Why MongoDB’s Earnings Just Broke the “Death by SQL” Narrative
For the last 12 months, the software market has been obsessed with a single, terrifying question: “Will AI kill the Database?”
The bear case was elegant: AI writes code. AI prefers simple, free databases like PostgreSQL. Therefore, complex, expensive platforms such as MongoDB are dead.
On Monday, MongoDB (MDB) took that narrative and shredded it.
The stock was up nearly 25% this morning after a blowout Q3 earnings report that didn’t just beat estimates—it fundamentally reframed the company’s role in the AI stack. With Atlas revenue accelerating to 30% growth and customers flocking to its new “Vector Search” capabilities, MongoDB proved it isn’t a legacy dinosaur. It is the Memory Layer for the AI revolution.
For now, forget the “Earnings Beat” hype and look at the structural reality. Instead of a database company, you are buying the only platform that allows AI to “remember” what it just learned.
The Narrative Flip: From “Legacy” to “AI Essential”
The market loves binary outcomes.
- The Old Story: “Developers love PostgreSQL (an open-source relational database). It’s free, it has extensions like pgvector, and it’s good enough for AI.”
- The New Story: “Good enough is not enough for production.”
- The Shift: As companies move from “Toy AI” (chatbots) to “Real AI” (Enterprise Agents), they are hitting a wall with SQL databases.
- The Problem: AI data is messy. It’s unstructured (text, images, video). Forcing this data into the rigid rows and columns of a SQL database is like trying to fit a square peg in a round hole.
- The MongoDB Solution: Its “Document Model” (JSON) is flexible by default. It swallows messy data without complaining. This earnings reports’ 30% Atlas growth proves that as AI apps scale, they are breaking PostgreSQL and migrating to MongoDB.
The Valuation Sanity Test: The “Reasonable” Rocket?
Let’s look at the price tag compared to the other “Data” darlings.
- MongoDB P/S: 14x Sales (Post-Jump).
- Snowflake P/S: 20x Sales.
- Palantir P/S: 95x Sales.
The Distortion: Despite the massive jump, MongoDB is still trading at a significant discount to the “Pure Play” AI data stocks.
- The “Perfection” Scenario: To justify 14x sales, MongoDB needs to prove it can sustain 20%+ growth rates for 5 years.
- The Math: Q3 revenue grew 19% total, but the future engine (Atlas) grew 30%. As the legacy “Enterprise Advanced” business shrinks and Atlas becomes 90% of the pie, the entire company’s growth rate will mathematically accelerate towards that 30% mark. You are buying an accelerating asset at a stable multiple.
The Black Box: The “Vector” Trap
What are you actually buying? You aren’t just buying storage; you are buying “Context.”
- The Asset: Atlas Vector Search.
- The “Why”: AI Models (LLMs) have amnesia. They hallucinate. To fix this, you need RAG (Retrieval Augmented Generation)—feeding the AI your private data so it tells the truth.
- The Trap: Companies started building RAG with dedicated Vector Databases like Pinecone. Vectors are how AI converts text into numbers
- The Problem: Now you have two databases: one for your app (Postgres) and one for your vectors (Pinecone). You have to keep them in sync. It’s a nightmare.
- The MongoDB Win: They built a “All-in-One” platform. You store your customer data and your vectors in the same place. It kills the complexity. Customers are buying MongoDB to delete Pinecone, not to replace Oracle.
The Competitive Analysis: The “Good Enough” Threat
This is the single biggest risk to the thesis.
- The Competitor: PostgreSQL + pgvector.
- The Bear Case: PostgreSQL is open-source (Free). The pgvector extension allows it to do vector search. For 80% of startups, this is “free and fine.”
- The Moat: Scale.
- PostgreSQL: Great for a prototype. But once you hit terabytes of data, managing “sharding” (splitting data across servers) in SQL is painful.
- MongoDB: Was built to shard. It scales horizontally by default.
- The Verdict: MongoDB wins the “Day 2” battle. Startups pick Postgres on Day 1. They migrate to MongoDB on Day 2 when their app explodes and the database crashes.
The “Consumption” Risk
Here is the fatal flaw that could kill the momentum.
- The Model: MongoDB Atlas is a Consumption model. You pay for what you use.
- The Risk: In a recession, companies optimize code to use less data. We saw this in 2023 (“The Optimization Cycle”).
- The AI Twist: AI is Consumption-Heavy. Every time an AI Agent “thinks,” it queries the database 10 times. AI is naturally inflationary for MongoDB’s revenue. The risk is that if the “AI Bubble” bursts and usage drops, MongoDB’s revenue falls faster than a subscription model would.
Our Take
MongoDB is the “Swiss Army Knife” of the AI era. It isn’t the specialized scalpel (Pinecone), and it isn’t the cheap hammer (Postgres). It is the one tool that does everything well enough to run a massive business.
- Bull Case: The “One Database” thesis wins. Companies consolidate their messy stack (SQL + NoSQL + Vector) into just MongoDB, driving growth back to 30%+.
- Bear Case: “Good Enough” open-source wins. Developers stick with free Postgres, and MongoDB is left fighting for the high-end enterprise niche only.
The Prediction: The “Death by SQL” narrative is over. At $400, you are paying a fair price for the Operating System of AI Data. It’s not a 100x lottery ticket, but it’s arguably the safest way to bet on AI software actually working.
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