MongoDB (MDB) Q1 FY2024 Earnings Review

After guiding for a sequential 4% drop in revenue for Q1, MongoDB delivered a strong beat. More importantly, their preliminary estimate for Q2 revenue would achieve a reacceleration of annual growth if they outperform at the same level as Q1. The revenue projection for Q2 even leapfrogged past the analyst consensus for Q3. While the market expected some conservatism, this level of outperformance caught investors by surprise, with the stock surging 28% the next day.

Equally impressive were improvements in profitability. In the past, MongoDB has been discounted for poor operating leverage. The transition to 2023 has brought record levels of operating income and FCF, closing the gap with peers in the software infrastructure space. This also led to a significant beat and raise on EPS, which we don’t often see with high growth SaaS companies.

Even customer activity notched records. Both total customer additions and those with spend over $100k in ARR represented all-time quarterly highs. Of new customers, over 200 companies are categorized in the burgeoning AI industry, providing another catalyst as these start-ups are landing new capital at levels on par with the Covid beneficiaries of 2020-2021.

MongoDB has emerged as a hot stock once again, with its valuation multiple now pressing up against the top of its peer group. The stock has more than doubled in 2023 and reached its 52-week high recently. MongoDB is well-positioned to capitalize on tailwinds from AI, as enterprises revamp their data infrastructure to deliver new insights and services from their proprietary data sets. With new product announcements at MongoDB.local in June, MongoDB is further supporting the case for consolidation of application workloads onto its developer data platform.

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In this post, I review the Q1 results and try to project the trajectory for the rest of 2023. I also loop back on MongoDB’s product strategy and update their positioning with all the major announcements from MongoDB’s recent annual user conference. This event brought new capabilities that should enable the next phase of MongoDB’s growth.

MongoDB Product Strategy

MongoDB’s product vision is to deliver a reliable, scalable and easy to use data platform to serve as the transactional store for modern software applications. This hinges on their flexible document-oriented data model, which aligns to how developers work with data within applications. They have extended the platform to address multiple data storage workloads from the same interface, allowing engineering teams to reduce the number of database point solutions that must be supported. This consolidation lowers vendor costs, reduces infrastructure management overhead and simplifies application architecture.

Long ago, the MongoDB team recognized that the document model could be extended to support the query and storage patterns of several other popular data schemas. To abstract the differences, MongoDB built a Unified Query API which facilitates the translation of different data models into the core document store. Adjacent data types can be easily modeled in a document form and then queried through the API using the same access patterns. These extensions address most “document adjacent” data models like key-value, time series, graph, geospatial, search and even denormalized relational.

MongoDB remains the most popular non-relational database solution on the market. With their expansion to address other document-adjacent data types, MongoDB is delivering a broader multi-model data platform. The platform has also been extended to support specific application delivery patterns like mobile, in-app analytics, charting and search.

As the data platform’s applicability expands, it becomes suitable as the backing data store for many applications within the same enterprise. Most modern enterprises allow developers the freedom to choose their technology stack from a preset selection of options. MongoDB’s go-to-market with large enterprises involves getting anointed as one of these sanctioned solutions. After that, the broad popularity, ease of use and familiarity of MongoDB with developers drives expansion to serve new application workloads and support legacy database refreshes.

As developers were empowered by enterprise engineering teams to make tool selection, the result has been a sprawl of point database solutions to address multiple data types and workloads. When enterprise IT budgets are flush, this additional overhead is tolerated. When budgets retract, however, engineering leaders will look for ways to reduce expenses. Consolidation of vendors naturally provides savings through economies of scale.

The MongoDB value proposition is to provide a single platform to replace most database types for modern application development. As software applications are increasingly designed (or refactored) around individual microservices, a single data model can be provisioned for each. MongoDB can address most non-relational models from the same platform. Even relational data models that tolerate some denormalization can be represented by the document model.

The advantages of a single data platform revolve around cost savings through volume discounts, fewer vendor agreements, higher developer productivity and reduced DevOps overhead. MongoDB delivers all of these benefits. Additionally, MongoDB clusters can be located on and share data across all three hyperscalers (GCP, AWS and Azure). This cross-cloud capability is appealing as enterprise development teams seek to avoid lock-in with one hyperscaler.

MongoDB continues to add support for new data types. With each, they expand the addressability of a customer’s application footprint. As an enterprise can have multiple database vendors within their infrastructure, MongoDB measures their success with a customer by the number of application workloads they address over time. Growth in workloads feeds their net ARR expansion rate, which remains above 120%.

Through the addition of new data types and workloads, the MongoDB sales team can make the argument to customers that the platform can be leveraged for many types of applications. Following the sprawl of point solutions for every data workload over the past 5 years, this consolidation strategy has a number of targets. These extend to dedicated database solutions for time series, graph, key-value, search and basic analytics.

