Sector

TechnologyDbaaSTech - Infrastructure

Background

Ro Nagpal is an investor at Holocene Advisors. We cover Mongo’s creative approach to R&D, learn how database product advantages compound, and look at what protects MongoDB from larger players like Microsoft and Amazon.

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Date

November 3, 2021

Episode Number

33

MongoDB Business Breakdown

Background / Overview

MongoDB, founded in 2007 by Dwight Merriman, Elliot Horowitz, and Kevin Ryan, emerged from their experience at DoubleClick, an ad tech company acquired by Google. The founders identified a gap in existing database solutions, particularly Oracle’s relational databases, which struggled with the scalability and flexibility required for internet-scale applications. MongoDB developed a NoSQL, document-based database to address these needs, focusing on dynamic scalability and developer ease of use. Headquartered in New York, MongoDB serves over 29,000 customers across 100 countries, with a workforce of approximately 4,500 employees (based on public data). The company operates in the database software category, competing in both on-premise and cloud-based environments, with a significant shift toward its cloud offering, Atlas, in recent years.

MongoDB’s story is one of a compounder, transitioning from an on-premise license model to a cloud-native, subscription-based model. Its open-source roots have driven widespread developer adoption, while strategic acquisitions and leadership under CEO Dev Ittycheria have refined its go-to-market (GTM) strategy and product-market fit.

Ownership / Fundraising / Recent Valuation

MongoDB went public in 2017 (NASDAQ: MDB) and has not been referenced in the transcript as having recent private equity or sponsor ownership. Its market capitalization is not explicitly stated in the transcript, but public data suggests it fluctuates between $15-20 billion as of early 2025, depending on market conditions. The transcript does not provide specific enterprise value (EV) or valuation multiples from recent transactions, so we avoid speculative figures. The company has raised capital through its IPO and secondary offerings, with no recent fundraising events highlighted in the transcript. For valuation context, MongoDB’s revenue multiple can be inferred as high, given its 40% revenue growth and slightly negative EBITDA, typical of high-growth SaaS businesses.

Key Products / Services / Value Proposition

MongoDB’s core product is its NoSQL document database, designed for flexibility, scalability, and developer productivity. Unlike relational databases (e.g., Oracle’s SQL-based systems), which rely on predefined schemas and rigid table structures, MongoDB’s document model allows developers to store data in a JSON-like format, accommodating dynamic and unstructured data. This makes it ideal for modern applications like mobile apps, e-commerce platforms, and IoT systems.

Key Offerings:

1. MongoDB Enterprise Advanced: An on-premise or self-managed database solution with advanced security, management, and support features.
2. MongoDB Atlas: A fully managed cloud database-as-a-service (DBaaS) offering, hosted on AWS, Azure, or Google Cloud. Atlas is the fastest-growing segment, approaching $500 million in revenue (out of $800 million total) and growing 80% year-over-year (YoY).
3. MongoDB Community Edition: An open-source version, freely downloadable, driving developer adoption and prototyping.

Value Proposition:

• Scalability: Dynamically scales on commodity cloud infrastructure, handling high-traffic events (e.g., Super Bowl pizza orders) by adding or removing resources as needed.
• Developer Productivity: Intuitive document model reduces development time, enabling faster iteration and deployment.
• Flexibility: Supports diverse use cases, from e-commerce to IoT, without requiring schema redesign for new data types.
• Cloud-Native: Atlas eliminates infrastructure management, offering instant updates and security patches.
• Open-Source Advantage: Free access to the Community Edition lowers barriers to entry, fostering a large developer community.

The transcript highlights unique use cases, such as weather.com leveraging MongoDB for multiple weather applications and an IoT platform in Norway processing 25,000 data points per second for 7,000 wind turbines. These demonstrate MongoDB’s ability to handle high-velocity, diverse data workloads.

Segments and Revenue Model

MongoDB operates two primary segments:

1. Subscription Revenue (Atlas and Enterprise Advanced): Accounts for ~$800 million, or nearly 100% of total revenue, growing 40% YoY. Atlas is the dominant growth driver, contributing ~$500 million and growing 80% YoY.
2. Services Revenue: Minimal, includes professional services like consulting, but not a significant contributor.

Revenue Model:

MongoDB follows a usage-based, subscription model for Atlas, where customers pay based on data storage, compute, and transaction volume. This aligns with cloud trends, ensuring revenue scales with customer workloads. Enterprise Advanced uses a traditional license model, with annual or multi-year contracts based on the number of servers or cores. The open-source Community Edition drives adoption but generates no direct revenue, acting as a funnel for paid subscriptions.

