MongoDB, Inc. (NASDAQ:MDB) Q3 2026 Earnings Call Transcript

MongoDB, Inc. (NASDAQ:MDB) Q3 2026 Earnings Call Transcript December 1, 2025

MongoDB, Inc. beats earnings expectations. Reported EPS is $1.32, expectations were $0.805.

Operator: Good day, and thank you for standing by. Welcome to the MongoDB’s Third Quarter Fiscal Year 2026 Earnings Conference Call. [Operator Instructions] Please be advised that today’s conference is being recorded. I would now like to turn the conference over to your speaker for today, Jess Lubert, VP of Investor Relations. Please go ahead.

Jess Lubert: Thank you, operator. Good afternoon, and thank you for joining us today to review MongoDB’s third quarter fiscal 2026 Financial Results, which we announced in our press release issued after the close of market today. Joining me on the call today are CJ Desai, President and CEO of MongoDB; and Mike Berry, CFO of MongoDB. Following our prepared remarks, Dev Ittycheria, MongoDB’s former President and CEO and current member of the Board will join us for Q&A. During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities. Our opportunity to win new business, our expectations regarding Atlas consumption growth, the impact of non-Atlas business and multiyear license revenue, the long-term opportunity of AI, our financial guidance, and underlying assumptions and our investments and growth opportunities in AI.

These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could cause actual results to differ materially from our expectations. For a discussion of material risks and uncertainties that could affect our actual results, please refer to the risks described in our quarterly report on Form 10-Q for the quarter ended July 31, 2025, filed with the SEC on August 27, 2025. Any forward-looking statements made on this call reflect our views only as of today, and we undertake no obligation to update them except as required by law. Additionally, we will discuss non-GAAP financial measures on this conference call. Please refer to the tables in our earnings release on the Investor Relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measures.

With that, I’d like to turn the call over to CJ.

Chirantan Desai: Thank you, Jess, and thank you to everyone for joining. I’m honored and genuinely excited to speak with you as the CEO of MongoDB. This is an incredible company and stepping into this role is a privilege. I want to start by thanking our customers, partners and employees for everything you have done to build MongoDB into what it is today. I especially want to acknowledge Dev whose leadership and vision created a phenomenal company, which has strong momentum and a tremendous market opportunity ahead. Many have asked why I chose MongoDB, I had multiple opportunities to lead other technology companies but MongoDB stood apart. We are at a true inflection point driven by major shifts across cloud, data and AI. MongoDB has the potential to become the generational modern data platform of this evolving era, an opportunity that comes once in a lifetime.

I am a truly customer-obsessed leader. So during my diligence, I spoke with multiple customers. Across these conversations, the message was clear. MongoDB already powers core, mission-critical workloads were enterprises that are modernizing their technology stack. At the same time, MongoDB is uniquely positioned at the center of the AI platform shift. Few technology companies have that combination of durable core strength and emerging platform relevance. Throughout my career, I have driven product to platform transformation at some of the most respected technology companies. Looking at MongoDB today, I see all the ingredients needed to build an iconic modern data platform company. World-class technology, a strong innovation engine, deep developer and customer pool and exceptional talent.

We have everything required to become the generational data platform of choice in the AI era. Now onto this quarter’s results. Atlas performance was strong, accelerating to 30% year-over-year growth, up from 29% in Q2 and 26% in Q1. We generated total revenue of $628 million (sic) [ $628.3 million ] , up 19% year-over-year and above the high end of our guidance, driven by strength in Atlas. We delivered non-GAAP operating income of $123 million (sic) [ $123.1 million ] or a 20% non-GAAP operating margin. We ended the quarter with over 62,500 customers adding 2,600 in the quarter and 8,000 year-to-date, reflecting 65% growth in customer additions on a year-to-date basis driven by the strong performance of our self-serve motion. Q3 was an exceptional quarter that was driven by our continued go-to-market execution and the broad-based demand we are seeing across business.

