MongoDB, Inc. (MDB) Q2 2024 Earnings Call Transcript

Good day, and thank you for standing by. Welcome to the MongoDB Second Quarter Fiscal Year 2024 Earnings Conference Call. At this time, all participants are in a listen-only mode. After the speakers' presentation, there will be a question-and-answer session. Please be advised that today's conference is being recorded. I would now like to turn the conference over to your speaker for today, Mr. Brian Denyeau. Please go ahead, sir. The floor is yours.

Thank you, Lisa. Good afternoon, and thank you for joining us today to review MongoDB's second 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 our 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 April 30, 2023, filed with the SEC on June 2, 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 the most directly comparable GAAP financial measure. With that, I'd like to turn the call over to Dev.

Thank you, Brian, and thank you to everyone for joining us today. I am pleased to report that we had another exceptional quarter as we continue to execute well despite challenging market conditions. I will start by reviewing our second-quarter results before giving you a broader company update. We generated revenue of $424 million, a 40% year-over-year increase and above the high end of our guidance. Atlas revenue grew 38% year-over-year, representing 63% of revenue, and is now a $1 billion-plus revenue run-rate product. We generated a non-GAAP operating income of $79 million for a record 19% non-GAAP operating margin, and we had another solid quarter of customer growth, ending the quarter with over 45,000 customers. Overall, we delivered an exceptional Q2. We had a healthy quarter of new business acquisitions, led by continued strength in new workload acquisition within our existing customers. From a new logo perspective, we added 1,900 new customers in the quarter. Our direct sales team had another strong quarter of Enterprise customer additions. Finally, our Enterprise Advanced and other non-Atlas business significantly exceeded our expectations, another indication of our strong product-market fit and the appeal of our run-anywhere strategy. Moving on to Atlas consumption trends, the quarter played out slightly better than our expectations. Michael will discuss consumption trends in more detail. Finally, retention rates remained strong in Q2, reinforcing the mission-criticality of our platform, even in a difficult spending environment. As we've told you in the past, our market is different from most other software markets because the unit of competition is a workload, not a customer. We started customer relationships by acquiring the first workload, and we grew from there acquiring incremental workloads over time. Over the last few years, we have oriented our entire company around winning more workloads. Starting with product. At our New York user conference held in June, we made a number of product announcements that will position us to capture more workloads faster. We introduced Atlas Stream Processing, which enables developers to work with streaming data to build sophisticated event-driven applications. The flexibility of the document model and the power of the MongoDB Query Language provide a compelling and differentiated way to process streaming data compared to alternative approaches. Our early access program has been meaningfully oversubscribed as customers realize they can use a familiar and easy approach to work with streaming data and immediately see value. We announced the general availability of Relational Migrator, which makes it easier for customers to migrate their existing relational applications to MongoDB. We are seeing increased adoption across industries and geographies. For example, a leading international retailer was able to leverage Relational Migrator to dramatically accelerate their migration of Oracle. We also announced Atlas Vector Search, which enables developers to store, index, and query vector embeddings instead of having to bolt on vector search functionality separately, adding yet another point solution and creating a more fragmented developer experience. Developers can aggregate and process the vectorized data they need to build AI applications, while also using MongoDB to aggregate and process data and metadata. We're seeing significant interest in our Vector Search offering from large and sophisticated enterprise customers, even though it's still only in preview. As one example, a large global management consulting firm is using Atlas Vector Search for internal research applications that allow consultants to semantically search over 1.5 million expert interview transcripts. Over time, AI functionality will make developers more productive with the use of code generation and code assist tools that enable them to build more applications faster. Developers will also be able to enrich applications with compelling AI experiences by enabling integration with either proprietary or open-source large language models to deliver more impact. Now, instead of data being used only by data scientists to drive insights, data can be used by developers to build smarter applications that truly transform the business. These AI applications will be exceptionally demanding, requiring a truly modern operational data platform like MongoDB. In fact, we believe MongoDB has an even stronger competitive advantage in the world of AI. First, the document model's inherent flexibility and versatility renders it a natural fit for AI applications. Developers can easily manage and process various data types all in one place. Second, AI applications require high-performance, parallel computations, and the ability to scale data processing on an ever-growing base of data. MongoDB supports these features with capabilities like sharding and auto-scaling. Lastly, it is important to remember that AI applications have the same demand as any other type of application: transactional guarantees, security