Datadog Company Profile (2026)
Company Profile | Observability, DevOps & Cloud Security | Updated August 2026
Datadog: The AI-Powered Observability and Security Platform for Cloud Applications
Datadog is a cloud software company providing observability, application performance monitoring, infrastructure monitoring, log management, cloud security, user-experience monitoring, incident response, and AI-assisted operations. The company generated approximately $3.43 billion in fiscal 2025 revenue and reported second-quarter 2026 revenue of approximately $1.12 billion, up 36% year over year.
Executive Summary
Datadog, Inc. provides a software-as-a-service platform that helps organizations monitor and secure cloud applications, infrastructure, networks, databases, logs, user experiences, and software-development environments.
The company’s core proposition is unified visibility. Modern applications are distributed across public clouds, private data centers, containers, microservices, serverless platforms, databases, APIs, and third-party services. Datadog collects telemetry from these systems and presents it through a common platform.
Datadog has expanded from infrastructure monitoring into application performance monitoring, log management, distributed tracing, digital experience, cloud security, software delivery, incident response, data observability, and AI operations.
The company reported fiscal 2025 revenue of approximately $3.43 billion, up 28% year over year. In the second quarter of 2026, revenue reached approximately $1.12 billion, representing 36% year-over-year growth.
Artificial intelligence is increasing the complexity of production systems. Organizations need to monitor models, data pipelines, inference services, GPU infrastructure, applications, security events, and customer experiences. Datadog is positioning its platform as an observability and security layer for the AI era.
Company Overview
Datadog was incorporated in Delaware in June 2010. Its principal executive offices are located at 620 Eighth Avenue in New York City, and its common stock trades on Nasdaq under the symbol DDOG.
The company was founded by Olivier Pomel and Alexis Lê-Quôc, who had previously worked on infrastructure and operations challenges at technology companies. Datadog was created to help development and operations teams understand the performance of distributed applications.
Datadog serves organizations across financial services, technology, retail, healthcare, media, manufacturing, telecommunications, government, and other industries.
The company’s customers range from startups and digital-native companies to large enterprises with complex multicloud deployments. Its platform is typically adopted by developers, site-reliability engineers, operations teams, security teams, platform engineers, and IT leaders.
Company History
Datadog was founded in 2010 as cloud computing and software development began shifting toward distributed architectures. Applications were increasingly built from multiple services running across cloud infrastructure, making it harder for teams to understand failures and performance problems.
The company initially focused on infrastructure monitoring and the ability to collect metrics from servers, cloud services, databases, networks, and applications. Its SaaS delivery model allowed customers to deploy monitoring without managing a large monitoring platform themselves.
Datadog expanded into application performance monitoring, log management, distributed tracing, user-experience monitoring, synthetic testing, and network monitoring. These products enabled customers to connect infrastructure events with application performance and user impact.
The company became publicly traded in 2019. It continued expanding its product portfolio through internal development and acquisitions, entering security, cloud-cost management, software delivery, data observability, and artificial-intelligence operations.
Datadog’s platform strategy is based on encouraging customers to adopt multiple products. A customer may begin with infrastructure monitoring and later add logs, APM, security, database monitoring, synthetics, incident response, or AI tools.
In 2025 and 2026, Datadog expanded AI-related capabilities, including Bits AI SRE Agent, data observability, storage management, feature flags, and other tools designed to help engineering and operations teams manage complex systems.
Leadership
- Olivier Pomel – Co-Founder and Chief Executive Officer – Leads Datadog’s product strategy, business direction, platform expansion, and enterprise growth.
- Alexis Lê-Quôc – Co-Founder and Chief Technology Officer – Helps guide the company’s technology architecture, engineering, observability platform, and product innovation.
- Engineering and product teams – Datadog’s technical organization develops telemetry collection, analytics, visualization, security, cloud monitoring, AI operations, and developer tools.
- Go-to-market organization – Sales, customer success, partners, and developer-relations teams support adoption across startups, mid-market companies, enterprises, and global organizations.
Olivier Pomel and Alexis Lê-Quôc are Datadog’s co-founders, serving as chief executive officer and chief technology officer, respectively.
Products & Services
Infrastructure Monitoring
Datadog Infrastructure Monitoring provides visibility into servers, virtual machines, containers, cloud resources, networks, databases, and other infrastructure components.
It collects metrics, events, tags, and other telemetry that allow teams to monitor system health, identify bottlenecks, detect abnormal behavior, and understand the relationship between infrastructure and applications.
Application Performance Monitoring
Application Performance Monitoring, or APM, helps developers and operations teams understand how applications behave in production. It provides visibility into requests, services, dependencies, databases, errors, latency, and resource usage.
APM is particularly important in microservice environments where a single user request may travel through many services, APIs, databases, and external dependencies.
Log Management
Datadog Log Management collects, indexes, searches, analyzes, and monitors logs from applications, infrastructure, security systems, and cloud services.
Logs provide detailed information about what happened inside a system. When combined with metrics and traces, they help teams investigate incidents and identify the root causes of failures.
Distributed Tracing
Distributed tracing follows requests as they move across services and infrastructure. It helps teams identify latency, dependency failures, bottlenecks, and application-performance problems.