At the same time, we significantly outperformed on operating margin, demonstrating that we can drive durable revenue growth while simultaneously expanding profitability. Now let me explain why I see such a large opportunity ahead for both core operational data and emerging AI workloads. Our core business is strong across self-served and enterprise customers even before any AI tailwinds. In my first 3 weeks, I’ve met with over 30-plus customers from AI-native companies to C-suite technology leaders at Fortune 500 companies. Those conversations have only strengthened my conviction in MongoDB’s opportunity. Customers already depend on us for mission-critical workloads today, and they are leaning in even further, betting on MongoDB to power the AI applications that will shape their future.

The expansion opportunity in front of us is immense. We already serve more than 70% of the Fortune 100 and many of the world’s largest banks, health care organizations and manufacturers run their mission-critical workloads on MongoDB. Even with this foundation, there is still significant room to broaden our footprint within the enterprise. A strong example of this expansion opportunity is a major global insurance provider that has adopted MongoDB broadly across its enterprise. The company selected MongoDB Atlas to modernize several mission-critical systems, including its next-generation policy administration platform, analytics rating engine, unstructured data repositories and hundreds of supporting services. Since moving its policy platform to Atlas, the insurer has expanded from just a small set of regions to nationwide and significantly accelerated the rollout of new products and distribution channels.

Standardizing on Atlas has given the organization the scalability and reliability to improve customer experience, support more advanced data and AI capabilities and increased development velocity, all central to its transformation and growth ambitions. All of this momentum in the core business is happening before the AI wave has meaningfully impacted our results. We are still early, but the signs are encouraging from AI-native start-ups building intelligent applications on MongoDB to large enterprises developing AI agents that will reshape how they operate. AI applications must connect what LLMs know with what companies know, which is their proprietary data, systems and real-time context. This is fundamentally an information retrieval problem, and it requires a very different architecture than the last generation of software.

Rapidly evolving AI models uncover new complex properties about entities and rigid tabular stores cannot deliver the real-time high accuracy performance that AI systems require. At the same time, AI is dramatically increasing the speed at which applications are built and iterated and fixed database schemas simply cannot keep pace. This is where MongoDB has a structural advantage. Our document model, natively, JSON is built for diverse class changing and interdependent data. Our integrated search, vector search and Voyage embeddings removed the need for brittle bolt-ons, and we are seeing industry-leading results. Number one, on the Hugging Face retrieval embedding benchmark with Voyage MongoDB models and the #1 vector database on DB engines.

Advances in our embedding and reranking models drive meaningful accuracy gains. Enabling AI applications to deliver more grounded responses with fewer LLM hallucinations, while lowering storage cost and query cost through smaller, more efficient embeddings. Because all of this is delivered in a unified platform that runs anywhere, customers can keep operational and AI workloads together, simplify their architecture and innovate faster. As AI adoption accelerates, MongoDB’s positioned not just to participate in the wave, but to help define it. we are already beginning to see this play out with AI-native customers like Mercor, which is redefining hiring with its fully automated platform that uses AI to assess and match talent with the opportunities they are best suited for.

Mercor uses MongoDB Atlas to store the AI data behind its platform that directly connects professionals to AI model training and evaluation roles. Originally, a self-serve customer, the company is also utilizing Voyage embeddings and Atlas Vector Search. Atlas has scale to support Mercor’s 50% month-over-month growth, allowing the company to keep its software engineering team lean and agile as it expands to over $10 billion in value. This is just one example of how customers are building AI-native applications and companies on MongoDB. We are also seeing meaningful traction among large enterprises that are starting to build AI applications that have a material impact on their business. For example, a highly influential global media company aim to increase engagement via enhanced content recommendation for its vast repository of multimodal assets across its 70-plus websites.

That existing stack powered by Elasticsearch hit a performance wall struggling with the complexity of new embedding models. Recognizing that [ rigid ] systems stifle innovation, the engineering team re-architected on MongoDB Atlas and MongoDB Atlas Vector search. Working with MongoDB experts to deliver a proof of concept in just weeks, they integrated Voyage AI models directly alongside their data. The solution scale effortlessly, cutting latency by 90% and reducing operational spend by 65% and driving a 35% increase in click-through rates, ultimately providing millions of global readers with a seamless, deeply personalized discovery journey. The bottom line is that the business is performing exceptionally well. Existing customers are expanding with us and net new customer additions continue to show strength.

