MongoDB, Inc. (NASDAQ:MDB) Q3 2024 Earnings Call Transcript

MongoDB, Inc. (NASDAQ:MDB) Q3 2024 Earnings Call Transcript December 5, 2023

MongoDB, Inc. beats earnings expectations. Reported EPS is $0.96, expectations were $0.49.

Operator: Thank you for standing by and welcome to MongoDB’s Q3 Fiscal Year 2024 Conference Call. At this time, all participants are in a listen-only mode. After the speakers’ presentation, there’ll be a question-and-answer session. [Operator Instructions] As a reminder, today’s call is being recorded. I would like to turn the call to your host Mr. Brian Denyeau from ICR. Please go ahead.

Brian Denyeau: Great. Thank you, Valerie. Good afternoon, and thank you for joining us today to review MongoDB’s Third Quarter Fiscal 2024 Financial Results, which we announced in our press release issued after the close of market today. Joining me on the call today are Dev Ittycheria, President and CEO of MongoDB; and Michael Gordon, MongoDB’s COO and CFO. During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities, the benefits of our product platform, our competitive landscape, customer behaviors, our financial guidance, and our planned investments. These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could cause actual results to differ materially from our expectations.

For a discussion of the material risks and uncertainties that could affect our actual results, please refer to the risks described in our quarterly report on Form 10-Q for the quarter ended July 31, 2023, that was filed with the SEC on September 1, 2023. Any forward-looking statements made on this call reflect our views only as of today and we undertake no obligation to update them except as required by law. Additionally, we will discuss non-GAAP financial measures on this conference call. Please refer to the tables in our earnings release on the Investor Relations portion of our website for a reconciliation of these measures to their most directly comparable GAAP financial measure. With that, I’d like to turn the call over to Dev.

Dev Ittycheria: Thank you, Brian, and thank you to everyone for joining us today. I’m pleased to report that we had another strong quarter as we continue to execute well, despite challenging market conditions. I will start by reviewing our third-quarter results before giving you a broader company update. We generated revenue of $433 million, a 30% year-over-year increase and above the high end of our guidance. Atlas revenue grew 36% Year-over-Year, representing 66% of total revenue. We generated a non-GAAP operating income of $79 million for an 18% non-GAAP operating margin and we had another solid quarter of customer growth and in the quarter with over 46,400 customers. Overall, we delivered a strong Q3. We had a healthy quarter of new business acquisitions, led by continued strength in new workload acquisition within our existing customers.

In addition, our Enterprise Advanced business again exceeded our expectations, demonstrating strong demand for our platform and the appeal of our run-anywhere strategy. Moving on to Atlas consumption trends, the quarter played out in line with our expectations. Michael will discuss consumption trends in more detail later. Finally, retention rates remained strong in Q3, reinforcing the mission criticality of our platform, even in a difficult spending environment. This quarter, we held our most recent global customer advisory board meeting, where customers across various geographies and industries came together to share feedback and insight about the experience using MongoDB. From these discussions, as well as our ongoing C-Suite dialog with our customers a few themes emerged.

First, AI is in nearly every conversation with customers of all sizes. We’re seeing great early feedback from our partnership with AWS’s CodeWhisperer, the AI-powered coding companion that is now trained on MongoDB data to generate code suggestions based on MongoDB’s best practices from over 15 years of history. Microsoft GitHub Copilot is also proficient at generating code suggestions that reflect best practices, enabling developers to build highly-performing applications even faster on MongoDB. And with the recent advances in Gen AI, building AI applications is no longer the sole domain of AI or ML experts, increasingly it’s software developers who are being asked to build powerful AI functionality directly into their applications. We are well-positioned to help them do just that.

We saw exceptional interest in our Vector Search public preview and we announced general availability yesterday. Customers are building a range of AI use cases from semantic search to retrieval augmented generation or RAG where organizations can leverage the use of their private data, to increase the accuracy of LLMs. For example, UKG, a human capital and workflows management technology serves over 80,000 plus customers around the globe chose to use MongoDB Atlas Vector Search for an AI-powered assistant that helps guide their customers employees, people managers, and HR leaders. They chose Atlas Vector Search because of its minimal added architectural complexity, flexibility to handle the rapidly changing data as applications evolve and the scale to handle large workloads.

UKG is not alone. In our recent state of AI survey report by Retool, Atlas Vector Search received by far the highest net promoter score from developers compared to all other vector databases available in the market. Moreover, developers can combine Vector Search with any other query capabilities available on MongoDB, namely analytics, tech search, geospatial, and time series. This provides powerful ways of defining additional filters and vector-based queries that other solutions just cannot provide. For example, you can run complex AI enriched queries such as find pants, shirt, shoes in my size that look like the outfit in this image within the particular price range and have free shipping or find real-estate listings with houses that look like this image that were built in the last five years and/or in that area within seven miles west of Downtown Chicago with top-rated schools.

Second, customers feel more pressure than ever to modernize their data infrastructure, they are aware that their legacy platforms are holding them back from building modern applications designed for an AI future. However, customers also tell us that they lack the skills and the capacity to modernize. They all want to become modern but are daunted by the challenges as they are aware it’s complex — it’s a complex endeavor that involves technology, process, and people. Consequently, customers are increasingly looking to MongoDB, to help them modernize successfully. We launched Relational Migrator early this year to help customers successfully migrate data from the legacy relational databases to MongoDB. Now, we’re looking beyond data migrations to the full lifecycle of application modernization.

