MongoDB (MDB) Q2 FY2023 Earnings Report

After fairly positive earnings in Q1, MongoDB’s Q2 report reflected increased pressure from the macro environment. Product-market fit, competitive position and GTM motion with large customers all remain consistent. What appears to have changed is a marked slowdown of utilization expansion for specific customer segments, compressing revenue growth as a consequence of MongoDB’s consumption model. This impact is being compounded by the mix shift of EA license revenue to Atlas.

In spite of this, management is continuing to invest heavily to drive large customer growth. Sales and Marketing spend increased significantly in Q2, highlighted by a surge in sales headcount and the resumption of in-person events, particularly the MongoDB World conference. These are resulting in growth of Direct Sales customers, which hit record net additions in Q2 and now account for 86% of total revenue. As new salespeople ramp up, more customers should push over the $100k ARR spending threshold.

In this post, I review the results, unpack how these are related to customer behavior and provide some signals that investors can monitor to track MongoDB’s progress going into 2023. For the next couple of quarters, I think that MDB stock will be under pressure. However, when headwinds to customer expansion abate, the reversal could be swift, reflecting a similar pattern that we observed in 2021, magnified by the greater mix of Atlas revenue. This could provide a favorable set-up for second half of 2023, as the company laps this year’s results. However, that means investors would need to stay invested and have faith in MongoDB’s product vision.

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Let’s start by reviewing the results themselves, by looking at the growth metrics, profitability measures and customer activity. Then, I will loop back on MongoDB’s product strategy and the value proposition for enterprise engineering organizations. Finally, I examine how the next year may play out and risk/reward trade-offs that investors can consider. Of course, any speculation is based on the macro environment. If business activity in general remains pressured through 2023, then the timeline to re-acceleration may be extended.

For additional research on MongoDB’s performance, please see the post-earnings review from our partners at Cestrian Capital Research.

Growth Metrics

Q2 revenue was $303.7M, up 52.8% annually and 6.4% sequentially. This beat the company’s guidance from Q1 for $279M – $282M (up 41.2% y/y) by about $23M at the midpoint and over 11% of annualized growth. In Q2 of last year, the company grew revenue by 43.7% annually and 9.4% sequentially. The sequential growth this year in Q2 is behind the pace of last year.

To stay on pace with the sequential growth from prior year, MongoDB would have needed about $8.5M more of revenue. The gap is partially explained by management’s projection from the Q1 report, where they expected a $4-5M impact from the current global macroeconomic environment. Adding in that buffer would have brought sequential growth to 8.0%, still slightly below last year’s pace.

Atlas now makes up 64% of total revenue and grew 73% y/y in Q2. This compares to 60% of total revenue and 82% y/y growth in Q1. If we go back to Q2 of last year, Atlas made up 56% of total revenue and growth was 83% y/y. Atlas growth did step down in this Q2, but the 80%+ growth from a year ago (and subsequent quarters) was preceded by lower Atlas growth in the 60-70%% range. If we look at sequential growth in Atlas revenue, the rate accelerated slightly over Q1’s 10.8% to 13.5% in Q2. Annualized, this would represent about 66% growth.

For Q3, leadership projected a revenue range of $300M – $303M, for 32.9% annual growth at the midpoint. This beat the analyst estimate for $294.6M or 29.8% annual growth. This range represents about a 1% sequential decrease from the Q2 actual revenue result ($303.7M in revenue). Guiding down from Q2 to Q3 is a change from prior guidance patterns. In Q2 of last year, management provided forward guidance for Q3 that was 2.2% above actual Q2 revenue, representing a swing of about 3% of sequential growth this year. In Q3 of last year the actual sequential revenue growth was 14.2%, or a 12.0% increase over the original target.

Going back 2 years, the Q3 estimate was about even with the Q2 actual. This resulted in 9.0% of sequential growth, meaning the increase from estimate was about 9%. Two years ago represented the summer of 2020, during the height of Covid. This level of beat provides a more reasonable target. This means that we could optimistically expect about 8% of sequential revenue growth for the Q3 actual. This would bring Q3 revenue to $325.6M and annual growth to 43.5%.

For the full year, leadership raised the revenue target by $19M at the midpoint to a range of $1.196B to $1.206B for an annual growth rate of 37.4% y/y. The prior range provided with Q1 results was $1.172B – $1.192B for annual growth of 35.2% and a raise of about 2% from prior guidance of 33.5% growth. The $19M raise was less than the $23.2M beat in Q2, effectively lowering the guide by $4.2M for the next 2 quarters.

As part of Q1’s results, management had factored in $30M – $35M of annualized revenue to account for macro impact. The actual impact for Q2 and the full year appears greater than what they had projected in the Q1 results. I think investors (myself included) were assuming that this downward adjustment was conservative and they might beat it.

Starting with our self-service channel. You’ll recall that we experienced slower than historical consumption growth in Europe in Q1 and in the U.S. in May. The May consumption patterns generally continued for the remainder of Q2 and self-serve did modestly better than our expectations.

Moving on to the mid-market channel. For context, the customers in this channel tend not to be traditional medium-sized businesses. This channel includes a disproportionate share of digital native, fast-growth companies that have built their businesses on MongoDB. Our expectation that the mid-market slowdown we saw in Europe in Q1 would become global in Q2. This is what we experienced, but the slowdown was more significant than we had expected, specifically the digit layer subset of the mid-market experienced slower growth in their applications as a result of macroeconomic conditions, and therefore, their underlying consumption growth of MongoDB slowed as well.

Finally, turning to enterprise, our largest channel. As in Q1, we had not seen an impact on consumption but we expected a modest impact to manifest itself in Q2. Here, consumption growth was above our expectations in North America, while in Europe, we experienced greater-than-expected macroeconomic headwinds. The slowdown in Europe was evidenced across all subregions and industries.

mongoDB Q2 Fy2023 Conference Call

Management acknowledged on the earnings conference call that performance was worse than they had expected. They discussed the different factors at play. It’s instructive to break these out by customer types:

Self-serve. These are generally small businesses and individuals who sign-up and consume MongoDB resources directly online and don’t interact with a salesperson. Consumption softness first observed in Q1 continued through Q2, but performed better than expected.

