Software applications are transforming how organizations engage with customers and operate their businesses. Companies across all industries are re-platforming their businesses to cloud infrastructures to enable this digital transformation.
Monitoring software is at the foundation of an organization’s IT stack. Without monitoring, organizations are blind to factors that impact the performance, reliability, scalability, and availability of systems in which they have invested large amounts of resources. Once installed, monitoring becomes integral to an organization’s performance and deeply embedded into business and operational workflows.
Historically, engineering teams have been siloed, making developing next-generation applications in dynamic cloud environments challenging. Legacy commercial and homegrown technologies were designed to work with monolithic, static, and on-premise environments.
Datadog was started to break this model and facilitate collaboration among development and operations teams, enabling the adoption of DevOps practices. In this strategy story, we will analyze the business model of Datadog and understand what does Datadog and how does it work.
What is Datadog? How does Datadog work?
Datadog, founded in 2010, is an observability service for cloud-scale applications, providing monitoring of servers, databases, tools, and services through a SaaS-based data analytics platform.
The business model of Datadog is built on a SaaS platform that integrates and automates infrastructure monitoring, application performance monitoring, and log management to provide unified, real-time observability of Datadog’s customers’ entire technology stack. Organizations use Datadog to
- enable digital transformation and cloud migration,
- drive collaboration among development, operations, and business teams,
- accelerate time to market for applications,
- reduce time to problem resolution,
- understand user behavior, and
- track key business metrics.
Datadog’s goal is to build a real-time data integration platform to turn chaos from disparate sources into digestible and actionable insights.
- In 2012, Datadog launched its first use case with infrastructure monitoring, purpose-built to handle increasingly ephemeral cloud-native architectures. This enabled Datadog to be deployed on Datadog’s customers’ entire cloud IT environments and gave Datadog’s product broad usage.
- In 2017, Datadog launched its APM product, designed to be broadly deployed in very distributed micro-services architectures.
- In 2018, Datadog was the first to combine the “three pillars of observability” with the introduction of Datadog’s log management product.
- To allow for full-stack observability, in 2019, Datadog launched user experience monitoring and announced network performance monitoring.
- In 2023, Datadog offers end-to-end monitoring and analytics powered by a common data model that is extensible for potential new use cases.
Datadog employs a land-and-expand business model. Datadog’s customers can expand their footprint with the company on a self-service basis. Datadog’s customers often significantly increase their usage of the products they initially buy from Datadog and expand their usage to other products Datadog offers on its platform. Datadog grows as customers expand their workloads in the public and private cloud.
Datadog’s Platform
Datadog’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.
Datadog’s platform is also modular and includes infrastructure monitoring, application performance monitoring, log management, user experience monitoring, and network performance monitoring, as well as a range of shared features such as sophisticated dashboards, advanced analytics, collaboration tools, and alerting capabilities.
Each of Datadog’s products is fully capable stand-alone, so clients can choose to use different capabilities incrementally or deploy many at once. When deployed together, Datadog’s products automatically enable cross-correlation, allowing customers to gain greater visibility across their infrastructure and applications to troubleshoot problems more rapidly.
Datadog’s platform is supported by hundreds of integrations to seamlessly aggregate metrics and events across all systems and services that power digital businesses. Datadog’s easy-to-use platform is deployed through a self-service installation process. Users can derive value from Datadog’s platform within minutes without specialized training, heavy implementation, or customization.
What is the business model of Datadog?
Value Proposition
Accelerate digital transformation: Datadog enables customers to take full advantage of the cloud to develop and maintain mission-critical applications with agility and confidence.
Reduce time to problem detection and resolution: Using infrastructure, APM, log data, and data from integrations in Datadog’s unified platform, Datadog’s customers can quickly isolate the root cause of application issues in one place where they otherwise would be required to spend hours trying to investigate using multiple tools.
Improve agility of development, operations, security, and business teams: Datadog eliminates the historical silos of development and operations teams and provides a platform that enables efficient and agile development through the adoption of DevOps and DevSecOps.