The net benefit is significant for engineering teams. This consolidation of database engines onto a single platform reduces costs (single vendor economies of scale), simplifies access (single programming interface) and lowers maintenance overhead (back-ups, configuration, etc.). Where MongoDB’s solution represents an adequate replacement for a point database (time series, key-value, search, graph, geospatial, etc), an engineering team can consider the migration of an application workload to it. Additionally, new applications suited for a non-relational data store can be spun up on the MongoDB platform from the beginning.

Recent Product Announcements

Looking forward, MongoDB’s product strategy is to continue to improve the capabilities of existing data workloads and to add support for new ones. This theme underscored their recent MongoDB.local user conference in June 2023. The team unveiled a number of new capabilities and programs, which should drive further growth.

With the rapid rise of AI as a central theme for many enterprises, MongoDB demonstrated they have been keeping up. The platform has even emerged as a preferred component of the modern AI data stack, with many AI companies represented among recent customer additions. Fortunately, the MongoDB product team anticipated the opportunity to serve AI workloads back in 2022, when they began work on their first major product announcement.

Vector Search Workload

As I discussed, MongoDB’s product vision is to enable development teams to utilize one data store for all of their application workloads. To realize this strategy, they have been adding new processing engines to the platform to support different data structures. This provides developers with a single interface to query. For DevOps teams, they have less infrastructure to manage.

To demonstrate their continued momentum in addressing as many transactional workloads as possible, MongoDB announced the addition of support for vector search in MongoDB Atlas. Vector search represents a key capability in the interaction with AI models. As models are trained, data is represented as vectors in N-dimensional space. In the simplest terms, vectors consist of a large set of numbers, unique to each object being modeled by the AI system.

Objects in this context can be anything relevant for the AI model within the business space being represented. General examples include text strings, images, videos, audio files, etc. They can also be any physical or logical construct with parameters, like automobiles, clothing, buildings, medical conditions, insurance coverage and more. In each case, the vector represents the “fingerprint” for each object within that AI model. The relationship between objects is then calculated by the distance between vectors across its entirety, or by combinations of dimensions.

To query the model, vector search retrieves data based on the expressed relationship desired (often nearest neighbor). These results are used to feed different machine learning operations, like similarity, recommenders, personalization and Q&A. The vector space also provides long term memory for LLMs trained on an enterprise’s proprietary data.

In a typical configuration, an enterprise would feed their proprietary data into an AI model to create embeddings, which are represented as vectors. Those vectors can then be stored in a vector database (like Chroma, Pinecone, etc.). Separately, metadata to describe each of the objects in the vector database would be stored in a transactional database (relational, NoSQL, etc.) close by.

If the enterprise is already a MongoDB customer, they could store the object metadata in an Atlas document store for easy retrieval. The platform could also provide some or all of the source data, depending upon how extensive MongoDB’s penetration is. With the addition of vector search, the MongoDB team has added an important capability that really extends their reach into the typical AI workflow. The enterprise can now use MongoDB for all data storage and retrieval steps in delivering an AI-enabled experience, allowing the customer to realize the benefits of a single data store. Additionally, as MongoDB is available on all three hyperscalers, customers can move data around seamlessly, making use of the best (and cheapest) location to run their AI models.

Vector Search is integrated with two popular open source frameworks, LangChain and LlamaIndex, which provide tools for accessing and managing common LLMs for a variety of applications (like from Anthropic, Hugging Face, OpenAI, etc.). Anticipating that vector search would become an increasingly important workload, the MongoDB team began a private preview of the capability six months ago with select partners and customers. As a result of that testing and iteration, Vector Search is now being offered in public preview.

With over 200 customers identified as AI companies reported in Q1 and “thousands” referenced during the Investor Session, MongoDB is rapidly making inroads with the AI community. This could become a large tailwind of additional consumption, beyond the gain from the resumption of secular growth from digital transformation and cloud migration. Of all the publicly traded software infrastructure providers I cover (besides maybe Snowflake), MongoDB appears the best positioned to capitalize on the surge in demand for AI data processing.

Stream Processing

Stream processing involves taking action on data in a real-time stream, generally in order to query and manipulate the data while it is in-flight. Examples of actions are creating a materialized view, aggregating, filtering, cleaning and alerting on the data. Stream processing is usually performed in coordination with a data streaming platform like Apache Kafka or Confluent Cloud.