Mid-market. This category includes a lot of fast-growth, digital native companies that selected MongoDB as their primary database from the beginning. The slowdown in this category observed in Europe in Q1 extended globally in Q2. Additionally, it was more pronounced than expected.

Enterprise. Enterprise customers represent MongoDB’s largest source of revenue. In Q1, MongoDB didn’t experience an impact on consumption, but they modeled for some in Q2. As it turns out, consumption growth in North America for enterprise customers was above management’s expectations. But, Europe experienced macroeconomic headwinds greater than anticipated. This resulted in less Atlas consumption. Customers in this category would be Boots, Conrad Electronic and Otto.

The full year guidance assumes that these trends continue. Focusing on the mid-market, digital native customers, we can infer a few examples of companies that likely experienced these consumption slowdowns, including Boxed and Coinbase. What’s noteworthy is that these customers spiked their usage in 2020 – 2021, due to ongoing Covid restrictions and the frothy financial conditions in 2021.

As Covid receded and the macro environment tightened, these companies’ year/year growth in utilization decreased. Looking at Boxed as an example, their usage spiked 30x year/year in late 2020 and that elevated use likely continued into 2021. They were initially using MongoDB EA and migrated to MongoDB Atlas on Google Cloud. While they highlighted cost savings moving to the cloud relative to their growth, their cloud spend doubled over this period.

“We chose MongoDB at the start,” says Boxed CTO and co-founder William Fong, “because we were confident it ran at scale and we really liked the flexibility of the document data model that let us rapidly add to and change database components without spending lots of time executing schema migrations. It played a pivotal part as we were figuring out who we wanted to be.”

Fong continues, “By now, we’ve got more than 50 self-developed applications running across the company, from customer-facing through to the MongoDB Atlas main data platform, through a pipeline into a data warehouse that supports our analytics.”

Boxed CTO, Blog Post

In 2022, as Covid receded and macroeconomic conditions dampened consumer demand, this extreme usage spike moderated. Also, as with other e-commerce and delivery businesses, year/year transaction volume on their platform likely flatlined or even decreased. Because of MongoDB’s pricing model for Atlas based on consumption, this would cause normal revenue growth from workload expansion to moderate. Atlas is less insulated from this effect than a typical SaaS model with annual contracts.

As a sidenote, in these cases of the digital natives, MongoDB is still a critical component of their software infrastructure. It is not an “optional” or less important system than say security or observability. If MongoDB were removed, then companies like Boxed could not conduct business. This is the same for other digital natives that built their platform on MongoDB from the beginning.

For larger enterprises that might be introducing MongoDB workload by workload, it would be reasonable to assume that in some cases, MongoDB is not yet at the backbone of the software infrastructure and that the legacy relational database is probably the last component to migrate to a more modern data architecture. Even if MongoDB is relegated to an ancillary application first (usually characterized as lower traffic with more tolerance for downtime), it would still be considered mission critical. That application would not work without its database – the difference is that the users might be willing to wait wait a few minutes for recovery from an issue.

Analyst: When you look at your Atlas consumption model, do you think that Atlas’ alignment with your end customers’ demand trend means that you’re more or less seeing the macro downturn in essentially real time, something that maybe is a little more unique here versus other software vendors? Do you think that this alignment also means that you can see the benefits of economic recovery sooner than other annual subscription software models?

CFO: Yes. We do think it’s a more real-time reflection given that we’re really sort of a second order effect of the underlying activity in their applications. And obviously, we’re not macroeconomists, and so I’m not forecasting recovery, we guess theoretically that if there were increases in activity and increases in underlying usage, that would drive incremental consumption of our platform.

MongoDB Q2 FY2023 Conference CAll

What I think is also interesting about these cases with the digital natives is to consider the impact of Covid surge versus macroeconomic (or even specific vertical) growth. We could acknowledge that the Covid surge, like the case with Boxed, likely began tempering in 2021 and then really dropping in 2022. However, MongoDB was able to absorb the 2021 drop because other digital businesses were excelling in 2021, like crypto platforms and gaming, which represent two other large digital native customer categories.

As an example, Coinbase is a heavy user of MongoDB. This interview between Jim Cramer and MongoDB’s CEO in December 2021 highlights the important relationship between the two companies. As investors know, crypto trading was at a peak in late 2021 and likely drove heavy usage of MongoDB. In their Q3 FY2022 earnings reported in December of 2021, MongoDB’s CEO even highlighted the increase in usage of Atlas by Coinbase in an interview with Business Insider. The use of Coinbase was also mentioned in the Q3 FY2022 earnings call as a reference example.

Coinbase, which is dedicated to increasing economic freedom in the world by building a more accessible, transparent, and equitable financial system, is the trusted choice of more than 73 million individuals, businesses, and institutions to interact with the crypto economy. The company is moving more workloads to MongoDB Atlas so it can build new products and services quickly and meet the scale and availability requirements of the unprecedented growth in the cryptocurrency markets.

MOngoDB Q3 Fy2022 Earnings call

Relationships like those likely drove outperformance for Atlas on a consumption basis in Q4 of last year and even coming into Q1. However, the rapid decline in crypto market activity in 2022 would immediately translate into lower usage of Atlas, simply because there are fewer transactions. Atlas is still a critical component of Coinbase’s trading platform, but if the amount of trading activity drops, then Atlas revenue recognition would flatline or decrease as smaller Atlas clusters could handle the load.

As 2022 is progressing, we are experiencing a confluence of factors causing consumption growth moderation for MongoDB Atlas. First, Covid-driven businesses are reverting to more normal activity. Second, businesses that thrived on financial looseness in 2021 (crypto, FinTech) are also experiencing a moderation of business. Third, the macroeconomic situation is reducing overall consumer demand for many products, with inflation limiting purchasing power. This is most pronounced in Europe and impacted enterprise customers there.

