What is MongoDB (MDB)?: A growth company expanding around Atlas into “operational data + search + AI,” while its profit model has yet to be firmly established

* This report is prepared based on data as of 2026-01-08.

What does MongoDB do? (For middle schoolers)

MongoDB provides a “place to store data (a database)” used by enterprise applications and services. Apps need to “store / retrieve / search” many kinds of information—user profiles, orders, inventory, logs, messages, and more. MongoDB’s pitch is that it makes this fast and straightforward, while also being a strong fit for modern software development where requirements change constantly.

In simple terms, it’s like replacing a store’s back-office work (inventory, orders, customer records) that once lived in messy paper ledgers with digital systems that can adapt as things change. As the store (the application) scales and the number of products handled (data types) expands, MongoDB’s value is that it remains easier to operate and maintain over time.

Who does it create value for? (Customers)

• Organizations from large enterprises to mid-sized companies to startups (used across industries)
• Developers building applications in-house (adoption often begins at the practitioner level)
• In some cases, government/public-sector customers (including constrained environments such as on-prem requirements)

Core offerings: Cloud-first (Atlas) + self-managed (Enterprise)

1) MongoDB Atlas (cloud): the largest revenue pillar

Atlas is the managed cloud offering where MongoDB runs the database for the customer. That lets customers offload work like scaling, backups, monitoring, and incident response, and keep their focus on building the application. Even by reported mix, cloud is the majority; in FY2026 quarters (Q1–Q2), just over 70% of revenue is said to be Atlas-derived.

2) Enterprise Server / Enterprise Advanced (self-managed): smaller than Atlas, but important

For organizations that can’t move to the cloud because of regulation, confidentiality, or legacy constraints, MongoDB can run on their own servers or in their data centers. Recently, the company has pointed to steps like offering through public-sector marketplaces, aimed at making procurement easier even in high-security environments.

How it makes money: Subscription + (for Atlas) usage-linked billing that scales with consumption

The core revenue model is subscription (recurring billing).

• Atlas: often functions like pay-as-you-go; as a customer’s application scales, usage typically rises, which can make revenue structurally easier to expand
• Self-managed (Enterprise): licenses, support, add-on features, etc. It can be long-lived, but the primary growth engine generally tilts toward Atlas

The key dynamic is that customer success (usage expansion) often flows through to MongoDB’s revenue growth. The trade-off is that usage-based billing can also make growth more variable when customers “optimize” (pull back usage to manage costs).

Why it is chosen (value proposition): reducing “development and operations hassle” more than raw speed

Value 1: Resilient to change, enabling faster development

Applications typically see frequent requirement changes. MongoDB positions itself as well-suited to environments where “data shapes change often,” and it’s valued for making it easier to keep up with changes and additions.

Value 2: Easier to scale globally in the cloud (operational offload)

Atlas is often positioned as a way to scale more easily while reducing operational burden during global rollouts and periods of rapid usage growth.

Value 3: From “storage” to “search and AI” (expanding integration)

Modern applications increasingly want not just storage, but also “search,” “analytics,” and “AI utilization” in an end-to-end workflow. MongoDB is leaning into this by integrating adjacent capabilities such as full-text search and vector search (searching by semantic similarity).

Initiatives looking ahead: three that could become the next pillars

1) Extending AI-oriented features (search/vector search) beyond the cloud

In September 2025, MongoDB announced it would extend search and vector search—previously Atlas-centric—to self-managed deployments (Community Edition / Enterprise Server). The goal is to support AI application development not only in the cloud but also on-prem, expanding the footprint (this is in preview, and the level of real-world adoption remains a forward-looking item to watch).

2) Application Modernization Platform (AMP): using AI to support “rebuilds”

In September 2025, MongoDB announced MongoDB AMP (AI-enabled application modernization support). Instead of simply selling a database, it can support MongoDB adoption as customers modernize legacy applications into newer architectures—potentially serving as an on-ramp that reduces adoption friction.

3) AI and security integration with hyperscalers (especially Microsoft/Azure)

In communications in the second half of 2025, MongoDB highlighted collaboration with Microsoft (AI development, security, governance integration). The intent is to become a “standard data foundation” for enterprises building AI applications on Azure, which could help in landing larger customers.

Long-term fundamentals: strong revenue growth, but profits are not yet stable

For long-term investors looking at MongoDB, the first thing to internalize is that “revenue growth” and the “profit model” are not moving in lockstep.

Revenue: strong growth even over 10 years

• Revenue CAGR (FY, 5-year): +36.6%
• Revenue CAGR (FY, 10-year): +46.3%
• Revenue scale (FY): $0.65bn in FY2016 → $20.06bn in FY2025

Revenue on its own clearly reads as high growth, but the profit and capital-efficiency metrics below have not progressed in the same way.

