Thank you for standing by and welcome to MongoDB's earnings call. At this time, all participants are in a listen-only mode. After the speaker's presentation, there will be a question-and-answer session. I will now turn the conference over to your host, Mr. Brian Denyeau.

Thanks, Valerie. Good afternoon, and thank you for joining us today to review MongoDB's fourth 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, our expectations for the macroeconomic environment in fiscal 2025 and the impact of AI, 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 cause actual results to differ materially from our expectations. For discussion of 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 October 31, 2023, filed with the SEC on December 7, 2023. Any forward-looking statements made in this call reflect our views only as of today, and we undertake no obligation to update them except if 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 reconciliation of these measures to the most directly comparable GAAP financial measures. With that, I'd like to turn the call over to Dev.

Thanks, Brian, and thank you to everyone for joining us today. I'm pleased to report that we had another strong quarter that capped off an impressive year as we continue to execute well to capture a large market opportunity. I will start by reviewing our fourth quarter and full year results before giving you a broader company update. Starting with the fourth quarter, we generated revenue of $458 million, a 27% year-over-year increase and above the high end of our guidance. Atlas revenue grew 34% year-over-year, representing 68% of revenue. We generated non-GAAP operating income of $69.2 million for a 15% non-GAAP operating margin, and we ended the quarter with over 47,800 customers. Overall, we are pleased with our performance in the fourth quarter. We had a healthy quarter of new business led by continued strength in new workload acquisition within our existing Atlas 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, and we saw stronger consumption than in Q4 last year. Michael will discuss consumption trends in more detail. Finally, retention rates remained strong in Q4, reinforcing the quality of our product and the mission criticality of our platform. Stepping back and looking at fiscal '24 as a whole, I'm proud of what we accomplished. We achieved revenue growth of 31% and a non-GAAP operating margin of 16%, well above our initial expectations. Atlas grew 37% year-over-year, and we added over 7,000 customers, ranging from AI startups to Fortune 500 companies. We had a record year of fast-paced innovative product releases such as Vector Search, Queryable Encryption, and the preview of Atlas Stream Processing, reinforcing why so many customers and developers choose MongoDB's developer data platform. Finally, we continue to innovate on our go-to-market motion to drive workload acquisition. As we look into fiscal '25, let me share with you what I see in the market. First, I'm excited about our opportunity to win new business. In today's digital world, customers express their business strategy through software. The software development strategy that one of the most important investments a company can make is in the productivity of its software developers. Consequently, customers are gravitating towards MongoDB as their next-generation developer data platform standard. Second, I see stable consumption growth going into next year. Atlas consumption trends have been steady for several quarters now, and we experienced less consumption variability in fiscal '24 compared to fiscal '23. Ultimately, the main driver of Atlas consumption is the growth in the underlying application usage, and we see stable usage growth across our portfolio of workloads. Third, while I strongly believe that AI will be a significant driver of long-term growth for MongoDB, we are in the early days of AI, akin to the dial-up phase of the Internet era. To put things in context, it's important to understand that there are three layers to the AI stack. The first layer is the underlying compute and LLMs. The second layer is the fine-tuning of models and building of AI applications. And the third layer is deploying and running applications that end users interact with. MongoDB's strategy is to operate at the second and third layers to enable customers to build AI applications by using their own proprietary data together with any LLM, close or open source, on any computing infrastructure. Today, the vast majority of AI spend is happening in the first layer, that is investments in compute to train and run LLMs. Neither are areas in which we compete. Our enterprise customers today are still largely in the experimentation and prototyping stages of building their initial AI applications, first focusing on driving efficiencies by automating existing workloads. We expect that it will take time for enterprises to deploy production workloads at scale. However, as organizations look to realize the full benefit of these AI investments, they will turn to companies like MongoDB, offering differentiated capabilities in the upper layers of the AI stack. Similar to what happened in the internet era when value accrued over time to companies offering services and applications leveraging the built-out Internet infrastructure, platforms like MongoDB will benefit as customers build AI applications to drive meaningful operating efficiencies, create compelling customer experiences, and pursue new growth opportunities. We already see our platform resonating with innovative AI startups building exciting applications for use cases such as real-time patient diagnostics for personalized medicine, cyber threat data analysis for risk mitigation, predictive maintenance for maritime fleets, and auto-generated animations for personalized marketing campaigns. Finally, our competitive position is getting stronger. Our win rates remain very high across all competitors. We rarely compete with legacy database providers as enterprises understand that they need to move away from inefficient and brittle legacy technology. We also rarely run into niche database players since customers are overwhelmed by the proliferation of point solutions that are hard to manage and add limited value. Our main competition remains the cloud players. They offer a wide array of database options, relational and non-relational, and benefit from their size and reach. We compete well against these players due to the flexibility and scalability of our document architecture. The