MongoDB Atlas’ consumption model is impacted immediately by these factors. This is different from their Enterprise Advanced (licensed product for self-hosting), for which customers purchase longer duration contracts. Usage by EA customers would not reflect this business activity slowdown immediately. As MongoDB has increased the penetration of Atlas from 44% two years ago (Q2 FY2021) to 64% now, the impact of the consumption model becomes more acute.

Finally, patterns in EA purchases can have an outsized effect on revenue recognition. When a customer purchases an EA license, a greater share of the revenue is recognized upfront. This is different from Atlas where revenue generation for a new customer starts at $0.

We expect a sequential decline in Enterprise Advance in Q3 as the renewal base is sequentially lower. Looking into Q4, we expect a seasonal uptick in revenue from EA. But recall, we faced a very difficult year-over-year comparison given strong EA new business activity we saw last year.

MongoDB Q2 EArnings Call

This means that EA will contribute less to Q3 revenue than it normally would. For Q4, MongoDB does normally experience an increase in EA renewals, as those generally align with the end of the year. But, Q4 of last year was particularly strong, which will lower the potential year/year growth rate this year.

Overall, I think the combination of reduced consumption and the product mix shift from EA to Atlas is creating a difficult period for revenue growth through the end of this year. As we look to 2023, however, the situation could reverse. If the macroeconomic situation improves or at least stabilizes, then the headwind to consumption with digital natives and regional enterprise customers will abate. This will allow normal expansion to resume, as well as any “catch up” in activity following the macro depression.

As Atlas will represent an even larger percentage of total revenue next year, increases in consumption by customers will have an immediate effect on revenue and will likely be more pronounced from any catch up. Additionally, as we pass Q2 of FY2024 (next year), the year/year comparison will become much easier. That means that if we are in a state of economic recovery by Q2-Q3 of next year, the reversal in impact from higher consumption will drive significant year/year revenue growth in second half of 2023. This should start to be noticeable in increasing sequential revenue growth.

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Profitability Measures

Non-GAAP gross margin showed some improvement year/year, growing from 72% a year ago to 73% in Q2. For Q1, it reached 75%, which represented a high point. Management attributed the year/year improvement to efficiency gains in Atlas. However, broader Atlas adoption is also a headwind to gross margins, as its gross margin is lower than that for the EA licensed software. Gross profit in Q2 was $223.2M, up from $144.0M last year (55% growth) and $214.3M last quarter. The lower 4.2% sequential growth in gross profit as compared to revenue growth of 6.4% sequentially is likely pressuring operating margins.

Non-GAAP loss from operations was $12.4M in Q2, for an operating margin of -4.1%. This compares to a loss of $4.0M a year ago, or an operating margin of -2.0%. What is discouraging about the operating loss is the performance relative to Q1, which delivered $17.5M in operating income for an operating margin of 6.1%. This reversed what appeared to be a trend of improving operating leverage from Q2 of last year.

Loss from operations was better than their original estimate for Q2, issued with the Q1 report, for a loss of $18.0 – $16.0M, representing a beat of about $4.6M at the midpoint. For Q1, management had estimated an operating loss of -$5.0M to -$2.0M and beat that by $21.0M. That outsized revenue beat in Q1 magnified the reversal in op margin performance.

The swing in operating margin to a loss in Q2 can be attributed to a few factors. First, MongoDB resumed travel and in-person events. In June, they held their annual user conference MongoDB World, which attracted 3,000 attendees to New York. In Q1’s report, management mentioned that “we also benefited in Q1 from the timing of certain expenses, which we now expect to occur later in the year.” These expenses were pushed out from Q1, bolstering operating income in that quarter, and shifting them to later this year.

In Q2, MongoDB consumed $44.7M in cash from operations and generated negative FCF of $48.6M for a FCF margin of -16.0%. This compares to -$22.7M a year ago and a FCF margin of -11.4%. This represented a substantial step backwards from the prior quarter and year/year trends. In Q1, MongoDB generated positive FCF of $8.4M for a FCF margin of 2.9%, the second quarter in a row of positive FCF. 

Looking at spending allocations by department on a Non-GAAP basis provides some color:

• S&M: Increased from $84.1M to $143.7M, up 70.9% y/y. S&M now makes up 47.3% of revenue, versus 42.3% last year. This would include the cost of the MongoDB World event.
• R&D: Increased from $44.5M to $64.7M, up 45.4% y/y. R&D now makes up 21.3% of revenue, versus 22.4% last year.
• G&A: Increased from $19.4M to $27.2M, up 40.2% y/y. G&A now makes up 6.4% of revenue, versus 9.8% a year ago.

The y/y increase in S&M of 71% was far higher year/year than revenue growth of 53%. For R&D and G&A, the relative percentage of revenue ticked downward, showing some leverage and reflecting the fact that they both grew more slowly than revenue. For S&M, though, costs in Q2 increased significantly. In Q1, S&M grew by 53% y/y, compared to 57% growth in revenue and made up 40.5% of total revenue.

The 10-Q report provides some insight into what drove the higher costs in S&M. On a GAAP basis, S&M costs increased by $72.2M or 66% year/year. That increase was made up of $38.1M (53% of increase) in sales and marketing headcount. Another $24.9M (34.5% of the increase) of cost increase was associated with costs to support those personnel, including higher travel related to in-person events, greater commissions expense and computer hardware and software expenses. Finally, they allocated $7.0M (9.7% to total increase) of increased spending to marketing programs, which includes the return to in-person attendance for MongoDB World.

We know that the resumption of travel for in-person events and MongoDB World are new expenses this year. We don’t know the total of those, but I would estimate $10M-$15M, or about 3-5% of operating margin performance. Given that the company delivered Non-GAAP loss from operations of $12.4M in Q2, for an operating margin of -4.1%, they would have reached break-even if we exclude these one time events.

The 10-Q provided some additional insight into hiring trends as well. I created the following chart of total employee counts and included those attributed to Sales and Marketing from the quarterly data. 

