Nvidia, CrowdStrike & MongoDB Earnings Reviews

This article contains full reviews of Nvidia, CrowdStrike & MongoDB earnings.

Housekeeping:

A favor request – please respond to this send with a one-word email. This will help greatly with deliverability on the new platform. Thank you.
Content tagging in the categories on the new site is currently a mess. I'm going to need to manually do it myself when I can find the time to make sure it's perfect. If you're struggling to find an article in the archive, please reach out and we'll help.
If you responded to an email or reached out to customer service with a question since the new site launched, I did not receive it. Settings were mismarked and have since been fixed. If you're still having issues with anything, please reach out! We're here to help. Apologies for the inconvenience.
If you're not getting emails successfully delivered to your inbox, there's a very good chance they're going to your "promotion" folder. Adding this email address to your contacts or safe-sender list will fix that.
- If you're still having any issues at all, please let us know. We will help!
Max subs – the new Discord room is part of your existing subscription! It's where I field questions, share real-time news alerts and invite you all to collaborate. Sign up (must be Max sub to join)! Instructions can be found here.
- Upgrade to Max here.
The Snowflake snapshot was sent in the Discord room. That review will be emailed this week. Headline numbers looked really good

In case you missed it from this earnings season:

& my current portfolio/performance.

1. MongoDB (MDB) – Earnings Review

a. MongoDB 101

MongoDB is a key player in data storage and analytics, with a document-oriented setup. This differs from legacy relational-style databases and next-gen versions like Snowflake’s. How so? Relational databases store data in rigid rows and columns linked by pre-set relationships. These databases look like giant Excel spreadsheets and use structured query language (SQL) to work. The datasets are fixed, with formatting and filtering more limited.

Per leadership, relational databases cannot seamlessly handle unstructured data like MongoDB’s “not only SQL” (NoSQL) data lake can. MDB's Document-oriented set-up can organize and use unstructured data is better in this regard, which is vital in the age of GenAI and agentic AI. Relational and SQL are fine for traditional machine learning, but less effective for agentic, multi-step, goal-oriented tasks. It’s worth noting that Snowflake and Databricks would argue that their modern relational databases have been retrofitted to handle scalable unstructured and semi-structured data ingestion, but MongoDB leading in this area is a commonly held opinion.

MongoDB’s NoSQL document-oriented database is called Atlas.
The newest version of its database software that powers Atlas is called MongoDB 8.1. This offers 20% to 60% performance boosts vs. the old version and better time series (timeline-based) data services.

💡

“MongoDB’s platform enables developers to more easily represent the messiness of real-world data. This includes understanding relationships between structured and unstructured data, and managing data that is constantly evolving and changing. This fundamental architectural advantage provides customers greater flexibility, faster time to market, and the ability to scale without rearchitecting.” – CEO Dev Ittycheria

Atlas’s cloud-native database architecture uses a group of servers (or a cluster) to actually store data for eventual product creation within its platform. The nature of MongoDB’s Atlas product allows clusters to be easily added to or subtracted from for seamless tweaking as needs fluctuate. It also offers MongoDB Search for data querying and MongoDB Data Lake for housing unstructured data for querying and analytics.

More Core Atlas Products:

Atlas App Services is a server-less toolkit for developers to help them build modern apps.
Vector Search allows clients to seamlessly scrape insights from data. It allows for theme-based querying rather than just word-based. It also provides retrieval-augmented generation (RAG). This pushes “semantic” search results into associated large language models (LLMs) to uplift querying precision.
Atlas Stream Processing allows for real-time data ingestion. That matters a lot for app developers who constantly toy with, split-test and render every single little detail within their apps. Real-time access to data querying helps make that process painless.
Atlas Search Nodes (nodes = servers) automate the optimal usage of compute capacity and separate database & search functions to enable easier, more affordable scaling.

Product Expansion & AI:

MongoDB thinks it’s great at automating the cumbersome data modernization work needed for migrations. Its Relational Migrator (RM) handles and takes client headache out of this process. Now, it’s focused on leveraging RM to let companies actively re-code their legacy applications and create more of an end-to-end, full-service migration service. They call this the “modernization factory.” With this holistic strategy, MongoDB doesn’t just aid with data migration, but also direct automation of software modernization to seamlessly run apps through its document-oriented foundation.

