Sector

SaaSTech - Infrastructure

Background

Peter Offringa is the author of Software Stack Investing. We cover Datadog’s unique product development cadence, how they’re able to grow their top line at 60% a year while staying profitable, and why Web3 might be their biggest competitive threat.

Cover

Date

September 8, 2021

Episode Number

25

Datadog Business Breakdown

Background / Overview

Datadog, founded in 2010 by Olivier Pomel and Alexis Lê-Quôc, is a SaaS-based monitoring and analytics platform that provides real-time visibility into the performance of enterprise IT systems, spanning infrastructure, applications, and logs. Headquartered in New York, the company emerged from the founders’ experiences at Wireless Generation, where they faced challenges with siloed development and operations teams. This inspired the creation of a unified platform to address DevOps inefficiencies. Datadog serves a diverse customer base, including SMBs, mid-market, and enterprise clients, with notable users like Peloton and Instacart. The company processes over 10 trillion events daily, monitors millions of servers, and integrates with over 450 external systems. As of 2021, Datadog had over 16,400 customers and was approaching $1 billion in annual revenue, with a market cap exceeding $40 billion.

Ownership / Fundraising / Recent Valuation

Datadog went public in 2019, and while the transcript does not provide specific details on private fundraising rounds or private equity ownership, it notes the company’s market cap was over $40 billion in 2021. No specific enterprise value (EV) multiples or recent transactions are mentioned, but the company’s high growth and profitability suggest a premium valuation typical of SaaS businesses with strong unit economics. The lack of debt or significant M&A financing discussions implies a capital structure leaning heavily on equity post-IPO.

Key Products / Services / Value Proposition

Datadog’s core offering is a unified platform that provides observability across three pillars: infrastructure monitoring, application performance monitoring (APM), and log analysis. These pillars address the increasing complexity of cloud-based software stacks, enabling DevOps teams to troubleshoot performance issues efficiently. The platform’s value proposition lies in its ability to consolidate disparate monitoring tools into a single interface, reducing friction between development and operations teams. Additional products include user experience monitoring, network monitoring, serverless monitoring, database monitoring, security monitoring, and incident management. The platform’s ease of activation, extensive integrations (over 450), and predictable usage-based pricing enhance its appeal. For example, Peloton used Datadog’s APM to reduce leaderboard response times by 80-90%, improving user experience and server efficiency. Instacart leverages log analytics and security monitoring to track user sessions and combat credential stuffing.

Segments and Revenue Model

Datadog operates as a single-segment business focused on observability, with revenue derived from usage-based subscriptions across its product suite. The revenue model is a la carte, allowing customers to select specific products (e.g., infrastructure, APM, logs) and pay based on usage metrics like hosts or data volume. This contrasts with competitors’ all-in-one pricing models, offering flexibility and predictability. Revenue is driven by three levers: (1) new customer additions (30%+ annual growth), (2) expansion of existing customer spend (dollar-based net retention rate >130%), and (3) new product adoption. Large customers (ARR >$100K) contribute ~80% of revenue, highlighting the importance of enterprise accounts.

Splits and Mix

• Customer Mix: Evenly split between SMBs, mid-market, and enterprises. Large customers (>100K ARR) grew from 590 at IPO (2019) to over triple that by 2021, driving 80% of revenue.
• Product Mix: Infrastructure monitoring is the largest contributor, followed by APM and logs. Newer products (e.g., security, incident management) are gaining traction, with revenue scaling based on product maturity.
• Geo Mix: Not specified, but global cloud adoption suggests broad geographic exposure.
• Channel Mix: Direct sales to developers, supported by a strong developer-focused go-to-market (GTM) strategy, including conferences and online onboarding.
• End-Market Mix: Serves digital natives (e.g., Peloton, Instacart) and traditional enterprises adopting cloud infrastructure.

Historical mix shifts show increasing reliance on large customers and newer products, with 70% of revenue growth in the latest quarter attributed to existing customer expansion. No specific EBITDA splits are provided, but high-margin products like APM and logs likely contribute disproportionately to profitability.