EPS (accounting profit): consistently negative on an FY basis; growth rates are difficult to assess

FY EPS is consistently negative from FY2016 to FY2025 (e.g., -1.73 in FY2025). As a result, 5-year and 10-year EPS CAGR cannot be calculated, which makes it hard to evaluate the business through a long-term “profit growth rate” lens.

Margins: gross margin is high, but operating and net margins are still negative

• Gross margin (FY2025): ~73.3% (consistently high over time, up from ~68.0% in FY2016)
• Operating margin (FY2025): ~-10.8% (improving from ~-111% in FY2016)
• Net margin (FY2025): ~-6.43% (improving from ~-113% in FY2016)

Free cash flow (FCF): turned positive in recent years, but long-term CAGR is difficult to assess

• FCF (FY): -$0.47bn in FY2016, +$1.15bn in FY2024, +$1.21bn in FY2025
• FCF margin (FY2025): +6.01%
• Operating CF margin (FY2025): +7.49%

Because FY includes multiple negative years and only turns positive later, 5-year and 10-year FCF CAGR cannot be calculated, which makes simple growth-rate comparisons structurally difficult.

Lynch-style “company type”: looks like a Fast Grower, but in practice a more cyclical-leaning hybrid

On revenue growth alone, MongoDB looks like a Fast Grower. But because profits (EPS) and ROE have not compounded consistently, the closest fit is better described as a hybrid with a cyclical tilt. Here, “cycle” is less about macro sensitivity and more about inconsistent profit generation, which can translate into sharper swings for investors.

• Revenue 10-year CAGR (FY): +46.3%
• ROE (latest FY): -4.64%
• EPS (TTM): -0.872, and EPS YoY (TTM): -67.8%

Near-term momentum (TTM / last 8 quarters): revenue is growing but decelerating; profits are weak; FCF is strong

Looking at whether the long-term “type” is also showing up in the near term, MongoDB reflects ongoing revenue growth alongside a slower growth rate versus the mid-term average, plus a widening gap between accounting earnings and cash flow.

Overall assessment: Decelerating

Overall momentum is classified as Decelerating. The main drivers are revenue growth running below the 5-year average and EPS worsening.

Revenue: growth continues, but decelerating versus the past 5-year average

• Revenue (TTM): $23.17bn
• Revenue YoY (TTM): +20.9%
• Revenue CAGR (FY, 5-year): +36.6%

The latest 1-year (TTM) revenue growth of +20.9% is below the 5-year average (FY CAGR +36.6%). This isn’t simply a “FY vs TTM” artifact; it more cleanly reads as still growing, but at a slower rate.

EPS: still loss-making, and deteriorating YoY

• EPS (TTM): -0.872
• EPS YoY (TTM): -67.8%

Because the 5-year average EPS growth rate cannot be calculated, a strict “faster/slower than the long-term average” comparison isn’t possible. As context, the TTM trend correlation over the last two years is +0.87, which leans toward improvement, while the most recent YoY has worsened—an observed “twist.”

FCF: materially improved (though profits are still negative)

• FCF (TTM): $3.55bn
• FCF YoY (TTM): +139.6%
• FCF margin (TTM): +15.3%
• Net income (TTM): -$0.71bn

“Accounting losses” and “meaningfully positive FCF” are showing up at the same time. For investors, the key is separating whether earnings are being held down by investment, or whether underlying unit economics are weak (there is no basis to conclude either within the scope of the source material; this is simply the observed setup).

Operating margin (FY): loss has narrowed over the last three years

• FY2023: -27.0%
• FY2024: -13.9%
• FY2025: -10.8%

Margins have improved over the last three FY years, but they remain negative as of FY2025, and it’s still hard to say the company has clearly entered a sustainably profitable growth phase.

Financial health: strong liquidity, but weak interest coverage on a profit basis

When thinking about bankruptcy risk, it helps to separate not just the level of debt, but also liquidity (cash on hand) and interest-paying capacity (profit strength).

Short-term funding: high liquidity

• Current ratio (FY2025): 5.20
• Cash ratio (FY2025): 4.16
• Debt-to-equity (FY2025): 0.013 (around 0.012 on a quarterly basis as well)

These point to a sizable cash cushion, at least from a near-term liquidity standpoint.

Interest-paying capacity: difficult to characterize as strong on a profit basis

• Interest coverage (FY2025): -15.26

With negative interest coverage, it’s hard to argue the company is “comfortably covering interest expense” from a profit perspective. The profile is therefore mixed: strong liquidity, but profit strength is not yet there.