Companies in nearly every industry and across every geography are choosing MongoDB because we deliver the features, performance, cost effectiveness, and AI readiness they need in single data platform. Given the continued robust performance of Atlas, along with the healthy underlying fundamentals we are seeing in the business, we are raising our financial guidance for the fourth quarter and the full fiscal year 2026 and reiterating our commitment to the long-term financial model outlined at our recent Investor Day. Over the next few months, my focus is straightforward. Deepening customer relationships, advancing our innovation agenda as we build the generational modern data platform for the multi-cloud and AI era, scaling our go-to-market efforts and supporting our people so they can do their best work.

I believe MongoDB is a company that has only begun to realize its vast potential and I look forward to unlocking this potential in the years to come. With that, I’ll now hand the call over to Mike to discuss the financial results and outlook in greater detail. Mike?

Michael Berry: Thank you, CJ. I want to extend a big welcome to you from all of the employees at MongoDB. We are excited to have you join the team. I look forward to working with you to continue to execute on our business plans and drive meaningful shareholder value. I also want to thank Dev for the partnership and our time working together. I believe we accomplished a lot in a short period of time and appreciate all of your guidance and leadership. Best of luck in the next stage of your life journey. Okay. Now let’s move on to the financial results. I will begin with a detailed review of our third quarter results and then finish with our outlook for the fourth quarter and fiscal ’26. I will be discussing our results on a non-GAAP basis unless otherwise noted.

As CJ mentioned, we had another strong quarter as we exceeded all of our guidance ranges and are increasing our full year outlook across the board. In the third quarter, total revenue was $628.3 million, up 19% year-over-year and above the high end of our guidance. Shifting to our product mix. Atlas revenue outperformed our expectations as year-over-year growth accelerated to 30% in the third quarter and now represents 75% of total revenue. This compares to 68% of total revenue in the third quarter of fiscal ’25 and 74% last quarter. In the third quarter, Atlas consumption growth was relatively consistent with last year’s growth rates which drove the acceleration in revenue as well as growth in absolute revenue dollars for the third straight quarter.

Atlas growth was driven by continued strength with our largest customers in the U.S. and broad-based strength in EMEA. This strength is being driven both by new workloads and growth of existing workloads. We believe these dynamics reflect our growing strategic importance to many customers and our ability to win more critical workloads due to the strength of Atlas. You can see that progress in our total company net ARR expansion rate, which increased to 120% in the third quarter, up from 119% last quarter. Turning to non-Atlas. Revenue came in ahead of our expectations in the quarter as we continue to have success expanding within our existing non-Atlas customer base. Non-Atlas ARR, which reflects the underlying revenue growth of this product without the impact of changes in duration grew 8% year-over-year.

We continue to see consistent trends in non-Atlas in the third quarter, which reflects the desire of some of our largest customers to build with MongoDB long term for their most mission-critical applications. We also benefited from higher-than-expected multiyear revenue in the third quarter as approximately 2/3 of the non-Atlas revenue outperformance versus the high end of guidance was attributable to multiyear outperformance. We had another strong quarter for customer adds as we grew our customer base by approximately 2,600 sequentially, bringing the total customer count to over 62,500, which is up from over 52,600 in the year ago period. The growth in our total customer count is being driven primarily by Atlas which had over 60,800 customers at the end of the third quarter compared to over 51,100 in the year ago period.

We ended the quarter with 2,694 customers with at least $100,000 in ARR, representing 16% growth versus the year ago period. Moving down the income statement. Gross profit for the third quarter was $466 million, representing a gross margin of 74%, which is down from 77% in the year ago period. Our year-over-year gross margin decline is primarily driven by Atlas growing as a percent of the overall business. Although Atlas gross margins are slightly below the total company gross margins they continue to improve year-over-year. Our income from operations was $123 million for a 20% operating margin compared to 19% in the year-ago period. We are very pleased with our stronger-than-expected operating margin results, which benefited from both our revenue outperformance and lower-than-expected operating expenses.