Looking at Sales and Marketing headcount in Q2, we see a substantial increase, up 56% year/year and 16% sequentially. MongoDB doubled the number of new hires in Sales and Marketing in Q2, as compared to the average of the prior four quarters. Besides MongoDB World, this explains the increase in operating expenses in Q2, which is also the source of the worsening operating margins through the rest of the year. Management didn’t speak specifically to this increase on the earnings call, beyond generally discussing their investment in pursuing customer demand and the long-term opportunity.

For MongoDB, new salespeople would be specifically focused on Direct Sales customers. I imagine that as these new personnel ramp up, they will drive growth in the Direct Sales customer segment. Additionally, it usually takes 6-12 months for new salespeople to be productive, so the impact of this investment would likely hit in 2023 and wouldn’t be reflected in the remainder of 2022.

Of course, the counter argument is that sales efficiency is decreasing and it requires more salespeople to manage each customer. The increase in Direct Sales customers would imply that is not the case, but it does introduce execution risk.

R&D headcount increased by 31% year/year. That compares to the increase in spending of 45% year/year and seems inline with expectations. I like the higher investment in R&D, as those engineers will continue building out new capabilities on the MongoDB platform to drive additional application workloads.

Looking forward, management lowered guidance for the remainder of the year and missed analyst estimates for income. For Q3, they are projecting a non-GAAP loss from operations of $10M to $8M. This is better than the actual loss of $12.4M in Q2. If we split the outperformance in Q1 and Q2 (about $12M) and add back to the estimate, MongoDB could deliver break-even operating margin in Q3. Analysts had modeled a non-GAAP EPS for Q4 of $(0.13) and MongoDB set their estimate at $(0.19) to $(0.16), representing the lower expectations.

For the full year, management reduced the estimate for operating loss from a range of $(9M) to $1M set in Q1 to $(13M) to $(8.0M) in Q2. At the midpoint, this represents a decrease of about $6.5M. Based on the full year revenue estimate for $1.2B, this is a decrease of about 0.5% in operating margin. If MongoDB outperforms in both Q3 and Q4 in line with historical beats, they will end the year with slightly positive operating margin.

FY2022 loss from operations was $24.7M for an operating margin of -2.8%. This means that MongoDB could still end the year with an annual improvement in operating margin of about 3-4%. In FY2021, operating loss was $49.6M for an operating margin of -8.4%. On an annual basis, MongoDB improved operating margin by 5.6% from FY2021 to FY2022, and could show further improvement for FY2023.

Customer Activity

In Q2, total customers increased by 1,800 sequentially to reach 37,000. As you can see below, this was the slowest rate of total customer growth in two years. This was reflected in Atlas customer growth as well. On the surface, this decrease implies a reduction in customer demand for Atlas.

However, we need to consider what drives total customer growth. The majority of MongoDB Atlas customers are “self-serve”, which means they sign-up online through the MongoDB web site. Self-serve customers are typically small businesses or even individuals with a side project. There is also a free tier that doesn’t require a credit card to start, but imposes usage limits to remain free. Free user sign-ups are not included in the total customer count. It is possible that more new MongoDB Atlas users opted for free in Q2, as a consequence of the macro environment.

“MongoDB delivered strong second quarter results, highlighted by 73% Atlas revenue growth and a record number of net additions of direct sales customers. We are seeing robust growth in new workloads being deployed on our platform, which is indicative of the critical role we play in enabling customers to build and run mission critical applications that transform their business,”  said Dev Ittycheria, President and Chief Executive Officer of MongoDB.

MongoDB press Release, Q2 Fy2023 Results

If total customer growth lagged, why did management emphasize strength in customer demand as part of their prepared remarks? They are referring to Direct Sales customers, which experienced a record number of sequential additions. The definition of Direct Sales customers is a little opaque, “Direct Sales Customers are customers that were sold through our direct sales force and channel partners.” Direct Sales customers are contrasted from self-serve. Based on commentary from management, new customers of this type are either directly brought on the platform by salespeople or assigned a salesperson after growing to a meaningful size from the self-serve channel.

According to the 10-Q, Direct Sales customers contribute 86% of total revenue. Given that these are the most significant customers, it is a positive signal that their additions are accelerating. The contribution of Direct Sales customers was 87% in Q1 and 84% in Q2 of last year. I would attribute the sequential quarterly drop to variability in the metric, as new Direct Sales customers come on board at a lower spend level. I like that Direct Sales customers are driving the vast majority of total revenue, as the sales team can focus their efforts on this cohort.

Direct Sales customers will take some time to ramp their spend, particularly for new customers to the platform. As we know with Snowflake, it can require a couple of quarters to perform data migrations and increase traffic on new applications. Given the surge in Q2 Direct Sales customers, it’s possible this will translate into a greater revenue contribution by 1H2023.

The other metric associated with larger customers is the number of $100k ARR customers. This count grew sequentially by 83 in Q2 to reach 1,462. This count is up about 6% sequentially and 30% year/year. The absolute number of 83 additions is the second largest q/q increase in the past two years, ticking up from Q1’s 72, but lower than the record of 106 additions in Q4.

A footnote in the Earnings Report regarding the count of $100k customers states: Represents the number of customers with $100,000 or greater in annualized recurring revenue (“ARR”) and annualized monthly recurring revenue (“MRR”). ARR includes the revenue we expect to receive from our customers over the following 12 months based on contractual commitments and, in the case of Direct Sales Customers of MongoDB Atlas, by annualizing the prior 90 days of their actual consumption of MongoDB Atlas.

It’s also possible that the headwinds attributed to Q2 Atlas consumption impacted the $100k customer count more acutely than normal. That is because the calculation for a $100k ARR customer is based on annualizing revenue for the prior period (either quarter or month) based on their billing cycle. If some customers reduced their consumption in Q2, they might have fallen below the $100k threshold, working against the sequential growth. Other software companies calculate this metric based on spend from the prior 12 months, reducing the impact of the most recent period.

We would have a better view into these dynamics if MongoDB reported the actual Net Expansion/Retention Rate. They provide the “Net AR Expansion Rate”, but do not quantify the actual value. They state whether the value is above their reporting threshold of 120%, which it has been for many quarters. This means that existing customers increased their spend on MongoDB services by more than 20% as compared to the prior year. Since we don’t know the exact value, we can’t use it to gauge the amount of customer spend expansion or compression quarter to quarter.

