To The Moon

AI Turns Databases Into New Infrastructure Winners: What MongoDB Signals For Snowflake

$MongoDB Inc.(MDB)$   has now delivered back-to-back upside quarters in FY2026. After a big beat in Q2, fiscal Q3 (for the quarter ended October 31, 2025) again came in well ahead of Wall Street expectations and the company raised full-year guidance on both revenue and earnings. Shares jumped roughly 15–20% in after-hours trading as investors repriced the stock on stronger growth and margins.

For most retail investors, "database" sounds like an IT plumbing topic, not an AI story. But in 2025, the logic is flipping: if AI is the new "application layer," then databases are increasingly the "data operating system" that everything sits on. 

What Does a Database Actually Do and Main Database Categories

A database is a structured place to store information so it can be organized, searched and reused efficiently. A database management system (DBMS) is the software layer that lets applications create, read, update and manage that data safely, handling permissions, concurrency and reliability in the background.

Main database categories can be roughly split into two big buckets for this discussion: relational databases (RDBMS) and NoSQL. 

A relational database is essentially a set of highly structured tables, with strict columns, data types and relationships. Classic examples include Oracle Database, Microsoft SQL Server, MySQL and PostgreSQL. They are great for transactions, strong consistency and complex joins.

By contrast, NoSQL is a family of non-relational databases designed to handle a different set of problems: very high concurrency typical of Internet and mobile workloads, flexible or frequently changing schemas, and deployments spread across hundreds or thousands of servers where horizontal scaling is essential. Instead of rigid tables, NoSQL systems often use documents, key-value pairs, wide columns, or graphs, trading some relational features for scalability and agility.

MongoDB today is not just a single database binary but a full data platform that aims to give developers a one stop data foundation for modern applications and AI features. 

Commercially, the business roughly splits into self managed MongoDB (enterprise and community editions installed and operated by customers), which is still meaningful but no longer the main growth driver, and MongoDB Atlas, the fully managed cloud service that runs on AWS, Azure and GCP and bills on a usage based model across compute, storage, network and various add on services.

MongoDB's Earnings and What They Mean for Snowflake

MongoDB's latest quarter showed a clean beat on both growth and profitability. 

Revenue came in at 628.3 million dollars, up 19% yoy and above Street expectations, with Atlas growing about 30% and reaching 75% of total revenue. Non GAAP EPS was 1.32 dollars and free cash flow reached 140.1 million dollars, pushing free cash flow margin above 20 percent. Management also raised FY2026 revenue guidance to roughly 2.44 billion dollars and EPS to about 4.8 dollars, a meaningful reset of earnings expectations in a single year, helped by stronger Atlas usage and tighter cost discipline. At the same time, leadership is transitioning to CJ Desai as CEO, bringing a track record from Cloudflare and ServiceNow, where he helped scale revenue from roughly 1.5 billion to over 10 billion dollars.

Based on the after-hours price of $400, on a static basis, MongoDB's CY2025 non-GAAP P/E is 83x and P/S is 13.3x.

The deeper takeaway, though, is how clearly AI is turning the database into a growth engine again. 

Management highlighted workloads that are essentially AI native: vector search, embeddings, reranking models and retrieval augmented generation (RAG). All of these depend on the database layer as the system of record for embeddings, documents and context that must be stored and queried at scale. AI is no longer a cosmetic feature on top of MongoDB; it is directly driving higher database consumption, improving monetisation and making the database a strategic component in the AI stack rather than a background utility.

For $Snowflake(SNOW)$  , MongoDB's print reinforces the story on three levels. At the sector level, it validates that enterprises are willing to increase spend on AI friendly databases and data platforms, not just on GPUs and accelerators. Snowflake, positioned as an AI focused data cloud, sits in the same structural tailwind of data and AI budget reallocation. At the product level, MongoDB is embedding vector search and model capabilities directly into the database, while Snowflake is doing something similar through its AI Data Cloud and Cortex, letting customers run models on top of a unified data layer. In both cases, the strategy is to turn the data platform into the operating system for AI applications, not just a passive store of records.

At the expectations level, investors have now seen the pattern they want to reward: AI that translates into higher cloud consumption, sustained product growth and healthier free cash flow, rather than AI slideware and promises. MongoDB delivered exactly that mix this quarter. The implication is that Snowflake's upcoming results will be judged against the same template: whether it can show that AI driven workloads are visibly lifting product usage, that growth remains durable, and that the economics of the data platform are improving at the same time.

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