Welcome to PyUncut — where we turn market noise into meaning. Today we’re diving into one of the most fascinating rivalries in the data-driven world: MongoDB, Snowflake, and Datadog. Three companies powering the modern cloud and AI infrastructure — and three stocks that tell us where digital intelligence is heading.
🎙️ Opening: The Data War Has Begun
Every decade has its platform wars.
In the 2000s, it was Microsoft vs Google.
In the 2010s, it was AWS vs Azure.
And now, in the 2020s, the battlefield is data — who stores it, secures it, and analyzes it fastest.
The front-runners?
- MongoDB (MDB) — the developer’s database.
- Snowflake (SNOW) — the data cloud for analytics.
- Datadog (DDOG) — the monitor and observability brain of the cloud.
They each serve a different layer of the AI-cloud stack, but their destinies are increasingly intertwined.
Investors are asking: Which one is the better long-term bet?
🏢 1. The Business Models at a Glance
| Company | Core Focus | Primary Revenue Driver | Key Growth Tailwind |
|---|---|---|---|
| MongoDB (MDB) | Flexible NoSQL database | Atlas cloud service (DBaaS) | AI data storage, vector search, developer adoption |
| Snowflake (SNOW) | Cloud-native data warehouse | Consumption-based analytics | Generative AI data pipelines, enterprise adoption |
| Datadog (DDOG) | Cloud monitoring and observability | Usage-based SaaS | Infrastructure monitoring for AI and cloud workloads |
Let’s break that down in plain English:
- MongoDB is the foundation layer: it stores, structures, and retrieves data for apps and AI models. Think of it as the “filing cabinet” for modern codebases.
- Snowflake is the insight layer: it lets enterprises run massive queries across oceans of structured and semi-structured data.
- Datadog is the watchtower: it monitors what’s happening across servers, APIs, and apps in real time — crucial for uptime and performance.
Each company sells a critical “picks-and-shovels” tool for the AI gold rush.
💰 2. Financial Snapshot (2025 Consensus Estimates)
| Metric (FY2025E) | MongoDB | Snowflake | Datadog |
|---|---|---|---|
| Revenue | $2.1 B | $3.3 B | $3.5 B |
| YoY Growth | 23 % | 24 % | 26 % |
| Gross Margin | 75 % | 78 % | 82 % |
| Operating Margin | ~7 % | 6 % | 17 % |
| Free Cash Flow (FCF) | $250 M | $600 M | $900 M |
| Market Cap (Oct 2025) | $22 B | $52 B | $48 B |
Datadog leads on profitability and cash generation, while Snowflake still commands the biggest valuation premium because of its enterprise footprint and near-monopolistic position in large-scale analytics.
MongoDB, though smaller, is growing rapidly and expanding its margins — making it the potential dark horse in this race.
⚖️ 3. Valuation Multiples — Growth vs Reality
| Valuation Metric | MongoDB | Snowflake | Datadog |
|---|---|---|---|
| EV / Sales | 10.5× | 15× | 13× |
| EV / EBITDA | 58× | 72× | 50× |
| P / FCF | 80× | 85× | 55× |
| PEG (Price/Earnings to Growth) | ~1.8× | ~2.3× | ~1.6× |
Interpretation:
- MongoDB looks cheaper on a relative basis — its valuation has compressed after last year’s correction.
- Snowflake trades at a premium — the “safe haven” play in enterprise data.
- Datadog is somewhere in between: steady margins, but volatility in consumption trends keeps the multiple moderate.
In short: MongoDB offers more upside if growth stabilizes; Snowflake offers predictability; Datadog offers profitability now.
📈 4. MongoDB’s DCF Model — What the Numbers Say
Let’s stress-test MongoDB’s intrinsic value using a simple 10-year DCF (Discounted Cash Flow) model, based on 2025 consensus.
Base inputs (2025 starting point):
- Revenue: $2.1 B
- Growth: 20 % for next 5 years, then tapering to 8 %
- Operating margin expansion from 7 % → 20 % by 2033
- Tax rate: 20 %
- Discount rate (WACC): 9 %
- Terminal growth: 3 %
Base-case intrinsic value: ≈ $330 / share
Current price (Oct 2025): ~$314
→ Implied Upside: ~5 %
Not screaming cheap — but fair for a long-term compounder.
Sensitivity Table (DCF per Share)
| Scenario | Growth (Next 5 yrs) | Terminal Margin | Fair Value / Share | Upside vs $314 |
|---|---|---|---|---|
| Bull | 25 % | 22 % | $410 | +30 % |
| Base | 20 % | 20 % | $330 | +5 % |
| Bear | 15 % | 15 % | $240 | −24 % |
The DCF shows MongoDB is priced for solid — not spectacular — execution. If Atlas continues its strong growth and the company capitalizes on AI-data workloads, upside opens fast. But a stumble could send it below $250.