fact that our open platform can run anywhere and avoids lock-in and MongoDB's popularity among developers around the world. Finally, when you look at our newer products, we see increased success competing against the established players in those markets. We find that the same principle applies as in the core database market. Customers don't want to manage a myriad of point solutions and prefer consolidating their spend with strategic vendors, especially in the current cost-conscious environment. In summary, we expect the environment in fiscal '25 to be largely similar to the environment we experienced in fiscal '24. With that backdrop, let me tell you what our priorities are going into next year. First, we'll continue pressing our product advantage in the core database, since we believe customers will place an even greater premium on performance and scalability in the AI-enabled world. In addition, we'll continue maturing our newer products, including additional features of Vector Search, GA of Atlas Stream Processing, and enhancements to other offerings. Second, we will remain singularly focused on new workload acquisition as the key long-term driver of our business. We will continue fine-tuning incentives to ensure that our entire go-to-market organization is focused on identifying and sourcing new workload opportunities. In addition, we will leverage our expertise and learnings from our self-serve business to use product-led growth techniques to increase the adoption of Atlas by other development teams within our existing large enterprise accounts. Third, we are focused on growing sales capacity. As we told you in the past, we were slow to grow capacity in fiscal '24, especially in the first half due to macro uncertainty. Given that the market is more stable now and that we remain under-penetrated compared to our opportunity, we'll increase the pace of go-to-market investments in fiscal '25. Fourth, we will continue investing to become a standard in more of our customer base. We intend to double the size of our strategic account program and dramatically expand our account-based marketing efforts in our largest accounts. Finally, we remain focused on locking in the relational migration opportunity. To remind everyone, there are three elements to migrating an application: transforming the schema, moving the data, and rewriting the application code. Our current relational migrator offering is designed to automate large parts of the first two elements, but rewriting application code is the most manually intensive element. GenAI holds tremendous promise to meaningfully reduce the cost and time of rewriting application code. We will continue building AI capabilities into Relational Migrator, but our view is that the end solution will be a mix of products and services. This year, we are investing in several pilots leveraging AI for relational migrations paired with services to substantially simplify and scale the process. Now I'd like to spend a few minutes reviewing the adoption trends of MongoDB across our customer base. Customers across industries around the world are running mission-critical projects on MongoDB Atlas, leveraging the full power of a developer data platform, including ZF, Forbes, and Swiss Federal Railways. ZF, a global technology company supplying systems for passenger cars, commercial vehicles, and industrial technology, needed a central database solution with broad functionality to support more than 300,000 commercial vehicles connected to ZF infrastructure. ZF originally began using MongoDB on-premise in 2014 and migrated to MongoDB Atlas to modernize the architecture behind its new fleet orchestration solution. The team now uses time series and online archive to reduce the overall data storage size, as well as MongoDB Atlas Search to manage indexes and Atlas Charts to display billing information. MongoDB's developer data platform enables ZF to release new features faster as innovative technologies like drones and autonomous vehicles continue to come to market. In any case, PicPay and Anywhere Real Estate are examples of customers turning to MongoDB to free up the developers' time for innovation while achieving significant cost savings. Anywhere Real Estate, a global leader in residential real estate services whose brand portfolio includes Better Homes and Gardens, Century 21, Coldwell Banker, Corcoran, ERA, and Sotheby's International Realty, is leveraging MongoDB Atlas and Atlas Search to greatly enhance its search capabilities. Their previous solution was too costly and operationally burdensome to maintain. Now with Atlas Search, they can ingest data from hundreds of MLS sources, aggregate the data, and provide customers with a search solution that efficiently delivers accurate and up-to-date information, saving time and lowering costs. Anywhere is also exploring the use of Atlas Vector Search to provide semantic search and GenAI features to millions of consumers. Samsung Electronics, ArcelorMittal, and Citizens Bank are turning to MongoDB to modernize applications. Samsung Electronics' digital appliances division transitioned from their previous MySQL database to MongoDB Atlas to manage their clients' data more effectively. By leveraging MongoDB's document model, Samsung's smart home service can collect real-time data from the team's AI-powered home appliances and use it for a variety of data-driven initiatives such as training AI services. Their migration to MongoDB Atlas improved response times by more than 50%, and disk read latency was reduced from 3 seconds to 18 milliseconds, significantly improving availability and developer productivity. Let me wrap up by saying that I remain highly confident about our ability to execute on our long-term growth opportunity. We are pursuing one of the largest and fastest-growing markets in all of software, with significant expansion opportunities in both new and existing customer accounts. While it's early days, we expect that AI will not only support the overall growth of the market but also compel customers to revisit both their legacy workloads and build more ambitious applications. This will allow us to win more new and existing workloads and to ultimately continue to establish MongoDB as a standard in enterprise accounts. Before I turn it over to Michael, I would like to personally invite all of you to attend the investor session at MongoDB.localNYC to be held at the Javits Center on May 2nd. Please email [email protected] if you're interested in attending. With that, here's Michael.

