```htmlQuality of Earnings Report: Datadog, Inc. (DDOG)

Quality of Earnings Report

Datadog, Inc. (NASDAQ: DDOG)

Date of Report: June 17, 2025

Executive Summary

This Quality of Earnings (QoE) report provides an assessment of Datadog, Inc. ("Datadog" or "the Company"), a leading monitoring and analytics platform for developers, IT operations teams, and business users in the cloud age. The analysis covers the three fiscal years ending December 31, 2022, 2023, and 2024.

Datadog has demonstrated robust revenue growth, driven by strong market demand for its cloud-native observability solutions and successful expansion of its platform capabilities. The Company's business model is primarily subscription-based, offering high gross margins and recurring revenue streams.

Normalized EBITDA analysis indicates underlying profitability trends after adjusting for significant non-recurring items, primarily stock-based compensation. While reported net losses have been influenced by these non-cash expenses and investments in growth, the normalized figures provide a clearer view of operational cash flow generation potential.

Key strengths include a scalable platform, a large and growing total addressable market (TAM), high customer retention (Dollar-Based Net Retention Rate typically >130%), and continuous product innovation.

Potential risks and areas for further due diligence include the high level of stock-based compensation, competitive pressures in the observability market, reliance on cloud infrastructure providers, and the sustainability of high growth rates as the company scales. Margin sustainability, particularly operating margins, requires ongoing monitoring as the Company balances growth investments with profitability.

Overall, Datadog exhibits strong earnings quality characteristics in its core recurring revenue, but diligence should focus on the path to sustained GAAP profitability and the impact of market competition on long-term pricing power and margins.

1. Company Overview

Datadog is a monitoring and security platform for cloud applications. It integrates and automates infrastructure monitoring, application performance monitoring, log management, and security monitoring to provide unified, real-time observability of its customers' entire technology stack.

1.1. Business Model

Datadog operates on a Software-as-a-Service (SaaS) model. Customers primarily enter into subscription agreements for access to its platform. Pricing is typically based on the number of hosts, volume of data ingested, number of users, and products utilized.

1.2. Core Revenue Streams

• Subscription Fees: The vast majority of revenue is generated from subscription fees paid by customers for using Datadog's platform.
• Usage-Based Fees: Some revenue may be tied to consumption levels beyond initial subscription tiers.

1.3. Cost Drivers

• Cost of Revenue: Primarily consists of expenses related to third-party cloud infrastructure (e.g., AWS, Azure, GCP), personnel costs for customer support and platform operations, and software license fees.
• Research and Development (R&D): Significant investment in personnel to enhance existing products and develop new ones.
• Sales and Marketing (S&M): Costs associated with acquiring new customers and expanding relationships with existing ones, including sales commissions and marketing programs.
• General and Administrative (G&A): Includes costs for finance, legal, human resources, and other administrative functions. Stock-based compensation is a significant component across all operating expense categories.

2. Financial Performance Analysis

The following analysis is based on Datadog's publicly available financial data for the fiscal years 2022, 2023, and 2024. (Note: 2024 data is presented as if it were actual, for illustrative purposes of a QoE report dated mid-2025).

2.1. Key Financial Highlights

Note: Reported EBITDA is calculated as GAAP Net Income (Loss) + Interest Expense (Income), Net + Taxes + Depreciation & Amortization. Actual figures may vary based on specific disclosures. FY 2024 data is illustrative.

2.2. Revenue Growth and Quality

Datadog has demonstrated exceptional revenue growth, with a CAGR of 56.3% over the analyzed period. This growth is driven by:

• Expansion within existing customers (high Dollar-Based Net Retention Rate).
• Acquisition of new customers, particularly larger enterprises.
• Introduction of new products and platform modules, leading to upselling opportunities.

The revenue is predominantly recurring and subscription-based, which indicates high quality and predictability.

2.3. Margin Analysis

Gross margins are strong and improving, consistently around 77-80%, reflecting the scalability of the SaaS model and effective management of cloud infrastructure costs. Operating margins have shown improvement, moving from a loss position towards profitability on a GAAP basis. However, operating expenses, particularly R&D and S&M, remain high as the company invests in future growth. Stock-based compensation significantly impacts GAAP operating margins.

3. Quality of Earnings (QoE) Analysis

QoE analysis aims to identify non-recurring, non-cash, or unusual items to arrive at a "normalized" view of earnings that better reflects sustainable operational performance.

3.1. Key QoE Adjustments

For a high-growth tech company like Datadog, common adjustments include:

• Stock-Based Compensation (SBC): A significant non-cash expense. While a real economic cost to shareholders, it's often added back for EBITDA normalization to assess cash-generating capability.
