# MongoDB Reports Strong Q4 2025 Earnings Amidst Growth Initiatives

MongoDB’s Strategic Growth Through AI Integration and Modernization Efforts

Modernizing outdated and intricate custom applications presents substantial challenges—effort, cost, and risk are all significant factors. In fiscal ’25, our pilot programs showed that combining AI tools with services can significantly shorten the modernization cycle. As we look ahead, we plan to deepen our customer engagements, aiming for app monetization to play a vital role in our business growth in fiscal ’27 and beyond. Our initial focus will target Java applications running on Oracle, a common choice that often includes thousands of complex stored procedures needing careful analysis, conversion, and testing for successful monetization.

We are addressing these issues through a blend of AI technologies along with inspection and verification performed by our delivery teams. Although this work is complex, the revenue generated from modernizing these applications is noteworthy. For instance, we successfully revamped our financial application for one of Europe’s largest Independent Software Vendors (ISVs), and discussions are underway to modernize their broader legacy infrastructure. As I look towards fiscal ’26, I anticipate significant growth for Atlas, driven by a robust market, an exceptional product, and effective go-to-market strategies.

We foresee continued strong win rates as we expand workloads across our customer base. Building on our land-and-expand strategy will allow us to further enhance workload acquisition. In fiscal ’25, we experienced increased sales force productivity, and we project ongoing improvements into fiscal ’26. Furthermore, we are making substantial investments to establish ourselves as a standard in more of our clients’ operations.

We are not limited by market constraints, even with our largest accounts. At the end of the fiscal year, we had 320 customers contributing over $1 million in Annual Recurring Revenue (ARR), demonstrating a year-over-year growth rate of 24%. This growth solidifies our transition toward upper-market strategies. As part of this shift, we plan to inject significant incremental resources into our strategic accounts program during fiscal ’26.

Turning our attention beyond fiscal ’26, I am eager about our long-term prospects, especially regarding the evolving demands of databases in the AI era. Our customers are increasingly seeking ways to integrate AI into their businesses. AI is ushering in rapid changes and every company is expected to adapt. We are witnessing a remarkable shift that is reshaping industries, accelerating innovation, and redefining competitive dynamics like never before.

We often say the pace of change will be so rapid that tomorrow’s plans will unfold today. Companies that can quickly transform and adapt will thrive, while those that cannot will fall behind. AI is transitioning software from a static tool to a dynamic decision-making system.

Unlike past technologies, AI-powered applications will continuously learn from real-time data. However, the speed of adaptation depends heavily on the data infrastructure in place—legacy systems cannot keep pace. Traditional technology stacks were crafted for stability, not continuous change, which creates significant barriers. Complex architectures, slow batch processing, and rigid data models hinder development and make updates both time-consuming and risky. The emergence of AI will only intensify these challenges.

MongoDB was architected for adaptability; designed from the very beginning to break the limitations imposed by legacy databases. It enables companies to scale effectively, adapt rapidly, and innovate at the speed demanded by AI. Our flexible document model accommodates any type of data, while our seamless scalability ensures high performance, even for unpredictable workloads. Through our acquisition of Voyage AI, MongoDB enhances the reliability of AI applications by integrating real-time data with sophisticated embedding and retrieval models for accurate and relevant outcomes.

Moreover, we improve AI development processes by natively integrating vector and text search within our database. This approach simplifies the developer experience, lowering cognitive burdens while reducing system complexity, risks, and operational overhead—all while maintaining the transactional and security benefits inherent to MongoDB. Yet, technology alone does not solve everything. Our structured and solution-oriented approach addresses the complexities customers face in adapting to the rapid advancements of AI technologies and the dearth of in-house expertise. Our focus remains on accelerating customers from AI experimentation to production using best practices that mitigate risks and maximize impact.

Our acquisition of Voyage AI addresses a critical issue for customers: the risk of hallucinations in AI applications. AI systems excel where traditional software tends to struggle—especially in tasks requiring nuanced understanding and complex reasoning. Nonetheless, since AI models operate probabilistically rather than deterministically, there’s a chance they can generate misleading information.

This poses severe risks, particularly in high-stakes scenarios, such as a financial services agent managing client capital autonomously or a cancer screening application analyzing scans for potential early signs of pancreatic cancer. For any critical application, inaccurate results are unacceptable. The best assurance for producing accurate results lies in high-quality data retrieval, ensuring precision in extracting the most pertinent information from an organization’s data.