Net income in the third quarter was $115 million or $1.32 per share based on 86.9 million diluted shares outstanding. This compares to net income of $98 million or $1.16 per share on 84.2 million diluted shares outstanding in the year ago period. Turning to the balance sheet and cash flow. We ended the third quarter with $2.3 billion in cash, cash equivalents, short-term investments and restricted cash. During the quarter, we spent $145 million to repurchase approximately 514,000 shares which was executed under our previously announced $1 billion total share repurchase authorization. Operating cash flow was well above our expectations at $144 million, and free cash flow was $140 million, which compares to $37 million and $35 million, respectively, in the year ago period.

Our cash flow results were driven primarily by strong operating profit and improving working capital dynamics, particularly related to higher cash collections. We remain confident in our ability to drive higher and more consistent free cash flow going forward. Before we go into our guidance for the rest of fiscal ’26, let me recap some of the enhancements we have made to our approach to guidance since I joined MongoDB. Importantly, we are providing more visibility into our expectations for Atlas growth as well as non-Atlas ARR growth each quarter. That being said, we will continue to be prudent in our forecasting of multiyear deals and only include those deals where we have very clear visibility. Our goal is to give you a more transparent view into our expectations for the business and our approach to guiding the non-Atlas business.

Now let me share some of the assumptions driving our outlook for the rest of fiscal ’26. Number one, we are continuing to see strong momentum in Atlas, which has experienced relatively consistent consumption growth through the first 3 quarters of the year. And comparable seasonal patterns as compared to fiscal ’25. We are seeing strength with existing customers, along with momentum in new accounts as customers large and small increasingly recognize the strategic value of Atlas. As a result, we now expect Atlas to see approximately 27% revenue growth in the fourth quarter of fiscal ’26, which is higher than our previous expectations of growth in the mid-20% range. This outlook reflects our continued confidence in Atlas while taking into account the historical seasonal variability and consumption patterns during the holiday period.

Number two, we continue to experience steady ARR growth in our non-Atlas business and have good line of sight to several large multiyear deals we either already have or expect to close in the fourth quarter of the year. Based on these dynamics, we now expect our non-Atlas business to grow in the upper single-digit percent range year-over-year in the fourth quarter. Number three, we continue to make strategic investments in engineering, marketing and direct sales capacity to drive continued growth. Some of these planned investments have taken longer to implement than expected and have shifted into the fourth quarter of fiscal ’26 and fiscal ’27, which has benefited our operating margin during fiscal ’26. Fourth, we continue to make progress on free cash flow conversion, which is now expected to exceed 100% for fiscal ’26.

Finally, we will continue to execute our share buyback program to help offset dilution from employee equity awards. In addition to our buyback, this past quarter, we began settling the taxes due on the vesting of employee RSUs with cash instead of issuing new shares. We also expect to receive over 1 million shares of stock for the cap calls associated with our 2026 notes that mature in January 2026. All of these actions will help us manage share count for the long term and illustrates our commitment to being good stewards of your capital. Now let’s shift to guidance in the fourth quarter and fiscal ’26. For the fourth quarter, we now expect revenue of $665 million to $670 million, which equates to 21% to 22% year-over-year growth. We expect non-GAAP income from operations to be in the range of $139 million to $143 million for an operating margin of approximately 21%.

We expect non-GAAP net income per share to be in the range of $1.44 to $1.48 based on 86.5 million diluted shares outstanding. For fiscal ’26, we now expect revenue to be in the range of $2.434 billion to $2.439 billion, an increase of $79 million from the high end of our prior guide and representing full year revenue growth of 21% to 22%. We are raising our non-GAAP income from operation expectations by $109 million at the high end and are now targeting a range of $436.4 million to $440.4 million for an operating margin of approximately 18%. We expect non-GAAP net income per share to be in the range of $4.76 and to $4.80 based on 86.7 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the fourth quarter and fiscal ’26 assumes a non-GAAP tax provision of 20%.