and privacy requirements, text search, in-app analytics, and more. Our developer data platform gives developers a unified solution to build smarter AI applications. We are seeing these applications developed across a wide variety of customer types and use cases. For example, Observe.AI is an AI startup that leverages a 40 billion parameter LLM to provide customers with intelligence and coaching that maximizes the performance of their frontline support and sales teams. Observe.AI processes and runs models on millions of support touchpoints daily to generate insights for its customers. Most of this rich unstructured data is stored in MongoDB. Observe.AI chose to build on MongoDB because we enabled them to quickly innovate, scale to handle large and unpredictable workloads, and meet the security requirements of their largest enterprise customers. On the other end of the spectrum is one of the leading industrial equipment suppliers in North America. This company relies on Atlas and Atlas Device Sync to deploy AI models at the edge, enabling their field teams’ mobile devices to better manage and predict inventory in areas with poor physical network connectivity. They chose MongoDB because of our ability to efficiently handle large quantities of distributed data and to seamlessly integrate between the network edge and their back-end systems. As much as we innovate our products, we also continuously innovate in how we engage with our customers. We are highly focused on reducing friction in the sales process so we can acquire more workloads quickly and cost-effectively given the large size of our market opportunity. Historically, the most significant source of friction has been negotiating with customers to secure an upfront Atlas commitment since it can be hard for customers to forecast consumption growth for a new workload. Given our high retention rates and the underlying consumption growth, several years ago, we began reducing the importance of upfront commitments in our go-to-market process to accelerate workload acquisition. This year, we took additional steps in that direction. For example, we no longer incentivize reps to sign customers to one-year commitments. Obviously, this has short-term impacts on our cash flow, but positions us better for the longer term by accelerating workload acquisition. We are pleased with the impact these changes have had on the business in the first half of the year; specifically, new workload acquisition has accelerated, especially within existing customers. We believe that our efforts to reduce friction are resulting in more efficient growth, and we'll always look for ways to improve our go-to-market approach to make it even easier for customers to adopt new workloads onto our platform. Now, I'd like to spend a few minutes reviewing the adoption trends of MongoDB across our customer base. Customers across industries, including Renault, Hootsuite, and Ford are running mission-critical projects on MongoDB Atlas, leveraging the full power of our developer data platform. One of the 2023 MongoDB North American Innovation Award winners is Ford. With a focus on innovation, quality, and customer satisfaction, Ford is a leader in the automotive industry and a household name around the world. Ford is committed to developing advanced technologies that enhance the safety, performance, and sustainability of its vehicles. Their data explorer and transportation mobility cloud applications aggregate customer vehicle data from 24 different sources at a volume ranging up to 15 terabytes. Since migrating to MongoDB Atlas from their previous solution, Ford has seen a 50% performance improvement and faster rewrite times. Cathay Pacific, Foot Locker, and Market Access are examples of customers turning to MongoDB to free up developers’ time for innovation while achieving significant cost savings. Cathay Pacific, Hong Kong's home airline carrier, operating in more than 60 destinations worldwide, turned to MongoDB on their journey to become one of the first airlines to create a truly paperless flight deck. Flight Folder, their application built on MongoDB, consolidates dozens of different information sources into one place and includes a digital refueling feature that helps crews become much more efficient with fueling strategies, saving significant flight time and costs. Since the Flight Folder launch, Cathay Pacific has completed more than 340,000 flights with full digital integration in the flight deck. In addition to the greatly improved flight crew experience, flight times have been reduced, and the digital refueling has saved eight minutes on the ground on average. All these efficiencies have helped the company avoid the release of 15,000 tons of carbon and save an estimated $12.5 million. Powerledger, Wells Fargo, and System1 are among customers turning to MongoDB to modernize existing applications. System1, a customer acquisition marketing company that acquired MapQuest in 2019, found that at the time of the acquisition, MapQuest had a fragmented architecture that mixed disparate data persistence technologies with third-party services. System1 selected Atlas as a key piece of MapQuest's architecture transformation and has realized estimated cost reductions of 75% and performance improvements of 20% over its prior relational database solution. MapQuest is planning a number of future projects that use Atlas Search and time series collections to improve the user experience and create a feedback loop and location-based relevancy in different cities. In summary, I am incredibly excited about our second-quarter results. Our ability to win new workloads remained strong, and our run-anywhere strategy is resonating with customers. While it's early days for AI, we continue to see evidence that MongoDB will be a platform of choice for AI applications, just as we are for other modern and demanding applications. We continue to invest to maximize our long-term potential. With that, here's Michael.

