This article is based on reporting originally published by Forbes (forbes.com) in February 2020, covering Datadog’s Q4 2019 earnings results and platform strategy.
Strong Q4 2019 Results Signal Growing Demand for Cloud Observability
Datadog (DDOG) reported exceptionally strong results for the fourth quarter of 2019, providing tangible evidence that organizations of all sizes are increasingly selecting its monitoring and analytics platform — with demand concentrated heavily in cloud environments.
Datadog’s Q4 revenue jumped 84% year-over-year to $113.6 million, beating consensus analyst estimates by approximately 11%. By comparison, competitor New Relic (NEWR) reported 23% revenue growth to $153 million in the same quarter, while Dynatrace (DT) grew revenue 25% to $143.3 million, with annual recurring revenue (ARR) growth of 44%.
Rapid Customer Growth Across All Segments
Datadog closed Q4 2019 with approximately 10,500 total customers, representing 36% year-over-year growth. The company added roughly 1,000 net new customers in the quarter alone — nearly twice the number added in the same quarter the prior year.
Customer penetration was relatively balanced across enterprise, mid-market, and SMB segments. Key metrics from the period include:
- Average enterprise ARR: $230,000 — up 44% year-over-year
- Average mid-market ARR: $170,000 — up 55% year-over-year
- Customers with ARR ≥ $100,000: 858 — up 89% year-over-year
- Customers with ARR ≥ $1,000,000: 50 — up 72% year-over-year (compared to just 12 customers two years prior)
The dollar-based net retention rate remained above 130% for the tenth consecutive quarter as of Q4, driven by both expanded usage of existing products and successful cross-selling of newer offerings.
Multi-Product Adoption Accelerating
One of the most telling signals of Datadog’s platform momentum is the rapid shift toward multi-product adoption among its customer base:
- 60% of customers were using two or more products as of Q4 2019, up from 50% the prior quarter and just 25% a year earlier.
- ~25% of customers deployed across all three core observability pillars (infrastructure monitoring, APM, and log management), compared to only 5% a year prior.
- The majority of new logo deals already included two or more products at the time of signing.
This expansion reflects Datadog’s deliberate strategy of growing its platform incrementally, adding APM and log management capabilities over the preceding three years.
The Three Pillars of Datadog’s Observability Platform
Founded in 2010, Datadog serves a diversified user base that includes developers, IT operations teams, and business users. The core platform is built around three foundational observability capabilities:
- Infrastructure Monitoring: Provides visibility into hosts, containers, and cloud services across hybrid and multi-cloud environments.
- Application Performance Monitoring (APM): Traces requests across distributed services, helping teams identify latency bottlenecks and errors.
- Log Management: Centralizes and indexes log data, enabling correlation with metrics and traces for faster root-cause analysis.
Together, these three pillars address the growing complexity of modern IT stacks as organizations move workloads from static on-premises infrastructure to public and private clouds, while also deploying containers, microservices, and serverless architectures at scale.
New Products Expanding the Platform’s Scope
Datadog has consistently broadened its product portfolio beyond the three core pillars. Key additions launched around this period include:
Synthetics (Q3 2019)
Synthetics allows customers to simulate end-user interactions with web applications, providing proactive visibility into performance problems before real users are affected. This is particularly useful for establishing performance baselines and detecting regressions during deployments.
Network Performance Monitoring (Q4 2019)
An extension of Datadog’s core Infrastructure product, Network Performance Monitoring provides granular visibility into network flows across hybrid cloud and on-premises environments. It delivers real-time insights into performance metrics and service dependencies — critical for diagnosing issues that span multiple network segments.
Real User Monitoring (Q4 2019)
Real User Monitoring (RUM) extends Datadog’s user experience monitoring suite to capture real-time data about individual end-user sessions — including page load times, JavaScript errors, and user actions — enabling teams to prioritize frontend performance issues by actual business impact.
Security Monitoring (Beta, November 2019)
Launched in beta in November 2019, Security Monitoring is designed to break down silos between security teams, developers, and IT operations. It provides security teams with unified visibility into infrastructure metrics, distributed traces, and logs — enabling real-time threat detection and streamlined alert investigation. This product represented Datadog’s first significant move into the security observability space.
Why Datadog Is Winning in Cloud Environments
Several structural trends are driving Datadog’s growth:
- Cloud migration: As organizations move workloads to AWS, Azure, and Google Cloud, they require monitoring tools that can handle ephemeral, dynamic infrastructure — a poor fit for legacy on-premises monitoring tools.
- Microservices and containers: Distributed architectures dramatically increase the number of observable components, making unified platforms that correlate metrics, traces, and logs more valuable than point solutions.
- Serverless computing: Functions-as-a-service environments introduce new observability challenges around cold starts, execution duration, and invocation errors that traditional APM tools were not designed to address.
- Developer-led purchasing: Datadog’s developer-friendly instrumentation and SaaS delivery model align well with modern engineering teams who influence or own tooling decisions.
Financial Outlook for 2020
For Q1 2020, Datadog guided for revenue of $117–$119 million (approximately 68% growth at the midpoint), ahead of the consensus estimate of $108.8 million at the time of reporting.
Full-year 2020 revenue guidance of $535–$545 million represented approximately 49% growth at the midpoint, coming in roughly 7% above the then-consensus estimate of $504.3 million.
Datadog went public in September 2019 at $27 per share, opening at $40.35. At the time of the Forbes report (February 2020), the stock was trading around $47 per share, having recently set a post-IPO high of $50.12.
Key Takeaways for DevOps and Platform Engineering Teams
For engineers evaluating observability platforms, Datadog’s Q4 2019 trajectory highlights several important considerations:
- The consolidation of metrics, traces, and logs into a single platform reduces context-switching and accelerates root-cause analysis during incidents.
- Native support for containers, serverless, and hybrid cloud environments is increasingly table stakes for modern observability tooling.
- Expanding into security observability signals an industry trend toward converging DevOps and SecOps tooling under unified data pipelines.
- High net retention rates (above 130%) suggest that teams consistently find enough value to expand their usage — a useful proxy for real-world platform stickiness.
Note: Financial figures and product details in this article reflect Datadog’s reported results and announcements as of early 2020. Platform capabilities, pricing, and market positioning have continued to evolve since this period.