While we will provide detailed guidance for fiscal ’27 on our fourth quarter call, I would like to comment on how we are thinking about a few metrics as we sit here today. First, we remain committed to the long-term model presented at our Investor Day in September and continue to make great progress against all of the objectives highlighted at the event. We have seen strong margin expansion and free cash flow performance in fiscal ’26. And both of these metrics are tracking well above the long-term targets we discussed in September. As we look ahead to fiscal ’27, we will continue to make strategic investments to focus on driving growth going forward. With these planned investments and the timing of head count adds, we continue to target 100 to 200 basis points of margin expansion on average and 80% to 100% for free cash flow conversion outlined in our long-term model.

Second, our non-Atlas business is on track to exceed our prior expectations for fiscal ’26 due to the stronger performance, including greater-than-expected large multiyear deals. Given this outperformance and our current bottoms-up forecast for fiscal ’27, we currently do not expect non-Atlas multiyear transactions to provide either a meaningful headwind or tailwind to revenue in fiscal ’27. To summarize, we had another very strong quarter. We are pleased with our ability to drive both revenue growth across the business while increasing our operating profit expectations and driving meaningful free cash flow. We remain incredibly excited about the opportunity ahead, and we will continue to invest responsibly to drive long-term shareholder value.

With that, Lisa, we would now like to open the call up for questions.

Operator: Good day, and thank you for standing by. Welcome to the MongoDB’s Third Quarter Fiscal Year 2026 Earnings Conference Call. [Operator Instructions] Please be advised that today’s conference is being recorded. I would now like to turn the conference over to your speaker for today, Jess Lubert, VP of Investor Relations. Please go ahead.

Jess Lubert: Thank you, operator. Good afternoon, and thank you for joining us today to review MongoDB’s third quarter fiscal 2026 Financial Results, which we announced in our press release issued after the close of market today. Joining me on the call today are CJ Desai, President and CEO of MongoDB; and Mike Berry, CFO of MongoDB. Following our prepared remarks, Dev Ittycheria, MongoDB’s former President and CEO and current member of the Board will join us for Q&A. During this call, we will make forward-looking statements, including statements related to our market and future growth opportunities. Our opportunity to win new business, our expectations regarding Atlas consumption growth, the impact of non-Atlas business and multiyear license revenue, the long-term opportunity of AI, our financial guidance, and underlying assumptions and our investments and growth opportunities in AI.

These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could cause actual results to differ materially from our expectations. For a discussion of material risks and uncertainties that could affect our actual results, please refer to the risks described in our quarterly report on Form 10-Q for the quarter ended July 31, 2025, filed with the SEC on August 27, 2025. Any forward-looking statements made on this call reflect our views only as of today, and we undertake no obligation to update them except as required by law. Additionally, we will discuss non-GAAP financial measures on this conference call. Please refer to the tables in our earnings release on the Investor Relations portion of our website for a reconciliation of these measures to the most directly comparable GAAP financial measures.

With that, I’d like to turn the call over to CJ.

Chirantan Desai: Thank you, Jess, and thank you to everyone for joining. I’m honored and genuinely excited to speak with you as the CEO of MongoDB. This is an incredible company and stepping into this role is a privilege. I want to start by thanking our customers, partners and employees for everything you have done to build MongoDB into what it is today. I especially want to acknowledge Dev whose leadership and vision created a phenomenal company, which has strong momentum and a tremendous market opportunity ahead. Many have asked why I chose MongoDB, I had multiple opportunities to lead other technology companies but MongoDB stood apart. We are at a true inflection point driven by major shifts across cloud, data and AI. MongoDB has the potential to become the generational modern data platform of this evolving era, an opportunity that comes once in a lifetime.

I am a truly customer-obsessed leader. So during my diligence, I spoke with multiple customers. Across these conversations, the message was clear. MongoDB already powers core, mission-critical workloads were enterprises that are modernizing their technology stack. At the same time, MongoDB is uniquely positioned at the center of the AI platform shift. Few technology companies have that combination of durable core strength and emerging platform relevance. Throughout my career, I have driven product to platform transformation at some of the most respected technology companies. Looking at MongoDB today, I see all the ingredients needed to build an iconic modern data platform company. World-class technology, a strong innovation engine, deep developer and customer pool and exceptional talent.