Thanks, Dev. As mentioned, we delivered a strong performance in the second quarter, both financially and operationally. I'll begin with a detailed review of our second quarter results and then finish with our outlook for the third quarter and full fiscal year 2024. First, I'll start with our second quarter results. Total revenue in the quarter was $423.8 million, up 40% year-over-year. As Dev mentioned, we continue to see a healthy new business environment, especially in terms of acquiring new workloads within existing customers. To us, this is confirmation we remain a top priority for our customers and that our value proposition continues to resonate, even in this market. Shifting to our product mix, let's start with Atlas. Atlas grew 38% in the quarter compared to the previous year and represents 63% of total revenue compared to 64% in the second quarter of fiscal 2023 and 65% last quarter. In Q2, Atlas slightly declined as a percentage of revenue due to the exceptionally strong performance of our non-Atlas business, underscoring the demand for MongoDB regardless of where customers are in their cloud adoption journey. 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 affected by macroeconomic factors. Let me provide some context on Atlas consumption in the quarter. Consumption growth in Q2 was slightly better than our expectations. As a reminder, we had assumed Atlas would continue to be impacted by the difficult macro environment in Q2, and that is largely how the quarter played out. Turning to non-Atlas revenues. EA significantly exceeded our expectations in the quarter, and we continue to have success selling incremental workloads into our EA customer base. We continue to see that our customers, regardless of their mode of deployment, are launching more workloads at MongoDB and moving towards standardizing on our platform. The EA revenue outperformance was in part a result of more multiyear deals than we had expected. In addition, we had an exceptionally strong quarter in our other licensing revenues. On our last call, we mentioned that we would benefit from a few large multiyear licensing deals, most notably the renewal and extension of our relationship with Alibaba. We also closed some additional multiyear licensing deals in the quarter, which is a meaningful contributor to our outperformance and another sign of the popularity of MongoDB and the success of our run-anywhere strategy. As a reminder, under ASC 606 for both EA and licensing contracts, the term license component, even for multiyear deals, is recognized as upfront revenue. Turning to customer growth. During the second quarter, we grew our customer base by approximately 1,900 customers sequentially, bringing our total customer count to over 45,000, which is up from over 37,000 in the year-ago period. Of our total customer count, over 6,800 are direct sales customers, which compares to over 5,400 in the year-ago period. The growth in our total customer count is being driven primarily by Atlas, which had over 43,500 customers at the end of the quarter compared to over 35,500 in the year-ago period. It's 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. Let me double-click into our direct customer count. As Dev mentioned, we're becoming increasingly sophisticated in how we engage our customers. But some of those motions result in the line between our direct sales and our self-service channels becoming more fluid. I thought it would be helpful to highlight two particular inter-channel dynamics that impact the channel breakdown of our reported customer counts. While these customer movements represent less than 1% of our ARR, we do expect both of these trends to continue into the future, and so we wanted to make sure you understood how they affect our reported customer counts by channel. First, we are having increasing success leveraging cloud provider self-service marketplaces to drive new customer additions. Growing cloud marketplace volumes is a major secular trend, and we are the only ISV available on all three hyperscaler marketplaces. Customers can deploy Atlas in seconds through cloud provider consoles and can pay for it by drawing down their existing cloud commitments. This further reduces friction as it bypasses the need for our contract altogether. For this reason, our direct sales team has been directing certain new prospects to sign up using self-serve marketplaces. We've added several hundred customers using this approach in recent quarters, and these customers show up in our self-serve customer count even though we have a direct sales relationship with them. Second, we continually review and analyze product usage signals to determine the potential of our customers. Because we are focused on velocity and efficiency of new workload acquisition, we're very careful not to deploy our reps on accounts where we don't see significant incremental benefit from sales rep coverage. If we determine that a direct sales customer can be supported more cost-effectively in the self-serve channel, we'd prefer to free up the rep's time to focus on winning more new workloads. So far this year, we've moved over 300 small mid-market direct sales customers to the self-service channel. Moving on to ARR. We had another quarter with our net ARR expansion rate above 120%. We ended the quarter with 1,855 customers with at least $100,000 in ARR and annualized MRR, which is up from 1,462 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 second quarter was $329 million, representing a gross margin of 78%, which is up from 73% in the year-ago period. It is important to keep in mind that this quarter, we saw exceptional performance of our EA and licensing revenue, which contains a large upfront license component and very high margins, and therefore, we wouldn't expect to repeat this performance. Our income from operations was $79.1 million or a 19% operating margin for the second quarter compared to a negative 4% 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. Net income in the second quarter was $76.7 million or $0.93 per share based on 82.5 million diluted weighted average shares outstanding. This compares to a net loss of $15.6 million or $0.23 per share on 68.3 million base weighted average shares outstanding in the year-ago period. Turning to the balance sheet and cash flow. We ended the second quarter with $1.9 billion in cash, cash equivalents, short-term investments, and restricted cash. Operating cash flow in the second quarter was negative $25.3 million. After taking into consideration approximately $2 million in capital expenditures and principal repayments of finance lease liabilities, free cash flow was negative $27.3 million in the quarter. This compares to negative free cash flow of $48.6 million in the second quarter of fiscal 2023. Three things of note on our cash flow performance in the quarter. First, as many of you know, Q2 tends to be our seasonally lowest collections quarter of the year because of low contract volumes in Q1, as evidenced by our Q1 ending accounts receivable balance. Second, while our revenue reflects the ASC 606 treatment of multiyear EA and licensing deals, most multiyear contracts are still billed annually, so there's no equivalent benefit to cash flow. Finally, as Dev mentioned, we continue to de-emphasize the value of upfront commitments. So we're seeing fewer of them. In other words, we are intentionally collecting less cash upfront in order to win more workloads more quickly. As evidence of this, we grew Atlas revenue 38% year-over-year, while Atlas dollars committed upfront actually declined by 15% year-over-year. Lower upfront commitments only impact the timing of when our customers pay us, not the total payment. But this trend of declining upfront commitments will impact the relationship between our non-GAAP operating income and operating cash flow in the medium term. I'd now like to turn to our outlook for the third quarter and full fiscal year 2024. For the third quarter, we expect revenue to be in the range of $400 million to $404 million. We expect non-GAAP income from operations to be in the range of $41 million to $44 million and non-GAAP net income per share to be in the range of $0.47 to $0.50 based on an estimated diluted weighted average shares outstanding of 83.5 million. For the full fiscal year '24, we expect revenue to be in the range of $1.596 billion to $1.608 billion. For the full fiscal year 2024, we expect non-GAAP income from operations to be in the range of $189 million to $197 million and non-GAAP net income per share to be in the range of $2.27 to $2.35 based on an estimated diluted weighted average shares outstanding of 83 million. Note that the non-GAAP net income per share guidance for the third quarter and full fiscal year 2024 includes a non-GAAP tax provision of approximately 20%. I'll provide some more context on our guidance. First, we have modestly raised our outlook for the rest of the year, primarily to reflect a slightly stronger Q2 and therefore a higher starting ARR for the second half. We continue to expect that Atlas consumption growth will be impacted by the difficult macroeconomic environment throughout fiscal '24. Our revised full-year revenue guidance continues to assume consumption growth that is, on average, in line with the consumption growth we've experienced since the slowdown began in Q2 of last year, but with a slight seasonal benefit in Q3 and a slowdown in Q4 as observed over the last two years. Second, we expect to see a significant sequential decline in non-Atlas revenues in Q3 as we simply do not expect similar new business activity, especially when it comes to licensing deals. For that particular line of business, Q2 was just an extreme positive outlier. Third, we're raising our non-Atlas revenue estimate for the rest of the year, even though we don't expect our exceptional Q2 performance to repeat in the second half. Our results in the first half give us incremental confidence in our run-anywhere strategy. We continue to expect, however, that the difficult compare in the back half of the year will impact our non-Atlas growth rate. Finally, thanks to strong performance in Q2 and the increased revenue outlook, we're meaningfully increasing our assumption for operating margins in fiscal '24 to 12% at the midpoint of our guidance, an improvement of more than 700 basis points compared to fiscal '23, while continuing to invest to pursue our long-term opportunity. As you update your models, please keep in mind that the majority of our planned fiscal '24 hiring will actually occur in the second half of the year. To summarize, MongoDB delivered excellent second-quarter results in a difficult environment. We are pleased with our ability to win new business and are demonstrating the operating leverage inherent in our model. While we continue to monitor the macro environment, we remain incredibly excited about the opportunity ahead to maximize our long-term value. With that, we'd like to open it up to questions.