KPIs

• Customer Growth: 36% YoY, from 8,800 (June 2019) to 16,400 (2021).
• Large Customer Growth: Tripled since IPO, with >100K ARR customers driving 80% of revenue.
• Dollar-Based Net Retention Rate: >130% for four years, indicating 30%+ annual spend expansion per customer.
• Revenue Growth: 66% YoY in 2020 ($600M), tracking to ~$1B in 2021.
• Operating Margin: 11% in 2020, increasing in 2021.
• Sales & Marketing Efficiency: Dropped from 33% to 26% of revenue, with R&D now exceeding S&M at 30% of revenue.
• Event Processing: 10 trillion events/day, reflecting platform scale.

These KPIs indicate acceleration in customer acquisition, spend expansion, and operational efficiency, with no signs of deceleration.

Headline Financials

• Revenue Trajectory: Grew 66% YoY in 2020, with 2021 projected at ~$1B, driven by customer additions (36% YoY), spend expansion (>130% net retention), and new product adoption. Large customers and high-margin products (APM, logs) are key drivers.
• EBITDA/Operating Margin: Non-GAAP operating income of $60M in 2020 (11% margin), with margins expanding in 2021 due to declining sales and marketing costs (26% of revenue) and operating leverage.
• FCF: Not explicitly reported, but profitability and low capital intensity suggest positive FCF. SaaS businesses typically have high cash conversion due to upfront subscription payments and low capex.
• Long-Term Trends: Revenue and margins are trending upward, with R&D investment (30% of revenue) supporting product expansion and competitive differentiation.

Value Chain Position

Datadog operates midstream in the cloud infrastructure value chain, between cloud providers (e.g., AWS, Azure) and end-user applications. It provides observability tools that integrate with cloud infrastructure, applications, and databases, adding value by simplifying monitoring and troubleshooting. The GTM strategy targets developers and DevOps teams through direct outreach (conferences, online onboarding) and a “try-before-you-buy” model, enabling easy activation via a single agent. Datadog’s competitive advantage lies in its unified platform, extensive integrations, and rapid product development, which reduce customer reliance on fragmented point solutions.

Customers and Suppliers

• Customers: DevOps teams (developers and operations) reporting to CTOs/VPs of Engineering. Key clients include digital natives (Peloton, Instacart) and enterprises, with large customers (>100K ARR) driving 80% of revenue.
• Suppliers: Primarily cloud infrastructure providers (AWS, Azure, GCP), which host Datadog’s platform. Payments to these providers are a significant cost of revenue, impacting gross margins (75-80%).

Pricing

Datadog’s pricing is usage-based and a la carte, charged per product per month:

• Infrastructure Monitoring: ~$15/host/month.
• APM: ~$30/host/month.
• Log Analysis: ~$0.10/GB ingested.
• Security Monitoring: ~$0.20/GB analyzed.

This model offers predictability and flexibility, allowing customers to control costs by adjusting usage or adding products. The a la carte approach contrasts with competitors’ all-in-one pricing, appealing to customers who prefer paying only for needed features. Pricing is competitive, with volume discounts for large customers. The >130% net retention rate reflects pricing power, driven by infrastructure growth and product adoption, not price increases.

Bottoms-Up Drivers

Revenue Model & Drivers

Datadog generates revenue through usage-based subscriptions across its product suite. The revenue model is driven by:

1. Customer Additions: 36% YoY growth, adding new SMBs, mid-market, and enterprise clients.
2. Spend Expansion: >130% dollar-based net retention, driven by:
• Infrastructure Growth: Digital natives scale their cloud footprints, increasing hosts and data volume.
• Product Adoption: Customers expand from 1-2 products to 3-6, with newer products (e.g., security) gaining traction.
3. New Product Launches: 30-40% annual product catalog growth, with recent additions like security and incident management expanding the addressable market.

Pricing Drivers:

• Mission-Criticality: Observability is essential for uptime and user experience, supporting premium pricing.
• Customer Type: Large enterprises negotiate volume discounts, while SMBs pay standard rates.
• Mix Effect: High-margin products (APM, logs) increase blended ASP.
• Price Elasticity: Low, as customers prioritize reliability over cost.