Capex burden: small on a TTM basis

• Capex as a percentage of operating cash flow (TTM): ~1.1%

A relatively light capex load can be one structural reason FCF can run higher.

Cash flow quality: how to treat the “twist” of strong FCF but weak EPS

MongoDB’s TTM FCF has improved materially, with FCF margin up to +15.3%. At the same time, net income (TTM) is a loss of -$0.71bn.

This kind of gap can show up in growth companies due to “the timing of accounting expenses (e.g., personnel costs, sales, R&D) and investment,” but within the scope of the source material, we can’t pinpoint the drivers. Investors need to judge whether the FCF improvement reflects temporary factors or a more structural, durable shift.

Dividends and capital allocation: less an income stock, more about growth and cash generation

For the most recent TTM, both dividend yield and dividend per share cannot be obtained, making assessment difficult. On an FY basis, there are years where dividend payments (dividend per share recognition) can be confirmed; rather than concluding dividends are zero, it is better described as intermittently observed.

From a capital allocation standpoint, TTM FCF is positive (~$3.55bn) and capex needs are modest, which suggests some flexibility. However, based on the current data, it’s difficult to argue dividends are the primary return lever. The core framing here is not income, but growth and cash generation (and, if needed, other return mechanisms).

Where valuation stands today (historical comparison vs the company only)

Rather than benchmarking against the market or peers, this section places today’s valuation within MongoDB’s own distribution over the past 5 years (primary) and 10 years (secondary), labeling each metric as “within range / above range / below range.” Where FY vs TTM changes the picture, we treat it as a difference in appearance driven by period definitions.

Assumptions: share price as of the report date, and constraints of profit metrics

• Share price (as of the report date): $420.82
• EPS (TTM): -0.872 → P/E (TTM): -482.54x

With negative EPS, P/E isn’t useful for standard comparisons and a historical distribution can’t be built; accordingly, we only present the current value.

1) PEG: a current value exists, but historical distribution cannot be constructed, making positioning difficult

• PEG (current): 7.12

With the latest EPS growth rate (TTM YoY) at -67.8% (negative), there is no 5-year or 10-year PEG distribution available, so we can’t determine whether it sits inside or outside a historical range.

2) P/E: due to losses, limited to presenting the current value

• P/E (TTM): -482.54x

This also lacks a historical distribution, and there isn’t enough information to assess directionality over the last two years.

3) Free cash flow yield: above the upper end for both 5-year and 10-year history

• FCF yield (TTM): 1.04%
• 5-year normal range (20–80%): -0.37% to +0.75% → above range
• 10-year normal range (20–80%): -0.98% to +0.52% → above range

Historically, FCF yield is positioned toward the “higher-yield” end of the company’s own range. This does not imply future returns; it is strictly a placement versus MongoDB’s own history. Over the last two years, the indicated trend is upward.

4) ROE: within the historical range, but negative

• ROE (latest FY): -4.64%
• 10-year range (20–80%): -47.33% to +30.63% → within range

The 5-year range has an unusually high upper bound (and can be skewed by volatility in the equity base), so it should be interpreted carefully; as a matter of fact, it is within range. Directionality over the last two years lacks sufficient information and cannot be concluded.

5) FCF margin: above range for both 5-year and 10-year history

• FCF margin (TTM): 15.30%
• 5-year normal range (20–80%): -3.10% to +6.18% → above range
• 10-year normal range (20–80%): -32.19% to +1.11% → above range

Versus history, this places the company on the “stronger cash generation” end of its own range. Over the last two years, the indicated trend is upward.

6) Net Debt / EBITDA: above the historical range (note it is an inverse indicator)

• Net Debt / EBITDA (latest FY): 23.83
• 5-year normal range (20–80%): 1.75 to 10.20 → above range
• 10-year normal range (20–80%): 0.52 to 6.00 → above range

Net Debt / EBITDA is an inverse indicator where a smaller value (more negative) implies more cash and greater financial flexibility. The current value of 23.83 is above the historical range and is described as trending upward over the last two years. That said, this metric can look “extreme” when EBITDA (the denominator) is small; here we simply note the company is in a phase where it can present that way.

Why MongoDB has won (the core of the success story)

MongoDB’s success is not just about database technology; it’s about delivering “developer ease” and “operational ease” as a bundled experience that lowers total cost for application teams.