Its recent purchase of Voyage AI provides another product expansion opportunity. Voyage is a key player in GenAI and agentic AI model trustworthiness. While models routinely create delightful, jaw-dropping experiences, they’re also wrong a lot. Hallucination rates (wrong answer rates) can often reach 25%, making models really only useful when you know what a right answer should look like and are double-checking. That limitation puts a tight ceiling on utility and is what Voyage aims to fix. It has two products. First, is its set of Vector Embedding Models. As leadership puts it, these are the “bridge between models and a client's private data.” They allow for meticulous information transfer into models. It organizes data as a number sequence, more closely grouping data points in a much more standardized manner. All of this helps with machine learning accuracy rates and uncovering patterns to reduce model mistakes. Secondly, it offers reranking models. These ingest and reorganize materials based on data relevance for a given model input. They sift through all queried information to eliminate waste and inaccuracies. With Voyage, MDB gains a broader suite of GenAI services to round out a more cohesive platform.

MDB will most meaningfully benefit from AI app monetization, which comes after infrastructure. Anything they can do (like RM, MAAP and Voyage) to expedite client adoption is good for their core business… while creating more revenue opportunities.
Voyage released two new retrieval models with better model accuracy and price performance. Voyage 3.5 is the firm’s latest embedding model, which lowers data storage costs by over 80%.
MongoDB launched its own Model Context Protocol (MCP) service with Cursor, GitHub, Anthropic and other needed 3rd-party connections.
MongoDB AI Applications Program (MAAP) offers an environment, a series of templates, integrations and guardrails and 3rd-party integrations to diminish GenAI app creation friction.
- This is more of a professional service and support product for helping clients build apps. Atlas App Services is more of a self-service product for developers to build modern apps themselves.

In summary, it now has Atlas, significant help for app and data modernization, real-time data streaming, world-class search tools and highly-regarded embedding and reranking models under one roof.

Non-Atlas Business:

Enterprise Advanced (EA) refers to its on-premise (Atlas is cloud-based), database and app bundle. It allows companies to purchase licensing for subscription-based usage (rather than paying for consumption under Atlas).

Business Model:

Note that most of MongoDB’s growth is based on acquiring new customers and migrating their workloads onto the platform, as well as consumption-based revenue from customers using its data products and growing workloads.

b. Key Points

Document-oriented product niche is resonating in the age of AI.
Rounding out the product suite with compelling expansions.
Outperformance was partially driven by the higher-quality Atlas revenue bucket.
Go-to-market changes are working.

c. Demand

Beat revenue estimates by 7.4% & beat guidance by 6.8%.
- Its 18.1% 2-year revenue compounded annual growth rate (CAGR) compares to 23.2% last quarter and 26% two quarters ago.
- Fastest rate of Y/Y revenue growth in 6 quarters (helped by easy Y/Y comps).
Beat Atlas revenue estimates by 4%.
- This is by far the highest-quality MDB revenue. Especially good to see this segment outperform.
- Net new Atlas revenue was its highest in over a year.
Non-Atlas revenue (just revenue - Atlas revenue) actually rose by 10% Y/Y vs. expectations of a nearly 10% Y/Y decline. More on this later.
- Non-Atlas ARR rose by 7% Y/Y.
Slightly missed $100K+ ARR client estimates by 0.3% of 7 clients.
Beat billings estimates by 7%.

On customer growth, of the 2,500 Atlas clients it added this quarter, 300 came from Voyage AI M&A.

MDB’s non-Atlas revenue is very high margin business. It’s also unpredictable, considering it comes from multi-year contracts subject to hefty closure and timing uncertainty. Because of this and the consumption-based nature of MDB’s business, it is among the most aggressive guidance sandbaggers out there. And rightfully so when your business trends aren’t solely driven by highly visible and pre-set subscription rates. Like it should, it bakes significant pessimism into forward guidance to leave potential upside risk to results. When the worst-case scenario doesn't play out, it outperforms on revenue. And based on the sky-high margin nature of its non-Atlas business, when that segment outperforms on demand, the profit commensurate outperformance (as you’ll see below) is massive.

For Atlas, great consumption during the month of May (cited on the last call) powered the quarterly beat. This strength was especially pronounced in the all-important USA market, which is where go-to-market (GTM) changes (more later) occurred. That’s evidence of these changes working. For non-Atlas, revenue vastly exceeded expectations due to outperformance in multi-year deal closings. That bodes well for the on-premise demand runway, as some customers with especially sensitive data and apps continue to embrace a hybrid infrastructure approach. Atlas revenue is the more important bucket, but this is still encouraging. If you recall, MDB offered disappointing initial FY 2026 guidance. This was because of a large non-Atlas headwind via lapping abnormally successful years of growth. 2026 is going much better than expected in this area, offering more evidence that this team loves to under-promise and over-deliver. Importantly, guidance continues to assume this headwind persists. It was updated to reflect the successful quarter, but Q3 and Q4 expectations remain unchanged. That likely positions them for more outperformance. Specifically, the annual headwind is now expected to be $40M vs. $50M previously.