High-quality retrieval hinges on vector embedding and reranking models. Voyage AI’s models are highly regarded within the Hugging Face community for functions such as retrieval, classification, and clustering. These technologies are utilized by leaders in the AI space, including Anthropic, LangChain, Harvey, and Replit. Voyage AI is led by Stanford professor Tengyu Ma, who has a distinguished team assembled from top AI research labs at Stanford, MIT, Berkeley, and Princeton. This acquisition positions MongoDB to offer premier embedding and reranking capabilities that empower native AI retrievability.

Our strategy at MongoDB democratizes the process of creating trustworthy AI applications right from the start. Instead of assembling various components—like operational data stores, vector databases, and embedding models—MongoDB provides a cohesive developer experience. Consequently, MongoDB has redefined the database landscape for the AI era. Now, I would like to take a few moments to discuss adoption trends for MongoDB among our diverse customer base.

Across various industries and globally, customers are engaging with mission-critical projects in Atlas, harnessing the full capabilities of our platform. Examples include Informatica, Sonos, Zebra Technologies, and Grab. Southeast Asia’s leading super app, Grab, has successfully transitioned its key application, Grab Kiosk, to Atlas. This migration has granted Grab an automated, scalable, and secure platform, allowing engineering teams to concentrate on product development to keep pace with rapid growth. Leveraging Atlas has resulted in substantial efficiency improvements, cutting down database maintenance time by roughly 50%.

Organizations such as the Associated Press, the Catalan Department of Health, Urban Outfitters, and Lombard Odier are choosing MongoDB to modernize their applications. Urban Outfitters selected MongoDB’s platform to lay a flexible, scalable foundation for their infrastructure, aiming to unify data across multiple systems for enriched and consistent customer service experiences.

Urban Outfitters Enhances Operations with MongoDB’s Atlas Solution

The retailer identified its legacy database as inadequate for current needs. By adopting MongoDB Atlas and implementing a flexible document model, Urban Outfitters has accelerated development, increased scalability, and seamlessly integrated data. This strategic transformation has enabled Urban Outfitters to introduce AI-driven personalization and advanced search features, enhancing the shopping experience in both digital and physical venues. Companies like Swisscom, NTT Communications, and Paychex are also leveraging MongoDB to roll out AI-powered applications for their customers.

In just 12 weeks, Swisscom, Switzerland’s premier provider of mobile, internet, and TV services, successfully deployed a next-generation AI application powered by Atlas. They utilized Atlas to enhance the East Foresight library, converting unstructured data—including reports, recordings, and graphics—into vector embeddings interpretable by large language models. This advancement allows Vector Search to locate relevant context, resulting in more accurate and tailored responses for users. Overall, it was a strong Q4 for MongoDB.

During the fourth quarter, MongoDB experienced stabilizing Atlas consumption growth alongside a robust new business segment. The company expresses confidence in its capacity to seize long-term opportunities. With fiscal year 2026 being a transitional period, MongoDB aims to enhance its go-to-market motion while simultaneously investing in strategies to capitalize on the AI potential through both new AI applications and modernizing older applications. As Dev mentioned, here’s Serge with more details.

Serge Tanjga — Interim Chief Financial Officer

Thank you, Dev. I will provide a thorough review of our fourth quarter results and our outlook for the first quarter and fiscal year 2026. Let’s begin with the fourth quarter. Total revenues reached $548.4 million, reflecting a 20% year-over-year increase and surpassing the high end of our guidance.

Examining our product mix, Atlas revenue grew by 24% year-over-year, now comprising 71% of total revenue, a rise from 68% during both fiscal Q4 of 2024 and the previous quarter. Atlas revenue primarily hinges on the customer’s consumption of our platform, which closely correlates with end-user activity within their applications. In Q4, consumption exceeded our expectations.

When comparing this year’s Q4 to last year’s, both usage and consumption growth remained stable year-over-year. Although one quarter does not define a trend—especially during a holiday season marked by volatility—we are encouraged to observe signs of stability and growth in consumption. Regarding non-Atlas revenue, it outperformed our expectations, partly due to stronger-than-anticipated contributions from multiyear contracts, as highlighted by Dev. Under ASC 606 accounting, we recognize the entire term license component of a multiyear contract at its commencement.

This benefit from multiyear license revenue exceeded our Q4 guidance by over $10 million. It’s important to note that ASC 606 also adds variability to our non-Atlas revenue, complicating the tracking of underlying trends. To clarify, if we focus on non-Atlas ARR growth instead of revenue, we observe growth in the mid-single digits year-over-year in Q4 of fiscal year 2025, compared to low double-digit growth in the previous year’s quarter.