Thanks, Dev. As mentioned, we delivered a strong performance in the fourth quarter both financially and operationally. I'll begin with a detailed review of our fourth quarter results and then finish with our outlook for the first quarter and full fiscal year 2025. First, I'll start with our fourth quarter results. Total revenue in the quarter was $458 million, up 27% year-over-year, and above the high end of our guidance. As Dev mentioned, we had another quarter of healthy new business acquisition, demonstrating our product market fit and the mission criticality of our platform. Shifting to our product mix, let's start with Atlas. Atlas grew 34% in the quarter compared to the previous year and now represents 68% of total revenue compared to 65% in the fourth quarter of fiscal 2023 and 66% last quarter. 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. As a reminder, in Q4 fiscal '23, we had a higher than normal amount of revenue from unused commitments, making this a tough year-over-year comparison. Excluding the impact of unused commitments, Atlas year-over-year growth in Q4 was in line with the growth that we observed in Q3. Let me provide some additional context on Atlas consumption in the quarter. As we shared in our guidance last quarter, we were expecting consumption to be impacted by the seasonal slowdown in Q4 around the holidays. Week-over-week consumption growth in Q4 was stronger than in Q4 of last year and in line with our expectations. We've seen less consumption variability this year, and so as in Q3 we forecasted less of a seasonal impact than in prior years, and that's exactly what we saw. Turning to non-Atlas revenue, EA exceeded our expectations in the quarter, and we continue to have success selling incremental workloads into our existing EA customer base. Ongoing EA strength speaks to the appeal and success of our run anywhere strategy. The EA revenue app performance was in part a result of more multi-year deals than we had expected. As a reminder, the term license component for multi-year deals is recognized as upfront revenue at the start of the contract, and therefore includes term license revenue related to future years. Turning to customer growth, during the fourth quarter, we grew our customer base by approximately 1,400 customers sequentially, bringing our total customer count to over 47,800, which is up from over 40,800 in the year-ago period. Of our total customer count, over 7,000 are direct sales customers, which compares to over 6,400 in the year-ago period. The growth in our total customer count is being driven primarily by Atlas, which had over 46,300 customers at the end of the quarter, compared to over 39,300 in the year-ago period. It's important to keep in mind that the growth of our Atlas customer count reflects new customers to MongoDB, in addition to existing EA customers adding incremental Atlas workloads. Moving on to ARR, we had another quarter with our net ARR expansion rate above 120%. We ended the quarter with 2,052 customers with at least $100,000 in ARR and annualized MRR, which is up from 1,651 in the year-ago period. We also finished the year with 259 customers spending a million dollars or more annualized on our platform compared to over 213 a year ago. Moving down the income statement, I'll be discussing our results on a non-GAAP basis unless otherwise noted. Gross profit in the fourth quarter was $353.6 million, representing a gross margin of 77%, which is down from 78% in the year-ago period. As we said at the time, our gross margin in the year-ago period reflected a one-time benefit of roughly 2.5 percentage points related to one of our cloud partner contracts. Our income from operations was $69.2 million, or a 15% operating margin for the fourth quarter, compared to a 10% 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 fourth quarter was $71.1 million, or $0.86 per share, based on 82.9 million diluted weighted average shares outstanding. This compares to net income of $46.4 million, or $0.57 per share, on 80.8 million diluted weighted average shares outstanding in the year-ago period. Turning to the balance sheet and cash flow, we ended the fourth quarter with $2 billion in cash, cash equivalents, short-term investments, and restricted cash. Operating cash flow in the fourth quarter was $54.6 million and $121.5 million for the full fiscal year 2024. After taking into consideration approximately $4.1 million in capital expenditures and principal repayments of finance lease liabilities, free cash flow was $50.5 million in the quarter. This compares to free cash flow of $23.8 million in the fourth quarter of fiscal 2023. For the full fiscal year '24, free cash flow was $109.9 million compared to negative $24.7 million in fiscal '23. I'd now like to turn to our outlook for the first quarter and full fiscal year 2025. For the first quarter, we expect revenue to be in the range of $436 million to $440 million. We expect non-GAAP income from operations to be in the range of $22 million to $25 million and non-GAAP net income per share to be in the range of $0.34 to $0.39 based on 83.8 million estimated diluted weighted average shares outstanding. For the full fiscal year 2025, we expect revenue to be in the range of $1.9 billion to $1.93 billion, non-GAAP income from operations to be in the range of $186 million to $201 million, and non-GAAP net income per share to be in the range of $2.27 to $2.49, based on 85.1 million estimated diluted weighted average shares outstanding. Note that the non-GAAP income per share guidance for the first quarter and full fiscal year 2025 includes a non-GAAP tax provision of approximately 20%. I'll now provide some more context on our guidance, starting with the full year fiscal '25, where we're facing difficult compares in two ways. First, we expect to recognize close to zero revenue from unused Atlas commitments in fiscal '25, compared to over $40 million in fiscal '24. As you may recall, in fiscal '24, we changed our sales incentive structure to reduce the importance of upfront commitments. And so we saw far fewer upfront commitments. Therefore, as those fiscal '24 deals come up for renewal in fiscal '25, we expect to see limited revenue related to unused commitments. Second, in fiscal '24, we recognized approximately $40 million more in multi-year license revenue than we did in fiscal '23. As you know, our fiscal year '24 non-Atlas revenue benefited from a higher-than-usual amount of license revenue related to multi-year contracts, including our extended partnership with Alibaba. Clearly, we are pleased with the fiscal '24 performance, but it was unusual in terms of the magnitude of multi-year deals, and we don't expect similar performance in fiscal '25. As a result, we expect non-Atlas revenues to be modestly down in fiscal '25. Next, we expect Atlas consumption growth to be in line with the consumption growth we've experienced in fiscal '24. Finally, I want to provide some context to better understand our operating margin guidance. The $80-plus-million of fiscal '24 revenue that won't repeat in fiscal '25 was very high margin, making for an exceptionally tough operating margin compare. In addition, as we mentioned in the past, in fiscal '24 we began increasing our pace of hiring relatively late in the year. So the full cost from those investments will impact our fiscal '25 operating margin. We're expecting headcount growth in the mid-teens versus 9% growth in fiscal '24. And as Dev mentioned, we are prioritizing growth in sales productive capacity. Consequently, we expect to see a year-over-year operating margin decline while still delivering 500 basis points of margin expansion on a two-year basis. We believe this is the most appropriate way to understand our continued margin progression. Moving on to our Q1 guidance, a few things to keep in mind. First, we expect Atlas revenue to be flat to slightly down sequentially. Q1 has two fewer days than Q4 this year, which represents a revenue headwind. Also, the slower Atlas consumption growth during the holidays will have a bigger impact on Q1 revenue than it did in Q4, thereby negatively impacting sequential revenue growth. Finally, the sequential impact from the expected decline in unused Atlas commitments will be most pronounced in Q1, given that we made the changes in Q1 of last year. Second, we expect to see a meaningful sequential decline in EA revenue. As discussed in past years, Q4 is our seasonally highest quarter in terms of our EA renewal base, which is an excellent indicator of our ability to win new EA business. In Q1, the EA renewal base is sequentially much lower. Finally, we expect operating income to decline sequentially due to lower revenue as well as our increased pace of hiring. To summarize, MongoDB delivered strong fourth quarter results. We're pleased with our ability to win new business and see stable consumption trends in Atlas. We remain incredibly excited about the opportunity ahead and will continue to invest responsibly to maximize our long-term value. With that, we'd like to open it up to questions.

