marketplace where customers can access products built by its partners on top of the Datadog platform. The company’s products include:
Infrastructure Monitoring: The company’s Infrastructure Monitoring platform provides real-time monitoring of IT infrastructure across public cloud, private cloud and hybrid environments, as well as in containers and serverless architectures, ensuring performance, and availability of applications. All infrastructure data is located in one repository with automatic correlation, regardless of environment size or rate of change, to provide a fulsome view of everything that is occurring across the IT ecosystem.
Application Performance Monitoring (APM): APM provides full visibility into the health and functioning of applications regardless of the deployment environment. Code-level distributed tracing across microservices, hosts, containers and serverless computing functions allows the company’s customers to gain deep insights into application and code performance.
Log Management: Log Management for applications, systems and cloud platforms ingests data, creates indexes and enables querying of logs with visualizations and alerting to provide immediate insight into any performance issues. Logging Without Limits decouples the cost of log ingestion from processing, allowing customers to cost effectively collect a massive volume of logs and selectively process those they need to monitor. Flex Logs decouples storage from query, so customers can adjust their retention and querying capacity independently and serve more use cases within the Datadog platform.
Digital Experience Monitoring: Digital Experience Monitoring brings visibility up the stack to monitor the digital experience of the user and consists of Synthetics, Real User Monitoring (RUM), and Session Replay. Synthetics provides user-experience monitoring of applications and API endpoints via simulated AI-powered user requests to proactively track application performance and ensure uptime. RUM provides analysis and visualization of the performance of web browser and mobile applications as experienced by all actual users. Session Replay captures and visually replays users' web browser and mobile application experiences to help identify errors, application usage patterns, and design issues.
Continuous Profiler: Continuous Profiler measures code level performance in any environment through an always-on, and low overhead solution. This allows customers to quickly identify and optimize the slowest and most resource-consuming parts in application code in order to improve mean time to resolution, reduce application latency, and lower cloud costs.
Database Monitoring: Database Monitoring allows customers to view query metrics and explain plans from all of their databases in a single place. With Database Monitoring, they can quickly pinpoint costly and slow queries and drill into precise execution details to address bottlenecks. Additionally, query, host, and application metric correlation makes it easy to identify and understand the impact of resource constraints on database performance.
Data Observability: Data Observability brings production-level observability to the data engineering space and consists of Data Streams Monitoring (DSM) and Data Jobs Monitoring (DJM). DSM enables customers to easily track and improve the performance of event-driven applications. DJM helps data platform teams and data engineers detect, remediate, and optimize problematic Spark and Databricks jobs.
Universal Service Monitoring: Universal Service Monitoring automatically detects all microservices across an organization's environment and provides instant visibility into their health and dependencies—all without any code changes.
Network Monitoring: Cloud Network Monitoring enables the analysis and visualization of the flow of network traffic in on-premise, cloud-based or hybrid environments, helping customers determine when the network is the root cause of an issue. Network Device Monitoring seamlessly consolidates monitoring and troubleshooting of network hardware, such as routers, firewalls, switches, load balancers, and other network devices.
Error Tracking: Error Tracking reduces noise by intelligently grouping errors into issues across frontend and backend applications. By providing rich debug context, down to lines of code, Error Tracking helps users identify root problems, accelerating incident resolution.
Incident Management: Incident Management allows users to declare incidents, investigate root cause and dependencies, collaborate around a shared view of the incident, follow to resolution, and auto-generate post-mortem documentations, all within the Datadog platform.
Workflow Automation: Workflow Automation enables customers to easily automate and orchestrate processes across their tech stacks, with hundreds of out-of-the-box actions and dozens of customizable blueprints. Workflow Automation enables automated actions based on observability insights for faster incident remediation, proactive prevention, and improved security.
Observability Pipelines: Observability Pipelines enables IT and security teams to cost-effectively collect, transform, and route logs, metrics, and traces from any source to any destination at scale.
Cloud Cost Management: Cloud Cost Management provides granular visibility into costs across cloud resources, automatically surfacing changes in cloud costs and visualizing cost data alongside metrics, traces, logs, and other data, and making it easy to find and fix cost inefficiencies.
Cloud Security Management: Cloud Security Management delivers vulnerability management, automated compliance checks, continuous posture management, real-time threat detection, identity risk assessments, and a comprehensive resource inventory across the entire cloud infrastructure, all in a unified platform for seamless collaboration and faster remediation. With a unified platform and real-time observability context, DevOps and security teams can quickly remediate issues and continuously improve their organization's security posture.