Additionally, we noted that customers with no historical Atlas revenue are increasingly deploying a larger share of their workloads on Atlas. In terms of customer growth, we added about 1,900 customers in Q4, bringing our total customer count to over 54,500, up from over 47,800 during the same period last year. Out of this total, more than 7,500 are direct sales customers, an increase from over 7,000 a year ago.

The growth within our overall customer base is primarily driven by Atlas, which includes more than 53,100 customers at the end of Q4, compared to over 46,300 a year before. Importantly, this growth among Atlas customers signifies new MongoDB clients and existing enterprise agreement customers who are adding more Atlas workloads. In Q4, our net ARR expansion rate was around 118%, a drop from historical performance due to a lesser contribution from expanding customers.

At the quarter’s conclusion, we had 2,396 customers spending at least $100,000 in ARR and annualized MRR, an increase from 2,052 last year. As Dev mentioned, we also finished the year with 320 customers who are spending $1 million or more annually, compared to 259 a year ago. Moving to the income statement, I will be discussing our results on a non-GAAP basis unless otherwise noted.

For Q4, our gross profit totaled $411.7 million, resulting in a gross margin of 75%, a drop from 77% in the prior year. This decline is partly due to the increasing contribution of Atlas to our overall business. Our operating income for this quarter was $112.5 million, yielding a 21% operating margin, up from 15% a year earlier. This performance was bolstered by our revenue exceeding expectations.

Moreover, we benefitted from the timing of recruitment efforts throughout the year. Net income for Q4 stood at $108.4 million, or $1.28 per share based on 84.6 million diluted weighted average shares. This contrasts with a net income of $71.1 million, or $0.86 per share, with 82.9 million diluted shares from the same quarter last year. Now, let’s look at the balance sheet and cash flow.

At the end of Q4, we had $2.3 billion in cash, cash equivalents, short-term investments, and restricted cash. During the fourth quarter, we also redeemed our 2026 convertible notes, resulting in a debt-free balance sheet. Operating cash flow for Q4 was $50.5 million, after accounting for approximately $27.6 million in capital expenditures and principal repayments of finance lease liabilities. Our free cash flow was $22.9 million for the quarter, compared to $50.5 million in the previous fiscal period.

Our capital expenditures for Q4 included roughly $24 million for purchasing IPv4 addresses, as previously discussed. This marks the conclusion of our IPv4 address acquisitions. Now, I’d like to detail our outlook for the first quarter and the full fiscal year 2026. For Q1, we anticipate revenues between $524 million and $529 million.

Non-GAAP income from operations is projected within a range of $54 million to $58 million, and non-GAAP net income per share is expected to fall between $0.63 and $0.67, based on an estimated 86 million diluted weighted average shares outstanding. For the full fiscal year 2026, we envision revenues ranging from $2.24 billion to $2.28 billion, with non-GAAP income from operations anticipated between $210 million and $230 million. We expect the non-GAAP net income per share to lie between $2.44 and $2.62, based on an estimated 87.3 million diluted weighted average shares. Please note that this non-GAAP net income guidance for Q1 and fiscal year 2026 includes a non-GAAP tax provision of approximately 20%. I will now provide further clarity on our guidance, focusing on the full year.

To reiterate what Dev stated, we expect relatively stable…

MongoDB Projects Strong Atlas Growth Amid Margin Declines for FY26

MongoDB anticipates consumption growth that will strongly benefit from workloads acquired in fiscal year ’25, significantly boosting performance compared to fiscal year ’24. The company revamped its sales compensation plans at the start of the previous fiscal year, focusing on the scale of new workloads acquired. MongoDB believes these changes are cultivating positive outcomes.

Subscription Revenue Outlook

Despite the positive Atlas projections, MongoDB foresees a decline in non-Atlas subscription revenue, expected to drop in the high-single digits this year. This decline is attributed to an estimated $50 million headwind from the multiyear license revenue in fiscal year ’26. This estimate stems from a detailed analysis of their non-Atlas renewal base. After experiencing two years of robust multiyear performance, the company forecasts a reduced mix of this revenue, which is anticipated to fall below historical trends. This shift occurs because fewer large non-Atlas accounts are poised to sign multiyear deals in fiscal year ’26.

Operating Margin Expectations

MongoDB projects an operating margin of 10% at the midpoint of its range, a decline from the 15% achieved in fiscal year ’25. Three primary factors contribute to this margin drop. First, the absence of the high-margin multiyear license revenue that significantly bolstered fiscal year ’25 will impact the margins, particularly in the second half of the year. Second, the company is investing heavily in R&D, including the recent acquisition of Voyage AI, aiming to differentiate itself from competitors in performance and scalability. Lastly, increased marketing investments will help elevate awareness of MongoDB’s capabilities, positioning the company favorably in comparison to legacy rivals.