We have everything required to become the generational data platform of choice in the AI era. Now onto this quarter’s results. Atlas performance was strong, accelerating to 30% year-over-year growth, up from 29% in Q2 and 26% in Q1. We generated total revenue of $628 million (sic) [ $628.3 million ] , up 19% year-over-year and above the high end of our guidance, driven by strength in Atlas. We delivered non-GAAP operating income of $123 million (sic) [ $123.1 million ] or a 20% non-GAAP operating margin. We ended the quarter with over 62,500 customers adding 2,600 in the quarter and 8,000 year-to-date, reflecting 65% growth in customer additions on a year-to-date basis driven by the strong performance of our self-serve motion. Q3 was an exceptional quarter that was driven by our continued go-to-market execution and the broad-based demand we are seeing across business.

At the same time, we significantly outperformed on operating margin, demonstrating that we can drive durable revenue growth while simultaneously expanding profitability. Now let me explain why I see such a large opportunity ahead for both core operational data and emerging AI workloads. Our core business is strong across self-served and enterprise customers even before any AI tailwinds. In my first 3 weeks, I’ve met with over 30-plus customers from AI-native companies to C-suite technology leaders at Fortune 500 companies. Those conversations have only strengthened my conviction in MongoDB’s opportunity. Customers already depend on us for mission-critical workloads today, and they are leaning in even further, betting on MongoDB to power the AI applications that will shape their future.

The expansion opportunity in front of us is immense. We already serve more than 70% of the Fortune 100 and many of the world’s largest banks, health care organizations and manufacturers run their mission-critical workloads on MongoDB. Even with this foundation, there is still significant room to broaden our footprint within the enterprise. A strong example of this expansion opportunity is a major global insurance provider that has adopted MongoDB broadly across its enterprise. The company selected MongoDB Atlas to modernize several mission-critical systems, including its next-generation policy administration platform, analytics rating engine, unstructured data repositories and hundreds of supporting services. Since moving its policy platform to Atlas, the insurer has expanded from just a small set of regions to nationwide and significantly accelerated the rollout of new products and distribution channels.

Standardizing on Atlas has given the organization the scalability and reliability to improve customer experience, support more advanced data and AI capabilities and increased development velocity, all central to its transformation and growth ambitions. All of this momentum in the core business is happening before the AI wave has meaningfully impacted our results. We are still early, but the signs are encouraging from AI-native start-ups building intelligent applications on MongoDB to large enterprises developing AI agents that will reshape how they operate. AI applications must connect what LLMs know with what companies know, which is their proprietary data, systems and real-time context. This is fundamentally an information retrieval problem, and it requires a very different architecture than the last generation of software.

Rapidly evolving AI models uncover new complex properties about entities and rigid tabular stores cannot deliver the real-time high accuracy performance that AI systems require. At the same time, AI is dramatically increasing the speed at which applications are built and iterated and fixed database schemas simply cannot keep pace. This is where MongoDB has a structural advantage. Our document model, natively, JSON is built for diverse class changing and interdependent data. Our integrated search, vector search and Voyage embeddings removed the need for brittle bolt-ons, and we are seeing industry-leading results. Number one, on the Hugging Face retrieval embedding benchmark with Voyage MongoDB models and the #1 vector database on DB engines.

Advances in our embedding and reranking models drive meaningful accuracy gains. Enabling AI applications to deliver more grounded responses with fewer LLM hallucinations, while lowering storage cost and query cost through smaller, more efficient embeddings. Because all of this is delivered in a unified platform that runs anywhere, customers can keep operational and AI workloads together, simplify their architecture and innovate faster. As AI adoption accelerates, MongoDB’s positioned not just to participate in the wave, but to help define it. we are already beginning to see this play out with AI-native customers like Mercor, which is redefining hiring with its fully automated platform that uses AI to assess and match talent with the opportunities they are best suited for.