In the prepared remarks, MonogDB’s CEO highlighted a number of new customers wins (copied from prepared remarks):

• Multinational trillion-dollar financial services company chose MongoDB to power the next-generation trading platform to cover all of their various trading businesses with one solution. Since launching the new service, the customer has been able to decommission eight legacy trading systems and realized cost savings of almost $50M in annualized expenses.
• Leading Canadian security provider migrated its IoT and AI security platform away from an open-source relational database to Atlas. With the ability to use MongoDB’s native charting to distribute their database over lower cost instances, the company has been able to significantly reduce their database spend by 60%, which is particularly compelling given the open-source nature of their prior solutions. Another one of the customer’s priorities is to invest in their core competencies and outsource or eliminate everything else.
• Global travel technology leader is in the process of getting out of the business of managing their own data centers. They turned to MongoDB in order to modernize a key legacy application and move it to the cloud. The monolithic application was originally built on Oracle and Elasticsearch but the customer decided to migrate the application to Atlas because they couldn’t meet their time line and performance requirements with their existing solution.
• Web 3.0 pioneer started off by building and managing their own computing infrastructure. However, the development team quickly reached a point where they were overwhelmed by day-to-day tasks of managing infrastructure. By migrating to MongoDB Atlas, the company saved three years in development time and reduce the need to hire 40 developers.
• Fortune 500 consumer technology leader turned to MongoDB to replace its existing compliance platform as it needed to double the performance while lowering cost and enabling real-time visibility of operations. MongoDB was able to address the performance requirements while lowering costs by 70%.
• One of the world’s largest healthcare companies, historically an Oracle shop, was unable to meet their desired performance expectations and had to spend tens of thousands of hours just to maintain their existing environment. Over the last few years, they have implemented MongoDB for the most demanding new projects, such as the COVID vaccine application as well as their digital health app.
• One of the world’s premier commercial investment banks signed a multimillion-dollar agreement as a signal of their desire to use MongoDB as a preferred platform for mission-critical workloads. The bank’s leadership team also observed how overwhelmingly popular MongoDB had become with their development teams across the organization. As a result, they decide to make MongoDB one of their enterprise standard offerings as part of the journey to the cloud.
• A leading German retailer, Conrad Electronics, built an online B2B marketplace on MongoDB. Turning to MongoDB for simplicity as the marketplace grew, it moved from MongoDB Community Edition to Atlas for further scalability and to reduce management complexity. The Conrad Electronics database now hosts over 8M active SKUs, and is estimated to reach 100M SKUs by 2024.
• Locus Robotics, a leading warehouse robotics company leverages Atlas to store data uploaded from their physical warehouses, including metadata, logs, configurations, and reports. Their Locus Cloud warehouse orchestration platform utilizes Atlas to store and access data for operational reports, ensuring seamless and reliable functionality for their customers. Locus Robotics selected MongoDB Atlas because they needed a fully managed data platform to allow them to build faster and handle vast amounts of data.
• In their efforts to improve their automation processes, e-commerce platform Rent The Runway selected and implemented MongoDB Atlas as a database platform to streamline how garments are sorted and cleaned in their fulfillment centers. With Atlas, Rent The Runway is able to seamlessly extract data and real-time analytics from the robotic sorting arm and X-ray machines, resulting in a 67% decrease in processing time.

Based on the number and scope of customer highlights, I have a few observations from this quarter. First, management provided more customer examples than in past quarters – there were 10 highlights this quarter, versus just 4 examples in Q1. This felt like an effort to double-down on their assertion that new customer demand is still robust, in spite of softening consumption trends.

Second, several of the customer highlights mentioned cost savings realized by moving to MongoDB Atlas, either through lower license fees from multiple alternate solutions or less staffing support required to maintain open source projects. This represents a new marketing message, reflecting the benefits of consolidating multiple application workloads onto the MongoDB platform. This aligns with messaging from other software infrastructure providers in recent quarters and plays well with overall enterprise sentiment given the macro environment.

Product Strategy

MongoDB is pursuing a TAM estimated to be $85B this year by IDC. IDC categorizes MongoDB’s market as “database management systems software.” This category encompasses several segments of databases, all within the general scope of transactional database use cases, which MongoDB can address.

IDC estimates the TAM will grow by about 12% a year to reach $138B by 2026. This scope primarily encompasses transactional databases – the market for data warehouses and analytics is separate. Demand for transactional databases is benefitting from significant tailwinds, primarily digital transformation and automation of business processes. New enterprise efforts to harness smart devices to optimize operations (Industrial IoT) are driving an exponential increase in data volumes. This data is collected by front-line transactional databases, processed and then shipped to larger analytics stores for deeper analysis.

As enterprises consider solutions to capture and process all this data, either as part of a new digital transformation project or an upgrade to an existing legacy system, they will generally consider a modern database offering. While a 12% CAGR doesn’t sound exciting on the surface, in a large market like databases, it provides a lot of growth for leading providers. A better way to consider the opportunity is that $11B of new database spend will be introduced in 2023. MongoDB has a reasonable chance of landing a fair percentage of that. Add to that some portion of the existing spend that is considered for an upgrade.

Beyond the upgrade motion, enterprises are also creating new applications as part of digital transformation. This may be to create a new digital experience for customers, partners, supply chain providers or employees. For these new applications, the engineering team can choose a modern database solution that can address their data workloads. If the functionality is powering a site search, they may look for a search index. If it is powering a new IoT data collection service, the time series data model would be well suited. Or, they might be planning a new mobile app that needs a system to keep the device’s cache in sync with the central data store.

In all these cases, a database platform that can address multiple data access patterns provides some benefits. The developers have fewer interfaces and data storage technologies to learn. The DevOps team has less dependencies to manage. The leadership team can consolidate spend to fewer vendors and achieve volume based discounts. Consolidating database vendor sprawl results in lower cost and higher efficiency for the engineering team.