Our first question comes from Sanjit Singh of Morgan Stanley. Your line is open.

Thank you for taking the question. Michael, I wanted to walk through the guidance with you a little bit. This time last year, coming out of Q4 '23, it was a pretty different environment, a much more cautious environment. I think usage growth was particularly impacted in Atlas last year. This year coming into fiscal year '25, things feel a little bit healthier or at least stable in sort of how you guys are framing it. And yet the guidance, the initial guidance for growth looks a lot like the initial guidance growth for last year. Just trying to understand, and I know you talked about some of the one-time impacts from last year, but just in terms of a better spending environment versus an initial guide that looks also pretty similar to last year, could you sort of frame out like the conservatism that you have embedded in guidance.

Yeah. So a couple of things. Thanks for the question. Yeah, I think the key thing is, once you adjust for those one-time items that we called out, we see a stable environment. That's what we described and experienced in Q4 compared to Q3. So we feel good about that. I think just to underscore, there's the $80 million in revenue that won't repeat both from the unused commitments as well as from the multi-year deals. And when you adjust from those, we feel good about the dynamic. To your question, which is sort of embedded in that around, sort of I'll call it, guidance approach or approach to guidance, we haven't changed our view as it relates to guidance. We have seen more stable consumption that obviously gives us higher confidence, in part given the less variability that we've seen over the course of fiscal '24. I think we also have a better understanding of the underlying seasonality of the business. We had updated at the end of last year in our call, in our Q3 call, around the success that we're having on EA, and we had updated those EA new business assumptions, and so that our guidance reflects kind of continued strength there. So I think that's how we approach it, but there are no fundamental changes, but hopefully we've given you a fair amount of the piece parts so people can help do the math.

Yeah, I really appreciate that. And thank you for breaking out that $80 million between unused commitments and the multi-year term license field. On the unused commitment side of the house, that $40 million, can you give us a sense of how that flowed through to the balance of the year? Obviously, it doesn't look like it all came in Q4, but the prior year in Q4, you also mentioned a $7 million impact to Atlas revenue that quarter. Was that impact worse or better this year when we think about the unused commitment contribution to this quarter's Atlas results?

The key point is that the $40 million will be realized throughout the year. It corresponds to actual contracts and influences a relatively small number of customers and a minor portion of the commitments. This situation will diminish over time as we've previously discussed. It's particularly important to note that on a sequential basis, it will be most noticeable in Q1 due to the initial wave of renewals where we don't have commitments. We wanted to highlight this to ensure everyone can grasp that dynamic when analyzing the sequential results.

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

Thank you. Congrats on a nice Q4. Question also a little bit on guidance, Mike. The last two quarters before Q4, we talked about EA seeing a little bit of a tailwind from customers kind of maybe modernizing on-premise rather than going to Atlas to kind of still modernize but maybe not spending all the money to go to the cloud. Is that trend still valid? And if you think about the multi-year commitments, obviously you had the $10 million, $15 million for the Alibaba deal, but then the other stuff is like customers that are just doing this work. Do you think that will change and people go back to like shorter commitments or is it just more that you're kind of thinking about the renewal pool? Thank you.

There are several factors to consider. Regarding the multi-year aspect, we have observed a lot of variability in deals and have communicated that, particularly in relation to ASC 606, which has led to increased variability and decreased comparability for Enterprise Advanced. While Atlas has become a larger part of our business, the situation with EA remains dynamic. We anticipate continued multi-year deals, but in fiscal '24, we encountered more than expected, not only in EA but also across the broader non-Atlas landscape. This level of activity is unlikely to be replicated in fiscal '25, which is why we wanted to highlight and quantify it. As for modernization, every customer has their unique IT strategies, including their deployment and cloud approaches, which they manage. Our goal is to make MongoDB accessible for them. The Run Anywhere strategy has shown great success, reflected in the performance of EA. Additionally, we’ve noticed that even businesses or those in regulated sectors that cannot fully transition to the cloud are seeking ways to modernize their applications and infrastructure. MongoDB and Enterprise Advanced are becoming recognized as a pathway to the cloud, even if a complete migration is not feasible. Ultimately, this aligns with our strategy, making it easier for customers when they eventually decide to move to the cloud.