Volume Drivers:

• Industry Growth: Cloud infrastructure spending grew 36% in 2021 ($200B run-rate), driving demand.
• Switching Costs: High, due to platform entrenchment and integration complexity.
• Network Effects: Limited, but scale (10T events/day) enhances platform reliability.
• Repeat Rates: High, with >130% net retention indicating sticky customers.

Absolute Revenue: $600M (2020) to ~$1B (2021), with 70% of growth from existing customer expansion.

Mix:

• Product: Shifting toward APM, logs, and newer products.
• Customer: Increasing reliance on large customers (80% of revenue).
• Organic Growth: Dominant, with M&A (e.g., Sqreen, Undefined Labs) supplementing product expansion.

Cost Structure & Drivers

Variable Costs:

• Cloud Infrastructure: Payments to AWS, Azure, GCP for hosting, the largest cost of revenue, limiting gross margins to 75-80%.
• Support Staff: Scales with customer growth but minimal compared to hosting costs.

Fixed Costs:

• R&D: 30% of revenue, driving product development and competitive differentiation.
• Sales & Marketing: 26% of revenue (down from 33%), reflecting efficient developer-focused GTM.
• G&A: Not specified, but typical SaaS overhead (admin, facilities) is low.

Cost Analysis:

• % of Revenue: S&M (26%), R&D (30%), COGS (~23%), G&A (est. 10-15%).
• % of Total Costs: COGS (30%), R&D (40%), S&M (30%), G&A (10%).
• Operating Leverage: High, as fixed costs (R&D, S&M) grow slower than revenue, expanding margins (11% in 2020, increasing in 2021).
• Contribution Margin: High for APM and logs, lower for infrastructure due to hosting costs.
• Gross Margin: Stable at 77%, targeting 75-80%.
• EBITDA Margin: 11% in 2020, expanding due to S&M efficiency and revenue growth.

FCF Drivers

• Net Income: Positive non-GAAP operating income ($60M in 2020), supporting FCF.
• Capex: Low, as SaaS businesses rely on cloud providers, minimizing capital intensity.
• NWC: Favorable, with upfront subscription payments reducing receivables days. No significant inventory or payables cycles.
• Cash Conversion Cycle: Short, enhancing FCF.

Capital Deployment

• M&A: Strategic acquisitions (e.g., Sqreen for security, Undefined Labs for pre-production) expand the product suite and TAM. Datadog integrates acquired technology into its platform, avoiding standalone products to minimize customer confusion.
• Organic Growth: Dominant, with 30-40% annual product catalog expansion.
• Buybacks: Not mentioned, likely minimal given growth focus.
• Synergies: M&A enhances upsell opportunities, contributing to >130% net retention.

Market, Competitive Landscape, Strategy

Market Size and Growth

• Market Size: Gartner estimates the observability market at $44B by 2024. Cloud infrastructure spending ($200B run-rate in 2021, 36% YoY growth) suggests a 5-10% allocation to monitoring, implying a $10-20B current TAM.
• Growth: Driven by cloud adoption, digital transformation, and increasing software complexity.
• Industry Growth Stack: Cloud spending growth (36%) exceeds GDP and inflation, with volume (new adopters) and price (premium features) contributing equally.

Market Structure

• Competitors: Fragmented, with open-source solutions, legacy providers (IBM, BMC), and modern players (Dynatrace, Splunk, New Relic, Elastic).
• MES: Moderate, as scale (10T events/day) requires significant infrastructure, limiting new entrants.
• Penetration: Low, with Datadog’s ~$1B revenue capturing <10% of TAM.
• Cycle: Early growth phase, with no signs of overcapacity or discounting.

Competitive Positioning

Datadog leads in observability due to its unified platform, rapid product cadence, and developer-focused GTM. It competes on simplicity, integration, and flexibility, positioning itself as a premium but cost-effective alternative to point solutions.

Market Share & Relative Growth

• Market Share: Small but growing, with ~$1B revenue in a $10-20B market.
• Relative Growth: 66% YoY revenue growth outpaces competitors (e.g., Dynatrace at ~33%, Splunk/New Relic slower).
• Customer Growth: 36% YoY vs. Dynatrace’s 23%, reflecting stronger adoption.