At our local London event, we unveiled a Query Converter, which uses genetic AI to analyze existing sequel queries and stored procedures and convert them to work with MongoDB’s query API. Customers already use the tool successfully to convert decades-old procedures to modernize their back-end with minimal need for manual changes. While it’s still early days, we’re continuing to invest in the Query Converter and other AI features with the goal of significantly reducing the effort involved in monetizing legacy applications to run on MongoDB. To be clear, application modernization will take time to ramp, but as one of the largest long-term growth opportunities for our business. Third, our run-anywhere strategy continues to be a real differentiator as customers greatly appreciate the optionality that our platform provides as they manage often conflicting priorities on the way to the cloud.

On one hand, the movement to the cloud continues unabated. Customers in industries and geographies were at first hesitant to move to cloud, such as financial services in Southern Europe are Now moving to the cloud with urgency to become more nimble and to reduce costs. Many of our customers find that the all-in cost of maintaining legacy workloads on-prem is higher than the cost of migrating them to the cloud. On the other hand, our largest enterprise customers tell us they are planning to maintain a meaningful on-prem footprint for the foreseeable future. The reason for keeping workloads on-prem include regulatory requirements, the desire to keep using their existing on-prem infrastructure, or the enormity of the task of migrating all their apps to cloud.

In the meantime, they still want to modern data platform to deploy new and existing applications, the continued outperformance of EA business demonstrates that our customers value our ability to run anywhere and to future-proof their eventual move to the cloud by building on EA. Finally, our customers remain focused on cost management, they’re looking to do more with less by consolidating vendors and reducing the complexity of the data architecture. MongoDB dramatically increases developer productivity and supports a wide variety of use cases, eliminating the need for many point solutions. This combination resonates with customers in this macro-environment. For example, Atlas search now powers the homepage of one of the most recognizable sports media brands in the world.

The customer replaced an incumbent search technology with Atlas search, because they were drawn to the operational ease of running search queries alongside other queries on Atlas as well as the overall cost-savings from consolidating functionality onto a single platform. In short, customers view MongoDB as a true partner, a partner that not only accelerate the pace of innovation, but also drives them to become more efficient. We are deepening investments in our product, partnerships, and customer-facing teams to continue to enable customers to do both. Now I’d like to spend a few minutes reviewing the adoption trends of MongoDB across our customer base. Customers across industries and around the world are running mission-critical applications on Atlas, leveraging the full power of our developer data platform.

These customers include AT&T, Fishbowl by Glassdoor, and Trend Micro. AT&T selected Atlas as a key element of their modernization journey. The location match and application validates 380 million unique customer addresses and handles about 14 million transactions per day. But the various disparate data management solutions led to technical depth and there were duplicative sources of information. The company turned to Atlas as a developer data platform to simplify their data infrastructure, merged their data into a single view, and freed their teams from managing database operations. Now they rely on Atlas change streams to easily track changes with data as well as Atlas’s native search capabilities and built-in geospatial functions to quickly identify location information and accelerate time-to-market for mission-critical products and services.

EY, Delivery Hero, and ASAP Log are examples of customers turning to MongoDB to free up their developer’s time for innovation, while achieving significant cost-savings. One of the 2023 MongoDB Innovation Award winners is EY. Ernst & Young LLP manages high volumes of transactional data and its clients and internal teams work under strict timelines to file taxes and meet regulatory deadlines. The cloud-based global VAT reporting tool or GVRT automates and digitizes the preparation of 242 different types of returns across 79 countries. EY migrated from their previous non-relational database solution to Atlas and experienced a significant performance boost, reduced cost by as much as 50% and are able to scale without limitations to handle increased data volumes, transactional loads, and concurrent user requests during peak periods.

Evernorth Health Services, a division of the Cigna Group Manulife and [indiscernible] are turning to MongoDB to modernize applications. Manulife, one of the largest life insurance companies in the world, migrated to Atlas when it became clear that their relational database caused a drag on innovation and increased the time to bring new digital products to market. Manulife selected Atlas, because of the flexible document model speeds up development, scales easily, supports asset transactions, and offers seamless data migration. Using Atlas Device Sync, they successfully launched one critical app’s offline mode to ensure uninterrupted app usage went offline or in low connectivity areas to improve mobile data synchronization. Using Atlas allows Manulife to broaden its digital capabilities and enhance the [indiscernible] of customer interactions cost-effectively.

In summary, I’m pleased with our third-quarter results, our run-anywhere strategy allows customers flexibility over where they deploy and MongoDB is emerging as a platform of choice for their AI-powered applications and customers are using MongoDB to modernize and become more efficient. Before I turn it over to Michael I’m excited to share that Ann Lewnes, the former Chief Marketing Officer and Executive Vice-President of Corporate Strategy and Development at Adobe just joined MongoDB’s Board of Directors, and her leadership roles at Adobe from 2006 to 2023, she was instrumental in driving Adobe’s transition from a perpetual to subscription-based business model, and as experienced marketing to creative professionals, whether they are in a small agency, a medium-sized business, or very large enterprise.

If you replace creative professionals with developers, this strategy is very similar to what MongoDB is doing and Ann did it at the next level of scale. Prior to Adobe, Ann held a variety of leadership positions at Intel during that 20-year tenure at the company, including Vice-President of Sales and Marketing. We’re thrilled for the exceptional perspective Ann will bring to the Board. With that, here’s Michael.

Michael Gordon: Thanks, Dave. As mentioned, we delivered a strong performance in the third quarter, both financially and operationally. I’ll begin with a detailed review of our third-quarter results, and then finish with our outlook for the fourth-quarter and full-fiscal year 2024. First I’ll start with the third-quarter results. Total revenue in the quarter was $433 million, up 30% year-over-year, and above the high end of our guidance. As Dev mentioned, we continue to see a healthy new business environment demonstrating our product market fit and the mission criticality of our platform. Shifting to our product mix, let’s start with Atlas. Atlas grew 36% in the quarter compared to the previous year and represents 66% of total revenue compared to 63% in the third quarter of fiscal 2023 and 63% last quarter.