Thank you. One moment while we can file the Q&A roster. And our first question today will be coming from Raimo Lenschow of Barclays. Your line is open.

Thank you. Good afternoon. Congrats on a great quarter. Two quick questions. First, the new trends you saw for EA this quarter, you called out bigger commitments from existing customers and taking more workloads back towards Mongo. Is that a new trend? Was it just special this quarter? What are you seeing there? Because that's against what we see from everyone else. And then the second question is on the newer products like the streaming and vector databases. How does that feed into the revenue model for Mongo? That’s it for me. Thank you.

Sure. So Raimo, the trends for EA, I think, indicate our run-anywhere strategy. We've been very committed to that strategy since the beginning. As you know, we started with EA and then introduced Atlas. But the whole point is that we give customers choice, and we want to meet customers where they are in terms of what deployment model they want to use. I think this is just a reflection of the quarter where we had a number of customers who wanted to double down on EA. We also had some other non-Atlas business come in during the quarter, which showed up in our results. But it's really confirmation that we give customers choice, and they truly appreciate that, which is evident in the results. Regarding the streaming and vector, those will show up in the Atlas revenue line as incremental consumption. There won't be a separate SKU. But what it will do is drive, as those workloads come on, that will drive incremental consumption of Atlas, which will reflect in the Atlas revenue line.

Yes. I would just add that also as part of the broader developer data platform, it gives us the opportunity to win more workloads in the beginning. So you've got both new workload penetration, which Dev mentioned, but also the increased Atlas number is where it will show up from a revenue standpoint.

Okay. Perfect. Thank you. Congrats.

Thank you.

Thanks, Raimo.

Thank you. One moment for our next question. And our next question will be coming from Keith Weiss of Morgan Stanley. Your line is open.

This is Keith Weiss on for Sanjit Singh. One question for Dev and one for Michael. Dev, you guys talked about, I think, last quarter, 1,500 AI companies using MongoDB. You talked a lot about your applicability for AI workloads. I think a question that a lot of investors have is the time frame for when this actually creates real impacts and when it becomes a significant tailwind for MongoDB. When will we see that more significantly in Atlas revenues? And then the question for Michael. We talked about the commitments coming down, the Atlas commitments coming down and that being a drag on operating cash flow. Any sense you could give us on how long that drag on operating cash flow persists? Is there any way to size that impact over time?