Enable operational efficiency: Datadog eliminates the need for heavy implementation costs and professional services. Datadog has several integrations with key technologies from which Datadog’s customers can derive significant value, avoiding internal development costs and the professional services required to create those integrations.
Datadog Products
Datadog’s platform consists of products that can be used individually or as a unified solution. It includes a Marketplace where customers can access products built by Datadog’s partners on top of the Datadog platform. Datadog’s products include:
Infrastructure Monitoring: Datadog’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 the performance and availability of applications.
Application Performance Monitoring (APM): APM provides visibility into the health and functioning of applications regardless of the deployment environment. Distributed tracing across microservices, hosts, containers, and serverless computing functions allows Datadog’s customers to gain deep insights into application performance.
Log Management: Log Management for applications, systems, and cloud platforms ingests data, creates indexes, and enables querying logs with visualizations and alerting to provide immediate insight into any performance issues.
User Experience Monitoring: User experience monitoring monitors the customer’s digital experience and comprises two products – Synthetics and Real User Monitoring, or RUM. Synthetics provides user-experience monitoring of applications and API endpoints via simulated AI-powered user requests to track application performance and ensure uptime. RUM analyzes and visualizes the performance of front-end applications as experienced by all users.
Continuous Profiler: Continuous Profiler measures code-level performance in any environment through an always-on and low overhead solution. This allows customers to identify and optimize the most resource-consuming parts in application code to improve mean time to resolution, enhance user experience and reduce cloud provider cost.
Database Monitoring: Database Monitoring allows customers to view query metrics and explain plans from 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.
Network Monitoring: Network Monitoring enables the analysis and visualization of network traffic flow in cloud-based or hybrid environments, allowing customers to monitor the flow of network traffic without sacrificing performance.
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.
Cloud Security Posture Management: Cloud Security Posture Management allows customers to assess and visualize their cloud environments’ current and historic security posture, automate audit evidence collection, and catch misconfigurations that leave their organizations vulnerable to attacks.
Cloud Workload Security: Cloud Workload Security performs deep, in-kernel analysis of workload activity across customers’ hosts and containers to uncover threats.
Incident Management: Incident management allows users to declare incidents, investigate root causes and dependencies, collaborate around a shared view of the incident, follow to resolution, and auto-generate post-mortem documentation, all within the Datadog platform.
CI Visibility: CI, or Continuous Integration, Visibility provides deep insight into the health and performance of customers’ CI environment. Datadog auto-instruments pipelines and tests so customers can dive into traces for problematic builds and executions.
Marketing Strategy of Datadog
Datadog’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 onboarding 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, APAC, 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.
Datadog focuses on multi-touch marketing efforts on the strength of Datadog’s product innovation and Datadog’s domain expertise. Datadog targets the development and IT operations community through Datadog’s 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.
Revenue Model
Datadog made $1.03 billion in 2023. The business model of Datadog generates revenue from the sale of subscriptions to customers using Datadog’s cloud-based platform. Usage is primarily measured by the number of hosts or indexed data volume. A host is generally defined as a server in the cloud or on-premise.
Datadog’s infrastructure monitoring, APM, and network performance monitoring products are priced per host, and Datadog’s logs product is priced primarily per log events indexed and secondarily by events ingested. Customers also have the option to purchase additional products, such as additional container or serverless monitoring, custom metrics packages, anomaly detection, synthetic monitoring, and app analytics.
Datadog Competitors
The worldwide monitoring and analytics market is and has been highly competitive for decades and is rapidly evolving. Datadog’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, Datadog’s competitors are diversified technology companies and systems management vendors, including IBM, Microsoft Corporation, and SolarWinds Corporation.
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- With respect to APM, Datadog’s competitors are Cisco Systems, Inc., New Relic, Inc., and Dynatrace Software, Inc.
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- With respect to Log Management, Datadog’s competitors are Splunk Inc. and Elastic N.V.
- With respect to Cloud monitoring, Datadog’s competitors are native solutions from cloud providers such as Amazon Web Services, or AWS, Microsoft Azure, and Google Cloud Platform, or GCP.