This is the foundation of MongoDB’s product strategy. They seek to be in the consideration set for a greater number of application workloads. Workloads are the unit of measure for market penetration in application databases. The application database market isn’t winner-take-all, like CRM or ERP. An engineering team usually has more than one database type in use at any time. A database vendor could land within an enterprise for a single use case on one application and expand into more workloads over time.

For MongoDB, this product strategy translates into platform improvements along three vectors. All of these enable customers to apply MongoDB to more of their application workloads.

• Make it easier to migrate a legacy (relational) database to MongoDB.
• Allow MongoDB to be applied to more data workloads.
• Support additional application deployment architectures.

This provides the framework upon which the product announcements at MongoDB World hang. By supporting more data models and deployment architectures, MongoDB moves beyond its nucleus of centralized, document-oriented databases. This opens up a larger share of the $96B of application database spend in 2023 for MongoDB to capture.

This high level strategy for capturing more application workloads was introduced at last year’s virtual user conference in July. Leadership labeled MongoDB as the application data platform. This extended MongoDB’s base beyond powering document-oriented workloads to supporting a broader set of the typical data storage patterns within modern applications.

This is a reaction to the state of “sprawl” within the transactional database market. Developers have to choose from multiple options within each category of database, generally aligned with a single data model. A perusal of the listings on DB-Engines demonstrates this bounty of choices – they include about 400 different databases in their popularity tracker.

Reducing the number of different databases that back enterprise software applications generates efficiencies, simplicity and cost savings for the engineering team. Consolidation may not be appropriate for every data access pattern, but the pendulum has swung too far in the direction of purpose-built databases over the past 10 years. We won’t have one general purpose database platform to rule them all, but we don’t need 400 options either.

With these advantages in mind, MongoDB’s product strategy is to continue to improve the MongoDB data platform to become a suitable replacement for more flavors of databases. During their annual user conference in 2021, the product team presented the slide above, showing common database workloads for a typical customer application and popular point solutions for each. MongoDB’s long-term goal is to replace many of these. As part of MongoDB World this year, they announced a number of platform improvements that move MongoDB a few steps closer to this vision. I have highlighted in green boxes the targeted data workloads that received the most focus in this year’s announcements.

On the Q2 earnings call, management highlighted traction in three areas of expanding workload penetration. These were the relational migrator, time series data and search.

Relational Migrator

MongoDB’s largest set of entrenched workloads to target are relational databases. First, I acknowledge that MongoDB won’t replace every SQL database out there. That is not reasonable or likely. However, as legacy applications are upgraded and new applications are planned, MongoDB’s data platform would be a suitable choice for many database implementations that previously defaulted to relational. Application re-architectures, particularly a refactoring into microservices, drives this motion. A service-oriented architecture makes it much easier to keep some workloads on relational, and move the rest to another model.

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 a migration. 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 one of MongoDB’s big product announcements in June, called the Relational Migrator. The goal of this product is to provide engineering teams with a tool to easily connect to a relational database, analyze its table structure, map that to the document model and then manage the data migration.

With these inherent design and architectural advantages, many engineering organizations are choosing the document model for new and upgraded application workloads, often over the relational model. In fact, CTO Mark Porter claims that he has met with over 400 CTO’s in the last two years and every one of them has an “off relational plan”, or at least an intent to do so. While a strong statement, I can visualize some percentage of databases that would fall into this category.

The Relational Migrator will be released initially to internal pre-sales and professional services teams within MongoDB to utilize. This will provide value to customers, while refining the tool’s functionality in a controlled environment. In 2023, a cloud-hosted version of the tool will be released for customers to use in a self-serve fashion.

We also announced Relational Migrator, a product that simplifies the process of migrating workloads off relational databases and onto MongoDB. Our early access program is oversubscribed, and we’re getting great customer feedback. Given most companies increased focus on cost management, we believe this technology will accelerate customer confidence in re-platforming applications of relational databases.

MongoDB Q2 Earnings call

On the Q2 earnings call, the CEO shared in his prepared remarks that their early access program for the Relational Migrator is oversubscribed. This product should help accelerate the migration of legacy relational workloads to the document model. This migration would allow for more consolidation of workloads onto MongoDB and reduce costs. The cost and headcount savings were highlighted as part of the CEO’s customer wins in his prepared remarks.

Time Series

Customers have been using MongoDB for time series data for years, but the MongoDB team realized that the platform could better serve this use case. A year ago, they started a series of improvements to make time series workloads easier to manage, faster to query, less error prone and less expensive to maintain. The team created a new data collection within MongoDB that stores time series data in an optimized format.

The graphic above lists all the improvements made over the past year. Sharding support allowed time series data to be written to multiple nodes in parallel, which is important to accommodate the extremely high throughput of most IoT systems. They also added cardinality improvements for better performance, compression techniques, methods to fill data gaps and more efficient data archiving.

Geo indexing, introduced in version 6.0, is particularly powerful, as that allows developers to slice time series data by geographic location, which is a common use case. The MongoDB team combined two primitives within the platform to deliver this capability. They brought their geo-spatial libraries to the time series processing engine and unified them to support the creation and querying of indexes by geographic spacial definitions.

Several customer highlights from the earnings call reflect these usage of times series capabilities. These primarily revolve around IoT data collection. Examples include the leading Canadian security provider, a large healthcare company, the leading warehouse robotics company and Rent The Runway, for their warehouse automation processes. In all these cases, MongoDB is being used to collect time series data and then process it to manage operations and identify potential issues.

Search

The traditional method for enabling application search involved setting up a stand-alone tier of servers with a search index loaded onto them. These search servers were optimized for data retrieval, often employing a reverse index and the open source library Lucene. In order to keep the search index updated, engineering teams would need to maintain a data synchronization process between the search index and the primary database.

This created extra overhead for engineering teams in managing two separate systems. At minimum, they would set up a separate search tier using popular solutions like Elasticsearch and Solr. Teams also had to set up a dedicated ETL process to pull data out of the primary data store and write it to the search index. These resulted in labor costs and expense for the commercial version of the search servers (Elastic or Lucidworks).