So, Keith, on AI, obviously, we're excited about the opportunity that AI presents. We continue to add many more AI customers this quarter. In the short term, we're really excited about some of the use cases we're seeing. We talked about Observe.AI, the management consulting company, a more traditional company using MongoDB for very impactful AI use cases. In the longer term, we believe our developer data platform's value proposition will just drive more AI adoption. People want to use one compelling, unified developer experience to address a wide variety of use cases, of which AI is just one of them, and we're definitely hearing from customers that this approach is far more preferable versus bolting on a bunch of point solutions. So we're excited about the opportunity, and I think you had some questions on partners. I do want to mention that we're seeing a lot of work and activity with our partner channel on the AI front as well. We're working with Google in the AI startup program, and there's a lot of excitement there. Google had their next conference this week. We're also collaborating with Google to help train Codey, their code generation tool, to help people accelerate the development of AI and other applications. We're seeing lots of interest in our own AI innovators program, and we've had many customers apply for that program. So we're very excited about the interest we're generating.

And on your other question, Keith, it's been a multiyear journey where we've been focused on reducing friction and accelerating new workload adoption. As we called out, we continue to make additional steps, and Dev mentioned some of the specific incremental steps this year. I think it's part of a transition. If you look at the Atlas revenue growth, Atlas grew 38% year-over-year, but dollars collected upfront shrank 15%. That gives you a sense of the magnitude or the divergence showing up in the operating income versus OCF bridge. There will be a transition time period, but it will settle into a more normalized level; however, I think we've still got a little bit more transition to go as we work through the balance of the year.

Got it. Thank you, guys.

Thank you, Keith.

Thank you. One moment for our next question. And our next question will be coming from Kash Rangan of Goldman Sachs. Your line is open.

I'm sorry, I didn't hear my name. Thank you very much and congrats, Dev and Michael on the quarter. It's hard to put up this kind of operating margin performance being a database company at the scale that you're operating. So kudos on that. The relational migrator came off of beta and became generally available this quarter, so I wonder if that had any particular impact on the EA business because you've certainly upped your modest expectations. I want to get a little bit more detail on how that pipeline of Relational Migrator beta customers should play out. Is it going to be showing up in Atlas? Or is it going to be showing up in the enterprise, the on-prem version? And on AI, just curious if you can quantify the level of consumption impact in the future to Atlas that you could attribute to the different new things that Mongo is working on, whether it's AI or streaming. How should we think about the incremental opportunities for consumption afforded by some of the new things you launched at MongoDB Live in New York a couple of months back? Thank you so much. Congrats.

Sure. Regarding relational migrators, it's important for investors to know that this is really a high-end enterprise play. That's where the bulk of the legacy relational market is. Relational Migrator is designed to help customers reduce the switching cost of migrating off relational databases to MongoDB for both EA and Atlas. So it's dependent on the customer's choice of their deployment model, but it's mainly designed to reduce the switching costs. I would say that there wasn't a real impact in terms of revenue of customers using Relational Migrator because we just made it generally available in June. But there's a tremendous amount of excitement. We have a large pipeline of customers who are very interested and are starting to use Relational Migrator, and projects have begun, but there was no real impact on the quarter. Regarding your second question about the new products and the impact of AI long-term, we definitely believe it will have a big impact long term. We think that things like Vector Search just make it much easier to build smarter applications on MongoDB. That unified developer experience is a key differentiator. There's a strong interest in our Public Preview product. We also see a lot of interest in Stream Processing. Stream Processing is a use case that's really optimized for MongoDB. The data is typically JSON, and the variability of the data lends itself to a document model that's much more flexible. It's obviously very developer-oriented, while all the alternatives are using rigid schemas and are much more complicated to use. So we think we have a big opportunity there. It's hard to quantify that impact long-term, but I can tell you that we are really excited, and the interest level on the new products is incredibly high.

Fantastic. Thank you.

Thank you, Kash.

Thank you. One moment for the next question. Our next question will be coming from Brad Reback of Stifel. Your line is open.

Great. Thanks very much. I'm not sure if Dev or Michael will answer this. But going back to the commentary on fewer upfront Atlas commits. Oftentimes, when customers sign multiyear deals and pay upfront, they get a better rate. So if we were to think about not having them pay you upfront and making long-term commitments, is that a net margin benefit for you guys on the pricing side?

Yeah. So a couple of things. Thanks, Brad, for the question. In general, for us, even before the evolution and changes in multiyear deals, typically, they were not all paid upfront. Typically, ours has been annually billed. But yes, to your point, as we've reduced upfront commitments, you have a couple of dynamics. The key one is when we are not motivating it or providing an incentive to our sales force, and it winds up being customer-driven, the leverage in that negotiation shifts. On the margin, that is helpful for the ultimate pricing or discount and winds up with better pricing for us, less discounting to the customer.