Tracing is essential for cloud-native applications because individual services may appear healthy while the overall request path is slow or failing.
Cloud Security
Datadog provides security monitoring, cloud-security posture management, vulnerability management, application security, workload protection, identity monitoring, and security analytics.
The integration of observability and security allows teams to connect security signals with application, infrastructure, and user data. This can improve detection, investigation, and response.
Digital Experience Monitoring
Digital Experience Monitoring tracks the performance and availability of websites, mobile applications, APIs, and user interactions.
Products such as browser monitoring, mobile monitoring, synthetic tests, and real-user monitoring help organizations understand what customers experience rather than only what internal infrastructure reports.
Database Monitoring
Database Monitoring provides visibility into database performance, queries, resource utilization, locks, errors, and workload behavior.
Databases are often a major source of application latency and reliability problems. Monitoring them alongside applications and infrastructure helps teams connect database behavior with user-facing outcomes.
Incident Management
Datadog provides tools for alerting, incident response, on-call coordination, service ownership, investigations, and post-incident analysis.
The goal is to reduce the time required to detect, understand, communicate, and resolve production incidents.
Bits AI and AI Operations
Datadog is incorporating artificial intelligence into observability and operations. AI tools can summarize incidents, identify relevant telemetry, assist with investigations, recommend actions, and automate parts of site-reliability engineering.
In 2025, Datadog announced the general availability of Bits AI SRE Agent and other products covering data observability, storage management, and feature flags.
AI & Observability Strategy
AI applications create new observability requirements. Teams need to track model latency, token usage, inference cost, data quality, prompt behavior, retrieval performance, model errors, GPU utilization, and application outcomes.
- Monitoring AI models and inference services.
- Observing GPU, accelerator, memory, and network utilization.
- Tracking data pipelines, retrieval systems, and vector databases.
- Detecting security risks in AI applications and APIs.
- Correlating application performance with customer experience.
- Automating investigation and response through AI agents.
- Managing cost and performance across multicloud AI systems.
Datadog describes its platform as bringing applications, infrastructure, data, models, and security into one place. This reflects a broader shift from traditional application monitoring toward full-stack observability for AI-enabled systems.
The company’s challenge is ensuring that AI features produce reliable recommendations and do not add unnecessary cost, noise, or operational risk. Customers will expect AI agents to be explainable, secure, controllable, and integrated into existing workflows.
Business Model
Datadog operates primarily as a cloud-based subscription software company. Customers typically pay based on usage, monitored hosts, data volume, products adopted, and other consumption metrics.
The usage-based model aligns revenue with the scale of a customer’s infrastructure. As customers deploy more applications, logs, services, containers, cloud resources, or security workloads, their Datadog usage can increase.
Datadog also uses a land-and-expand strategy. Customers may begin with one product and later adopt additional modules. This increases the platform’s value and raises the potential annual contract size.
Large customers are strategically important. Datadog reported 603 customers with at least $1 million in annual recurring revenue at the end of 2025, up from 462 a year earlier.
Financial Performance
| Metric | Figure or description |
|---|---|
| Legal name | Datadog, Inc. |
| Founded | 2010 |
| Founders | Olivier Pomel and Alexis Lê-Quôc |
| Headquarters | New York City, New York, United States |
| Public listing | Nasdaq |
| Stock symbol | DDOG |
| Fiscal 2025 revenue | Approximately $3.43 billion |
| Fiscal 2025 revenue growth | 28% year over year |
| Q2 fiscal 2026 revenue | Approximately $1.12 billion |
| Q2 fiscal 2026 growth | 36% year over year |
| Q2 fiscal 2026 non-GAAP operating income | Approximately $257 million |
| Large customers | 603 customers with $1 million or more in ARR at the end of 2025 |
Datadog reported fiscal 2025 revenue of approximately $3.43 billion, up 28% from the prior year. Fourth-quarter revenue reached approximately $953 million, up 29% year over year.
In the second quarter of 2026, revenue increased to approximately $1.12 billion, representing 36% year-over-year growth. Non-GAAP operating income reached approximately $257 million, with a non-GAAP operating margin of 23%.
Datadog’s financial model benefits from recurring SaaS revenue, customer expansion, high gross margins, and usage growth. Its main financial risks are cloud infrastructure costs, sales and marketing expenses, usage optimization by customers, competition, and the possibility that customers consolidate monitoring tools.
Competitive Landscape
Datadog competes with observability platforms, cloud-provider monitoring tools, application-performance vendors, log-management companies, security platforms, open-source technologies, and specialized monitoring providers.
| Competitor group | Examples | Competitive overlap |
|---|---|---|
| Observability platforms | New Relic, Dynatrace, Splunk, Grafana Labs | Infrastructure monitoring, APM, logs, traces, dashboards, and user experience |
| Cloud-provider tools | AWS CloudWatch, Microsoft Azure Monitor, Google Cloud Operations | Cloud monitoring, logs, metrics, alerts, and application operations |
| Security platforms | Splunk, CrowdStrike, Palo Alto Networks, cloud-security vendors | Security monitoring, detection, cloud posture, and incident response |
| Open-source tools | Prometheus, Grafana, OpenTelemetry, Jaeger | Metrics, visualization, traces, instrumentation, and monitoring infrastructure |
| Developer platforms | GitHub, GitLab, cloud DevOps ecosystems | Software delivery, CI/CD, feature management, testing, and developer operations |
Datadog’s major differentiator is the breadth of its integrated SaaS platform. Customers can collect and analyze multiple types of telemetry without assembling a monitoring stack from many separate tools.