The transcript emphasizes Atlas’s dominance, with 50% of revenue and accelerating customer acquisition (8,000 new customers annually vs. 3,500 a few years ago). The average customer spends ~$27,000 annually, though this varies widely (some spend millions, others minimal amounts).

Splits and Mix

Revenue Mix:

• Product Mix: Atlas (62.5% of revenue), Enterprise Advanced (37.5%, declining share), Community Edition (0% direct revenue).
• Geo Mix: Operates in 100 countries, with Atlas enabling sales in regions like India without physical sales presence. No specific geo breakdown provided.
• Customer Mix: Ranges from startups to enterprises (e.g., ADP, weather.com). Long-tail customers dominate, with 29,000 total and 8,000 new annually.
• Channel Mix: Direct sales for enterprise customers, self-serve for smaller customers via the website (no credit card needed for Community Edition).
• End-Market Mix: Diverse, including e-commerce, gaming, IoT, payroll, and weather applications. No specific revenue split by vertical.

EBITDA Mix:

The transcript does not provide segment-level EBITDA, but Atlas likely has lower gross margins than Enterprise Advanced due to cloud infrastructure costs (AWS, Azure, Google Cloud). Company-wide gross margins are 72%, with slightly negative EBITDA, reflecting high sales and marketing (S&M) spend (45% of revenue).

Mix Shifts:

• Historical: Shift from on-premise (Enterprise Advanced) to cloud (Atlas), with Atlas now approaching majority revenue.
• Forecasted: Continued Atlas dominance as workloads move to the cloud (only 20% of workloads are cloud-based today). The transcript suggests a 10-15 year runway for cloud migration, driving organic growth.

KPIs

• Customer Growth: 29,000 total customers, adding 8,000 annually (up from 3,500 a few years ago), indicating acceleration.
• Revenue Growth: 40% YoY overall, 80% YoY for Atlas.
• Churn: Estimated <5% (likely <2%, comparable to Oracle’s 2%), reflecting high stickiness due to database entrenchment.
• Same-Store Sales: 20-25% annual growth in existing customer spend, driven by workload expansion and new applications.
• Customer Acquisition Cost (CAC): ~$30,000-$40,000 per logo, with a decades-long customer lifespan.
• Downloads: 100 million in the last 12 months, matching the total from 2007-2020, signaling strong developer adoption.

These KPIs show acceleration in customer acquisition and usage, underpinned by low churn and high developer engagement.

Headline Financials

Long-Term Financial Trends:

• Revenue: Growing at 40% YoY, with Atlas outpacing the legacy business. The transcript suggests sustained growth as cloud workloads increase.
• EBITDA Margin: Currently breakeven, but expected to expand as fixed costs (S&M, R&D) become a smaller percentage of revenue.
• FCF: Not explicitly stated, but high S&M (45%) and R&D spend, plus cloud infrastructure costs, likely result in negative FCF. Future FCF improvement depends on operating leverage and reduced CAC relative to customer lifetime value (LTV).

Value Chain Position

MongoDB operates midstream in the technology value chain, providing database software that sits between raw storage/compute infrastructure (e.g., AWS, Azure) and front-end applications (e.g., mobile apps, websites). Its primary activities include:

• Product Development: Building and maintaining the database software, including the storage engine and cloud features.
• Sales and Marketing: Enterprise sales for large clients, self-serve for smaller customers, and developer marketing to drive adoption.
• Customer Support: Ensuring uptime, security, and feature updates for Atlas users.

Supply Chain:

• Upstream: Relies on cloud providers (AWS, Azure, Google Cloud) for Atlas infrastructure. No significant physical inputs, as MongoDB is software-based.
• Downstream: Sells to developers, IT teams, and enterprises building applications. Customers integrate MongoDB into their tech stacks, which include front-end frameworks and cloud infrastructure.

GTM Strategy:

MongoDB’s GTM combines bottom-up developer adoption with top-down enterprise sales:

• Bottom-Up: Open-source Community Edition and self-serve Atlas allow developers to try MongoDB without upfront costs, driving viral adoption (100 million downloads in the last 12 months).
• Top-Down: Enterprise sales target large organizations, upselling Atlas or Enterprise Advanced to customers with growing workloads.
• Hybrid Motion: Developers adopt the free version, build prototypes, and convert to paid subscriptions as usage scales, with enterprise reps nurturing high-potential accounts.

MongoDB’s value-add lies in its developer-friendly document model and cloud scalability, differentiating it from rigid relational databases and less flexible competitors.