Excellent. And then on your commentary about second half hiring outpacing the first half. Would it be correct to assume that the hiring environment is a little less competitive, so you might actually be able to find people more easily and get better pricing for them as well? Thanks.

Yeah. What I would say, Brad, is that, in general, the frothiness of a few years ago has abated, but for certain skill sets, there's still a significant premium for talent. We don't want to lower our bar just to optimize on cost. We pride ourselves on recruiting the best in this industry. We focus on paying market rates. While it's a little easier because the market is softer, I wouldn't suggest that all of a sudden we are getting employees at a massive discount.

Yeah. I would think about it as availability rather than cost and then throw in some dynamics around different return-to-office models and other factors. I think that incrementally is likely to provide opportunities in the back half of the year. We’ll pursue those as they present themselves.

Excellent. Thank you.

Thank you.

Thank you. One moment for the next question. Our next question will be coming from Karl Keirstead of UBS. Your line is open.

Okay. Great. Maybe this one to Mike. Michael, I wouldn't normally ask about the other segment, but it's such an outlier if I could ask a two-parter. First, what surprised on the upside there? Was the Alibaba deal much larger than you thought? Or did you grab a few others? Maybe you could unpack that? And secondly, you did tell us that the second half guidance assumes a significant decline in the non-Atlas business. Is it fair to assume that this other category might return to the levels it was at pre the July quarter? Thank you.

Yeah. Thanks, Karl. No, other deals, not Alibaba. Alibaba was baked in at the time of the last guidance call. So it was the incremental deals that surprised us to the upside there. Yes, obviously, it’s a volatile or variable, especially given the ASC 606 and the nature of it where it goes, given the lumpiness of the term license revenue. Yes, I think that this is not repeatable performance, and I think it should settle back down to a lower and more normalized level.

Okay. And then if I could ask a follow-up, Mike, you did a good job explaining the changes in the model and the licensing on cash flow, but it's not a metric you often talk about, but your deferred revenue balance was actually down year-over-year. This is highly unusual. Is this basically the same explanation impacting DR?

Yeah. I think it's the same explanation or discussion overlaid with our recurring discussion around billings. That’s not a metric we focus on and we sort of discourage people from using. We’re focused on winning new workloads rather than large upfront commitments. But one of the ways that plays out is absolutely in deferred, and anyone still doing deferred or calculated billings will affect that as well.

Okay. Awesome. Thanks so much.

Thank you. One moment for the next question. Our next question is coming from Rishi Jaluria of RBC. Your line is open.

This is Rich calling on for Rishi Jaluria today. Thanks for taking my question. So I guess if we look at the workloads you have in front of you with Vector Search, Relational Migrator, and Streaming, and you could even throw in application search, which was more of a driver last year. If we had to stack-rank each of those workloads in terms of your positioning to win and your overall opportunity in each of those use cases, how would you go about doing that?

Yeah. So Rich, thanks for your question. I would say, obviously, the general operational workload, or what you would call the OLTP workload, is still our bread and butter. Workloads that people come to us with, Relational Migrator would just be more of that because it's about migrating operational workloads of relational databases to MongoDB. The other products really depend on the use cases that customers are interested in. For example, Atlas Device Sync is focused on the enterprise mobility play, like point-of-sale devices for the retail industry or connected cars in automotive or instrumenting the factory floor. It really depends on the use cases. In application search, we're really seeing an acceleration of large workloads for that product. So we're excited about the size of the business we're seeing there. Obviously, Vector is still in public preview, but we hope to have GA sometime next year. We've received plenty of early and high interest from enterprises. Streaming is something we're very excited about. This is more for event-driven real-time applications. It's very suitable for MongoDB due to the flexibility of JSON data. The variability of the data makes it a very compelling play. I’d say it depends on customer use cases; it enables us to go after more workloads quickly.

The other thing that I’d add, which is probably implicit, but I think it's important to make explicit, is one thing that the slice-by-slice view misses is the aggregate benefit of delivering the whole platform, right, in providing a common integrated unified experience to developers so they don’t have to use a bunch of point solutions. That's really a key part of the strategy.

Got it. That makes perfect sense. Thank you.

Thank you. One moment for the next question. Our next question will be coming from Brent Bracelin of Piper Sandler. Your line is open.

Good afternoon. This is Brent. I wanted to talk a little bit about AI. Mongo has been at the leading powering new apps for the better part of the decade. We're all trying to figure out what this AI-first world looks like. Given your purview as a new app enabler, what's your sense in the next three to four years? How many of these new apps are going to layer in large language models, and what is the net result on the database?