As a reminder, we recognize Atlas revenue primarily based on customer consumption of our platform and that consumption is closely related to-end user activity of the application, which can be impacted by macroeconomic factors. Let me provide some context on Atlas consumption in the quarter. Week-over-week consumption growth in Q3 was in line with our expectations and stronger than Q2. As a reminder, we were expecting a seasonal uptick in consumption in Q3 compared to Q2 based on what we’ve experienced and shared with you last year. We had forecasted that seasonal improvement to be less pronounced this year compared to last year. Given that overall, we’ve seen less consumption variability this year and that is exactly how the quarter played out.

Turning to non-Atlas revenues, EA exceeded our expectations in the quarter as we continue to have success selling incremental workloads into our existing EA customer base. Ongoing EA strength speaks to the appeal and the success of our run-anywhere strategy. The EA revenue outperformance was in part a result of more multiyear deals than we had expected. As a reminder, the term license component for multiyear deals is recognized as upfront revenue at the start of the contract and therefore includes term license revenues from future years. Turning to customer growth. During the third quarter, we grew our customer base by approximately 1,400 customers sequentially, bringing our total customer count to over 46,400, which is up from over 39,100 in the year-ago period.

Of our total customer count over 6,900 are direct sales customers, which compares to over 5,900 in the year-ago period. The growth in our total customer count is being driven primarily by Atlas, which had over 44,900 customers at the end of the quarter compared to over 37,600 customers in the year-ago period. It is important to keep in mind that the growth in our Atlas customer count reflects new customers to MongoDB in addition to existing EA customers adding incremental Atlas workloads. During the quarter, we moved approximately 350 accounts, representing negligible ARR. Out of our self-serve customer count because they’re better classified as subsidiaries of other customers or they are now users of our free tier. Taking that into account our self-serve net additions and overall net additions remain consistent with our historic healthy trends.

In terms of our direct sales, net additions, new sales activity remains healthy. Our reported direct sales net-adds continue to reflect the dynamics we discussed last quarter related to leveraging cloud provider marketplaces to fulfill new direct sales, customer additions and the movement of some small mid-market direct sales accounts to self-serve. Moving onto ARR, we had another quarter with our net ARR expansion rate above 120%, we ended the quarter with 1,972 customers with at least $100,000 in ARR and annualized MRR, which is up from 1,545 in the year-ago period. Moving down the income statement, I’ll be discussing our results on a non-GAAP basis unless otherwise noted. Gross profit in the third quarter was $335.3 million, representing a gross margin of 77%, which is up from 74% in the year-ago period.

Our year-over-year margin improvement is primarily driven by improved efficiencies that we’re realizing in Atlas. Our income from operations was $78.5 million or an 18% operating margin for the third quarter compared to a 6% margin in the year-ago period. Our strong bottom-line results demonstrate the significant operating leverage in our model and are a clear indication of the strength in our underlying unit economics. The primary reason for our operating income results versus guidance is our revenue outperformance. In addition, our operating income benefited from the timing of new hires. Finally, Q3 benefited from the timing of marketing programs, internal events, and other expenses, which we now expect to incur in Q4. Net income in the third quarter was $79.1 million or $0.96 per share, based on 82.7 million diluted weighted-average shares outstanding.

This compares to a net income of $18.7 million or $0.23 per share on 80.4 million diluted weighted-average shares outstanding in the year-ago period. Turning to the balance sheet and cash flow, we ended the third quarter was $1.9 billion in cash, cash equivalents, short-term investments, and restricted cash. Operating cash flow in the third quarter was $38.4 million, after taking into consideration, approximately $3.5 million in capital expenditures and principal repayments of finance lease liabilities free cash flow was $35 million in the quarter. This compares to a negative free cash flow of $8.4 million in the third quarter of fiscal 2023. I’d now like to turn to our outlook for the fourth-quarter and full-year fiscal year 2024. For the fourth quarter, we expect revenue to be in the range of $429 million to $433 million.

We expect non-GAAP income from operations to be in the range of $35 million to $38 million and non-GAAP net income per share to be in the range of $0.44 to $0.46 based on $83.2 million estimated diluted weighted-average shares outstanding. For the full-year fiscal 2024, we are increasing our outlook across the board. We now expect revenue to be in the range of $1.654 billion to $1.658 billion, non-GAAP income from operations to be in the range of $236.3 million to $239.3 million, and non-GAAP net income per share to be in the range of $2.89 to $2.91 based on $82.5 million estimated diluted weighted-average shares outstanding. Note that the non-GAAP net income per share guidance for the fourth-quarter and full-fiscal year 2024 includes a non-GAAP tax provision of approximately 20%.

I’ll now provide some context on our guidance. First, we expect Q4 Atlas consumption growth to be impacted by the seasonal slowdown around the holidays. Second, as you think about both the sequential and year-over-year revenue growth of Atlas in Q4, keep in mind that in Q4 last year, we had several million dollars more of revenue coming from unused commitments, which we do not expect to occur this quarter. Third; as a result of our strong execution so far this year, we are again raising our non-Atlas revenue expectations for Q4. However, we expect that our non-Atlas revenues will decline sequentially, Q4 versus Q3. This is different from our normal pattern as we usually see a seasonal uptick in Q4 due to greater renewal activity. The reason for the sequential decline this year is because of the strength we’ve seen in Q3, including the benefit of multiyear EA deals.