MongoDB’s addition of a stream processing capability represents an interesting move. First, it recognizes the increased importance of real-time data as an input to newer AI models and the logical next step in data-driven applications. As LLM’s are being applied to different use cases, like customer service, the AI agent interface will only be effective if it is provided access to the latest customer data. This can’t be a snapshot from the prior day. This requirement elevates the value of real-time data distribution, which is easily facilitated by data streaming platforms like Confluent.

While somewhat competitive to Confluent, I think MongoDB’s investment in a stream processing capability validates Confluent’s strategy to incorporate Apache Flink (through its Immerok acquisition) into the Confluent platform. Additionally, I think the primary use of the Atlas Stream Processing offering will be to handle data that will ultimately be stored in Atlas. Providing a materialized view and/or prep functions in advance of storage would be a useful addition.

MongoDB Atlas Stream Processing is available to customers in private preview. For MongoDB, this provides yet another workload that would be relevant for customers building an AI system.

Isolated Search Workloads

Atlas Search has become increasingly popular with customers. As investors will recall, MongoDB launched Atlas search back in 2020 to provide customers with a means to address common application search use cases (full text, faceted, geospatial) from the same data store. Prior to this, customers would have to stand up a separate search cluster (usually Elasticsearch or SOLR) to deliver search functionality for their application. This represented another system to maintain, as well as the implementation of a data syncronization job to keep the search index updated.

As most popular search implementations are built on Apache Lucene, the MongoDB team simply integrated Lucene into their data platform. With Atlas Search, MongoDB supports the primary search types from the same interface as the core database. This has the benefit of eliminating the overhead of maintaining a separate search cluster and data syncronization process. Those are all managed by MongoDB Atlas behind the scenes.

Atlas Search has become so popular with customers that they are migrating increasingly critical search workloads to MongoDB. This has created new scalability challenges, where the search workload might consume more resources than the database. In order to address this in an efficient way for the customer, MongoDB added Dedicated Search Nodes. This allows customers to scale up their search workload separate from the operational database. They get better observability, control and cost efficiency for heavy search usage. Dedicated Search Nodes is now available in preview mode.

Relational Migrator to GA

About a year ago, MongoDB announced the Relational Migrator as a controlled access tool to be used by their Field Engineering teams to help customers plan their migration off of a relational database onto MongoDB. When it was first released, MongoDB planned to use feedback from their internal teams and customers to further shape the product, with an expectation that it would be ready for general availability in 2023. As part of the MongoDB.local conference, the team brought Relational Migrator to GA, meaning that customers can freely use the tool to plan and execute their migrations.

As background, MongoDB’s largest set of entrenched workloads to target for migrations are relational databases. Before MongoDB and other alternate data stores were available, relational databases were the only option for application development. As legacy applications are upgraded and new applications are planned, MongoDB’s data platform is a suitable choice for many database implementations that previously defaulted to relational. Application re-architectures, particularly a refactoring into microservices, also drives this motion.

A service-oriented architecture makes it much easier to keep some workloads on relational, and move the rest to a non-relational schema. While MongoDB wouldn’t be appropriate to replace every relational implementation, it could be applied to many of them. In the past, engineering teams would shoehorn all data models into relational. Some concepts, like user profile data, are much better suited to a document model, where new fields can be easily appended and look-ups are by a single primary key.

Migrating off of an entrenched relational database is a difficult exercise. It requires changes to the data model, the application code and movement of the data. In the past, engineering teams had to create their own tooling to support these migrations. MongoDB sales support and professional services would assist in these projects by providing basic scripts to automate some of the work. A flexible, UI-driven tool was needed as much of the effort can be redundant across implementations.

This was the genesis for the Relational Migrator. The goal of this product is to provide engineering teams with the tools to easily connect to a relational database, analyze its table structure, map that to the document model and then manage the data migration. It also provides support in rewriting application code to access data through the MongoDB connector, as a replacement for the SQL code that queried the relational database.

The Relational Migrator is now available for a number of popular cloud and on-premise relational databases. Data can be ported to either MongoDB Atlas or Enterprise Advanced. Further, customers can opt for a single migration of the data (if downtime is acceptable) or through a continuous synchronization process (minimal disruption).

While the majority of MongoDB’s growth is driven by workloads from new applications or microservices, they are smart to facilitate a direct migration off of a relational database for the instances where the customer wishes to further consolidate their data storage solutions onto a smaller set of providers. Relational Migrator will facilitate a steady stream of additional workloads from the refactoring of older applications to take advantage of newer data models.

Atlas for Industries

Similar to Snowflake’s ecosystem of industry specific solutions, MongoDB announced Atlas for Industries to provide customers with industry-specific expertise, programs, partnerships and integrated solutions. Within each industry, MongoDB will develop unique capabilities and achieve relevant certifications that make their offering more valuable for customers within that category. Additionally, each industry selected already has a critical mass of customers using the MongoDB platform, allowing the MongoDB professional services team to share best practices and security considerations.