Q1 Forecast Considerations

As for Q1 guidance, MongoDB expects flat to slightly positive sequential Atlas revenue. It’s important to note that Q1 has three fewer days than Q4, contributing to seasonal slowdowns in Atlas consumption growth during the holidays, which negatively affects incremental Q1 revenue. The company also anticipates a notable declining trend in Enterprise Agreement (EA) revenue, as Q4 typically represents the peak period for EA renewals. This seasonal fluctuation leaves a lower EA renewal base in Q1, affecting overall revenue. Additionally, operating income is expected to pare down sequentially due to this revenue dip and increased hiring rates.

Impact of Voyage AI Acquisition

Concerning the recent acquisition of Voyage AI, which totaled $220 million—with most shareholders opting for MongoDB stock—the company plans a $200 million stock buyback to counteract the dilution. In fiscal year ’26, revenue generated from this acquisition will likely be minimal as integration efforts commence. However, expenses related to expanding the Voyage team to enhance innovation and integration capabilities will lead to modest dilution in operating margins throughout the year.

In conclusion, MongoDB reported robust fourth-quarter results and is satisfied with its customer acquisition efforts and stable consumption trends in Atlas. The company expresses optimism about future opportunities and remains committed to prudent investments aimed at maximizing long-term value. With that, I would like to open the floor to questions. Operator?

Questions & Answers:

Operator

And our first question will come from the line of Raimo Lenschow with Barclays. Your line is open.

Raimo Lenschow — Analyst

Thank you. I have two quick questions. First, regarding the multiyear licensing situation, you’ve historically outperformed in this area. Given your current guidance, is the downturn a result of just a lower renewal portfolio, or do you perceive a behavioral change as well?

Serge Tanjga — Interim Chief Financial Officer

Thank you for your question, Raimo. In fiscal year ’24, we experienced exceptional multiyear performance primarily due to our Alibaba deal. Entering fiscal year ’25, we anticipated a $40 million headwind based on long-term trends. However, we saw a lower headwind after strong Q3 and Q4 results. Looking ahead, the renewal base is diminished due to the many multiyear deals signed in fiscal years ’24 and ’25, which sets up a smaller opportunity for fiscal year ’26. Thus, it’s not a trend change; rather, it’s about the reduced opportunity set for multiyear deals.

Raimo Lenschow — Analyst

Understood. For my second question about the Voyage AI acquisition, how do you plan to integrate this technology into your organization and market it beyond your existing customer base?

Dev C. Ittycheria — President and Chief Executive Officer

Thanks for the question, Raimo. Voyage AI currently offers its models to other entities, and we intend to continue this practice. This approach helps attract new users to MongoDB. In the short to medium term, we plan to integrate Voyage AI into the MongoDB platform, with features like automatic embeddings for seamless data entry, enhancing developer experiences. We have additional product plans that we will discuss in future calls, ensuring broad accessibility, even for non-MongoDB customers.

Company Leaders Discuss AI Acquisition and Operational Strategy

Operator

Thank you. One moment for our next question. That will come from the line of Sanjit Singh with Morgan Stanley. Your line is open.

Unknown speaker — — Analyst

Great. Thank you, team. This is [Inaudible] stepping in for Sanjit. My first question builds on Raimo’s inquiry regarding the acquisition of Voyage AI. Could you clarify the reasons behind this decision and how it enhances your existing portfolio? Specifically, what capabilities do existing or new customers gain with this technology that were not available before? Furthermore, regarding your operating expense guidance related to these investments, what changes in the past 90 days led to your decision for these incremental investments? You mentioned reallocating investments last quarter, and I’m curious what prompted this shift.

Dev C. Ittycheria — President and Chief Executive Officer

Thank you for the question. I’ll address the Voyage AI acquisition first, then pass it to Serge for the operational expenses. From a customer perspective, a major concern in deploying mission-critical AI applications is the risk of hallucinations. AI systems are probabilistic, which means their outputs cannot always be guaranteed. In regulated sectors, such as financial services and healthcare, where accuracy is paramount, this risk can hinder the adoption of AI technologies.

The acquisition of Voyage AI aims to reduce this concern by offering advanced embedding and reranking models. To explain, consider a large language model (LLM) as the brain and the database as memory. The embedding models serve the purpose of efficiently directing queries. Imagine having an expert like Albert Einstein on your team. When you ask him a question, he needs to find the right information to craft an answer. Instead of perusing every book in a vast library, embedding models guide him to the relevant section, aisle, shelf, and chapter, facilitating precise and high-quality responses.