Application Security Management: Application Security Management (ASM) delivers continuous, real-time visibility into attacks that target customers' web applications, serverless applications, and APIs. ASM is automatically integrated with APM distributed traces and code-level context, empowering Dev, Ops, and security teams to build and run secure applications in production. Application Vulnerability Management continuously monitors customers' production environments for both code-level and open-source vulnerabilities.
Cloud SIEM: Cloud SIEM (Security Information and Event Management) allows customers to detect threats in real time and investigate security signals across metrics, traces, logs, and other data. It provides the engineering organization, including Dev, Ops, and security teams, visibility into common data sources, in order to better operationalize IT security.
Sensitive Data Scanner: Sensitive Data Scanner helps businesses meet compliance goals by discovering, classifying, and redacting sensitive data, in real-time and at scale. Datadog scans for patterns of sensitive data upon ingestion and then hashes or redacts it, following built-in or user-defined rules to support compliance with GDPR, HIPAA, CCPA, and more.
CI Visibility: CI, or Continuous Integration, Visibility provides deep insight into the health and performance of customers' CI environment. Pipeline Visibility allows platform engineers to detect and find the root cause of slow or failing pipelines, builds, and jobs across all their CI providers in a single place. Test Visibility auto-instruments every test service to help developers detect and resolve slow, failing, and flaky tests.
LLM Observability: LLM, or Large Language Model, Observability provides end-to-end tracing of LLM chains with visibility into input-output, errors, token usage, and latency at each step. LLM Observability seamlessly correlates LLM traces with APM and utilizes cluster visualization to identify drifts, enabling users to swiftly resolve issues and scale AI applications in production, while improving accuracy and reducing privacy risks.
Event Management: Event Management uses AIOps to intelligently aggregate and consolidate alerts into one consistent view to help centralized operations teams discover and resolve issues faster. Event Management helps teams paint a complete picture of incidents and their underlying causes by reducing noise and enriching events with observability context.
Sales and Marketing
The company’s sales team is segmented into four revenue-generating areas: an enterprise sales team that sells to large businesses; a high velocity inside sales team that is focused on acquiring new customers; a customer success team that handles new customer on-boarding and expansions in existing customers; and a partner team that works with resellers, system integrators, referral partners and managed service providers. Each of these teams is further split regionally for geographic coverage across the Americas, Asia-Pacific, or APAC, and Europe, the Middle East and Africa, or EMEA, regions. The sales teams work with marketing to actively pursue leads generated from marketing programs and help take prospective customers through an evaluation and purchase process.
The company focuses its multi-touch marketing efforts on the strength of its product innovation, the value it provides, and its domain expertise. The company targets the development and IT operations community through its marketing activities, using diverse tactics to connect with prospective customers, such as content marketing, email marketing, events, digital advertising, social media, public relations, partner marketing and community initiatives. The company offers prospective customers free trials to help them understand the power of its platform. The company also hosts and present at regional, national, global and virtual events to engage both customers and prospects, deliver product training, share best practices and foster community.
As of December 31, 2024, the company had many employees in its sales and marketing organization, including sales development, field sales, sales engineering, technical solutions, business development, sales operations, sales strategy, customer success and marketing personnel.
Competition
The company’s unified platform combines functionality from numerous traditional product categories, and hence it competes in each of these categories with different vendors:
With respect to on-premise infrastructure monitoring, the company competes with diversified technology companies and systems management vendors, including IBM, Microsoft Corporation, and SolarWinds Corporation.
With respect to APM, the company competes with Cisco Systems, Inc., New Relic, Inc. and Dynatrace Software Inc.
With respect to Log Management, the company competes with Cisco Systems, Inc., and Elastic N.V.
With respect to Cloud monitoring, the company competes with native solutions from cloud providers, such as Amazon Web Services, or AWS, Microsoft Azure, and Google Cloud Platform, or GCP.
Intellectual Property
As of December 31, 2024, the company owned forty-two patents globally, nine patent applications pending for examination in the United States, three pending PCT applications, and four pending foreign patent applications. The pending U.S. patent applications, if issued, would be scheduled to expire between 2039 and 2043. Despite the company’s pending patent applications, there can be no assurance that its patent applications will result in issued patents. As of December 31, 2024, the company owned nine registered trademarks in the United States and one hundred twenty-four registered trademarks in various non-U.S. jurisdictions. However, as the company has expanded internationally, it has been unable to register or obtain the exclusive right to use the Datadog trademark in certain jurisdictions, and as it continues to expand may face similar issues in other jurisdictions.
History
Datadog, Inc. was founded in 2010. The company was incorporated in Delaware in 2010.