Combined with MongoDB’s foray into supporting operations for AI, industry participants can benefit from MongoDB’s experience as they establish their own AI strategies. Because data is the critical differentiator for enterprises in building competitive advantage around AI offerings, MongoDB’s visibility into what is working effectively across their customer base will become a valuable input for industry-specific solutions.

As part of the announcement, MongoDB will establish financial services as the first industry specific offering. This leverages MongoDB’s deep experience servicing customers in the finance industry. Currently, 19 of the 20 largest banks in North America use the MongoDB platform within their infrastructure. This provides MongoDB with the insight to layer on new capabilities that meet requirements unique to a particular industry segment.

As an example of a value-add capability for financial services, MongoDB achieved the Amazon Web Services (AWS) Financial Services Competency. This involved demonstrating MongoDB’s strong capabilities around security, reliability and system management for usage specific to financial operations. These were validated by the AWS team through direct testing.

As part of the MongoDB Atlas for Financial Services launch, MongoDB has achieved Amazon Web Services (AWS) Financial Services Competency. To obtain this competency, MongoDB was tested against strict security, operational, and reliability requirements, validating that MongoDB and AWS can help financial institutions get ideas to market faster, while reducing cost and enhancing business agility. This builds on MongoDB’s long standing relationship with AWS, including MongoDB Atlas’ availability in the AWS Marketplace.

MongoDB Press Release, June 2023

MongoDB plans to expand Atlas for Industries programs to other industry categories over the course of the next year. They have programs on deck for manufacturing and automotive, insurance, healthcare, retail, telecommunications and the public sector. Interestingly, these have a lot of overlap with Snowflake’s Industry Solutions.  For MongoDB, I think Atlas for Industries will provide another competitive advantage in attracting enterprise customers in targeted categories, providing a further catalyst for growth.

Hyperscalers

MongoDB enjoys strong relationships with all three hyperscalers. While the hyperscalers offer competitive database products, they have also all struck productive co-selling relationships with MongoDB. These incentivize their sales teams to work closely with their counterparts at MongoDB to land enterprise customers on that hyperscaler with MongoDB as the primary database.

In March 2022, AWS took their relationship to the next level when they announced an expanded collaboration with MongoDB. The agreement with AWS builds on the prior multi-year relationship between the two companies, aimed at driving customer adoption of MongoDB Atlas on AWS.

In an effort to further improve the customer experience, both companies have agreed to collaborate across sales, customer support, solution architecture, marketing and other areas to make MongoDB Atlas a better experience for developers on AWS globally. This includes increased workload migration incentives and enhanced tools to help customers move from legacy technologies in on-premises data centers to MongoDB Atlas on AWS. Finally, the partnership will support MongoDB’s expansion into more AWS Regions across the globe and the US Public Sector with FedRAMP authorization.

During the Investor Session at MongoDB.local, MongoDB’s CEO conducted a Partner Spotlight discussion with the Managing Director of the AWS Marketplace. The AWS leader highlighted many of the benefits of working closely with MongoDB. AWS smartly recognizes that they can strike a win-win relationship in this case. First, they can keep the enterprise customer happy by allowing their developers to remain on MongoDB. At the same time, AWS can sell them many other adjacent services for application hosting, storage and networking. Second, AWS still generates revenue from MongoDB’s usage of the underlying compute and storage, as Atlas is hosted on AWS.

For investors who have followed the MongoDB story for a while, you will recall that this mutually beneficial collaboration wasn’t always the case. In 2019, AWS announced DocumentDB, which represented a hosted version of MongoDB. This directly competed with MongoDB. Many analysts predicted the product would be a MongoDB killer.

Fast forward to today and DocumentDB did not kill MongoDB. The product still exists, but has not broadened its penetration beyond a few AWS customers. According to DB-Engines, AWS DocumentDB’s popularity ranking is way down at position 151, while MongoDB is ranked at position 5. Even expanding the comparison of scores on DB-Engines to include more modern solutions from the hyperscalers, the proprietary hyperscaler products are scored far below MongoDB.

Additionally, those relative positions haven’t changed much over time. Amazon DynamoDB and Azure Cosmos DB are still far below MongoDB in terms of overall developer popularity. In fact, none of the newer, proprietary database solutions (those not derived from open source or less than 10 years old) from the hyperscalers have even broken into the Top 10.

I think at this point, we can dispense with the thesis that newer specialized database solutions from the hyperscalers will dislodge MongoDB. The data just doesn’t bear this out. As an aside, the same pattern applies to Snowflake, relative to hyperscaler data warehouse solutions like Redshift or BigQuery. The point is that in the database category, the independent providers tend to do a better job at building a solution that appeals broadly to developers across all cloud providers.