Finally, thanks to the strong performance in Q3, and increased revenue outlook again, we’re again increasing our assumption for operating margins in fiscal 2024 to 14% at the midpoint of our guidance, an improvement of more than 900 basis points compared to fiscal 2023. Our significant margin improvement this year is primarily driven by our revenue outperformance and the fact that we didn’t increase our pace of investment as the revenue outlook improved until relatively late in the year. As a result, we achieved greater margin expansion this year than we think is desirable in the short term given the long-term market opportunity ahead of us. To summarize, MongoDB delivered excellent third-quarter results in a difficult environment, we’re pleased with our ability to win new business and are demonstrating the operating leverage inherent in our model, while we will continue to monitor the macro-environment we remain incredibly excited about the opportunity ahead and we’ll continue to invest responsibly to maximize our long-term value.

With that, we’d like to open it up to questions, operator.

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Q&A Session

Operator: [Operator Instructions] Our first question comes from Sanjit Singh of Morgan Stanley. Your line is open.

Sanjit Singh: Thank you for taking the questions and congrats on the results in Q3. When we look at the Atlas growth this quarter, Michael, I know you mentioned that — that the consumption trends were better than expectations, in terms of the seasonality that you were anticipating what has shown up in the business for the past two years, was that more pronounced than you expected, or did that come in below in terms of the seasonal uplift that you’ve seen in Q3.

Michael Gordon: Yes, so just to clarify Q3 for Atlas came in line with our expectations. We had seen and called out last year seasonal — seasonality for Q3. We had expected that and incorporated that into our forecast. What we have seen is less variability over the course of the year and so, we assume that Q3 seasonality would be more muted this year and that’s exactly what happened and how the quarter played out.

Sanjit Singh: Understood. Thank you, Michael, for the clarification. And then, Dev I guess a much bigger-picture question, it’s — I think the company was started in 2007, so it’s about 17 years into the journey. And if you compare that to some of the large and competitive players in the space like an Oracle, around this time 17 to kind of 20 years, they started to move away from like the data management database space and into like the application software market. Is that a vision, particularly as we are in the dawn of the e-compute cycle with AI, is that an analogy that applies to Mongo in terms of its long-term roadmap in your view?

Dev Ittycheria: Yes, thanks for your question Sanjit, what I would say is we’ve been very clear that we’re really pushing our developer data platform strategy and we think the market is way larger today than it was for Oracle, when they were essentially 17 years old and our developer data platform strategy is really very simply enabling developers to use MongoDB for a wide variety of use cases across a wide variety of deployment models, whether it’s on-prem, on the cloud, multi-cloud or even at the Edge. And so that’s really our strategy. We don’t see any need to get into quote-unquote the application space itself. And we think that we have a lot of runway ahead of us.

Sanjit Singh: I appreciate it. Thanks, Dev. Thank you.

Dev Ittycheria: Thanks, Sanjit.

Operator: Thank you. One moment, please. Our next question comes from the line of Raimo Lenschow of Barclays. Your line is open.

Raimo Lenschow: Thank you. Congrats from me as well. I wanted to ask on EA first. Another very strong quarter there, can you talk a little bit more on the drivers here, because I do seem to remember you kind of double down on sales capacity I think it was [indiscernible] guide that you wanted to put on existing accounts, is that kind of a main driver or is it more like modernization or kind of picking up a lot more than what we thought would be possible. Can you speak to that, because it’s like the second quarter and early where we kind of have better numbers and I have one follow-up for Michael.

Dev Ittycheria: Yeah, Raimo. With regards to our EA outperformance, I think it really speaks to our run-anywhere strategy where customers really value the ability to build on MongoDB and essentially future-proof their deployment model, whether they stay on-prem or to move to the cloud or move from one cloud to another cloud and the fact that they can do that without having to rewrite the application is very compelling for customers. As you also said, a lot of customers still, especially the largest customer, still have a lot of sunk costs and they want to leverage that — their existing data infrastructure. So, lot of customers have told us that they will still deploy infrastructure on-prem for the foreseeable future, but what — but by building on MongoDB, they get the benefits of a modern platform and the optionality to also move to cloud when they’re ready to do so.

Michael Gordon: Yeah, the other thing which I know you know, Raimo, but just for the broader audience, we run the business on a channel basis, sales isn’t oriented around products and then also, as we’ve said before, EA tends to be — the additional sales of EA tend to be to existing customers, we don’t tend to land a ton of new brand-new customers on EA.

Raimo Lenschow: Okay, perfect. And then the follow-up is more, it’s around AI, so, if I look at the demos that you guys have on Vector Search and how Search is getting a lot better, that seems very compelling and it seems like really straightforward for a client to improve their customer experience they use it for a customer [indiscernible] up for example, what is the — what are the implications for gross margins for you Michael like, do you have to do a lot more compute here to be able to handle it. How should we think about that in terms of extra revenue, but also extra costs coming through? Thank you.

Michael Gordon: Yes. So I think it’s a little too early to tell, there’s obviously plenty of variability in the workloads depending on the nature of what the underlying application is. So, I think it’s a little early to give a strong direction to that. I think more broadly on margins, we’ve certainly been very happy with the margin progress that we’ve made I referenced, continued efficiencies that we’re driving in Atlas. Atlas is roughly two-thirds of the business, so as that continues to increase Atlas still is lower-margin overall. And so that will have some impact over the next several years. But we’re really pleased on the margin front, but I think too early to make a specific call or quantification on the gross margin impact of AI.

Dev Ittycheria: Yes. And Raimo, just to add to that. One of the announcements we also made was that we can now do workload isolation. So, for Search or Vector Search functionality you can scale those nodes independently of the overall cluster. So, what that really does is allow customers to really configure those clusters to have the right level of performance at the most efficient cost. So, we’ve been very sensitive to making sure that based on the different use cases you can scale up and down different nodes based on your application needs, by definition that will be a very compelling value proposition for customers.

Raimo Lenschow: Okay, perfect. Thank you.