The MongoDB team saw an opportunity to to make this process more efficient. In 2020, they introduced Atlas Search in GA. For MongoDB’s implementation, the search engine is integrated directly into the platform and sits beside the database. This provides a unified and fully managed system. Developers don’t need to maintain two sets of servers. They also avoid the overhead of managing a data sync mechanism, writing custom transformation logic and then remapping search indexes every time the database schema is updated.

MongoDB’s Atlas Search implementation is built using the open source Lucene search libraries. This is the same core search functionality at the center of Solr and Elasticsearch. While Lucene provides a lot of the basic search functionality, there are a number of use cases within search that need to be implemented outside of what comes with the library. The MongoDB team has been adding these in incremental releases over the last two years.

With the MongoDB 6.0 release, customer engineering teams will have access to search facets, cross-collection searching, stored source fields and embedded documents in arrays. These further round out the use cases that Atlas Search can address, with facets being the major addition. On the Q2 earnings call, the CEO referenced search-related functionality being harnessed by the the global travel technology leader and Conrad Electronics.

Other Capabilities

When MongoDB is talking about analytics, they are referring to “in-app” analytics. These are not the rich data visualizations constructed by aggregating multiple data sources in a data warehouse. MongoDB has no aspirations to move down the stack to power those types of big data workloads in the domain of Snowflake or Google BigQuery.

MongoDB is targeting data visualization and analytics use cases that are generated in near real-time using the data set stored within a single application’s transactional database. The purpose is not to help corporate executives or analysts understand business trends. Rather, these use cases typically drive a recommendation or decision made within the application context itself.

These use cases require the data processing framework illustrated in the screenshot above. The application needs access to three different databases for these kinds of logic decisions, including the operational data, a time series data store (as many of these use cases are associated with time series data) and the analytical database to query for recommendations. MongoDB is targeting all of these workloads, represented within the green box above. Having one database eliminates the ETL and synchronization jobs necessary to keep the three separate databases aligned. MongoDB isn’t targeting the space at the bottom of the diagram, occupied by the enterprise data warehouse and data lake.

In order to consolidate all of these functions into one platform, MongoDB has built in many capabilities that enable robust in-app analytics. These include a flexible data model, a framework to aggregate and query data within time windows, support for long-running queries that generate a snapshot view and workload isolation. The last item prevents analytics load from affecting the performance of operational database transactions.

The highlight of MongoDB’s general platform capabilities announcements from the MongoDB World conference was Queryable Encryption. Given the broader sensitivity around security and data breaches, this release was well timed. With Queryable Encryption, developers will have access to a broader set of query patterns (referred to as expressive queries) without having to worry about the security of the data on the database server. This capability will be available in preview mode as part of MongoDB’s 6.0 release. Users of MongoDB will get access to the capability automatically, without having to re-architect their systems.

On the Q2 Earnings Call, the CEO provided some updates on customer interest with Queryable Encryption and Analytics capabilities.

We announced Queryable Encryption, an industry-first feature that allows customers to query data while it remains encrypted. Given the heightened focus on security and privacy, this announcement received a lot of attention from both customers and the academic community. And currently, over 60 companies, the majority of whom are Fortune 500 customers, are in development with this feature.

We also received positive feedback related to our analytics announcements. Most notably, Atlas Data Federation and Atlas Data Lake storage are seeing healthy early adoption in use cases related to the ingestion, data transformation, and curing of large volumes of data to provide greater insights from data generated by applications on Atlas.

MongoDB q2 earnings call

Competitive Landscape

I have provided in-depth analysis of the competitive landscape in prior blog posts. MongoDB is the most popular document-oriented database solution on the market. Additionally, with their expansion to other document-adjacent use cases (time series, search, key value), MongoDB is providing a broader multi-model data platform. The primary value propositions for engineering organizations in adopting MongoDB are:

• Cost savings and overhead reduction through platform consolidation.
• Higher developer productivity driven by an application-centric data model (removes impedance of translating to relational models).
• Reduced DevOps overhead with a managed, cloud-based platform.
• Multi-cloud data distribution. This allows the same data store to be addressed from applications running on multiple cloud vendors, reaching 81 global regions across GCP, AWS and Azure.

While there are a lot of database solutions, the competitive set hasn’t really changed in the last quarter. A reasonably objective indicator of data storage engine usage across all categories is provided by DB-Engines. They maintain a ranking of popularity of solutions on their web site, with an overall score and an indication of change in magnitude compared to the prior month and the prior year. This is constructed from a combination of inputs pulled from various public forums, discussion boards, web sites and job postings, which are all heavily developer influenced.

If we look at rankings for document databases and their multi-model adjacents, MongoDB is well ahead of any competitive offerings. The second place position goes to DynamoDB and that has a score less than 1/5 of MongoDB’s ranking. Further, no other offering is making significant progress up the rankings, with all solutions maintaining about the same relative level of popularity over time.

As a sidenote, Databricks made an appearance last month on the rankings, but I don’t consider them to be a direct competitor of MongoDB. Snowflake is categorized as a Relational database, and has about twice the score of Databricks, growing at the same rate.

A graph of rankings over time also shows the large gap that MongoDB is maintaining relative to other offerings. The only contender that has been perceptively improving their position over the last couple of years is DynamoDB. The DocumentDB product from AWS is down at position 24 with a score of 2. This product clearly didn’t gain popularity against MongoDB, even though AWS had originally unveiled it several years ago as a direct competitor.

While several of the multi-model databases in the rankings are offered by the hyperscalers, MongoDB enjoys a very productive partnership with all three of them. In 2022, MongoDB further solidified these relationships. In March, they announced an expanded collaboration with AWS.

The agreement with AWS builds on the current 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.

Not to be left out, Google Cloud Platform (GCP) struck a similar agreement with MongoDB in April. In this case, it is a pay-as-you-go offering available directly in the GCP console. Customers will just be billed for MongoDB Atlas based on their consumption, with no up-front commitments. This makes provisioning seamless, as customers can initiate the relationship through GCP and consolidate costs onto their existing GCP bill. Atlas is deeply integrated with a number of other GCP services including BigQuery, Tensorflow, Cloud Run, App Engine, EventArc, Cloud Functions, Google Kubernetes Engine (GKE) and Dataflow.