This is likely due to a combination of factors. They include a more complete feature set, a desire to avoid lock-in and more focused developer relations. For MongoDB and Snowflake, the ability to work across all three hyperscalers with the same interface represents a big advantage in my view. Every global enterprise engineering team has to consider the opportunity cost of maintaining all their data on a single hyperscaler. They must also be prepared for the optionality to support another hyperscaler without having to rewrite their application. With MongoDB, the API is the same across all providers.

Google Cloud Deal

Another catalyst for fostering a closer relationship between MongoDB and the hyperscalers is AI. During the user conference, MongoDB announced an expanded partnership with Google Cloud that enables developers to use state-of-the-art AI foundation models from Google to build new classes of generative AI applications.

This partnership delivered a couple of new capabilities. First, developers can use MongoDB Atlas Vector Search with Google’s Vertex AI platform to build applications with AI-powered capabilities for personalized and engaging end-user experiences. Vertex AI will provide an API to generate text embeddings from customer data stored in MongoDB Atlas, combined with PaLM text models to create advanced functionality. Examples of AI-powered capabilities that developers can derive from this combination include semantic search, classification, outlier detection, AI-powered chatbots and text summarization.

The other benefit coming out of the new partnership will be access to dedicated professional services teams that can help rapidly prototype applications by providing expertise on data schema and indexing design, query structuring and fine-tuning AI models. Developers can also tune models to further improve the performance of the model for specific tasks. Google Cloud and MongoDB are working closely together to make these experiences even more seamless with Google’s Generative AI capabilities built right into MongoDB Atlas.

When applications are ready for production, the MongoDB and Google Cloud professional services teams can optimize applications for performance and will support future feature development based on customer feedback.

I think this relationship further underscores the unique position that MongoDB occupies in the AI value chain, further solidified with the introduction of Vector Search. As Google is pushing their AI capabilities, this partnership with MongoDB provides developers with a reason to utilize Google Vertex AI for their AI workloads.

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Q1 Earnings Results

Now that we have reviewed MongoDB’s product strategy and recent announcements, let’s look at how these translate into the most recent earnings results. After a couple of mediocre quarterly reports, MongoDB’s Q1 earnings results were particularly well-received by the market. Prior to the report on June 1st, MDB stock had spent most of 2023 in the low $200 range. It even dipped to the $140’s briefly in November 2022.

The day after Q1 earnings, MDB stock shot up 28%. It recently reached a new 52 week high, after passing the $380 mark hit back in August 2022. It is still below the historical ATH of $590 reached back in November 2021. Looking forward, the key question for investors will be if MongoDB can reaccelerate its revenue growth from the 29% annual increase in Q1 and outperform their current full year estimate for 19% growth.

Beyond a return to durable elevated revenue growth, a rapid improvement in profitability measures are providing further support for the stock. Over the last year, MongoDB surged past Non-GAAP operating margin break-even to deliver 12% op margin in Q1. FCF margin was even better, hitting a record of 14%. During the Investor Session, the CFO set the long-term operating margin target over 20%.

The market seems to be anticipating improvements in revenue growth and operating leverage, with the P/S ratio kicking above 20 recently. MongoDB’s P/S ratio has been above 20 in the past, but revenue growth was well over 30% annually then. Accelerating revenue growth with record operating and FCF margin may provide enough momentum to push MDB back towards its ATH price over the next year.

With that, let’s review the results from Q1. Combined with the product development commentary from the recent Investor Session at MongoDB.local, we can try to discern some signals about the growth story looking forward to 2024.

Revenue

As part of MongoDB’s Q4 report in March, leadership forecast underwhelming preliminary revenue guidance for Q1 and the full year. At the time, it wasn’t clear if leadership was being conservative, or anticipated a more significant slowdown than what had already been experienced in the prior year (calendar year 2022). Because of the full year estimate for slower growth, analysts set a low bar for Q2 and Q3 revenue targets. This is what allowed MongoDB to really outperform in Q1.

MongoDB delivered $368.3M in revenue during the first quarter, which was up 29.0% annually and 1.9% sequentially over Q4. This beat the analyst estimate for $347.0M by a healthy margin, which would have represented 21.6% annual growth and -4.0% sequentially. MongoDB also surpassed their preliminary Q1 guidance for a range of $344M – $348M by $22.3M and 5.9% of sequential growth.