Customers and Suppliers

Customers:

• Demographics: Developers, IT teams, and enterprises across industries (e-commerce, gaming, IoT, payroll, weather).
• Key Examples: ADP (payroll app), weather.com (weather applications), IoT platform for wind turbines in Norway.
• Customer Concentration: No single customer dominates, with a long tail of 29,000 customers. The average spend is $27,000, but some spend millions.

Suppliers:

• Cloud Providers: AWS, Azure, and Google Cloud provide infrastructure for Atlas, representing a significant cost in the cost of sales (COGS).
• Developer Community: Contributes to open-source code, though MongoDB controls core development.
• No Major Dependencies: Unlike hardware-based businesses, MongoDB has minimal supplier concentration risk.

Pricing

MongoDB’s pricing is usage-based for Atlas, tied to storage, compute, and transaction volume, ensuring alignment with customer value. Enterprise Advanced uses a license-based model, priced per server or core, with annual or multi-year contracts. The open-source Community Edition is free, driving adoption but requiring conversion to paid tiers for commercial use.

Pricing Drivers:

• Industry Fundamentals: High demand for cloud databases supports premium pricing, though competition from AWS, Azure, and others caps price increases.
• Differentiation: Developer ease of use and scalability justify higher prices compared to commodity databases.
• Mission-Criticality: Low churn (<5%) reflects the mission-critical nature of databases, reducing price sensitivity.
• Mix Effect: Atlas’s lower gross margins (due to cloud costs) offset higher-margin Enterprise Advanced licenses, resulting in a blended 72% gross margin.

Contract lengths are not specified but likely range from monthly (Atlas self-serve) to multi-year (enterprise contracts), providing revenue visibility.

Bottoms-Up Drivers

Revenue Model & Drivers

MongoDB generates revenue through subscriptions, with Atlas as the primary driver. The revenue model is:

• Atlas: Pay-as-you-go, based on data volume, compute, and transactions. Customers pay ~$27,000 on average, with growth from increased usage (e.g., 5% more e-commerce sales) and new workloads (e.g., additional applications).
• Enterprise Advanced: License fees based on infrastructure size, with predictable annual contracts.
• Community Edition: Free, driving adoption and conversion to paid tiers.

Revenue Drivers:

• Volume:
• Customer Growth: 8,000 new customers annually, up from 3,500, driven by cloud adoption and open-source accessibility.
• Workload Expansion: Existing customers increase spend by 20-25% annually, adding new applications or scaling existing ones.
• Cloud Migration: Only 20% of workloads are cloud-based, with a 10-15 year runway for migration.
• Price:
• Usage-Based Pricing: Scales with customer growth, ensuring revenue aligns with value.
• Premium Features: Enterprise features (security, support) command higher prices.
• Competitive Pressure: AWS, Azure, and others limit pricing power, but MongoDB’s developer preference supports premium positioning.
• Mix:
• Product Mix: Shift to Atlas (lower margins but higher growth) from Enterprise Advanced.
• Geo Mix: Expansion into new markets (e.g., India) via cloud, without physical sales presence.
• Customer Mix: Long tail of smaller customers, with high-value enterprises contributing significant revenue.

Absolute Revenue:

• $800 million in 2024, with 40% YoY growth, driven by Atlas ($500 million, 80% YoY).
• Long-term growth expected as cloud workloads increase and new applications adopt MongoDB.

Cost Structure & Drivers

MongoDB’s cost structure consists of variable and fixed costs, with high operating leverage potential as revenue scales.

Variable Costs:

• Cloud Infrastructure: Payments to AWS, Azure, and Google Cloud for Atlas hosting, a major COGS component. Reduces gross margins to 72% (vs. near-100% for on-premise licenses).
• Support and Services: Costs for customer support and professional services, though minimal.
• Drivers: Cloud usage scales with Atlas revenue, but bulk purchasing and optimization could improve margins over time.

Fixed Costs:

• Sales and Marketing (S&M): 45% of revenue, high due to enterprise salesforce and developer marketing. Includes costs for acquiring 8,000 new customers annually at $30,000-$40,000 per logo.
• Research and Development (R&D): Significant spend to enhance the storage engine, add features (e.g., mapping functionality), and maintain open-source code.
• General and Administrative (G&A): Overhead costs, including facilities and admin, relatively small compared to S&M and R&D.
• Drivers: Fixed costs provide operating leverage, as revenue growth outpaces cost increases. S&M efficiency improves as self-serve adoption reduces CAC.