🔍 5. Narrative Breakdown — Strengths and Weak Spots
🧠 MongoDB: Developer’s Darling, AI-Ready
- Strengths: Rapid developer adoption, vector search integration, multi-cloud reach.
- Weakness: Lower profitability, execution-sensitive guidance, competition from AWS DocumentDB.
- Opportunity: AI-ready data infrastructure; Atlas can embed directly into GenAI pipelines.
- Risk: Valuation still premium; one weak quarter can reset sentiment.
❄️ Snowflake: The Enterprise Fortress
- Strengths: Deep enterprise contracts, data-sharing network effects, strong gross margins.
- Weakness: Slowing consumption growth; heavy dependence on macro IT budgets.
- Opportunity: “Snowflake Cortex” and AI models built on data lakes.
- Risk: Market already pricing perfection; any slowdown gets punished fast.
🐶 Datadog: Profit Powerhouse
- Strengths: High margins, cross-selling efficiency, expanding into security and AIOps.
- Weakness: Growth volatility when cloud workloads soften.
- Opportunity: Observability for LLM infrastructure — tracking model usage, latency, cost.
- Risk: If customers optimize spending (common in downturns), usage dips quickly.
🧮 6. Comparative Take — Who’s Winning the AI Stack?
| Category | Winner | Why |
|---|---|---|
| Growth Story | MongoDB | Smaller base, strong developer momentum |
| Profitability | Datadog | High FCF and stable margins |
| Enterprise Stickiness | Snowflake | Deep integration and contracts |
| AI Optionality | MongoDB & Snowflake | Core data foundation for model training |
| Valuation Comfort | MongoDB | Lowest relative multiple |
| Defensive Profile | Datadog | Cash-rich, diversified monitoring base |
So, there isn’t a single “winner.” Instead, think of them as a three-layer AI infrastructure portfolio:
- Store with MongoDB,
- Analyze with Snowflake,
- Monitor with Datadog.
If you owned all three, you’d effectively own the core arteries of the modern AI-cloud ecosystem.
📊 7. Long-Term Outlook — The 2030 Vision
Fast-forward five years.
If AI adoption keeps expanding and companies continue migrating data to the cloud, here’s a realistic projection:
| Year | MongoDB Rev ($B) | Snowflake Rev ($B) | Datadog Rev ($B) |
|---|---|---|---|
| 2025 | 2.1 | 3.3 | 3.5 |
| 2030 (Bull) | 5.0 | 7.5 | 8.0 |
| CAGR | 19 % | 18 % | 17 % |
By 2030, MongoDB could double or even triple revenue if it stays the default choice for developers building AI-driven apps.
Snowflake will remain the backbone for enterprise analytics and data governance.
Datadog will quietly dominate monitoring and security layers — a cash-machine business.
Each, therefore, has a distinct risk/reward personality:
- MongoDB = High growth, high volatility
- Snowflake = Stable, slow-compounder
- Datadog = Profit-driven resilience
💬 8. PyUncut Insight — Our Take
🎯 Our Take: MongoDB still offers the best long-term asymmetric upside.
At current prices, you’re paying near intrinsic value, but the optionality from AI vector data, developer loyalty, and Atlas’ growth curve could justify a re-rating above $400 in the next two years.
For now, treat dips below $280 as an accumulation zone.
💡 Investor Fit:
- Aggressive Growth Investors → MongoDB
- Moderate / Institutional → Snowflake
- Profit Stability Seekers → Datadog
🔭 Next to Watch:
- Atlas usage growth in MongoDB’s Q3 earnings
- Snowflake AI/LLM integration updates
- Datadog’s security revenue expansion
- Interest-rate impacts on software multiples
🎙️ Closing Thoughts
The AI era won’t crown a single champion — it’ll reward the ecosystem that makes data move, learn, and act.
MongoDB is betting on being the developer’s best friend.
Snowflake wants to be the enterprise data brain.
Datadog guards the gates and keeps the whole system awake.
For investors, the question isn’t which one will exist in 2030 — all three will.
It’s which one will compound value the fastest from here.
At PyUncut, we’re leaning toward MongoDB as the most mispriced growth engine among the trio — but with eyes wide open on execution.
So, next time you open an app, query a dataset, or monitor an API, remember: you’re probably touching one of these three silent giants of the digital age.
PyUncut Finance — Where Market Data Meets Meaning.
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