Neither Atlas nor non-Atlas enjoyed aggressive demand pull-forwards or any one-off event that propped up results. The beats were mainly structural in nature. Non-Atlas helped the beat more than Atlas, but Atlas still materially contributed.

d. Profits & Margins

Missed 74.5% GPM estimate by 70 bps.
- Outsized Atlas growth is a GPM headwind vs. their higher-margin licensing and on-premise business.
- Atlas was 74% of revenue vs. 63% two years ago.
Beat EBIT estimate by 51% & beat guidance by 52%.
Beat $0.66 EPS estimate by $0.42 & beat guidance by $0.44.
- EPS rose by 43% Y/Y.
Free cash flow (FCF) rose from -$4M to $70M Y/Y.

e. Balance Sheet

$2.3B cash & equivalents.
No debt.
10% Y/Y share dilution. This includes $200M in buybacks and Voyage AI M&A.

f. Guidance & Valuation

Raised annual revenue guidance by 3.5%, which beat by 2.7%.
- Revenue raise was $70M vs. the $38M Q2 outperformance, implying rising 2nd-half-of-year expectations.
Raised annual EBIT guidance by 17.5%, which beat by 16%.
- EBIT raise was $44M vs. the $28M Q2 outperformance, implying rising rest-of-year expectations (specifically stronger Q4 expectations considering Q3 EBIT guidance was roughly in line).
Raised annual $3.03 EPS guidance by $0.65, which beat by $0.59.
Raised implied annual low-20% Atlas revenue growth target offered at the start of the fiscal year to a mid-20% growth target.
Raised non-Atlas revenue growth guidance from nearly -10% Y/Y to about -5% Y/Y.

For Q3, revenue guidance was modestly ahead of expectations, EBIT was in line and EPS was $0.78 vs. $0.73 expected. They expect non-Atlas revenue growth to be roughly -20% Y/Y as it laps fantastic multi-year contract performance during Q3 2025. That will lead to EBIT margin falling Q/Q.

MDB trades for 79x forward EPS. EPS is expected to grow by 1% this year, 15% next year and 26% the following year.

g. Call Highlights

Why MongoDB is Winning & Well-Positioned:

The bulk of CEO Dev Ittycheria’s prepared remarks were spent on the 4 reasons why MDB had a great quarter and expects to have many more great quarters going forward. We’ll walk through those 4 items (which have considerable overlap) here.

First, MongoDB seamlessly scales with the big boys in a way that yields better up-time, security, scalability and overall performance. Atlas and its on-premise business are both capable of ingesting data and massive scale without any bottlenecks. That’s why it has 70% of the Fortune 500, 14 of the 15 largest healthcare firms and 9 of the 10 largest manufacturers in its customer base. These logos rely on massive scalability with optimal efficiency to ensure they can meet profit expectations quarter after quarter. MDB is a great partner in this pursuit.

Second, as the 101 section mentioned, document-oriented is best for the “most mission-critical and transaction-intensive applications.” It’s perfectly capable of conducting full asset transactions containing many different documents and so more complexity. As an aside, this is why both Snowflake and Databricks have added JSON-powered Postgres products to better support online transaction processing (OLTP), which is already a core competency for MDB.

JavaScript Object Notation (JSON): Open-source format for data connections and exchanges. MongoDB heavily leans on JSON to power its NoSQL database services. When customers use Atlas, they’re probably using JSON-formatted data. MDB will then convert JSON files into Binary JSON (BSON) (which MongoDB created) for its internal storage and performance optimizations.
MongoDB views Databricks and Snowflake (both relational players) adding OLTP support via M&A is evidence of how hard this is to build internally, and also evidence of MDB’s core niche being extremely compelling. They see their OLTP capabilities as best-in-class and the “Lake Base” launch from Databricks does not change that opinion at all.
This transaction-intensive skill is why it has 90 Atlas customers with over 60 million customer records stores and is how it helped Deutsche Telekom 15Xed login capacity with better resiliency and customer engagement.