In fact, Q1 revenue even leapfrogged the analyst estimate for Q2 revenue, which was for $360.8M or 18.8% annual growth. Due to the outperformance in Q1, MongoDB leadership set the initial Q2 guide for a range of $388M – $392M, representing 28.4% annual growth and 5.9% sequentially. If we assume the same level of beat for Q2 actual revenue (+$22.3M), MongoDB could deliver $412.3M for annual growth of 35.8% and sequential growth of almost 12%.

This would represent a nice reacceleration of revenue growth over the Q1 rate. Given that annual growth decelerated in every quarter of the prior year and sequential growth never broke 10%, this would represent a favorable inflection. I think this turning point in the growth story is what reignited the performance of MDB stock.

For the full year, the company had set guidance for revenue in the range of $1.480B – $1.510B in the Q4 report, representing annual growth of 16.4%. After the Q1 beat, they raised this by $37M at the midpoint to $1.522B – $1.542B for 19.3% annual growth. The magnitude of the raise exceeded the $22.3M beat in Q1.

Looking to the second half of the year, the full year revenue guide currently implies flat sequential growth for Q3 and Q4. On an annual basis, revenue growth would drop below 20%. That assumption is based on adding the Q1 actual to the Q2 estimate (which is $758.3M) and subtracting from the midpoint of full year guidance of $1.532B, resulting in just $774M of revenue for the second half of FY2024. Dividing by 2 gives $387M a quarter, or less than the midpoint of the current Q2 guide.

I think the full year revenue projection is conservative and will be raised as MongoDB beats each subsequent quarterly estimate. Given their momentum in customer growth, I think it is unlikely they will revert to 0% sequential revenue increases.

MongoDB’s cloud offering, Atlas, grew by 40% y/y and contributed 65% of total revenue. In Q4, Atlas grew by 50% and also contributed 65% of revenue. By applying the percent of revenue for Atlas to total revenue, we can get an approximate value for Atlas revenue in each quarter. Atlas was responsible for about $239.4M of revenue in Q1, versus $234.8M in Q4, for 1.9% sequential growth. That represents a pretty substantial deceleration from the 11.7% sequential growth in Q4. However, management pointed out that there are fewer days in Q1. Because Atlas is a consumption business, this would impact revenue generation by about 2-3%.

During the Investor Session, MongoDB’s CFO displayed an interesting slide that provided some insight into the change in y/y growth rates for Atlas. He was able to show how the macro headwinds that started in Q2 FY2023 (calendar 2022) translated into slower weekly sequential growth in Atlas consumption.

The slide illustrates the clear drop-off in growth rates starting Q2 last year and the pick-up in Q1. As we look forward to the 12 months, MongoDB can potentially reaccelerate sequential growth. This would be driven by increased consumption of Atlas by the combination of new customers, expanded workloads within existing customers and the natural growth of existing application usage.

Profitability

While the outperformance on revenue growth was refreshing, MongoDB’s improved profitability delivered another positive catalyst. In an environment where investor expectations have shifted towards a balanced mix of revenue growth and margin improvement, MongoDB responded well. They are demonstrating the ability to drive meaningful gross profit and free cash flow from their platform.

In Q1, profitability measures beat expectations and hit new records in several meaningful areas. Non-GAAP gross margin reached 76.0% in Q1, which is 100 bps above Q1 from a year ago. Gross profit grew by 30.6% year/year, which is about 160 bps faster than overall revenue growth. Improvements in gross margin continue to be driven by efficiencies in Atlas operations.

As a cloud-based service, Atlas will have lower gross margins than the pure software package Enterprise Advanced. Yet, in spite of Atlas’ contribution to revenue continuing to increase, MongoDB has been able to methodologically inch up gross margin. During the Investor Session, the CFO shared a long term target for gross margin of 70% or more.

The outperformance on revenue growth, gross margin and improved operating leverage are all combining to allow MongoDB to deliver strong Non-GAAP operating income. In Q1, MongoDB generated record operating income of $43.7M, representing an operating margin of 11.9%. This beat the company’s own guided range from Q4 for $10M to $13M by about 4x. This compares to $17.5M in Non-GAAP operating income a year ago and $37.2M in Q4.

The increase in operating margin follows a line of gradual improvement since the IPO. The lack of profitability had been a major investor complaint for a while, particularly as some software peers achieved positive operating margin much sooner than MongoDB.

On a per share basis, the Non-GAAP operating income outperformance drove a huge beat in EPS over analyst estimates. MongoDB delivered $0.56 of Non-GAAP EPS, versus the $0.19 consensus. It’s not often that we see beats of that magnitude on EPS with software companies.

Shifting to cash flow, the same story emerges. Q1 represented a record for FCF generation. MongoDB delivered $51.8M of FCF, for a FCF margin of 14.0%. A year ago, FCF was $8.4M for a 2.9% FCF margin. In Q4, it was $23.8M for a 6.6% FCF margin. FCF in Q1 doubled over Q4 and was a 6x improvement from a year ago. The high FCF margin allowed MongoDB to remain over the Rule of 40 for the quarter.