Mercor uses MongoDB Atlas to store the AI data behind its platform that directly connects professionals to AI model training and evaluation roles. Originally, a self-serve customer, the company is also utilizing Voyage embeddings and Atlas Vector Search. Atlas has scale to support Mercor’s 50% month-over-month growth, allowing the company to keep its software engineering team lean and agile as it expands to over $10 billion in value. This is just one example of how customers are building AI-native applications and companies on MongoDB. We are also seeing meaningful traction among large enterprises that are starting to build AI applications that have a material impact on their business. For example, a highly influential global media company aim to increase engagement via enhanced content recommendation for its vast repository of multimodal assets across its 70-plus websites.

That existing stack powered by Elasticsearch hit a performance wall struggling with the complexity of new embedding models. Recognizing that [ rigid ] systems stifle innovation, the engineering team re-architected on MongoDB Atlas and MongoDB Atlas Vector search. Working with MongoDB experts to deliver a proof of concept in just weeks, they integrated Voyage AI models directly alongside their data. The solution scale effortlessly, cutting latency by 90% and reducing operational spend by 65% and driving a 35% increase in click-through rates, ultimately providing millions of global readers with a seamless, deeply personalized discovery journey. The bottom line is that the business is performing exceptionally well. Existing customers are expanding with us and net new customer additions continue to show strength.

Companies in nearly every industry and across every geography are choosing MongoDB because we deliver the features, performance, cost effectiveness, and AI readiness they need in single data platform. Given the continued robust performance of Atlas, along with the healthy underlying fundamentals we are seeing in the business, we are raising our financial guidance for the fourth quarter and the full fiscal year 2026 and reiterating our commitment to the long-term financial model outlined at our recent Investor Day. Over the next few months, my focus is straightforward. Deepening customer relationships, advancing our innovation agenda as we build the generational modern data platform for the multi-cloud and AI era, scaling our go-to-market efforts and supporting our people so they can do their best work.

I believe MongoDB is a company that has only begun to realize its vast potential and I look forward to unlocking this potential in the years to come. With that, I’ll now hand the call over to Mike to discuss the financial results and outlook in greater detail. Mike?

Michael Berry: Thank you, CJ. I want to extend a big welcome to you from all of the employees at MongoDB. We are excited to have you join the team. I look forward to working with you to continue to execute on our business plans and drive meaningful shareholder value. I also want to thank Dev for the partnership and our time working together. I believe we accomplished a lot in a short period of time and appreciate all of your guidance and leadership. Best of luck in the next stage of your life journey. Okay. Now let’s move on to the financial results. I will begin with a detailed review of our third quarter results and then finish with our outlook for the fourth quarter and fiscal ’26. I will be discussing our results on a non-GAAP basis unless otherwise noted.

As CJ mentioned, we had another strong quarter as we exceeded all of our guidance ranges and are increasing our full year outlook across the board. In the third quarter, total revenue was $628.3 million, up 19% year-over-year and above the high end of our guidance. Shifting to our product mix. Atlas revenue outperformed our expectations as year-over-year growth accelerated to 30% in the third quarter and now represents 75% of total revenue. This compares to 68% of total revenue in the third quarter of fiscal ’25 and 74% last quarter. In the third quarter, Atlas consumption growth was relatively consistent with last year’s growth rates which drove the acceleration in revenue as well as growth in absolute revenue dollars for the third straight quarter.

Atlas growth was driven by continued strength with our largest customers in the U.S. and broad-based strength in EMEA. This strength is being driven both by new workloads and growth of existing workloads. We believe these dynamics reflect our growing strategic importance to many customers and our ability to win more critical workloads due to the strength of Atlas. You can see that progress in our total company net ARR expansion rate, which increased to 120% in the third quarter, up from 119% last quarter. Turning to non-Atlas. Revenue came in ahead of our expectations in the quarter as we continue to have success expanding within our existing non-Atlas customer base. Non-Atlas ARR, which reflects the underlying revenue growth of this product without the impact of changes in duration grew 8% year-over-year.