Thank you. Our next question comes from Kash Rangan of Goldman Sachs. Your line is open.

Hey, thank you very much. Congrats on the results. One quick one for Dev and one hopefully quick one for Michael as well. Dev, you talked about Generative AI applications. You described the three layer architecture. When do you think it hits the sweet spot of how MongoDB is positioned from a timing standpoint? When do these Generative AI applications start to really drive underlying need for the kind of databases that you're best suited for? One for Michael. In your assumptions, when I take away the $40 million of the upfront, that's like a couple of percentage points of growth. I'm just trying to understand what kind of consumption trends you are using to build guidance? Was it average of fiscal '24 consumption trends or weighted more to its second half or exiting fourth quarter? Any color there would be tremendously useful. And also want to ensure the sales force is still selling EA and can get comp for EA because it does not look like you're giving much weight for EA in your forecast. That's it for me. Thank you.

Thanks, Kash. I'll take the first question. Regarding the deployment of Generative AI applications by enterprises at scale, it depends on customers becoming comfortable with the technology and the advancements in these technologies in terms of performance and cost. If you've used ChatGPT or similar tools, you understand that the response time for these applications ideally falls between one to three seconds, depending on the query. While chatbots are a straightforward use case, integrating this technology into complex applications that make real-time decisions based on real-time data still presents challenges in performance and cost. Customers are in a learning phase, experimenting and prototyping, but widespread deployment of AI applications is still limited. This year will likely see customers rolling out a few applications to gain experience, and as they become more comfortable, they will gradually introduce more applications as the technology matures and costs decrease. We are optimistic about our positioning because our architecture, which includes a document model, flexible schema, real-time data handling, performance at scale, and a unified platform all contribute to our attractiveness. Additionally, the decision-making process for AI is increasingly centralized, allowing us to engage with higher levels in organizations since it’s viewed as a top-down initiative. This positions us as a modern platform suitable for new workloads and use cases. Overall, while we feel positive about our positioning, this year appears to be one of continued experimentation and the rollout of individual applications.

Thank you for your question regarding consumption. When considering our guidance and the factors we've shared, the impact of $80 million from unused commitments and multi-year outperformance will reflect about 500 basis points of headwind at the top line. Additionally, we anticipate that the non-Atlas segment will see a modest decline because $40 million of that segment is non-recurring. So, when combining all these aspects, it's likely you'll conclude that Atlas's consumption growth appears consistent, aligning with the stable trends we've observed throughout fiscal '24. We are basing this on our fiscal '24 numbers, taking into account some seasonal adjustments. Regarding your question about EA, we still sell it, but generally, it's to existing customers rather than new accounts, as we expand their MongoDB usage. Our sales representatives do receive compensation for EA sales, which relates to an earlier point made about their limited ability to influence IT deployment decisions on the customer side. Lastly, the results or expectations from EA are affected by difficult comparisons, partly due to that multi-year dynamic.

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

Good afternoon. Thank you. Michael, we're going to stick with the guidances here. If I look at last year, you guided to, I think, 16% growth. You ended up doing 31% for the full year. Even if I take out the $80 million tailwind you talked about, that's still 25% growth. You're guiding to 14% growth this year, again 5% headwind, so closer to 19% organically. Are you more confident kind of going into this year than last year just as you think about the trends? Is the 14% comparable to the 16% initial guide last year? Is it really more like 19% adjusted basis comparing to 16%? I know it's a little confusing, but getting a lot of questions on it, thanks.

I go back to my previous response regarding Sanjit's question. There hasn't been any fundamental change in how we determine our guidance. I believe we have more confidence now due to more data. Looking back a year ago, we faced significant macro uncertainty. Throughout fiscal '24, we observed narrower variability and more consistent results, which has increased our confidence. Additionally, we have gained more insight into the seasonality trends of Atlas. While Atlas is a substantial business, it is still relatively new, especially when considering our quarterly data points. This gives us a better understanding. Lastly, despite the challenging comparison for EA, we discussed in the second half of last year that there was only so much surprise we could expect from EA. During our third quarter call, we raised our expectations for what EA could achieve, and all of this has been factored into our guidance.

Helpful context there. Dev, when do you believe the growing interest in AI, particularly in Vector, will begin to significantly affect your business? It sounds like you anticipate another year of experimentation before we see substantial production changes. Is that accurate? Please share your current thoughts on when AI will start to make a noticeable impact on your business. Thank you.