The performance gains from using embedding models are considerable, and Voyage AI’s models rank exceptionally well on Hugging Face. Following the acquisition, many independent software vendors have reached out to us, mentioning improved performance after switching from previous providers to Voyage AI. This acquisition enhances the reliability of AI applications, enabling customers to address the most demanding use cases.

Serge Tanjga — Interim Chief Financial Officer

Now, regarding the operational expenses, you’re correct that we are reallocating while simultaneously reinvesting. Approximately 90 days ago, we discussed reallocating resources in our sales and marketing efforts, particularly reducing investments in the mid-market to focus more robustly on upmarket opportunities. We also mentioned deprioritizing certain products to concentrate on our core portfolio where traction and potential are evident.

We are investing beyond our reallocations, as planned. This decision stems from recognizing a unique opportunity in AI that allows organizations to reassess their infrastructure. We view this as a once-in-a-lifetime chance that warrants significant investment. Our aim is to avoid a scenario in five years where we question whether we invested adequately to capitalize on this significant opportunity. Our track record with margins strengthens our confidence. Historically, our margins were negative 30% at the IPO, but we have substantially improved in growth and margin expansion since then. Notably, in Q4, we achieved an operating margin of 21%, aligning with our long-term guidance. This gives us faith that not only is our business model sound, but it also scales effectively. Timing and strategic investment are crucial to optimizing the opportunities ahead.

Operator

Thank you. One moment for our next question. The next question comes from Mike Cikos with Needham. Your line is open.

Mike Cikos — Analyst

Hi, everyone. Thanks for taking my questions. I’d like to return to the topic of Atlas consumption trends. How have those trends developed through the year, and what specifics in Q4 contributed to the growth dynamics? Are there particular improvements in your sales initiatives that drove performance, or were there other factors impacting this growth?

Serge Tanjga — Interim Chief Financial Officer

I appreciate the question, Mike. Let me clarify things about Q4. Consistent with previous discussions, Q4 tends to be the seasonally slowest quarter for consumption growth due to the holiday slowdown, and this year was no exception. Q4 consumption was indeed lower than in Q3, which reflects typical holiday season volatility.

However, stability in consumption growth for fiscal year ’26 can be evaluated in three key components. The first component is the existing workload base, which forms the majority but also impacts percentage rates due to its size. The second component involves looking at prior year’s workloads, which remain significant and continue to grow. [Technical difficulties] allow us to assess ongoing trends effectively going forward.

MongoDB Discusses Fiscal Strategy Amid Transformative AI Adoption

In any given fiscal year, understanding workloads from both the previous and current year is crucial to counterbalance the growing base effect. Last year, we faced challenges in this area for three main reasons. First, the overall workload base slowed down, a trend attributed to broader macroeconomic factors that we identified in Q1.

Additionally, the workloads expected in fiscal year ’24 did not align with our projections for fiscal year ’25. Furthermore, the initiation of new workloads in fiscal year ’25 experienced a slow start due to operational issues, creating a headwind we contended with throughout the year. Looking ahead, we are forecasting a stable macro environment and growth in the base usage. While predicting the future is inherently uncertain, this is our current outlook.

We anticipate better performance in the workloads for fiscal year ’25, supported by data that reflects optimism. Our strategic focus on upstream markets suggests we will generate more new workload annual recurring revenue (ARR) and improved sales productivity in the fiscal year ’26 cohort. These factors should help stabilize consumption growth despite the larger base we are working with compared to last year.

Mike Cikos — Analyst

Thank you for that insight, Serge. For a follow-up question, I understand we’re referencing a $50 million multiyear headwind concerning the non-Atlas segment. Can you clarify how this might break down quarterly for Q1 and Q2?

Serge Tanjga — Interim Chief Financial Officer

This situation reflects the inverse of the strong performance we observed in Q3 and Q4. Therefore, I would categorize this as a phenomenon weighted towards the latter half of the fiscal year.

Mike Cikos — Analyst

Understood. Thank you for your clarity.

Operator

Thank you. One moment for our next question, which will come from Brent Bracelin with Piper Sandler. Your line is open.

Brent Bracelin — Analyst

Thank you and good afternoon. I would like to delve deeper into Atlas. Adjusting for unused credits from last year, the implied growth for Atlas in the low-20% range indicates a steeper decline. Could you elaborate on the factors driving this growth for Atlas?