• Developer adoption → internal standardization: once it wins in the field, it can spread across teams and applications (an indirect network effect)
• Mission-critical nature: once deployed, it sits close to the core of the application, which reduces the likelihood of replacement
• Operations, reliability, audit readiness: barriers aren’t just implementation skill, but also operating history, security posture, audit readiness, and community penetration

In particular, in public-sector and regulated environments, certifications can be decisive for adoption. MongoDB has indicated it is targeting FedRAMP High/IL5 for government cloud, which could be viewed as an effort to “raise the ceiling” on where it can be deployed.

Is the strategy consistent with the success story? (Narrative continuity)

Recent moves are broadly consistent with the core story (lowering total cost across development + operations, and pushing toward platformization). Two points stand out.

• The AI framing has become more central: search and vector search are moving from “nice-to-have adjacent features” toward “baseline requirements,” and capabilities that were cloud-only are being extended to self-managed deployments (supporting AI apps anywhere)
• Raising the bar for public-sector and regulated wins: by pursuing higher security certifications, the company is trying to expand the upper bound of where it can be adopted

Even in the numbers, the current setup—“revenue is growing but growth is slowing,” and “cash flow is improving while accounting profit remains negative”—fits a narrative of a business still in the middle of expansion and investment.

Invisible Fragility: structures that look like strengths can also become constraints

1) Revenue concentration: a high Atlas mix is both a strength and a dependency

Atlas representing just over 70% of revenue is a strength in that the growth engine is clear. At the same time, it increases reliance on cloud usage-based billing; if customers optimize spend (reduce usage), revenue growth can become more sensitive.

2) Differentiation expands into “integrated features,” increasing competitive dimensions

Adding search and vector search is a logical extension, but it also pulls MongoDB into a wider set of adjacent competitors, and customers often decide based on “what’s ultimately easiest and cheapest.” As differentiation shifts from the database itself to broader integration, the number of competitive dimensions—and the cost of explaining value—can rise, which can become a point of fragility.

3) Financial metrics can “look” worse abruptly (when denominators are small)

When profitability is weak, metrics like Net Debt / EBITDA can look extreme. Rather than treating that as a crisis by itself, it’s better framed as a potential fragility: if profit recovery takes longer than expected, the optics can deteriorate quickly (also tied to weak interest coverage).

4) Organization: shifting priorities and leadership turnover can reduce execution capability

External employee reviews (generalized) include comments about shifting priorities, management-layer changes, and organizational confusion. In a period where the company is expanding into integrated domains (search, AI, government readiness, etc.), execution consistency matters more, so this can’t be dismissed (reviews can be biased, so this is treated as a tendency rather than a statement of fact).

5) Limited supply-chain dependence, but significant dependence on cloud infrastructure

While hardware supply-chain exposure appears limited, the model is sensitive to cloud-side variables such as operational conditions and data transfer costs. That can be a less obvious constraint.

Competitive landscape: the fight is not only against “peer databases,” but also “compatible APIs” and “open standardization”

MongoDB competes in a very large market, but it’s also a category where “being average” is hard. Databases are mission-critical and naturally lend themselves to recurring revenue, while hyperscalers aggressively promote their own services—turning this into a contest of end-to-end capability across procurement, integration, and operations, not just product features.

Key competitors (examples)

• Amazon DocumentDB (AWS): a managed DB positioned as MongoDB-compatible
• Azure Cosmos DB (Microsoft): strong as a NoSQL platform, continuing to enhance search capabilities
• Google Cloud Firestore (MongoDB-compatible): generally available with MongoDB compatibility emphasized
• DocumentDB (open source under the Linux Foundation): built on PostgreSQL extensions, positioned with a MongoDB-compatible API, and could support the trend toward standardization and lock-in avoidance
• Couchbase (Capella): a potential NoSQL comp
• PostgreSQL ecosystem (including managed): absorbing document use cases via JSON and extensions, enabling architectures that “do not maintain a separate document DB”

Switching costs: high, but there is pressure that could lower them

• Factors that make replacement less likely: data migration, query differences, operational procedures, rebuilding audit/backup/monitoring
• Factors that can make replacement more likely: compatible APIs proliferate and “porting with limited code changes” becomes practical (lower switching costs)

Competitive KPIs (variables) investors should monitor

• How far MongoDB-compatible APIs expand into “practical use” beyond CRUD (aggregation, indexing, operational features)
• How persistently major cloud document DBs (AWS, Azure, etc.) continue improving performance, operations, and pricing
• Whether the Linux Foundation’s DocumentDB can create adoption pathways across multi-cloud/on-prem
• Whether AI search (full-text/vector) becomes commoditized as “the same everywhere,” or whether operational integration preserves differentiation
• Whether selection skews toward new workloads or toward replacements (the higher the replacement mix, the more the competition tends to center on price and portability)

Moat and durability: compounding developer standardization vs erosion from compatibility and standardization

MongoDB’s moat is less about a single feature advantage and more about the compounding loop of developer adoption → internal standardization → mission-critical embed. Beyond that, operating history, reliability, security, audit readiness, and the ability to make the broader ecosystem work (cloud integrations, data integrations, AI development tool integrations) can function as barriers.