Cost Analysis:

• % of Revenue:
• COGS: 28% (cloud infrastructure, support).
• S&M: 45%.
• R&D: ~20-25% (estimated, not specified).
• G&A: ~5-10% (estimated).
• % of Total Costs:
• S&M dominates due to high customer acquisition and enterprise sales focus.
• COGS is significant for Atlas but negligible for on-premise licenses.
• Contribution Margin: Atlas has lower contribution margins than Enterprise Advanced due to cloud costs, but high gross margins (72%) support profitability at scale.
• EBITDA Margin: Slightly negative, driven by high S&M and R&D. Margin expansion expected as fixed costs become a smaller percentage of revenue.

FCF Drivers

The transcript does not provide explicit FCF data, but we can infer:

• Net Income: Negative due to slightly negative EBITDA and interest/tax expenses.
• Capex: Likely low, as MongoDB is software-based with minimal physical infrastructure. Most capex is for internal systems or R&D facilities.
• Net Working Capital (NWC): Minimal inventory, but receivables may increase with enterprise contracts. Cash conversion cycle is likely short, given subscription-based revenue.
• FCF: Negative, driven by high S&M, R&D, and cloud infrastructure costs. Future FCF improvement depends on EBITDA margin expansion and reduced CAC.

Capital Deployment

• M&A: Strategic acquisitions (e.g., WiredTiger for storage engine, AMlab for cloud GTM) enhance capabilities. No large-scale M&A indicated, with product development primarily organic.
• Organic Growth: 40% revenue growth driven Dimensions: 1.5x1.5x0.5in
• Buybacks: Not mentioned in the transcript, likely minimal given negative FCF.
• Capex: Low, focused on internal systems rather than heavy infrastructure.
• Dividends: None, typical for high-growth SaaS companies.

MongoDB prioritizes organic growth, reinvesting cash into S&M and R&D to capture market share and drive cloud adoption.

Market, Competitive Landscape, Strategy

Market Size and Growth

The database market is segmented into:

• Mature (On-Premise): $70 billion, growing 8% YoY, dominated by Oracle ($17 billion revenue).
• New (Cloud): Estimated to reach $100 billion in 10 years, growing faster than on-premise. Total market projected at $200 billion in a decade.

Growth Drivers:

• Volume: Increasing number of cloud applications (Microsoft estimates 500 million new apps in the next few years).
• Price: Usage-based pricing aligns with cloud adoption, though competition limits price increases.
• Industry Growth Stack: Driven by digital transformation, big data, and cheap cloud storage/compute (AWS).

Market Structure

The database market is fragmented:

• Competitors: Oracle, AWS (Aurora, DocumentDB), Azure, Google Cloud, Snowflake (data warehousing), Redis, and others.
• Market Leaders: Oracle dominates on-premise; MongoDB leads NoSQL cloud databases, with ~30% of developers using it in the last 12 months.
• Minimum Efficient Scale (MES): Cloud databases require significant scale to optimize infrastructure costs, favoring large players like MongoDB and hyperscalers.
• Penetration: Only 20% of workloads are cloud-based, indicating early-stage adoption.
• Industry Traits: Low regulation, high innovation pace, and macro tailwinds (digital transformation).

Competitive Positioning

MongoDB positions itself as the developer-friendly, scalable NoSQL database for cloud-native applications:

• Price: Premium, justified by ease of use and scalability.
• Target Market: Developers and enterprises building modern apps (e.g., mobile, IoT).
• Differentiation: Document model, open-source accessibility, and Switzerland-like neutrality (runs on any cloud).

Market Share & Relative Growth

• Market Share: MongoDB captures ~30% of new cloud applications, disproportionate to its size, indicating strong developer preference.
• Relative Growth: 40% YoY revenue growth (80% for Atlas) outpaces the mature market (8%) and likely exceeds cloud market growth (estimated 20-30%).
• Trends: Gaining share in NoSQL, with competitors like AWS DocumentDB lagging in developer adoption (per developer comments).

Competitive Forces (Hamilton’s 7 Powers)

1. Economies of Scale: MongoDB’s large customer base (29,000) and developer community (100 million downloads) create scale advantages in R&D and infrastructure optimization. MES is high, limiting new entrants.
2. Network Effects: Developer adoption compounds as engineers move between companies, bringing MongoDB expertise (e.g., from Company A to B).
3. Branding: Strong developer reputation, with consensus as the easiest-to-use NoSQL database, supports premium pricing.
4. Counter-Positioning: Document model and open-source model are hard for incumbents like Oracle to replicate due to legacy architecture and business model inertia.
5. Cornered Resource: Access to top database talent, as MongoDB attracts the best NoSQL engineers, unlike hyperscalers’ broader focus.
6. Process Power: Superior storage engine and feature iteration (e.g., mapping functionality) outpace competitors, driven by real-time usage data.
7. Switching Costs: High, with <5% churn, as rewriting applications for another database is costly and disruptive.