I believe we'll start to see the impact when companies begin deploying AI applications at scale, which I think will take at least another year. However, we do notice some promising startups building on our platform, which reinforces our confidence in its suitability for advanced workloads. It's important to remember that we're still in the early stages of AI. The performance of many systems is currently acceptable but not exceptional, and the costs associated with inference are quite high, so organizations must be cautious about the types of applications they implement. There is ongoing discussion about the benefits of open versus closed-source large language models, and whether to use them for specific use cases or in more general capacities. A lot of learning is happening in this area. Additionally, a recent announcement revealed that another company has achieved better performance than GPT-4, indicating that there is considerable activity in this field. It's going to take some time for companies to feel secure in selecting a technology stack and deploying workloads at scale, although there are some who are moving more aggressively. Overall, across our customer base, we feel well positioned. Our unified platform, which efficiently manages data, metadata, and vector data, resonates with users. We remain open and adaptable, integrating not only with various large language models but also with different embedding models and emerging application frameworks that developers prefer. We believe we are in a strong position and will continue to expand our influence in this area, though it may take some time.

Thank you. One moment, please. Our next question comes from the line of Karl of UBS. Your line is open.

Okay, great. Maybe one for Dev and one for Mike. Dev, just because my first question follows on that, I'll go to you first. There are certainly some voices in the industry that would argue that even in advance of AI applications being deployed at scale, which you just said might take a year, enterprises might look to spend more to modernize their existing data stack and on data readiness in advance of those AI apps going into production. Are you seeing any of that type of behavior that could precede the in-production deployment timeframe?

Yeah, I touched a little bit about relational migrations. I mean, that's one way where a lot of people feel like they have a lot of data trapped in these legacy platforms. As we've shared, we've always had customers migrating from legacy SQL apps to MongoDB. But the hardest part was basically rewriting the application. Generative AI essentially lowers the cost to do so. We are running a bunch of pilots with customers. Customers are very aligned. We have access to senior-level decision-makers. And we're learning a lot. Obviously, we're learning about the effectiveness of some of these AI technologies. We're learning about how you have to handle old languages, old libraries, old packages, and the different versions. The variability in all that makes it clear that this will require a mix of product and services. Product alone today will not solve the problem. So we do think this is a big opportunity, but we're in the early days. And as I said in the past, even when I talked to investors about this pre-AI, there was no big red easy button to press to kind of migrate a SQL app to MongoDB. And while GenAI makes that easier, it's still going to take a little bit of time, but it's definitely exciting and there's a lot of customers leaning in. And so, we're excited about the option, but it's a lot of work to do.

Yeah. Okay. Thanks, Dev. And then for Mike. Mike, could you just because we're all trying to set up our models by quarter for fiscal '25, is there any way to be more descriptive about how that $80 million, the sum of those two pieces, tracked by quarter and in particular how much of the $80 million landed in the fourth quarter you just reported?

I don't have a quarterly breakdown to share. However, throughout our time as a public company, especially over the last three quarters, we've highlighted any unusual trends to help you understand what has influenced the results and what might affect future comparisons. Our past comments should provide a clear indication of these trends. We have been transparent regarding multi-year deals and instances of EA outperformance. As a general guideline, unused credits or commitments usually align with the renewal cycle, and we've mentioned the seasonality associated with that. Specifically for EA and other non-Atlas deals, Q2 of last year was significant. We discussed Alibaba and other deals that were finalized in that quarter, so just dividing by four wouldn’t give an accurate representation, as there are quarter-to-quarter variations. Lastly, we noted that for those analyzing the business sequentially, the Atlas impact for Q1 will be more significant due to the effects of recent changes.

Q2 last year was significant for Atlas, with major deals like Alibaba occurring during that period. It's important to note that you can't simply average it out over four quarters, as there are variations between each quarter. Additionally, for those analyzing the business sequentially, it's important to recognize how Atlas will influence the numbers in Q1, especially since this quarter will reflect a much stronger impact due to the recent changes.

Hello, can you hear me? The line is cutting out a bit.

Yes, we can hear you. Unfortunately, we can't hear the moderator, but we can hear you.

Terrific. Okay, well, thank you for having me on. I'd like to ask about Atlas Stream Processing. So that was announced in June 2023. I guess, can you just remind us of like what is Mongo's reason to win in that segment of the market and then any idea of when that product will likely go GA?

We announced the private preview of Stream Processing, which attracted hundreds of development teams. We are now in a public preview, allowing customers to start using the product today if they are interested. Our strong position in this market stems from a few key factors. First, our focus is solely on developers. The data primarily comes in JSON format, necessitating a flexible schema suitable for real-time applications. Given these attributes, we believe we are well-positioned, as many alternatives have a rigid or fixed schema. This rigidity makes it more challenging for customers to manage the varying data from these events. We are optimistic about our standing in this area. Regarding the timeline for general availability, we are currently collecting and responding to feedback. We want to ensure that we have addressed the essential issues before going generally available, but we are genuinely excited about the opportunities that stream processing presents.

Yeah, ideal. I might just also follow on that and then add in my kind of proper second question. The follow-on is, is Stream Processing embedded in the guidance for fiscal '25? And then the question I had is about the bottom line. The guidance, if I'm not mistaken, is a 10% op margins in fiscal '25. Assuming the same beat as you guys did this year, so if 6 points of beat would put us at about 16 points of op margin exiting fiscal '25, so kind of net-net flat year-on-year. Is that due to this kind of putting the kind of foot on the gas in terms of hiring and really trying to be aggressive at adding headcount? Thank you.