Looking forward, MongoDB expects the strong profitability performance to continue. For Q2, they are estimating Non-GAAP income from operations in the range of $36M – $39M. While below the $43.7M just delivered in Q1, it is significantly above the original estimate set by the company for Q1 of $10M – $13M. On an EPS basis, MongoDB leadership estimates Non-GAAP EPS in a range of $0.43 – $0.46, which beat the analyst estimate for $0.14 substantially.

For the full year, MongoDB leadership raised profitability estimates as well. For Non-GAAP operating income, they increased the range from $69M – $84M issued in Q4 to $110M – $125M in Q1. This translates into a Non-GAAP EPS range of $1.42 – $1.56, compared to analyst estimate for $1.04, and the company’s prior guidance of $0.96 – $1.10.

The majority of the gains in operating leverage has been generated by improvements in spend efficiency. Non-GAAP operating expense as a percentage of revenue has decreased from 113% in FY2018 to 70% in FY2023. Over the long term, the CFO shared an operating margin target of 20%+ during the Investor Session. Based on MongoDB’s current momentum, this should be easy to hit in the next couple of years.

Customer Activity

MongoDB carried the good news from their Q1 report into customer activity as well. The number of total customer additions hit a record in Q1 with 2,300 new customers joining the platform. This exceeded counts for at least the past 2 years, which even included the Covid-fueled IT spending surge in 2021. It also far exceeded the 1,700 customers added in Q4, which represented a concerning dip.

Of the customer additions, MongoDB leadership also cited over 200 customers that identify as AI companies. This perceived demand tailwind is driving a lot of the excitement around the stock. Considering the product announcements in AI made during the MongoDB.local event, like vector database support, this interest isn’t surprising. Nor is it necessarily new, as MongoDB leadership first mentioned Hugging Face as a new customer back in Q3 (admittedly, I missed the significance of this one, as I was ramping up on the AI story).

The shift to AI will favor modern platforms that offer a rich and sophisticated set of capabilities delivered in a performance and scalable way. We are observing an emerging trend where customers are increasingly choosing Atlas as a platform to build and run new AI applications. For example, in Q1, more than 200 of the new Atlas customers were AI or ML companies, while start-ups like Hugging Face, Tekion, One AI, and Nuro are examples of companies using MongoDB to help deliver the next wave of AI-powered applications to their customers.

MongoDB Q1 FY2024 Earnings Call, June 2023

In addition to a surge in total customer additions, MongoDB hit a new record on the number of sequential adds in $100k+ customers. This increased by 110 in Q1 to reach 1,761. This growth helped keep MongoDB’s net ARR expansion rate above 120%. MongoDB doesn’t report the actual value, but it is good to see the rate hasn’t dipped below this threshold with the slower overall growth.

Direct Sales customer additions dipped in Q1 to only represent 300 net adds over Q4, but management shared that the majority of the Direct Sales customers were in the Enterprise segment (their largest). As investors will recall, the Direct Sales metric represents those customers who get assigned a sales rep (versus self-serve), so variations in this metric could have other causes.

During the Investor Session, MongoDB leadership displayed a slide with customer logos segmented by industry. An important aspect of the MongoDB investment thesis is their penetration with many mainstream businesses. For example, some of their largest customers are in financial services, with examples like Wells Fargo and Morgan Stanley. They also enjoy broad penetration in other non-tech categories like healthcare, manufacturing, media and retail.

Intuitively, we would assume these companies (particularly financial services) would gravitate towards a relational model. Yet, they are embracing MongoDB’s non-relational workload support for a number of different applications. These customers appreciate the benefits of MongoDB’s high performance, versatility and presence across all cloud providers. This also highlights the fact that MongoDB isn’t embraced just by progressive developers at tech start-ups, but also engineering teams at established Fortune 100 enterprises.

In fact, during the Investor Session, MongoDB’s CFO shared a slide listing penetration among the Fortune 100, Fortune 500 and Global 2000. These numbers are higher than I expected, with over 60% of the Fortune 100 using MongoDB in some capacity. Even though MongoDB has a foothold in these large companies, there is still plenty of growth remaining. The CFO followed this slide with data showing that MongoDB’s share of total database spend for the Fortune 100 and Fortune 500 is below 2%.

The difference is explained by the number of workloads that these large enterprises use MongoDB to address. As discussed earlier, the application workload is MongoDB’s currency in large customer spending growth. MongoDB is often brought into the organization to provide the data store for a single application workload by a development team. Then, over time, the broader engineering organization becomes comfortable with the inherent advantages of MongoDB and begins applying it to adjacent applications.

