MongoDB (MDB) – Earnings Review – March 09, 2024
MongoDB is a key player in data storage and analytics with a document-oriented setup. Last earnings review, I covered MongoDB’s niche and product suite overall. I think that’s an important read for folks wanting to understand this business. It defines key terms with needed context. That can be found in part 3, section e of this article.
Its most exciting product is called MongoDB Atlas. This is a cloud-native database service that implements a group of servers (or a cluster) used to actually store the data. The nature of MongoDB’s product allows clusters to be easily added to or subtracted from for easier flexing up & down as needs fluctuate. It also offers MongoDB Realm as a mobile environment for app creation, MongoDB Stitch to build apps without servers or any needed infrastructure maintenance and MongoDB Search for data querying. Finally, it offers MongoDB Data Lake specifically for unstructured data, which directly competes with players like Snowflake. There are more products, but these are the big ones with new releases discussed below.
Results
- Beat revenue estimates by 5.1% & beat guidance by 6.3%.
- Net revenue retention was 120%.
- Crushed EBIT estimates by 86% & Crushed guidance by 89%.
- Beat $0.48 earnings per share (EPS) estimates by $0.38 & neat guidance by $0.41.
- Met free cash flow (FCF) estimates.
Gross margin (GPM) of 77% worsened from 78% Y/Y. This is due to a 250 basis point (bps) boost to last year’s GPM due to “one-time cloud contract benefits.” It expanded by about 150 bps Y/Y without this headwind.
Source: Brad Freeman – SEC Filings, Company Presentations, and Company Press Releases
Annual Guidance & Valuation
- Missed revenue estimates by 5.9%.
- Sharply missed EBIT estimates by 34%.
- Missed $3.22 EPS estimates by $0.84.
MDB trades for 163x next year’s earnings. Earnings are set to shrink Y/Y. Wait what? MongoDB’s guidance was shockingly poor, but there’s decent reasoning for this. The company pocketed $80 million in unused Atlas commitment revenue and multi-year licensing revenue (large Alibaba contract extension) last year. It expects that to fall to $0 this year, with non-Atlas revenue growth turning negative. This implies continued strong Atlas growth, which is by far its most important product. From a margin perspective, the $80 million in lost revenue was basically pure profit, which again won’t recur. These were the sources of the misses. (MDB’s team also loves to sandbag on their initial annual guides.)
The misses were not at all related to competition. Its win rates vs. all competitors rose Y/Y and its new product uptake is going very well. MongoDB is rapidly rounding out its product suite to consolidate vendors and cut costs within databases and app development. In a world obsessed with point solution displacement, this is a great focus area and is working. That’s likely why it now has 259 customers with over $1 million in annual business vs. 213 Y/Y.
Balance Sheet
- $2 billion in cash & equivalents.
- No traditional debt.
- $1.1 billion in senior convertible notes.
- Shares +4.1% Y/Y.
- $109.9 million in FCF vs. -$24.7 million Y/Y.
Call & Release Highlights
Atlas:
Atlas’s new workload growth was strong while its retention and consumption trends were too. It sees consumption growth continuing throughout next year. This product had a great quarter despite tough comps related to lapping unused commitment revenue. It greatly changed its sales team incentives to minimize this stream of revenue and focus on consumption growth. 34% Y/Y Atlas growth would have been 36% Y/Y without this headwind.
GenAI:
MongoDB released a few GenAI products this year. Vector Search makes it easy to query needed data with slick integrations to plug that data into GenAI apps. GenAI apps and models insatiably consume data. The more data, the better; the more relevant that data is, the better. MongoDB provides data scale and secure access to a client’s first party insights. It pairs these data skills with ample programming language frameworks to free developers to bring their work to their data. This new tool offers Semantic Search, which allows clients to seamlessly scrape insight from data. It allows for theme-and-idea-based querying rather than just word-based. It also provides retrieval-augmented generation (RAG). This pushes semantic search results into associated large language models to uplift querying precision. It also debuted Stream Processing to utilize data as it is generated in real time. This will surely be a popular tool for GenAI app builders too.
While these products are compelling, they’re not really moving the financial needle just yet. Almost all GenAI spending is happening within model training and inference. Very little spend has happened to date within the app layer of GenAI, which is where MongoDB expects to carve its niche. It provides endless and cohesive access to data and tools to automate app creation from within its platform. It marries these strengths with powerful integrations to ensure a developer can easily deploy apps in runtime elsewhere. It sees strong evidence and early cases of its platform being used to build GenAI apps. Most of the demand tailwind from this, however, will take place in the future.
2024 Priorities:
- Keep investing in the core platform and GenAI products like Vector Search.
- Obsess over new workload growth.
- Alter sales incentives to align go-to-market with workload growth maximization goal.
- Accelerate sales team growth.
Relational Database Migration Opportunity:
As the MDB intro I linked to at the beginning of this spells out, displacing legacy relational databases is MDB’s biggest opportunity. It’s great at automating the preparation and data movement of these antiquated products. It’s not great at rewriting application code as that data enters its ecosystem. It plans to lean into GenAI products to internally automate that code writing process. It thinks this will significantly diminish migration friction for new customers. They’re probably right.
Take
Great quarter and an awful annual guide. If history is any indication, it will raise that guidance consistently throughout the year. It loves to under-promise when it offers initial annual guidance just like it should. Atlas revenue and guidance were strong, the margin trend excluding special items last year was very strong and commentary surrounding competition was encouraging too. Like with Snowflake, the multiple is simply far too lofty for me to start a position just because I think guidance is conservative. At 163x forward EPS, perfection is needed and I’m not willing to assume perfection at this stage. This was not perfect, but I do see why bulls defended the print.