What can erode that moat is compatible APIs + open standardization. As “compatible is good enough” options improve, differentiation tends to migrate toward integration convenience and total operating cost, and comparisons versus cloud-native standard features can become the default.

Structural position in the AI era: not the side replaced by AI, but the “operational data layer” AI depends on

Potential structural tailwinds

• Data advantage: not from owning proprietary data, but potentially from sitting close to where customers’ operational data accumulates (the operational DB)
• Degree of AI integration: keeping storage + full-text search + vector search tightly integrated, and extending those capabilities to self-managed deployments to broaden applicability (preview)
• Lower adoption friction: expanding touchpoints with hyperscalers and AI development platforms, moving toward easier adoption (e.g., integrations where connectivity can be treated as a “tool” within Azure’s agent platform)

Where AI could become a headwind (commoditization risk)

As AI readiness becomes table stakes, differentiation from being “integrated and convenient” can compress, and evaluation can shift toward operational simplicity and cost. That keeps the risk alive that competition versus cloud-native standard features and adjacent tools intensifies.

Leadership and culture: CEO transition emphasizes “continuity,” but execution discipline is being tested

CEO transition (key event)

MongoDB announced a CEO transition effective November 10, 2025. Dev Ittycheria stepped down as CEO, and Chirantan “CJ” Desai became the new CEO. Ittycheria remains on the board and will support the transition as an advisor for a period. The company frames this as continuity rather than a strategic reset—an effort to carry the long-term strategy forward while handing leadership to someone positioned for the next phase of growth.

Context under the prior CEO: reducing complexity through integration

The prior CEO often framed “integration” (DB + search + semantic search) as a way to reduce customer complexity costs, and has been observed to keep some distance from over-hyping AI.

Context under the new CEO: customer proximity, category-defining products, scaled execution

The new CEO’s public comments emphasize staying close to customers, building category-defining products, and executing at scale, with references to “long-term sustainable and profitable growth.” Given how recent the transition is, how those prioritization boundaries show up in day-to-day execution remains a forward-looking item to watch.

Cultural observation points (fit with long-term investors)

• Potential positive: the prior CEO remains involved after the transition, which can reduce the risk of abrupt strategic shifts
• Caution: the broader an integration strategy becomes, the more priority conflicts can emerge, raising the execution bar
• Transition risk: immediately after a CEO change, decision cadence and evaluation criteria can shift; how “customer proximity” translates into trade-offs will matter

Customer positives and pain points (the “reasons” adoption expands and the “frictions”)

What customers value (Top 3)

• Development speed and resilience to change (easy to keep up with schema changes and feature additions)
• Reduced operational burden (Atlas reduces the hassle of management, scaling, and availability)
• Integration beyond the DB (extensions aligned to application requirements, such as search and vector search)

What customers are dissatisfied with (Top 3)

• Difficulty forecasting costs (usage-based billing; variability driven by multiple factors such as processing and transfer)
• Design and operational complexity that comes with “it can do everything” (optimal design requires expertise)
• Relative to incumbent DBs or specific cloud DBs, the need to justify why to replace (can become adoption friction)

Two-minute Drill (summary for long-term investors): how to understand and track this company

• MongoDB’s core proposition is serving as an application’s “system of record for operational data,” bundled with a developer and operations experience, in a model where usage expansion tends to drive revenue (Atlas-centric).
• Over the long run, revenue growth is strong (FY 10-year CAGR +46.3%), while EPS remains loss-making and ROE is -4.64% in the latest FY—evidence that the profit model is still not fully established.
• In the near term (TTM), revenue is up +20.9% but is slowing versus the 5-year average; EPS remains negative and worsens YoY, while FCF is strong at +139.6%, creating a notable “twist.”
• On the balance sheet, liquidity is strong (current ratio 5.20; cash ratio 4.16), but interest coverage is -15.26 and profit strength remains weak—“liquidity comfort” alongside “unfinished profitability.”
• Competition is not just feature-by-feature among databases, but a structural fight against cloud-native compatible APIs and open standardization that can reduce switching costs; differentiation increasingly shifts toward total cost (operations, governance, cost predictability).
• The AI era can be supportive (as the foundation layer through which AI accesses operational data), but if AI features commoditize, comparisons versus cloud-native standard offerings can intensify—making cost and ease of adoption the primary battleground.