Hamilton’s 7 Powers Analysis

1. Economies of Scale: Strong. Processing 10T events/day and hosting costs create scale advantages, reducing per-unit costs and enabling competitive pricing.
2. Network Effects: Weak. No direct user-to-user benefits, but platform reliability improves with scale.
3. Branding: Moderate. Strong developer reputation enhances pricing power and adoption.
4. Counter-Positioning: Strong. Unified platform and a la carte pricing counter legacy point solutions and all-in-one models.
5. Cornered Resource: Moderate. Technical founders and rapid product cadence are hard to replicate.
6. Process Power: Strong. Developer-focused GTM and easy onboarding (single agent) differentiate operations.
7. Switching Costs: High. Platform entrenchment and integration complexity deter churn.

Competitive Forces (Porter’s Five Forces)

1. New Entrants: Low threat. High MES, scale requirements, and switching costs deter entry. Partnerships with cloud providers (AWS, Azure) further entrench Datadog.
2. Substitutes: Moderate threat. Open-source solutions are cheaper but labor-intensive, while no/low-code platforms and Web 3.0 (e.g., blockchain) could disrupt long-term.
3. Supplier Power: Moderate. Reliance on cloud providers (AWS, Azure) impacts gross margins, but partnerships mitigate risks.
4. Buyer Power: Low. Diverse customer base and mission-criticality reduce price sensitivity. Large customers negotiate discounts but remain sticky.
5. Industry Rivalry: High. Fragmented market with Dynatrace and Splunk as key rivals, but Datadog’s growth outpaces competitors.

Strategic Logic

• Capex: Minimal, as cloud-based model reduces capital intensity.
• Economies of Scale: Achieved MES, with scale advantages in event processing and hosting costs. No diseconomies observed.
• Vertical Integration: Limited, focused on observability and adjacent areas (security, incident management).
• Horizontal Integration: Expanding into security and pre-production via M&A, increasing TAM.
• M&A: Strategic, enhancing product suite and upsell potential without diluting focus.

Valuation

With a market cap of $40B+ and ~$1B in 2021 revenue, Datadog trades at a ~40x forward revenue multiple, typical for high-growth SaaS companies with strong unit economics. The combination of 66% YoY growth, 11%+ operating margins, and >130% net retention justifies the premium. However, risks include potential paradigm shifts (e.g., Web 3.0) and competitive pressure from adjacent players (e.g., CrowdStrike). Long-term growth depends on capturing a larger share of the $44B observability market and expanding into adjacencies like security.

Key Takeaways

1. Unified Platform Advantage: Datadog’s consolidation of infrastructure, APM, and logs into a single interface addresses DevOps inefficiencies, distinguishing it from point solutions like Splunk and New Relic.
2. Land and Expand Model: Rapid customer additions (36% YoY), spend expansion (>130% net retention), and new product launches drive 66% revenue growth, outpacing competitors.
3. Developer-Focused GTM: Easy onboarding, extensive integrations, and a la carte pricing enhance adoption, reducing S&M costs (26% of revenue) and boosting R&D (30% of revenue).
4. Operating Leverage: High fixed costs (R&D, S&M) and low capital intensity yield expanding margins (11% in 2020), with gross margins stable at 75-80%.
5. Scalable Infrastructure: Processing 10T events/day creates scale advantages, reinforcing competitive positioning.
6. M&A Strategy: Acquisitions (e.g., Sqreen, Undefined Labs) expand the product suite and TAM, integrated seamlessly to avoid customer confusion.
7. Market Opportunity: A $44B observability market, growing with cloud adoption (36% YoY), offers significant headroom for Datadog’s ~$1B revenue.
8. Risks: Long-term threats include Web 3.0, no/low-code platforms, and adjacent competitors (e.g., CrowdStrike) entering observability.

Datadog’s success lies in its ability to simplify observability, execute rapidly, and align with developer needs, creating a virtuous cycle of growth and profitability in a fragmented, high-growth market.