Serge Tanjga — Interim Chief Financial Officer

Absolutely, let’s break this down. Overall, we expect consumption growth in fiscal year ’26 to remain stable relative to fiscal year ’25. To determine Atlas’s specific growth, you start with total guidance and account for the high-single-digit decline in the non-Atlas business. This provides a clearer view of Atlas, aligning with our expectations of stable consumption growth in fiscal year ’26. Regarding fiscal year ’25, it’s essential to focus on the exit rate compared to the average, which reflects the revenue slowdown across all quarters except for Q4 last year.

Brent Bracelin — Analyst

Got it. As a follow-up for you, Dev, AI workloads have largely been in an experimental phase over the past two years. We are now witnessing a transition toward production, with early signs of these functions impacting revenue. When do you expect to see a noticeable uptick in business from customers moving from experimentation to production?

Dev C. Ittycheria — President and Chief Executive Officer

We’ve noted a number of prominent AI companies leveraging Atlas, though I cannot disclose specific names. Generally, the journey for customers towards AI adoption will be gradual. Two significant challenges exist. Firstly, many organizations lack sufficient AI expertise, compounded by the rapid advancements in AI technology that create uncertainty on which stack to utilize. Secondly, there are concerns regarding the reliability of numerous applications.

Currently, use cases remain relatively simple, such as customer chatbots and basic document summarization. We are still in the early stages of AI integration, and I anticipate a gradual increase in complexity as companies become more comfortable with the technology.

Architecturally, our products offer significant advantages. Our document model accommodates different types of data, including structured, semi-structured, and unstructured data. Additionally, our integration of search capabilities, including Vector Search, is unique to our platform.

Moreover, with the launch of Voyage AI, we are introducing advanced embedding and reranking models to enhance quality and trust. All of these elements are designed to foster a smooth developer experience that minimizes friction, empowering customers to move quickly. We are confident in our position within this evolving landscape and eager about the prospects Voyage offers, even as we recognize that adoption will require time as customers acclimate to the technology.

Operator

Thank you. One moment for our next question, which will come from Karl Keirstead with UBS. Your line is open.

Karl Keirstead — Analyst

Thanks, Dev. In previous calls, you discussed the go-to-market shift aimed at targeting more opportunities upmarket. How is this transition reflected in your guidance? Are you anticipating some upside from this change in your revenue projections? Conversely, does this necessitate a level of investment in sales that might affect your margin outlook? How is this strategy progressing overall?

Dev C. Ittycheria — President and Chief Executive Officer

Thank you for the question, Karl. We are pleased with the progress we’re making, as evidenced by the increase in our $1 million customers, which is growing at a faster rate than our overall customer base. This indicates that our investments in targeting upper market segments are beginning to yield returns.

MongoDB’s Strategic Focus on Sales and AI Enhances Customer Engagement

In the past year, MongoDB has experienced notable sales productivity gains driven by the move-up market. This year, the company anticipates even more robust sales productivity improvements. While the company’s overall investment in sales resources has shifted from midmarket to upper-market sectors, it is essential to clarify that no significant increase in sales investment is planned.

MongoDB is prioritizing research and development (R&D) and enhancing awareness of its platform. The company emphasizes the importance of educating customers, as many remain unaware of MongoDB’s full capabilities and may not possess the necessary skills to utilize them effectively. This focus on education and customer engagement is crucial for MongoDB’s strategy in the upper market, where positive results are already visible.

Serge Tanjga — Interim Chief Financial Officer

In response, Tanjga confirmed that increased productivity is integrated into the company’s guidance moving forward.

Karl Keirstead — Analyst

Thank you both for the insights.

Operator

Thank you. One moment for our next question. We now turn to Kingsley Crane from Canaccord Genuity. Your line is open.

Kingsley Crane — Analyst

Hello. Thank you for taking my question. Turning to Voyage AI, you noted in your prepared remarks that technology alone is insufficient. Can you elaborate on how Voyage AI contributes to workload creation within AI applications and if it can help reduce vector storage costs similarly to your quantization efforts?

Dev C. Ittycheria — President and Chief Executive Officer

Certainly. Broadly speaking, we observe two significant challenges faced by our customers: a skills gap and a trust gap concerning AI. Voyage AI seeks to address the trust gap, enabling organizations to develop high-quality AI applications with confidence in the outcomes. However, the skills gap remains prevalent.

Thus, our strategy is not solely technology-centric; we adopt a solutions approach that combines technology with best practices and experience. This comprehensive method allows us to help customers tackle their business challenges effectively, rather than merely providing them with technology solutions.

Customers value this approach; for instance, we assist them in modernizing legacy applications and building new AI-driven applications. Regarding storage costs, our advancements in quantization have lowered storage expenses while enhancing the performance of our vector store. These embedding models serve to quickly identify relevant information based on queries directed at the application or underlying large language model (LLM), ultimately improving the reliability and accuracy of AI-generated results.