Third, they’re quickly rounding the product suite in pursuit of being a true platform play. They don’t just offer data storage. To actually create value from scalable data ingestion and organization, vector search, stream processing and Atlas Search Nodes all provide incremental utility vs. point solutions. From there, it ensures data and workloads can be seamlessly migrated and modernized via Relational Migrator and that it effectively vets and places that data into models to minimize inaccurate response rates. That’s so important in a world where 95% of AI spend still comes with negative return on investment (ROI). A lot of that is because answers are so frequently wrong and unreliable.

With all of this higher fidelity data usable, it also provides everything a company needs to build apps for the AI era. A true platform play… which is powering larger deals, better retention, broader differentiation and this successful quarter. It frees developers to shed tedious task requirements and “spend less time stitching together disparate systems." And for a company like Agibank in Brazil, it’s the end-to-end value creation that cut costs by 90% and boosted tech stack performance by 400%.

💡

“The choice for this application is not between MongoDB or Postgres. It’s between MongoDB or Postgres plus [other point solutions needed].” – CEO Dev Ittycheria

PostgreSQL (Postgres): Popular, open-source SQL database framework that hyperscalers and several other data vendors are using more frequently.
Leadership thinks companies picking Postgres over their JSON-based offering are doing so because they don’t know how much better JSON is. It's about awareness, not functionality. Customers routinely pick Postgres, run into performance bottlenecks and migrate to MDB.

The fourth and final point ties closely to the first 3. It entails leadership’s belief that they’re “emerging as the standard for AI applications.” That’s thanks to all the other product value and architecture we’ve already discussed. And fortunately, the AI app layer monetization journey remains in its infancy. The vast majority of financial value creation is still on the infrastructure side, and app monetization will surely follow… timing uncertain.

For now, they’re seeing customers experimenting with productivity tools for coding, document summarization, customer service etc. And early on, there’s more momentum on the self-serve side from AI-native startups vs. large enterprises thus far. That’s understandable; start-ups move more quickly. It talked about an electric vehicle platform using Atlas for their AV program and DevRev (private AI-native company) using it to pocket large efficiency gains. Traction is “real but early.” It also briefly mentioned “one of the fastest-growing startups in the Bay Area has bet big on MDB.”

More on AI:

The contribution from AI-native customers is still immaterial to MDB’s results. Outperformance was driven by core business momentum, as customers still haven’t gotten to the app transformation phases of their businesses.

Launched new Voyage context & reranking models.
Added a new LangChain (AI-native firm) partnership.
Added Temporal, LangChain and Galileo (not SoFi’s Galileo) to its AI partner roster.

Public Sector Focus:

MongoDB is pursuing FedRAMP High Impact Level 5 (IL5) authorization to accelerate public sector momentum. That has been a key unlock for other enterprise software firms operating in different categories, such as CrowdStrike. The same should be true here. In other positive government news, it was also added to the U.S. Intelligence Community portion of AWS’s marketplace.

Improving Go-To-Market:

As a reminder, MongoDB overhauled its go-to-market a few quarters ago. They moved resources from mid-market direct sales to large enterprises, as ROI there was higher. They pivoted to mid-market demand being addressed via self-serve channels and implemented a managed and self-serve go-to-market hybrid for customers wanting more freedom to build, without needing to handle back-end maintenance. That has been extremely popular and is leading to direct sales growth moving to self-serve. Next, they tweaked sales incentives to prioritize key performance indicator health. Finally, they’ve gotten louder and more proactive in messaging the value-proposition of the product suite. This quarter, that included a series of global developer events, with “hands-on” consulting to inspire product creation. It also includes more active pursuit of SQL developers unfamiliar with MDB’s products. Leadership thinks the excellent quarter is evidence of these go-to-market alterations being well-placed.

h. Take

Excellent quarter that validates the team’s confidence in their AI app layer positioning. While lumpy non-Atlas revenue outperformance did help, the beats were also driven by structural Atlas demand tailwinds and point to a sizable runway ahead. Voyage AI is the perfect complement to this product suite and drives incremental differentiation. I also think that + MAAP does make this a real platform play in enterprise software. I don’t believe that it’s fair to say relational database vendors like SNOW aren’t set to effectively compete for AI workloads, but I do think it’s safe to say that MDB and its differing foundation clearly are. The valuation is a bit stretched following today’s move, but the quarter warrants a larger premium than the company enjoyed before the report. Bulls should be very pleased.