This drives the growth of customer spend within each enterprise across two dimensions. First, as the customer’s business grows, the use of each application increases, generating more consumption of the MongoDB service backing it. Second, the addition of new application workloads starts new growth curves that stack on top of each other.

MongoDB’s CFO provided an example of this during the Investor Session, where a particular customer iterated through 5 application workloads. As time progressed, the ARR associated with each established workload increased from more utilization. Then, each subsequent workload stacked a new ARR curve on top of the prior ones.

Having managed a large-scale hosting infrastructure consisting of many microservices for a major consumer Internet property in the past, I can see the appeal of consolidating many data models onto a single platform. While it may not address every edge case, MongoDB can handle most requirements for document, key value, time series, wide column and search workloads. Additionally, some relational data models are fairly flat in practice (meaning denormalized with some data duplication for performance), which can easily be repurposed into a document model.

The benefits of a single platform like MongoDB for consolidating most (not all) workloads far outweigh any gaps in functionality. These include a single interface, consistency across cloud providers, lower cost (due to fewer vendors) and less DevOps overhead. Atlas makes this argument even more compelling, as the infrastructure management is outsourced to the experts at MongoDB.

In the Q1 earnings calls, management reported “record” number of new workload captures within existing customers. This supplemented the all-time-high number of total customer additions, emphasizing that MongoDB’s future revenue growth should be durable. They will continue to increase revenue as existing large customers apply MongoDB to more workloads within their organization. This motion is supported by MongoDB’s continued product expansion to address new data models.

Looking forward, future elevated growth will be maintained as new customers backfill any eventual saturation within large customers. As long as MongoDB maintains healthy net ARR growth with existing customers and continues to add new customers at a robust clip, then overall revenue growth should continue in the 30%+ range. New AI workloads provide another tailwind to prop up growth over the long term.

Investment Plan

In my review of MongoDB’s Q4 results, I was guardedly optimistic, predicting that the low preliminary full year guide was conservative. I cited a few factors that demonstrated MongoDB’s ongoing momentum and raised the potential benefit from AI.

In the Q1 report, MongoDB showed that these tailwinds are indeed in place. Performance surpassed expectations on almost every level, surprising investors with their momentum. The market rewarded the stock with a 28% bump the next day. At current prices, MDB is up more than 100% in 2023 and is sitting near its 52 week high, with the P/S ratio hovering around 20. This valuation has brought it above most peers and is now inline with the most highly valued stocks, like NET and SNOW.

I agree that the stock is pricey at this valuation, but also consider MongoDB a company I want to own. It is becoming increasingly clear that investment in modern data infrastructure will be a priority for enterprises going forward. Not only does this represent a continuation of previous digital transformation secular trends, but also is a requirement for any company to leverage AI to improve their service offerings.

As part of Accenture’s latest earnings call, they cited an internal survey in which executives at customer companies were asked about their plans for AI. The results indicated that 97% of executives expect generative AI to be transformative to their industry and that 67% of organizations are planning to increase their spending on technology in general, with prioritization for investments in data and AI.

And so while it is early days, we see generative AI as a key piece of the digital core and a big catalyst for even bigger and bolder total enterprise reinvention going forward. In fact, in a survey of global executives that we completed just last week, 97% of executives said Gen AI will be transformative to their company and industry and 67% of organizations are planning to increase their level of spending in technology, prioritizing investments in data and AI.

Accenture Q3 2023 Earnings call, JUne 2023

Given the critical position of data as a driver of AI, I have increased my portfolio allocations to data infrastructure providers (CFLT, SNOW, MDB and even arguably DDOG and NET). MDB now occupies equal standing with my other large positions. As data is becoming the competitive differentiator for realization of AI ambitions, providers of data infrastructure are well-positioned. Of these, I think MongoDB has significant optionality. They will benefit from increased consumption driven by the confluence of greater developer productivity, more AI-driven data processing demand and the ongoing trends of digital transformation and cloud migration.

Further Reading

• The Investor Session from MongoDB.local is available on YouTube. I recommend that investors watch the full 4+ hour segment for a complete overview of the product announcements, go-to-market strategy and financial insight.
• Our partners at Cestrian Capital Research cover MongoDB as part of their Cestrian Tech Select newsletter, which includes a broad range of technology companies with a deep focus on financial and chart analysis. They cover MongoDB as well and provide a useful supplement to my perspective.

NOTE: This article does not represent investment advice and is solely the author’s opinion for managing his own investment portfolio. Readers are expected to perform their own due diligence before making investment decisions. Please see the Disclaimer for more detail.