Kingsley Crane — Analyst

That was very helpful. For my follow-up, how have partnerships with Google Cloud Platform (GCP) influenced deal flows during the last quarter? You mentioned robust performance previously and indicated plans for further collaboration in Q4.

Dev C. Ittycheria — President and Chief Executive Officer

Our relationship with Google Cloud remains constructive and mutually beneficial. Overall, we maintain positive relationships with all the major hyperscalers. Depending on customer circumstances, some engage with a single hyperscaler, while others utilize multiple partnerships. We collaborate closely with GCP, AWS, and Azure, and I can confidently say that our partnerships with all three are productive.

Operator

Thank you. One moment for our next question. We now have Pat Walravens from Citizens Bank. Your line is open.

Patrick Walravens — Analyst

Thank you for the opportunity. Could you provide a high-level overview of the growth trajectory for non-Atlas offerings over the past five years? I’ve observed growth rates fluctuating—23% in 2021, 19% in 2022, and 25% in 2023. Were there periods that exceeded your expectations, and were there times when growth fell short? Please share your insights on the ebb and flow of non-Atlas performance.

Dev C. Ittycheria — President and Chief Executive Officer

We manage our business primarily by channel rather than by specific products. Our focus is customer-oriented and driven by demand. At the high end of the customer spectrum, clients appreciate having choices in how they operate. Many do not believe that all workloads should transition to the cloud; some prefer to maintain their technology stacks on-premises. This is especially true for large banks in Europe and several U.S. banks that have significant on-prem workloads.

Ultimately, our goal is to meet the diverse needs of our customers. Increasingly, we are observing clients historically linked to non-Atlas now deploying additional workloads on Atlas. Additionally, new capabilities like Voyage will only be available on Atlas, further driving adoption.

Serge Tanjga — Interim Chief Financial Officer

To echo Dev’s comments, I’d like to highlight a couple of points. In our prepared remarks, we noted that non-Atlas annual recurring revenue (ARR) growth for Q4 is projected to be in the mid-single digits, a slowdown from double-digit growth in Q4 of fiscal year 2024. This trend underscores our observation that more customers historically linked to non-Atlas are now implementing workloads on Atlas.

While some clients continue to deploy incremental workloads on EA, which will contribute to its ongoing growth, the increasing shift toward Atlas suggests a more significant trend in our ARR growth.

Patrick Walravens — Analyst

Understood. As a follow-up, and while you might not be able to comment extensively, could you provide insight into expectations beyond 2026? We hope to get a clearer understanding of your long-term strategy.

MongoDB Leadership Discusses Growth Strategies Amid Market Transition

Serge Tanjga — Interim Chief Financial Officer

In assessing our performance beyond fiscal ’26, we must consider both opportunities and constraints. A key factor is our current low market share among customers who are not predominantly using our Atlas platform. We anticipate steady growth in our Enterprise Account (EA) segment as we continue to expand our market presence.

Furthermore, we expect to see increased cloud migration among our existing customers, facilitating further growth by allowing us to capture additional Atlas workloads. However, the impact of our application modernization initiative remains uncertain. We believe that this effort will positively affect both Atlas and non-Atlas segments, although it is still in the early stages and outcomes are hard to predict.

Operator

Thank you. We will now take the next question from Brad Sills with Bank of America. Your line is open.

Brad Sills — Analyst

Thank you. Dev, you referred to fiscal ’26 as a transition year. Can you elaborate on what that entails? It appears that consumption patterns are stabilizing and that you’re seeing traction with new workloads. Last year included significant go-to-market changes, so it feels like progress should be expected this year. Can you clarify your thoughts on this transition?

Dev C. Ittycheria — President and Chief Executive Officer

Absolutely. I want to convey my optimism regarding our business outlook. I haven’t felt this level of excitement in quite some time. The rise of AI applications is aligning perfectly with our MongoDB platform. Our platform can proficiently handle various data types and supports essential features like lexical semantics search, which enhances user experience.

We are committed to making the necessary investments to ensure our growth trajectory. I am pleased to report that our Atlas business is showing signs of stabilization—a point noted by both Serge and me in our earlier remarks. While the Enterprise Account business has several variables influencing it over the long term, the overall market trends are favorable for us.

We are pursuing a significant market opportunity as businesses increasingly rely on custom software solutions. I strongly believe that as we differentiate ourselves in a competitive landscape, this will begin to reflect in our performance metrics over time.