Its main challenge is that observability is becoming more crowded. Cloud providers offer native monitoring, open-source tools remain popular, and major security vendors continue expanding into application and infrastructure telemetry.
Competitive Advantages
- Unified platform – Datadog connects infrastructure, applications, logs, security, databases, networks, and user experience.
- Cloud-native design – The platform was built for distributed, multicloud, containerized, and service-oriented environments.
- Land-and-expand model – Customers can add products as their infrastructure and operational needs grow.
- Strong developer adoption – Datadog is widely used by developers, platform engineers, operations teams, and security professionals.
- AI operations – AI features can help teams analyze telemetry, investigate incidents, and automate repetitive operational tasks.
- Usage-based growth – Expansion in infrastructure, data volume, services, and cloud workloads can increase customer spending.
Risks and Challenges
- Competitive pressure – Datadog competes with cloud providers, open-source tools, security vendors, and specialized observability platforms.
- Usage optimization – Customers may reduce telemetry volume, change retention settings, or consolidate products to control costs.
- Cloud dependence – Datadog relies on cloud infrastructure and must manage the cost of processing and storing customer telemetry.
- Platform complexity – Expanding into monitoring, security, developer tools, and AI increases product complexity and competition.
- Customer concentration – Large customers can generate significant revenue but may also have greater negotiating power.
- AI accuracy and security – AI-assisted operations must provide dependable analysis while protecting sensitive telemetry and customer data.
Future Outlook
Datadog is positioned to benefit from the continued expansion of cloud computing, microservices, containers, serverless applications, cybersecurity, and AI.
The company’s long-term opportunity is to become a central operating platform for engineering, security, and IT teams. Observability data can support not only incident response, but also capacity planning, application development, security investigations, compliance, and business analysis.
AI may increase the value of Datadog’s data because intelligent agents can analyze metrics, logs, traces, security events, and application context. However, customers will demand measurable improvements in resolution time, reliability, cost, and productivity.
Datadog’s future success will depend on maintaining product innovation, expanding enterprise adoption, managing usage-based pricing, integrating AI responsibly, and defending its position against cloud-native and open-source alternatives.
Key Takeaways
- Datadog is a cloud software company providing observability, monitoring, security, developer tools, and AI operations.
- The company was founded in 2010 and is headquartered in New York City. Its stock trades on Nasdaq under the symbol DDOG.
- Datadog generated approximately $3.43 billion in fiscal 2025 revenue, up 28% year over year.
- Second-quarter 2026 revenue reached approximately $1.12 billion, up 36% year over year.
- Its platform includes infrastructure monitoring, APM, logs, traces, databases, networks, cloud security, digital experience, incident management, and AI operations.
- Datadog’s SaaS model supports land-and-expand growth as customers add products and increase monitored workloads.
- The company competes with New Relic, Dynatrace, Grafana Labs, Splunk, cloud-provider tools, open-source monitoring, and security platforms.
- Its competitive advantages are platform breadth, cloud-native architecture, developer adoption, usage-based expansion, and AI-assisted operations.
- Its major challenges include competition, usage optimization, cloud costs, platform complexity, customer concentration, and AI reliability.
Frequently Asked Questions
What does Datadog do?
Datadog provides cloud-based observability and security software for monitoring applications, infrastructure, logs, databases, networks, cloud services, user experiences, and security events.
What is Datadog best known for?
Datadog is best known for infrastructure monitoring, application performance monitoring, log management, distributed tracing, dashboards, cloud monitoring, and its unified SaaS platform.
Is Datadog an AI company?
Datadog is primarily an observability and security software company. It is incorporating artificial intelligence into monitoring, incident investigation, site reliability engineering, security, and operations.
Who competes with Datadog?
Datadog competes with New Relic, Dynatrace, Splunk, Grafana Labs, cloud-provider monitoring services, open-source tools such as Prometheus and Grafana, and security platforms.
How does Datadog make money?
Datadog primarily generates revenue through cloud-based subscriptions and usage-based pricing tied to monitored hosts, data, products, services, and infrastructure activity.
What is observability?
Observability is the ability to understand the internal condition of a software or infrastructure system by analyzing telemetry such as metrics, logs, traces, profiles, events, and user-experience data.
- Datadog fiscal 2025 financial results. Official results.
- Datadog second-quarter 2026 financial results. Official results.
- Datadog annual report and company information. Annual report.
- Datadog observability and security platform overview. Official website.
Profile compiled August 2026. Financial figures, products, leadership details, market conditions, and technology roadmaps may change. Fiscal-year references follow Datadog’s reporting calendar. This article is intended for editorial and informational purposes and does not constitute investment advice.