Operator: [Operator Instructions] Thank you. One moment please. Our next question comes from the line of Karl Keirstead of UBS. Your line is open.

Karl Keirstead: Great, thanks. Maybe I’ll direct this question to Dev. Dev, I think a lot of people on the line are hearing this refrain about Customer’s enterprises wanting to get their data states in order in advance of moving forward on AI initiatives and you even spoke about customers feeling pressure to modernize their data infrastructure. I’d just like to ask, where we are in that journey Is this. Is this a conversation that your sales teams are having or do you feel like this is actually beginning to result in deals and revenue pull-through and if it isn’t yet roughly, when do you think that might start to translate to an actual revenue lift?

Dev Ittycheria: Yes. So I’ll make a couple of points. Karl, you’re absolutely right, there is a lot of focus on data because with AI, data in some ways becomes the new code, you can train your models with your proprietary data that allows you to really drive much more value and build smarter applications. Now, the key thing is that it’s operational data because with applications, this data is always constantly being updated and for many customers, most of those applications are right now running on legacy platforms, so that operational data is trapped in those legacy platforms. And you can really do a batch process of e-tailing all that data into some sort of warehouse and then still be able to leverage the real-time use of that data.

That’s why customers are now much more interested in potentially modernizing legacy platforms than they ever have before. I would say, Karl, to your second part of your question, I would say it’s still very, very early days. We definitely believe that this will be one of the largest long-term opportunities for our business, but we’re in the very early days and as I’ve said in the past, there is a risk of overestimating the impact of the short-term but underestimating the impact long-term, we definitely think this is a long-term impact.

Karl Keirstead: Okay, great. And if I could ask a follow-up to Mike on a different subject. Mike, you had talked on the last call a little bit more about this mix-shift away from multi-year Atlas commits and you were pointing to that as a reason for some of your metrics like DR and I think even cash flow to come under a little pressure. This quarter I see DR is still under pressure, cash flow was a little bit better, can you maybe revisit that phenomenon and describe how it’s impacting some of these metrics?

Michael Gordon: Yes, a couple of things. As we’ve said from the beginning. Some of those like calculated billings are deferred revenue metrics aren’t super helpful or don’t provide a ton of insight in terms of how we run the business. We’ve also talked about how over the last couple of years, one of the things we’ve been trying to do is reduce friction for the sales force. Some of that includes reducing the emphasis around upfront commitments. And so that helps accelerate you know lending new workloads and things like that and that will flow through or does flow through the financial statements as less upfront deferred and things like that. And so — but allows us to sort of synthetically cover more ground from a salesforce perspective.

And so you do see that, continue to go through, we shared the statistics last quarter that Atlas revenue growth last quarter was 38%, but dollars of committed Atlas declined 15% year-over-year, just as one way to try and help dimensionalize it, we also talked, I think, earlier in the year about how roughly 80% of Atlas doesn’t flow-through deferred and so I think all of those data points help kind of lineup to explain the rest of what you’re seeing and why that’s not sort of helpful forward-looking metrics like it might be in other companies. It doesn’t kind of give you the insight that maybe people are used to or hope that it will provide.

Karl Keirstead: Yes. That’s clear. Thanks a lot.

Michael Gordon: Thanks, Karl.

Operator: Thank you. One moment, please. Our next question comes from the line of Brad Reback of Stifel. Your line is open.

Brad Reback: Great, thanks very much, Michael, maybe following up on that last question in your commentary. At what point should DR stop being a headwind from a financial perspective, when should it stabilize or is this a multi-year trend as you kind of bleed it down?

Michael Gordon: So I’d say there are a couple of things, obviously it’s not something we guide to, it’s not a key thing that we focus on, but I think the trends will be. You’ve got sort of two factors, one is overall Atlas mix, right, and so to the extent that Atlas continues to grow, that will provide a headwind on this dynamic. And then similarly, even within the current Atlas footprint, there’s this historic commitments that we need to run-through renewal cycles and everything else and so I think this will take a little while still to play out.

Brad Reback: Great. And then switching gears. Dev, as customers begin to trial — excuse me, some of these Copilot code tools, we’ll say, what type of feedback have you gotten from them as it relates to the pace with which they’ve been able to reduce net new workload time-to-market. How much faster or efficient are customers getting using these tools?

Dev Ittycheria: Yes. We get different answers from different customers, really depends on which tool, they’re using without commenting on who is better or who is worst, we definitely see a difference in the quality of the output between different tools. I think it’s going to take some time for these tools to mature. So I think we’re seeing a lot of customers do a lot of testing and prototyping. I would also tell you that doing a lot of this on internal facing applications, because there’s still lots of questions about IP rights and what is potentially copyright-able then ultimately licensable, they offer this as a shrink-wrap software or service to their end-customers. So, we’re seeing more of this work on internally facing applications.

But the productivity gains, really do vary by tool and also very — do vary by the sophistication of the app being built. So, it’s hard for me to give you a real number. I know there are people out there to toating 30% or 40% improvements. But it really depends on the customer, and the use-case and the tool that they’re trying to use. It’d be hard for me to give you a specific number.

Brad Reback: Okay, thanks very much.

Dev Ittycheria: Thank you.

Michael Gordon: Thanks, Brad.

Operator: Thank you. One moment, please. Our next question comes from the line of Tyler Radke of Citi. Your line is open.

Tyler Radke: Yes, thanks for taking the question. So, earlier this quarter, you hired Mark Porter’s replacement, Jim Scharf from AWS, a lot of experience in the database industry. Can you just talk about some of his priorities, what you’re kind of accelerating in terms of the product roadmap to sell to larger enterprises?

