<div id="readability-page-1">Money Markets MongoDB Earnings Lift The Stock  ByTrefis Team ,  Contributor.  Forbes contributors publish independent expert analyses and insights. Building a platform to do the job of 1 million analysts for  Great Speculations Follow Author Dec 05, 2025, 05:45am EST Share Save Comment  CHONGQING, CHINA AUGUST 24: In this photo illustration, a smartphone displaying the logo of MongoDB Inc. (NASDAQ: MDB), a leading U.S. database software company, is seen in front of the company's latest stock market chart on August 24, 2025, in Chongqing, China. (Photo illustration by Cheng Xin/Getty Images) Getty Images Over the past 12 months, the software industry has fixated on a singular, daunting question: “Will AI eliminate the Database?”  The bearish argument was compelling: AI generates code. AI favors straightforward, free databases like PostgreSQL. Consequently, intricate, costly platforms such as MongoDB are doomed.  On Monday, MongoDB (MDB) dismantled that narrative.  This morning, the stock surged nearly 25% following an outstanding Q3 earnings report that not only exceeded expectations but also fundamentally redefined the company’s role within the AI ecosystem. With Atlas revenue advancing at a rate of 30% growth and customers flocking to its latest “Vector Search” functions, MongoDB established that it is not a relic of the past. It serves as the Memory Layer for the AI transformation.  For now, set aside the “Earnings Beat” excitement and focus on the underlying reality. Rather than merely a database entity, you are acquiring the only platform that enables AI to “retain” what it has just learned.  The Narrative Shift: From “Legacy” to “AI Essential”  The market thrives on binary results.  MORE FOR YOU The Old Narrative: “Developers are fond of PostgreSQL (an open-source relational database). It’s free, it has plugins like pgvector, and it suffices for AI.”  The New Narrative: “Good enough simply isn’t sufficient for production.”  The Transition: As organizations progress from “Toy AI” (chatbots) to “Real AI” (Enterprise Agents), they encounter limitations with SQL databases. The Challenge: AI data is disorganized. It comprises unstructured formats (text, images, video). Fitting this data into the strict rows and columns of an SQL database is akin to trying to insert a square peg into a round hole.  The MongoDB Advantage: Its “Document Model” (JSON) is inherently adaptable. It accommodates chaotic data effortlessly. The earnings report’s 30% growth in Atlas demonstrates that as AI applications scale, they are outgrowing PostgreSQL and shifting towards MongoDB.  The Valuation Reality Check: The “Reasonable” Rocket?  Let’s examine the valuation compared to other “Data” favorites.  MongoDB P/S: 14x Sales (Post-Jump).  Snowflake P/S: 20x Sales.  Palantir P/S: 95x Sales.  The Discrepancy: In spite of the substantial increase, MongoDB continues to trade at a considerable discount compared to the “Pure Play” AI data stocks.  The “Perfection” Scenario: To validate the 14x sales multiple, MongoDB needs to demonstrate that it can maintain growth rates exceeding 20% for 5 years.  The Calculation: Q3 revenue expanded by 19% overall, yet the future core (Atlas) saw a 30% increase. As the traditional “Enterprise Advanced” business contracts and Atlas accounts for 90% of the total, the entire company’s growth rate will inevitably accelerate towards that 30% threshold. You are investing in a rapidly expanding asset at a consistent multiple.  The Black Box: The “Vector” Dilemma  What are you genuinely acquiring? You aren’t merely purchasing storage; you are acquiring “Context.”  The Asset: Atlas Vector Search.  The “Why”: AI Models (LLMs) suffer from memory gaps. They can generate false outputs. To remedy this, you require RAG (Retrieval Augmented Generation)—providing the AI with your private data to ensure accuracy.  The Dilemma: Organizations began developing RAG with dedicated Vector Databases like Pinecone. Vectors represent the method by which AI translates text into numerical formats The Challenge: As a result, you end up with two databases: one for your application (Postgres) and another for your vectors (Pinecone). Synchronization is necessary, making it a logistical headache.  The MongoDB Advantage: They created an “All-in-One” platform. Customer data and vector data are stored in the same location. It simplifies operations. Businesses are choosing MongoDB to phase out Pinecone, not to replace Oracle.  The Competitive Landscape: The “Good Enough” Threat  This represents the most significant threat to the argument.  The Rival: PostgreSQL + pgvector.  The Bear Argument: PostgreSQL is open-source (Free). The pgvector extension enables vector search capabilities. For 80% of startups, this is “free and adequate.”  The Defense: Scale.  PostgreSQL: Excellent for prototypes. However, once you reach terabytes of data, managing “sharding” (distributing data across servers) in SQL is challenging.  MongoDB: Was designed to handle sharding. It scales horizontally by nature.  The Conclusion: MongoDB prevails in the “Day 2” scenario. Startups opt for Postgres on Day 1. They transition to MongoDB on Day 2 when their applications surge and the database encounters issues.  The “Consumption” Risk  This is the critical flaw that could derail the momentum.  The Model: MongoDB Atlas operates on a Consumption basis. You pay according to your usage.  The Risk: During a recession, companies refactor code to use less data. This trend was observed in 2023 (“The Optimization Cycle”).  The AI Angle: AI is inherently Consumption-Intensive. Each time an AI Agent “thinks,” it queries the database 10 times. AI naturally increases MongoDB’s revenue pressure. The concern lies in the possibility that if the “AI Bubble” bursts and usage declines, MongoDB's revenue may decrease more rapidly than a subscription model would.  Our Perspective  MongoDB embodies the “Swiss Army Knife” of the AI era. It is not a specialized scalpel (Pinecone), nor is it a budget hammer (Postgres). It is the single tool that performs a variety of tasks well enough to operate a large-scale business.  Bull Argument: The “One Database” thesis prevails. Companies consolidate their chaotic stack (SQL + NoSQL + Vector) into solely MongoDB, driving growth back above 30%.  Bear Argument: &#34;Good Enough&#34; open-source prevails. Developers remain loyal to free Postgres, and MongoDB is relegated to competing for the elite enterprise segment only.  The Forecast: The “Death by SQL” narrative has concluded. At $400, you are paying a fair price for the Operating System of AI Data. This isn’t a 100x gamble, but it’s arguably the most secure way to invest in AI software genuinely succeeding.  The Trefis High Quality (HQ) Portfolio , comprising 30 stocks, has a history of comfortably outperforming its benchmark which includes all three – the S&amp;P 500, S&amp;P mid-cap, and Russell 2000 indices. What accounts for this? As a group, HQ Portfolio stocks have yielded superior returns with lower risk relative to the benchmark index; offering a smoother investment experience, as seen in HQ Portfolio performance metrics .  Editorial Standards Reprints &amp; Permissions LOADING VIDEO PLAYER... FORBES’ FEATURED Video</div>

<p>MongoDB's stock surged nearly 25% after an outstanding Q3 earnings report, redefining its role in the AI ecosystem. With Atlas revenue growing at 30%, MongoDB is seen as essential for AI, shifting from a legacy database to an AI memory layer. Despite the stock jump, MongoDB trades at a discount compared to other AI data stocks. Its "Document Model" accommodates unstructured AI data, making it preferable over PostgreSQL for scaling AI applications.</p>