Yeah. So Brent, thanks for your question. I firmly believe that we in the industry tend to overestimate the impact of a new technology in the short term and underestimate the impact in the long term. There's a lot of hype in the market right now, and early-stage companies in AI have sky-high valuations. Some struggle to see how they can make money due to risk-reward mismatch, so there's a lot of hype. However, I believe that AI will significantly impact the industry and us long-term. I think nearly every application, both new and existing, will embed some AI functionality within the next three to five years. Let me remind you where we see AI impacting our business. One, developers will become far more productive with code generation and code assist tools. This will lead to more applications, meaning they need more databases and data platforms. Two, developers will use generative AI to build smarter applications. They generally prefer to use a single platform to process, analyze, and vectorize data rather than using multiple point solutions, which is why there’s so much interest in Vector Search, which I believe is a feature, not a product. It essentially enables people to merge private and public data for compelling experiences, and we’re seeing a lot of interest in the public preview right now. As we mentioned earlier, we've continued to add many more AI customers this quarter, and we think the impact will be big in the long term.

Super helpful color there. And a quick follow-up for Mike. The three-year annual growth rate for EA is over 20%. It slipped below 10% in Q1, and now it’s spiked to about 30% here in Q2, excluding the licensing multiyear deals. If you continue to see enterprise workload migrations happen, why can’t you continue to see strength in EA?

Yeah. So a couple of things. As Dev mentioned, some of the Relational Migrations will determine whether they land in Enterprise Advanced or Atlas based on the customers' cloud strategies and overall IT approach. Certainly, some of that could benefit EA, and we’ve continued to see robust adoption and the adoption of new workloads within that EA customer base. The key consideration this year is that we’ve had very strong results from EA, and as we think about EA on a comparative basis, it’s crucial to keep that in mind.

Okay. Thank you.

Thank you. One moment for the next question. Our next question will be coming from Jason Ader of William Blair. Your line is open.

Yeah. Thank you. Good afternoon. I wanted to get a sense on EA. You talked about doing a good job of existing customers adding incremental workloads. What's the main driver there? Is there something you're doing differently? Or do you think it's just maturity and customers getting more comfortable with you for more workloads?

Yeah. I think it's really about people recognizing that MongoDB is truly a standard. It's a platform they can bet on to run the most mission-critical use cases. The flexibility of the deployment models means they can start on-prem, but they can always migrate to the cloud. This built-in optionality makes going to EA much more comforting; it’s not like they’ll be locked into an on-prem solution or some proprietary cloud solutions. We've achieved maturity, and how we’re becoming a standard in many organizations makes people much more comfortable doubling down on EA.

Got you. And then just to follow up on that EA question. It seems like I don’t want to put words in your mouth, but it seems like you've been a little surprised at the strength of EA relative to Atlas over the last year. Atlas has been really strong too, but EA has, I think, surprised you more to the upside. What does that say about on-prem versus cloud, or self-managed versus fully managed? Any comments on that, Dev?

Yeah. What I would say is it reinforces that customers still prefer running workloads on-prem, that they want to manage workloads themselves versus using a managed service like Atlas. Customers value choice. The ability to have different deployment models is essential, and they appreciate that migrating between different deployment models is easier with MongoDB. What we see is customers valuing that choice as our run-anywhere strategy resonates with them.

I would just add, Jason, that the premise of your question is correct. We've been pleasantly surprised by the performance of EA. It’s been terrific to see, but it definitely has surprised us to the upside.

Great. Thank you.

Thank you. One moment for the next question, please. And the next question will be coming from Tyler Radke of Citi. Your line is open.

Yes. Thanks for taking my question. Atlas revenue grew by almost $30 million quarter-over-quarter, which is the highest you've ever seen it. That performance is better than any of the other consumption models you're seeing. It seems like the commentary at least on Atlas, consumption was pretty consistent with your expectations and still a bit below where it had been pre some of the macro challenges. So could you just kind of unpack what’s driving that strength and the revision back to record high levels of sequential dollar adds? Is it better pricing just given some of the sales changes you made, or perhaps maybe the new AI use cases that you talked about? If you could help us understand that a bit better. Thank you.

Yeah. Thanks for the question, Tyler. A few things. If you're looking at an absolute dollars basis, obviously, the business is much larger now. Secondly, if we're looking at the sequential from Q1 to Q2, remember, Q1 has fewer days, which is part of the dynamic you see historically. Third, consumption in Q1 was better than planned, resulting in a stronger ARR in Q2. That translates into better performance. The consumption itself was broadly in line with expectations. We’re continuing to assume a similar trendline going into the back half of the year, adjusted for seasonality.

Okay. That's helpful. And then a follow-up question, just in terms of the excitement around generative AI. I’m curious how you're using generative AI in products like Relational Migrator to automate the re-architecture process. Are you seeing greater appetite from customers to modernize legacy transactional applications? Is that picking up just due to the excitement around Gen AI? Thank you.