Porter’s Five Forces:

• New Entrants: Moderate threat. High MES and switching costs deter startups, but hyperscalers (AWS, Azure) can invest heavily.
• Substitutes: Low threat. Relational databases and other NoSQL solutions are less flexible for modern apps.
• Supplier Power: Moderate. Cloud providers (AWS, Azure) are critical but commoditized, with MongoDB’s multi-cloud strategy reducing dependency.
• Buyer Power: Low. Customers face high switching costs, and MongoDB’s developer love reduces price sensitivity.
• Industry Rivalry: High. Fragmented market with hyperscalers and niche players (Snowflake, Redis) competing on features and price.

Strategic Logic

• Capex Cycle: Minimal capex, with focus on R&D and S&M to capture cloud workloads. Offensive bets on Atlas expansion align with market trends.
• Economies of Scale: MongoDB operates at MES, with scale advantages in R&D and customer acquisition. No evidence of diseconomies, as the company remains agile.
• Vertical Integration: Limited, focused on software layer rather than owning infrastructure (outsourced to cloud providers).
• Horizontal Integration: Expanding use cases (e.g., IoT, gaming) and features (e.g., mapping) to address 80% of database needs.
• M&A: Targeted acquisitions (WiredTiger, AMlab) enhance capabilities without diluting focus. No large-scale M&A planned.
• BCG Matrix: Atlas is a Star (high growth, high share), Enterprise Advanced a Cash Cow (low growth, high share), and Community Edition a Question Mark (high growth, low revenue).

Valuation

The transcript does not provide MongoDB’s market cap or EV, but public data suggests a market cap of $15-20 billion as of early 2025. With $800 million in revenue, this implies a revenue multiple of ~19-25x, high but justified by:

• 40% YoY growth (80% for Atlas).
• Long-term cloud migration runway (10-15 years).
• High developer adoption and low churn (<5%).

EBITDA multiples are less relevant due to negative EBITDA, but future margin expansion could align valuation with peers like Datadog or Snowflake (20-30x revenue). Risks include competition from hyperscalers and potential tech shifts, which could compress multiples.

Key Takeaways and Dynamics

MongoDB’s business model is unique due to its:

1. Document Database Architecture: Enables flexibility and scalability for modern applications, unlike rigid relational databases. This architectural bet, validated by market share gains, compounds as developers adopt and spread expertise.
2. Open-Source Model: Drives viral adoption (100 million downloads) and lowers barriers to entry, with conversion to paid tiers as workloads scale. The licensing change (requiring code sharing) protects against hyperscaler copycats like AWS DocumentDB.
3. Hybrid GTM: Combines bottom-up developer adoption with top-down enterprise sales, optimizing CAC ($30,000-$40,000) and LTV (decades-long customers). The shift to self-serve Atlas enhances efficiency.
4. Cloud Transition (Atlas): From on-premise to cloud, Atlas ($500 million, 80% YoY growth) captures a larger TAM by eliminating infrastructure management and targeting new markets (e.g., India).
5. Developer-Centric Focus: Capturing the scarce developer resource through ease of use and community engagement creates a flywheel of adoption and loyalty.
6. Switzerland Positioning: Multi-cloud compatibility (AWS, Azure, Google Cloud) reduces lock-in risk, appealing to enterprises wary of hyperscaler dominance.

Critical Observations:

• Leadership Impact: CEO Dev Ittycheria’s focus on product-market fit and GTM alignment turned MongoDB from a developer-loved but under-monetized business (pre-2020) into a revenue-generating machine (40% YoY growth).
• Risks: Hyperscalers’ scale and resources pose a threat, though MongoDB’s developer preference and product iteration speed provide a moat. A tech shift (e.g., new database paradigm) could disrupt, but none is evident.
• Operating Leverage: High S&M (45%) and R&D spend suppress EBITDA, but fixed costs create leverage as revenue scales, promising future margin expansion.
• Market Mispricing: The market may underestimate MongoDB’s cloud runway and developer moat, viewing it as “just another database” rather than a category leader in NoSQL.

MongoDB’s success hinges on its ability to maintain developer love, iterate features rapidly, and capture cloud workloads, positioning it to take a disproportionate share of the $100 billion cloud database market.

Transcript