While Microsoft Azure has the most directly competitive offering in Cosmos DB, they too are starting to collaborate more. MongoDB’s CEO highlighted the improving relationship with the Microsoft Azure sales team on the Q2 earnings call.

Yes. And Mike, to your second question about our strategic relationships with the hyperscale vendors, they’re very strong. One is the function of how popular MongoDB is on their respective platforms.

We’ve been constantly told that we are one of the most popular technologies that developers are running on their platforms. What they’ve also seen is that they’re the net beneficiary of our growth and the Atlas growth on their platforms because Atlas not only drives more underlying consumption of storage and compute, but customers themselves end up using other ancillary services. So for the hyperscale vendor, it truly becomes a win-win relationship. And so you talked about the AWS relationship.

That relationship is going really, really well. There’s so much engagement happening in literally almost every theater of the world. The GCP relationship has historically been strong and remains so. And the Azure relationship is actually picking up, and we’re seeing a lot more activity with the Microsoft Azure team. And so in general, I would frame the relationships with the hyperscale vendors to be very, very good.

MongoDB Q2 Earnings call

Investor Take-aways

After two strong quarterly reports in Q4 and Q1, MongoDB’s Q2 results left investors with more questions than answers. Revenue performance beat expectations, but points to a significant slowdown in growth through the remainder of the year. Additionally, the reversal in operating and FCF margins magnified the market reaction, driving down the stock price about 25% the day following the release.

Each of these factors taken individually has a reasonable explanation. The slowdown in revenue growth can be attributed to second order effects stemming from business issues with segments of customers. Because Atlas generates revenue on a consumption basis, flat or even decreased year/year utilization compresses the normal expansion of spend. These effects were centered on digital native customers that experienced a surge of usage in 2020-2021 and enterprise customers in Europe. In both cases, the decreased utilization was driven by changes in their business activity, versus their interest in MongoDB.

The weakness in operating income and FCF have explanations as well. The resumption of travel and in-person events, particularly the MongoDB World customer conference, generated incremental costs in Q2 of about 3-5% of revenue. Also, MongoDB stepped up their hiring of Sales and Marketing personnel, more than doubling new hires in Q2 over the average of the four prior quarters. This resulted in a year/year increase in S&M expense of 71% versus 53% growth in revenue.

The highlight of the quarter was the record additions of Direct Sales customers. These are MongoDB’s most important segment of customers, contributing 86% of revenue. Total customer additions did step back in Q2, likely due to macro conditions and selection of the free tier on Atlas. Growth in $100k ARR customers also hit the second highest absolute net additions for the quarter, in spite of some of these customers likely dropping below the threshold due to the calculation based on most recent billing period.

This leaves investors with a quandary. On the surface, MongoDB’s performance looks worse than in Q1. One could conclude from these data points that MongoDB’s revenue growth is slowing quickly while costs are shooting up. Current investor sentiment offers little patience for negative FCF, particularly where the trend reverses. Additionally, management is being necessarily conservative for the remainder of the year, as they have to assume that the consumption drivers remain the same until the macro environment improves.

On the other hand, the increase in salespeople should drive further growth in Direct Sales customer additions and expansion. It will take some time for these salespeople to ramp up and even new Direct Sales customers to reach target utilization. These contributions should start to hit in 2-3 quarters or the first half of 2023. Additionally, if the macroeconomic situation stabilizes or improves by 2023, that will provide another tailwind to growth. The consumption model for Atlas cuts both ways – as customer business transactions increase again, Atlas will register the higher usage immediately. With easier year/year comparisons starting in Q3 2023, the second half of next year could yield re-accelerating revenue growth.

Long term, I think MongoDB’s product strategy and competitive position are intact. While there are alternatives in multi-model databases and even interesting offerings in relational, those enjoy far less popularity or adoption relative to MongoDB. The product strategy to land new customers and expand into adjacent application workloads is viable. New product launches this year will facilitate these additions of application workloads, driven by the relational migrator, time series enhancements for IoT, better search capabilities and real-time analytics.

I don’t primarily rely on sell-side analyst reports to gauge my investment thesis, but they are helpful to understand which way the market may be leaning, at least on the institutional side. Overall, analysts were fairly forgiving regarding the Q2 results. They acknowledge the near term headwinds and point to an opportunity for re-acceleration next year. Most acknowledged the long term view is still in place relative to the opportunity for MongoDB and their market position. This may reflect some defense of their prior recommendations, but their analysis and conclusions appear reasonable.  In the period since earnings, most analysts lowered their price targets, but all of them maintained their Buy equivalent rating except for Mizuho who has a hold rating. The average consensus price target for analysts issuing an update post-earnings is $388, about 60% higher than MDB’s current price.

After earnings, the stock dropped roughly 25% from $320 to $240. Assuming MongoDB hits its current full year revenue target of $1.2B, the end of year P/S ratio would be about 14. If MongoDB can deliver 40% revenue growth in 2023, the forward P/S drops to 10. Any outperformance this year or next would lower the forward multiple further. I think 40% revenue growth next year is a reasonable assumption based on past performance, the sales investment, expanded product reach and focus on Direct Sales customers. This would represent roughly similar growth as for 2022 on the whole.

Given MongoDB’s small share of its large market, their product positioning in it and the long tail of database upgrades, the company should be able to maintain 30% growth for several more years in the future. As they land and expand into larger Direct Sales customers, sales efficiency should improve allowing margins to increase. Architecturally, I see no reason MongoDB can’t achieve a similar margin profile to other software infrastructure providers that sit on hyperscaler infrastructure, like Datadog or Snowflake.

Given the pros and cons, and weighing the market’s reaction, I decided to reduce my allocation to MDB after earnings by about 1/3. I moved these funds into SNOW, as I saw more strengths in their quarterly report. Both stocks are down about the same amount this year, so I consider it a fair trade. I will continue to monitor MongoDB’s performance through the year and will be watching closely for any changes to the current trajectory they have charted and the opportunity for 2023.

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.