We have a few different questions to address. Our plans for Stream Processing are included in our guidance, but most of that will reflect in Atlas overall when considering the results. New workloads and similar initiatives typically start small but grow rapidly in the initial quarters. However, I wouldn’t expect it to be a significant factor in the fiscal '25 results, though we are very optimistic about its long-term potential. Regarding operating margin, our guidance indicates a decrease in operating margin compared to fiscal '24, but this will lead to a 500 basis points improvement over a two-year period. The rationale behind this is that, when looking at us since the IPO, we outlined a need for about 55 points of margin improvement to reach our target. With the fiscal '24 results, we effectively achieved 50 of those 55 points while still holding a 2% market share, so it’s logical to keep investing in this opportunity, particularly in enhancing our sales capacity and advancing our product roadmap. We will keep moving in this direction, which may cause a decline compared to last year, but will yield a positive 500 basis points improvement over two years.

Tremendous. Thank you so much.

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

Great. Thank you so much. I wanted to ask a question around the sales capacity. It sounds like, at some point last year, you realized that you had under-invested, maybe pivoted too much towards margin expansion and then are now catching up. In the guidance, if you could assume you had the sales capacity that you would prefer to be at this point, given the demand that you're seeing, would we be at a higher level of growth? I'm just trying to parse out how much of the guide is factoring in those constraints on sales capacity that you've talked about?

Yeah, so thanks for your question. Yes, we, given the macro uncertainty, especially coming out of Q4 of last year, we did slow down hiring quite meaningfully. And obviously that showed up in our numbers to the point that Michael talked about in our op margin as well. We obviously know that we have a big opportunity in front of us. So we are growing our headcount between the mid and high teens. We think that's appropriate relative to the opportunities we see. And yes, if we had more productive sales capacity, the guidance would probably be higher. There's no question about that.

I want to clarify that the slowdown in hiring was primarily due to macroeconomic factors and concerns about the broader environment. At that time, there were widespread layoffs across the industry. Fortunately, we managed to navigate through that period successfully. Looking back, we discussed during the last call that, with hindsight and considering our results, we could have started investing sooner. In the year-ago call, we mentioned that we aimed for single-digit headcount growth compared to a 30% increase the previous year, and we ended up adding 9%. This was at the upper end of what single digits would represent, partly due to the stabilization we experienced. Regarding the operational income guidance and other factors, those investments will have a greater impact on our fiscal '25 profit and loss statement, which is what we are currently factoring in.

Understood, thanks so much for that. And then one more, if I may, please. You guys have such broad exposure to different industries. With the advent of AI coming into your business and some of the early progress you're seeing, are there any verticals that you would point to where you're seeing more activity perhaps than others, any use cases you might point to, just to give us a sense for where that early adopter might come from? Thank you.

In terms of use cases, most customers are focusing on improving efficiencies in their operations because they have a good understanding of their current costs. This makes it easier for them to assess the value they can gain from new AI technologies. The initial wave of applications is aimed at cost reduction. Several customers have already announced their efforts to significantly lower costs in areas like customer support and customer service. This is not unexpected. Another area of potential is enhancing developer productivity and cogeneration. I believe these will be key focus areas where quick wins can be achieved. Subsequently, customers will likely concentrate on providing better experiences for their clients and exploring new growth avenues. I anticipate these developments will unfold in phases. While there are some limitations based on the regulatory environment in various industries, we generally observe high interest across nearly all sectors in which we operate.

That's exciting. Thank you so much, Dev.

Thank you.

Thank you, Brad. Our next question comes from Rishi Jaluria of RBC. Please go ahead.

Wonderful. Hey, Dev. Hey, Michael. Thanks so much for taking my question. I wanted to first start with relational migrator. Dev, can you talk a little bit about what demand for that looks like? And when customers are talking to you, are they more focused around the value prop being around cost savings that they get from moving from legacy relational databases over to MongoDB? Is it more about the flexibility around the technology itself? And maybe if you could tie in, how you expect now with GenAI's accelerator, how that can impact the timeline of migrating workloads from relational over to MongoDB? And then I've got a quick follow-up to Michael.

Certainly. When we engage with customers, it's important to remember that even at our IPO, we had a significant number of customers transitioning from relational databases to MongoDB. Their reasons generally fall into three categories. The first is that the relational architecture often creates inflexible data models, making it difficult for them to innovate and respond to customer needs. This leads to a perception that their capacity for innovation has diminished. The second reason is the limitations in scaling and performance, particularly as user numbers or data volumes increase, prompting the realization that they need to move away from outdated platforms. The third reason is the cost of the existing platform in relation to the return on investment it offers. Typically, customers' motivations can be categorized into one of these three areas, although some may experience a combination of two or all three. There is often a compelling event that drives urgency, such as achieving specific milestones or impending renewals with current vendors, which might motivate them to transition swiftly. Regarding the relational migration process, it consists of three components: transforming the schema from a traditional tabular format to a document-based format in MongoDB, migrating the data to this new schema, and rewriting the application itself. We have successfully completed numerous migrations before the advent of GenAI. Some customers choose to rewrite everything at once, while others opt for a gradual approach, incrementally shifting functionality from their existing application to the new one according to their specific use cases and business requirements. With GenAI, we anticipate that rewriting applications will become simpler, which will reduce the costs associated with migration and broaden the range of customers and workloads we can target. Additionally, there's a developing fourth reason for migration: enabling data and applications to be more AI-capable. Customers are increasingly interested in not just moving to a more modern platform but ensuring that their systems are optimized for AI. Regarding timing, I believe this year we will observe many pilot projects as companies explore these new AI capabilities. As technology evolves and we gain more insights, I expect to see the pace of migration accelerate significantly.