• M&A-Related Expenses: Costs associated with acquisitions (e.g., transaction fees, integration costs) are typically non-recurring.
• Litigation or Restructuring Costs: If material and non-recurring.

3.2. Normalized EBITDA Calculation (Illustrative)

Note: Stock-Based Compensation and M&A Related Costs are illustrative figures for QoE adjustment purposes. Actual SBC is disclosed in financial statements.

The normalized EBITDA shows a more robust profitability profile and consistent margin, highlighting the underlying cash generation potential of the business before significant non-cash charges and specific non-recurring items.

4. Growth Trajectory, Scalability, and Risks

4.1. Historical Growth Drivers

• Strong secular tailwinds from cloud migration and digital transformation.
• Successful "land and expand" strategy.
• Continuous platform innovation and expansion into adjacent areas (e.g., security, developer workflows).
• Effective go-to-market execution.

4.2. Future Growth Potential & Scalability

Datadog operates in a large and expanding TAM. Future growth is expected to be driven by:

• Continued adoption of cloud technologies.
• Further penetration of existing customers with more products.
• International expansion.
• New product development, including AI-powered features.

The business model is inherently highly scalable due to its SaaS nature. However, sustaining very high growth rates becomes more challenging as the revenue base increases.

4.3. Key Operational Risks and Dependencies

• Competition: The observability market is competitive, with players ranging from large diversified technology companies to specialized vendors. Pricing pressure or the need for increased R&D/S&M spend to maintain leadership are risks.
• Reliance on Public Cloud Providers: Datadog's services are hosted on public cloud infrastructure (AWS, Azure, GCP). Any disruption or significant cost increase from these providers could impact margins and service delivery.
• Talent Acquisition and Retention: Success depends on attracting and retaining highly skilled engineering and sales talent, particularly in a competitive labor market. High SBC is partly a tool for this.
• Innovation Pace: The market evolves rapidly; continuous innovation is critical to maintain a competitive edge.
• Market Saturation and Growth Deceleration: As the company matures, maintaining historical hyper-growth rates will be challenging.
• Path to GAAP Profitability: While normalized EBITDA is strong, investors will look for a clear path to sustained GAAP profitability. The level of SBC can be a concern for some investors regarding shareholder dilution and its impact on GAAP earnings.

5. Charts and Visualizations

Revenue Growth (USD Millions)

EBITDA Trend (USD Millions)

6. Conclusion and Key Considerations

Datadog presents a compelling investment case based on its leadership position in the cloud observability market, strong historical growth, high recurring revenue, and robust gross margins. The normalized EBITDA figures demonstrate healthy underlying profitability and cash flow generation potential.

Strengths:

• Strong, recurring revenue growth with high customer retention.
• Scalable SaaS business model with attractive gross margins.
• Large and expanding Total Addressable Market (TAM).
• Continuous product innovation and platform expansion.

Areas for Further Due Diligence / Risks:

• Sustainability of high growth rates as the company scales.
• Impact of intense market competition on pricing and market share.
• High levels of Stock-Based Compensation and its impact on GAAP profitability and shareholder dilution.
• Dependence on major cloud providers and the ability to manage these costs effectively.
• Execution risk in expanding into new product categories and geographies.

A thorough due diligence process should involve a deeper dive into customer cohort behavior, competitive positioning for new product offerings, a detailed review of SBC practices, and sensitivity analysis on growth and margin assumptions. The quality of Datadog's reported earnings is good from a revenue recurrence and gross margin perspective, with the primary complexities arising from growth-related investments and non-cash compensation practices typical of technology companies.

Citations & Sources (Illustrative)

Financial data and company information for a real QoE report would be sourced from:

• Datadog, Inc. SEC Filings (10-K Annual Reports, 10-Q Quarterly Reports). Example: "Datadog, Inc. Form 10-K for the fiscal year ended December 31, 2023."
• Datadog, Inc. Investor Relations website (earnings releases, presentations).
• Reputable financial data providers (e.g., Bloomberg, Refinitiv Eikon, S&P Capital IQ).
• Industry research reports from firms like Gartner, Forrester, IDC.

For this illustrative report, specific values for FY2024 and QoE adjustments are hypothetical and designed to demonstrate the structure and typical components of a QoE analysis. Real data from the sources above would be used in an actual engagement.

This report is for informational purposes only and does not constitute investment advice or a recommendation to buy, sell, or hold any security. The information herein is based on publicly available data and illustrative assumptions as of June 17, 2025. Financial data for FY2024 and specific QoE adjustments are illustrative. Actual results may differ.

```