Michael Gordon: Yes, so for those people who don’t know Jim’s background, he spent about 17 years at AWS. He had a variety of roles but last two meaningful roles was he ran the Dynamo business at AWS which is AWS’s fastest-growing and largest non-relational database business. And then he ultimately then took over that identity access management business, which if you think about it, every AWS customer has to use as a service that has to not only perform but perform at massive scale. The fact EV has dealt and built two mission-critical services for AWS was very appealing to us given our ambitions to kind of the level of scale that we expect our business ultimately get to at some point in time. He, obviously, brings us very strong technical DNA, he obviously has a lot of network of relationships in the industry.

So, we expect him to help us grow the team around the world leveraging his relationships and in terms of priorities. I mean, right now, he still kind of really assessing the current state of the business. He has been quite impressed with the quality of talent that we have. But he is really kind of what I have encouraged him to do is we need to go slow to go fast to take his time in terms of really understanding the business, understanding the team, understanding the code base before he starts really prioritizing what to do and it’s not like there’s some things massively broken, it’s really helping us set-up to scale to the next level.

Tyler Radke: Great. And a follow-up for Michael. I know you talked about how Atlas consumption during Q3 was in line with your expectations. I’m just curious, given there were a lot of volatility in at least the equity markets and the economy, did it — was there any more variability within the quarter, in other words, did it start weaker and stronger and then I’m just curious throughout the month of November, have things kind of further improved ahead of the holiday seasonality? Just any additional color would be helpful. Thank you.

Dev Ittycheria: Yes. So it like you said, Q3 Atlas results were in line with our expectations there was a seasonal benefit to Q3 relative to Q2. But given the fact that we’ve seen less variability in consumption in fiscal 2024, we had expected that to be smaller than it was in fiscal 2023 and that’s exactly how it played out. The seasonal improvement, as it relates to Q3 is a little bit more in the back-half of the of the — of the quarter. As it relates to Q4, typically the back half is weaker, given the holiday slowdown and I hope that helps people understand a little bit.

Operator: Thank you. One moment, please. Our next question comes from the line of Kash Rangan of Goldman Sachs. Your line is open.

Kash Rangan: Hey, thank you very much, Dev and Michael. Happy holidays. Congrats on the results. So, going into calendar 2024, how does the management team feel relative to going into calendar 2023 with respect to how macro conditions are no longer impacting or maybe they are impacting some aspects of the business, any verticals that stand-out that you feel particularly excited about? So just wanted to understand how MongoDB is therefore fitting into customer priorities as you get into 2024. Thank you so much.

Dev Ittycheria: Hey, Kash. Thanks for the question. I think compared from last year, this year we don’t see things getting worse, but we don’t see things getting better. Where I said, last year with the Fed raising rates, you could really sense that people are getting much more cautious and there was probably more negativity in terms of the outlook coming into calendar 2023. So, that being said, we definitely see innovation, being a priority for customers. We clearly are. I would say the distinction between must-to-have and nice-to-have clearly, in the first category. But customers are also, as I mentioned in the prepared remarks remain focused on being sensitive to costs and ensuring that any investments that may have a high ROI.

So we feel that we’re well-positioned in terms of use cases or segments. I would say in general there is no real kind of material change in any across any vertical industry or geography. We do see. I mean we were at Reinvent last week and we had an amazing set of conversations with lots of senior-level customers. I think we’re really viewed as a mission-critical platform by all our customers, and I think people view us as a platform that they can bet on long-term. And so we see less I would say focus on like point solutions and more about like trying to leverage MongoDB for more and more use cases and I would say that’s pretty consistent across industries and geographies.

Michael Gordon: Yes, the only other thing I’d add Kash is clearly, things have stabilized, we are not guiding fiscal 2025. But just looking out there is clearly a difficult EA and non-EA compare that people should sort of keep in mind, and. I think the big assumption or the big determinant will be people’s macro outlook in terms of how that affects the fiscal 2025 numbers. But those are probably the key things to keep in mind.

Operator: Thank you. One moment, please. Our next question comes from the line of Ittai Kidron of Oppenheimer. Your line is open.

Ittai Kidron: Thanks. Hey guys, nice numbers. Michael, I wanted to go back to one of the comments in your prepared remarks. I think you’ve talked about how you expect non-Atlas business to be down quarter-over-quarter in the fourth-quarter, because I think the third-quarter had multiple multiyear deals, correct me if I’ve got this wrong. I guess my question is, why would that affect 4Q unless there was a pull-forward also not just multi-years, is there a pull-forward element from 4Q into 3Q in your non-Atlas business?

Michael Gordon: No, it’s not about a pull-forward. It’s just when you take into account the 606 impact of a multi-year deal, you wind up recognizing a lot of upfront license revenue. And so when you think about what that means on a sequential basis, you see the difference in the delta there.

Ittai Kidron: Got it, helpful and then, therefore —

Michael Gordon: I think the only other thing, Ittai, just for people is, given the strength that we’ve seen of EA throughout the year, but including Q3 we effectively raised our outlook, if you will in Q4, in part given the strength of EA. Even though we don’t guide to product, Atlas is in line with our expectations. EA outperformed in Q3. And our full-year raise was more than the beat in Q3 and I think that shows that kind of continued strength of EA.

Ittai Kidron: Got it, helpful. And then, Dev, on Vector Search, I know this is kind of fresh out of the oven here, but maybe you can talk about the opportunity here on a per-customer basis. How do we think about the dollar potential here and is there one common vendor out there that you expect to see more in competition for those types of use cases?