Yeah. Regarding generative AI, we see opportunities where migrating via Relational Migrator involves three components: mapping the schema from the old relational database to MongoDB, moving the data appropriately, and rewriting some, if not all, of the application code. Historically, the last component has been the most manually intensive part of the migration. With code generation tools improving, there are opportunities to automate the rewriting of the application code. We are in the early days, and you'll see us continue adding functionality to Relational Migrator to help reduce switching costs, which presents a big opportunity for us. As for customer appetites for modernization, the recent trend indicates a preference to modernize rather than just lift and shift. This reflects a thoughtful approach to new workloads as companies recognize the importance of leveraging legacy data for competitive advantages while needing new modern platforms to accommodate high performance. The flexibility of our platform is driving more customers to MongoDB.

Thank you.

Thanks, Tyler.

Thank you. One moment for the next question. And our next question will be coming from Patrick Walravens of JMP Securities. Your line is open.

Hi. This is Owen for Pat. Thanks for taking the question, and congrats on the strong quarter. What will the pricing structure for some of the new features like Vector Search and Stream Processing be?

The pricing will be based on the consumption of the back-end infrastructure that supports those new capabilities. So they will show up as increased consumption of Atlas clusters or increased clusters, depending on the load of the application, and will be reflected on the Atlas revenue line.

Great. Thank you.

Thank you. One moment for the next question. And our next question will be coming from Michael Turits of KeyBanc. Your line is open.

Hello?

Hey, Michael.

My name got lost; I wasn't sure if it was me. Thanks. Quick one for you, Mike, and then one for Dev. Mike, are you able to comment on the linearity in the quarter relative to those consumption growth trends and how we exited? And then, Dev, for you, you just said that you think Vector Search is a feature, not a product. There are two databases deliberately out there in the market, and then you, as well as others who don’t have vector databases, including Google, are talking about the applicability of their databases for vector embedding. Can you talk about how that's playing out with customers in terms of their receptivity of looking for something besides a dedicated vector database for this? So the linear question and then that one.

Sure. Yeah, in linear, I don't think there's anything particularly notable to call out.

Regarding Vector Search, and I've shared this before, it's really a reverse index. It's an index built into all databases, and over time, I believe that Vector Search functionality will be embedded into all databases or data platforms. Yes, there are point products focusing solely on Vector Search. But it’s a point product that still requires integration with other technologies like MongoDB to store metadata and process all that information effectively. Developers have expressed a strong desire for a unified developer experience, which is a key differentiator. It removes friction. It’s much easier to build and innovate on a single platform rather than juggling multiple technologies. My strong belief is that ultimately, Vector Search will be embedded in many platforms, and our differentiation will always be a compelling and elegant developer experience.

Thanks, Dev and Mike.

Thank you. One moment for the next question. Our next question will be coming from Mike Cikos of Needham. Your line is open.

I'm sorry, I apologize; the operator couldn’t find me. Thanks for getting me on the call here, guys. If I could just follow up on, Dev, your comments there in response to Michael on the Vector Search. I know that we’re talking about developers and how they’re voicing their preferences for unified platforms, right? But I would think there’s probably also a benefit to having it all in a single platform as well, just because you're lowering the TCO for your customers as well. Right? They’re not paying a tax for the movement or duplication of all that data between different vendors. Is that also a fair assumption when I'm considering the potential you bring versus some of those more point features or databases out there?

With vectors, they are a mathematical representation of different types of data. Thus, there's not a ton of data, unlike application search, that presents significant benefits for storing everything in one platform versus having an operational database, a search database, and needing some glue to synchronize the data. However, it’s more about the user experience and development workflow that really matters. It really offers a unified, elegant method of using MongoDB to facilitate Vector Search functionality. It’s a more compelling differentiation compared to needing a separate vector solution that you'd need to provision, configure, and manage along with all the other tasks.

Got it. Thank you for helping clear my understanding on that. And then just a quick follow-up for Michael. Michael, I've received a few inquiries about the Q3 revenue guide, specifically as it pertains to Atlas. I think what people are looking at is the sequential growth of Atlas, which appears slower than daily consumption. What I’m getting is, were there any one-time events in Q2 that lead us to believe daily Atlas consumption won't accelerate into Q3? Or can you provide more insight?

No. The underlying reason for the Atlas increase is primarily derived from modest upside in Q2, with a stronger starting ARR for Q3. That strong performance from Q2 should continue contributing to our outlook as we move forward.

Terrific. Thank you very much, guys. I appreciate it.

Thank you, Mike.

Thanks, Mike.

Thank you. This concludes the Q&A session for today. I would now like to turn the call back over to Dev Ittycheria, CEO, for closing remarks. Please go ahead.

Thank you, everyone, for joining us today. I just want to reinforce that we had another strong quarter of new business performance, which really validates our value proposition and our run-anywhere strategy. Again, we remain focused on our North Star, which is acquiring new workloads, both from new customers and existing customers. We're innovating both on the product and go-to-market dimensions to accelerate workload acquisition. While it's early days, we believe that with the rise of AI, MongoDB will be a beneficiary as AI becomes more prominent. Thank you very much, and I appreciate all your time. Take care.

This concludes today's conference call. Thank you all for participating, and enjoy the rest of your evening. You may now disconnect.