Wonderful. Really helpful. And then, Michael, just quickly, you put up your first free cash flow positive year in public company history. Just as we think about the margin guidance for next year, how should we be thinking about cash conversion going forward? Thanks.

Yeah, so I think the two factors to think about in terms of cash conversion are, within Atlas, there's this dynamic where we are reducing and continuing to see less upfront Atlas. Obviously, we're hitting the anniversary of that, and that's what's creating the headwind on the revenue front and the tough compare. But that's with Atlas at 68% of revenue. And so if you assume that Atlas is going to increase as a percent of revenue, I think that will sort of further drive the divergence. And then the only other big delta is things related to SBC and stuff like that. But I think it's the Atlas dynamic when you think about potential changes from a cash conversion standpoint, I think is where I'd focus and the impact, if you assume that Atlas is going to be a larger percent of the business.

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

Hey, can you hear me okay?

Yeah. Hey, Tyler. Good evening.

Okay, cool. Okay, sorry, the line was a little choppy. Dev, I wanted to ask you just a question as it relates to competition. Obviously, it's been a busy week at both Snowflake and Databricks with Frank Slootman retiring. I hope you're not going anywhere soon. But Databricks announcing pretty impressive growth. I guess, how do you think about your positioning relative to those two vendors, especially with the new CEO, kind of more of a technical focus at Snowflake, and new store product coming out later this year? Do you expect to compete more, just frame for us how you're thinking about it, especially as the GenAI momentum increases over the coming years?

Yeah, so first of all, I'm very committed to MongoDB. I'm very excited about the opportunity here, so I have no plans to go anywhere. Second, in regards to Snowflake and Databricks, we don't typically compete with them, right? Because they're focused on analytical workloads. We're focused on operational workloads. So those are two very different sets of use cases. The big difference in terms of how customers buy typically, data warehouses and data lakes tend to be a centralized decision, organizations standardize on one platform, and then basically move their existing data to those platforms, where I would say operational platforms tend to be a more decentralized decision where different development teams, different lines of businesses, etc., based on the requirements for their application, will choose, will make their own independent decisions about what they think they need to do. And we've always talked about how we start with one team and then try and expand from there, and why there's so much focus on expanding within accounts and becoming a standard within an account, because then that accelerates the amount of workloads we capture. But those are two very different kind of customer buying behaviors in terms of analytical versus operational. With regards to the potential overlap, we are embedding more analytics capabilities. We have a very sophisticated aggregation framework, so people can do real-time processing of analytics on our platform with real-time data. Remember, data lakes and data warehouses have a batch process to get that data into their platforms, so they're not dealing with real-time data. And then with regards to Unistore, listen, there's like over 300 databases in the marketplace, so not sure, I have a lot of respect for the Snowflake people. I'm not sure, we've heard noises about Unistore for a long time, but we feel very comfortable and confident about our position just given the investments we made on our platform and the large customers we have. And frankly, the popularity of our platform with developers. And remember, developers are not a persona that these other players typically go after. They go after more the analysts and the data scientist community. We're very, very focused on developers.

That's helpful. Michael, I have a quick follow-up regarding your comments about the overage. I have a couple of questions. First, the $40 million figure you mentioned seems quite high compared to last year, especially since we only heard about several million in overage credits in Q4. Can you clarify if that amount aligns with what you've seen in previous years? Additionally, it seems that you don't anticipate this continuing next year. Is that due to a change in how you're recognizing overage revenue, or do you simply not expect it because all your contracts have been reset? Thank you.

There are just a couple of quick points I want to make. First, the several million refers to the additional amount beyond what we typically experience. This is our approach to highlight differences from normal behavior. Essentially, it was several million more than usual. The $40 million recognized in fiscal '24 is expected to go to zero. This change is directly due to the adjustments we've made in our go-to-market strategy, where we have not prioritized commitments since the beginning of the last fiscal year. Consequently, we have significantly fewer commitments. The revenue for unused credits reflects the income at the end of a contract term that hasn't been recognized through consumption. We anticipate this will decline because we've adopted a new strategy to promote greater workload adoption, which is the reason behind this shift. I hope this provides some clarity.

Thank you. That does conclude our conference for today. I'd like to turn the call back over to Dev, CEO, for any closing remarks.

Thank you. Again, I thank everyone for joining us today. I just want to reiterate that we had a strong quarter and year as we executed our opportunity. We do expect fiscal '25 to play out similarly to fiscal '24 with a healthy new business and stable consumption trends. We are very excited about the long-term AI opportunity, but still believe it's early days as customers are mainly in the experimentation and prototyping stages of building AI applications. And our priorities for fiscal '25 are to invest in deepening our product advantage while remaining focused on acquiring new workloads and establishing ourselves as the standard for building modern applications. So thank you again for joining us and we'll talk to you soon. Take care. Bye-bye.

Thank you. Ladies and gentlemen, this does conclude today's conference. Thank you all for participating. You may now disconnect. Have a great day.