Dev Ittycheria: Yes, so let me start with the second question first. I would say that I think six to nine months ago, there was a lot of interest in vector databases and there were some point solutions that got a lot of name recognition and a lot of people wondering is there a risk that could be disrupted by them. And at that point in time, we’ve made it clear that we believed vectors were really another form of an index and that every database platform would ultimately incorporate vectors into their architecture and the winner really would be the technology that made the vector functionality very integrated and cohesive as part of the development workflow. I would I would argue that that’s really played out. As I’ve said in the prepared remarks, there was a recent analysis done by a consultancy firm called Retool that really spoke to lots of customers and we came out on top in terms of NPS and by the way, our product is a preview product, it wasn’t even the GA product.

We’ve seen a lot of demand from customers and we feel like this is a big, big opportunity. Again, it’s early days, it’s going to take time to materialize, but this is again one of the other big growth opportunities for our business. That being said, in terms of the revenue opportunity, it is really hard to quantify now because the use cases that customers are starting with are still kind of, I would say, early in development, so because people are still playing around with the technology. But we are seeing you know as I mentioned, UKG, is using it to essentially provide a AI-powered assistance for its people, you know one energy — European energy company is using — has terabytes of geospatial data and is using vectors to basically get better insights in terms of the images that they’re getting from the work they’re doing in terms of drilling for oil.

So, it’s still very, very, early days. So, hard to give you like an exact number, even today even in our general non-AI workloads, the workload variety can vary a lot depending on the customer, the number of users, the amount of data. So, I think it’s going to be similar to our core business, which is that just really depends on the use case.

Ittai Kidron: Very good. Appreciate it. Thank you.

Operator: Thank you. One moment, please. Our next question comes from the line of Brad Sills of Bank of America. Your line is open.

Brad Sills: Great. Thanks so much. Wanted to ask a question around the customer count, greater than 100K, it looks like a real nice result this quarter. Is there any change going on there in terms of the trajectory or the path for customers to get to that level. In other words, are they starting bigger, are they landing bigger or are they just getting to that point faster and what would be driving those two things?

Dev Ittycheria: Yes, I’m glad you called that out, Brad. Yeah, we’re — I think we added 117 100K customers this quarter, which is the largest add. I think in the company’s history. What I think it really speaks to is that customers are increasingly viewing MongoDB as a mission-critical platform. They’re going to run more and more workloads on MongoDB. So, by definition, it’s rare that one workload on its own will drive that kind of revenue. So, the multiple workloads and really dealing us as a standard part of their infrastructure stack is what’s really driving that number. And we’re obviously happy to see the results of that and we think that that’s just indication, as I said earlier, where people are consolidating onto a few vendors. They recognize that we offer support for a broad set of use cases, we’re truly a general-purpose mission-critical platform and that their developers really love using MongoDB.

Brad Sills: Wonderful to hear. And then one more if I may please. On the commentary around customers viewing Mongo as that platform with some of these newer workloads besides Search like relational migrator, Atlas streaming, do you — are you finding that receptivity for customers who want to run Search within you know one single solution, is that also the case for streaming and relational, just trying to get a sense for those cycles and how those might ramp on that platform capability. Thank you.

Dev Ittycheria: Yes, so. Actually, yeah, one of the reasons we actually built Search is because we got feedback from our customers in many Instances lot of our customers were dual-homing data to MongoDB and to some sort of search database. So consequently, now that they had to manage two databases, keep that data in sync, but also manage the plumbing, the connected those two database platforms and customers told us [indiscernible] like, we don’t understand why you’re not offering a solution, because we much rather have it all in one platform with one API and that ultimately drove our desire to build-out our search functionality, which is really becoming more-and-more popular. So, the point for customers is that, if you can remove friction in terms of how they can use the platform, leverage the platform, have one set of kind of semantics In terms of — to address a broad set of use cases, it really simplifies the data architecture and the more you simplify data architecture, the more nimble, you can be and the more cost-effective you can be, and I think that’s what’s really resonating with customers.

Brad Sills: Thanks so much, Dev.

Operator: Thank you. One moment, please. Our next question comes from the line of Rishi Jaluria of RBC. Your line is open.

Rishi Jaluria: Wonderful, thanks so much for taking my question. Maybe I want to start by diving a little bit into relational migrator, Dev, I know you said it definitely early days, but where you are seeing usage of it, maybe can you give us a little bit of color, what sort of workloads are these customers are utilizing the tool for, what is kind of that timeline look like; any color you can give there in terms of early adoption would be really helpful and then I’ve got a quick follow-up.

Dev Ittycheria: Yes, so — again, as you can imagine, given the lot of these legacy platforms have been around four between 30 to 40 years. Lot of people have large repository of legacy apps and migrating off a legacy platform to another platform does require some work, it requires essentially three things; one, you have to map the scheme of the old platform onto the new platform, you then have to map move the data and then you have to rewrite the application code. And those three things took some time. So, we heard feedback. When we took the company public, you might remember that 30% of our net new business at the time we went public was actually relational migration. So customers were undertaking that heavy lifting because they were in such pain and wanted to move to a more modern platform.

But clearly, that pain can basically be a bit of a tax on switching costs. And so essentially rebuilt tooling based on feedback from customers to start automating the schema mapping and the data movement. Now, with the availability of Gen AI. You can also now start automating the code generation associated with rebuilding or rebuilding an application and essentially what rather than thing, but just moving the app in one last step, we can actually break down a monolithic relational app into microservices and start cleaning off different parts of the services first. So it can be a much more efficient and also more ROI, quicker ROI and some of the investments we’re making. So, there is a big, big opportunity here for us to do that, but again I want to be very clear, we view this as a long-term growth opportunity.

We’re still in the very early days. We’ve got some really interested customers who are doing some interesting things and working with us on pilots. Our engineering and product and field teams are really focused on this, but we are in the very, very early days of really automating relational migrated to the next level leveraging Gen AI.