Serge Tanjga — Interim Chief Financial Officer

In addition to Dev’s insights, I want to emphasize that this transition year involves focusing on major initiatives like application modernization and establishing dominance in the AI sector. While we anticipate only modest revenue contributions from these efforts this year, we expect them to become substantial growth drivers in the future.

Brad Sills — Analyst

Thank you. I’d also like to hear about the strengths in new workloads you mentioned. Are there specific categories where you are seeing this strength?

Dev C. Ittycheria — President and Chief Executive Officer

The short answer is that we are witnessing growth across the board. We are attracting new customers at both the upper and lower ends of the market. Our new customer acquisition for the past quarter was notably strong, and our foray into high-value accounts is resulting in a faster growth rate for our $1 million customer segment compared to our overall customer base.

Operator

Thank you for waiting. Our next question comes from Patrick Colville with Scotiabank. Your line is open.

Patrick Colville — Analyst

Thank you. Dev, I want to inquire about the competitive landscape today. How is MongoDB positioning itself against hyperscalers and Postgres currently? Also, are there any expected changes in the competitive environment by March 2024?

Dev C. Ittycheria — President and Chief Executive Officer

I’ll address two primary points. First, many tend to compare MongoDB directly with Postgres, but I believe that’s an inaccurate comparison. Postgres functions primarily as an OLTP database. A more apt comparison would involve integrating Postgres with Elastic and other advanced technologies. Customers often prefer the comprehensive solution that MongoDB offers rather than trying to stitch together disparate systems.

Secondly, MongoDB performs notably better than Postgres as an OLTP database. Postgres, grounded in a rigid relational framework, struggles with unstructured data and does not scale effectively, especially with complex JSON data. Unfortunately, Postgres has gained traction lately due to its open-source nature and associations with legacy database systems like Oracle and SQL Server.

Our win rate against Postgres is strong. When our sales team articulates the advantages of MongoDB, success in conversions markedly increases. Our challenge lies in increasing awareness and training potential customers about MongoDB, particularly those who might default to Postgres due to unfamiliarity with our platform.

Patrick Colville — Analyst

That’s very informative. Could you also touch on the hyperscalers briefly?

Dev C. Ittycheria — President and Chief Executive Officer

Hyperscalers do offer their variations of Postgres, but I’ve not seen as many clones recently. Our success rate against these clones remains robust. As we continue to advance in our strategic initiatives, I believe we will be well-prepared to navigate the competitive landscape ahead.

MongoDB Discusses Growth Strategy Amid AI and Hyperscaler Partnerships

During a recent earnings call, MongoDB’s President and CEO, Dev C. Ittycheria, provided insights into the company’s positive relationships with hyperscalers across the globe. He addressed questions about the challenges and opportunities related to the evolving landscape of artificial intelligence (AI) and the importance of flexible data architectures.

Positive Relationships with Hyperscalers

In response to inquiries, Ittycheria emphasized that MongoDB’s sales teams effectively partner with hyperscalers to enhance business outcomes. While there are instances where hyperscalers may attempt to leverage first-party services, he noted that cooperative partnerships generally yield better results as both parties work together. He affirmed, “There’s no structural issue with the hyperscalers,” reinforcing their commitment to these collaborations.

Opportunities in AI and Data Infrastructure

When analyst Rishi Jaluria asked about the shift of existing SQL applications to MongoDB, Ittycheria explained that the emergence of AI necessitates a significant transformation in how companies approach data management. He stated that traditional software solutions may not suffice as businesses increasingly need adaptable and flexible infrastructures to accommodate new AI-driven use cases. MongoDB’s document model supports quick changes, showcasing its foundation for flexibility. The recent acquisition of Voyage AI allows MongoDB to integrate various functionalities that enhance their capabilities in this area.

The Role of Relational Migrator

Jaluria also inquired about the momentum surrounding MongoDB’s relational migrator and its potential as a growth driver. Ittycheria noted the growing pain points experienced by customers with legacy systems, particularly those using Java applications on Oracle. He mentioned that many organizations are eager to modernize their processes to enable AI capabilities in their applications. As MongoDB pushes forward in this sector, they expect to see substantial growth reflected in FY ’27, following a scaling of operations in FY ’26.

Closing Remarks

Ittycheria concluded the call by highlighting MongoDB’s strong performance and optimistic outlook for the upcoming fiscal year. He expressed enthusiasm about the company’s long-term potential to meet the changing needs of database management during the AI era while emphasizing that MongoDB will judiciously invest in initiatives that will elevate their execution in the marketplace.

This conversation illustrates MongoDB’s proactive approach to staying relevant in an ever-evolving technology landscape, addressing both immediacy and long-term growth strategies.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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