Rishi Jaluria: All right, wonderful. That’s really helpful. And then I wanted to ask about MongoDB Serverless. I know this is something you’ve kind of had at least been talking about for a while, given a lot of the concerns around cloud optimization and rationalization, customers overpaying, and having to figure out how to optimize our footprint. It feels like that could be a natural tailwind for MongoDB Serverless especially because you were early to embrace it, can you talk a little bit about what you’re — what you’re seeing in terms of adoption there? How we should be thinking about that opportunity and maybe just kind of what this could look like over the next several years in terms of service adoption versus traditional consumption adoption in Atlas. Thanks.

Dev Ittycheria: Yes, so just to be clear, when we talk about serverless, basically what customers think about is that they don’t have to start thinking about capacity planning that the workload can scale-up and scale-down based on the needs of whatever the use cases and what the compute and other resource needs are, and so there’s been a lot of interest from customers. At first stage, there was a lot of the similar workloads, where they didn’t want to go provision a dedicated cluster. They wanted to be able to leverage our serverless functionality, we think long-term that almost every workload will become serverless because over time that’ll be the way most applications are provisioned. But we’re in the early days and the receptivity and use of our serverless functionality has been very high.

And you’re right, it’s — for those legacy platforms that they can offer similar solutions MongoDB becomes that much more attractive because a development team and architecture team doesn’t have to worry about capacity planning, they can just build the app and they know the background our infrastructure can scale-up and down as their usage — as the usage — as the usage goes up and down.

Rishi Jaluria: Wonderful. Really helpful. Thank you.

Operator: Thank you. One moment, please. Our next question comes from the line of Brent Bracelin of Piper Sandler. Your line is open. Brent Bracelin, your line is open.

Hannah Rudoff: Hi guys, this is Hannah on for Brent. Thanks for taking my question. Just one from me. The Subscription gross margins remained above 80% for the second straight quarter, even with that continued mix-shift to Atlas. I know you mentioned efficiency improvement to Atlas, but are we at a structural point, Michael, where the scale of gross margins can remain at that 80% plus range into the next year?

Michael Gordon: Yes, so what I would say is, I do not think — I think if you think about it, Atlas gross margins continue to be lower than Enterprise Advanced gross margins, and while we’re very pleased with that 80% margin performance on a subscription margin basis in Q3, Atlas is quote-unquote only two-thirds and so there is still a delta between the two and so I think that that will have a slightly dilutive effect on margins as Atlas increases as a percent of overall revenue.

Hannah Rudoff: Okay, it makes sense. Thank you.

Operator: Thank you. One moment please. Our next question comes from the line of Patrick Colville of Scotiabank. Your line is open.

Joe Vandrick: Hi, this is Joe Vandrick on for Patrick Coleville. As of 3Q, it looks like about 29% of direct sales customers are spending over 100K on the platform. Just curious where you think that percentage can trend over the longer term and kind of how big the opportunity is within these existing direct sales customer accounts. Thanks.

Michael Gordon: Yes, Joe. I would say that we still believe that we have a very small percentage of wallet share in most accounts. And so, obviously the smaller customer the bigger the wallet share. But in most direct sales customers, our percent of wallet share is still quite small. So we see a big opportunity there and as we talked about in terms of our new business, a big part of our new business came from acquiring new workloads with existing customers. And that is a big focus for our go-to-market teams and the runway is quite long for that trend to continue.

Joe Vandrick: Great and just one more for me. I mean, you kind of touched on this but what’s the feedback been from those customers who have used Vector Search in preview, and then obviously with Vector Search comes quite a bit more data. So, how are you making sure that customers don’t receive a surprise bill and end up unhappy?

Dev Ittycheria: Yes, so, as we mentioned earlier the feedback on our Vector Search has been very-high, even when it was in public preview, we’re getting a lot of feedback and then we saw this report that came out, obviously, we don’t talk to people who are using alternatives, we just talk focus on our own customers, but it was — we’re pleased to see that out of all the products available in the marketplace, our own preview product had the highest NPS score. So then if you unpack that. Why do customers like using MongoDB, because it is one tightly integrated solution, you can tightly integrate capturing Vector data, metadata, and then data regarding a particular use case and that becomes very, very attractive, it just becomes much more seamless and easier to use versus either using point solutions or some kludgy solution that’s been put together.

So, I think that’s a big reason about how we remove friction from a developers workflow and why the MongoDB approach makes it so much better to use instead of any other alternative approach. In terms of your question around the amount of data and data bills, obviously vectors can be memory intensive and the amount of vectors you generate will obviously drive the amount of usage on those nodes, that’s one of the reasons we also introduced dedicated search knows. So you can asymmetrically scale particular nodes of your application especially Search nodes without having to increase the overall size of your cluster. So you’re not — to your point, shocked with a big bill for underlying usage — for non-usage, right. So, you only scale to nodes that are really — need that incremental compute and memory versus nodes that don’t and that becomes a much more cost-effective way for people to do this and obviously that’s another differentiator for MongoDB.

Joe Vandrick: Got it. Thank you.

Operator: Thank you. So, that is all the time that we have today for today’s conference, I would turn the call-back over to Dev Ittycheria, CEO for any closing remarks.

Dev Ittycheria: Thank you. I appreciate everyone joining the call today. Again. I just want to reiterate that we had another strong quarter of new business performance, validating the value proposition of our developer data platform and our run-anywhere strategy. We’re seeing strong momentum on our AI strategy, especially Atlas Vector Search, which is emerging as a best-in-class solution for building powerful AI applications. And we continue to help customers drive greater efficiency, while also accelerating our pace of innovation. So with that. I appreciate your time and we’ll talk to you soon. Take care.

Operator: Thank you, ladies and gentlemen, this does conclude today’s conference. Thank you all for participating, you may now disconnect.