We continue to see consistent trends in non-Atlas in the third quarter, which reflects the desire of some of our largest customers to build with MongoDB long term for their most mission-critical applications. We also benefited from higher-than-expected multiyear revenue in the third quarter as approximately 2/3 of the non-Atlas revenue outperformance versus the high end of guidance was attributable to multiyear outperformance. We had another strong quarter for customer adds as we grew our customer base by approximately 2,600 sequentially, bringing the total customer count to over 62,500, which is up from over 52,600 in the year ago period. The growth in our total customer count is being driven primarily by Atlas which had over 60,800 customers at the end of the third quarter compared to over 51,100 in the year ago period.

We ended the quarter with 2,694 customers with at least $100,000 in ARR, representing 16% growth versus the year ago period. Moving down the income statement. Gross profit for the third quarter was $466 million, representing a gross margin of 74%, which is down from 77% in the year ago period. Our year-over-year gross margin decline is primarily driven by Atlas growing as a percent of the overall business. Although Atlas gross margins are slightly below the total company gross margins they continue to improve year-over-year. Our income from operations was $123 million for a 20% operating margin compared to 19% in the year-ago period. We are very pleased with our stronger-than-expected operating margin results, which benefited from both our revenue outperformance and lower-than-expected operating expenses.

Net income in the third quarter was $115 million or $1.32 per share based on 86.9 million diluted shares outstanding. This compares to net income of $98 million or $1.16 per share on 84.2 million diluted shares outstanding in the year ago period. Turning to the balance sheet and cash flow. We ended the third quarter with $2.3 billion in cash, cash equivalents, short-term investments and restricted cash. During the quarter, we spent $145 million to repurchase approximately 514,000 shares which was executed under our previously announced $1 billion total share repurchase authorization. Operating cash flow was well above our expectations at $144 million, and free cash flow was $140 million, which compares to $37 million and $35 million, respectively, in the year ago period.

Our cash flow results were driven primarily by strong operating profit and improving working capital dynamics, particularly related to higher cash collections. We remain confident in our ability to drive higher and more consistent free cash flow going forward. Before we go into our guidance for the rest of fiscal ’26, let me recap some of the enhancements we have made to our approach to guidance since I joined MongoDB. Importantly, we are providing more visibility into our expectations for Atlas growth as well as non-Atlas ARR growth each quarter. That being said, we will continue to be prudent in our forecasting of multiyear deals and only include those deals where we have very clear visibility. Our goal is to give you a more transparent view into our expectations for the business and our approach to guiding the non-Atlas business.

Now let me share some of the assumptions driving our outlook for the rest of fiscal ’26. Number one, we are continuing to see strong momentum in Atlas, which has experienced relatively consistent consumption growth through the first 3 quarters of the year. And comparable seasonal patterns as compared to fiscal ’25. We are seeing strength with existing customers, along with momentum in new accounts as customers large and small increasingly recognize the strategic value of Atlas. As a result, we now expect Atlas to see approximately 27% revenue growth in the fourth quarter of fiscal ’26, which is higher than our previous expectations of growth in the mid-20% range. This outlook reflects our continued confidence in Atlas while taking into account the historical seasonal variability and consumption patterns during the holiday period.

Number two, we continue to experience steady ARR growth in our non-Atlas business and have good line of sight to several large multiyear deals we either already have or expect to close in the fourth quarter of the year. Based on these dynamics, we now expect our non-Atlas business to grow in the upper single-digit percent range year-over-year in the fourth quarter. Number three, we continue to make strategic investments in engineering, marketing and direct sales capacity to drive continued growth. Some of these planned investments have taken longer to implement than expected and have shifted into the fourth quarter of fiscal ’26 and fiscal ’27, which has benefited our operating margin during fiscal ’26. Fourth, we continue to make progress on free cash flow conversion, which is now expected to exceed 100% for fiscal ’26.

Finally, we will continue to execute our share buyback program to help offset dilution from employee equity awards. In addition to our buyback, this past quarter, we began settling the taxes due on the vesting of employee RSUs with cash instead of issuing new shares. We also expect to receive over 1 million shares of stock for the cap calls associated with our 2026 notes that mature in January 2026. All of these actions will help us manage share count for the long term and illustrates our commitment to being good stewards of your capital. Now let’s shift to guidance in the fourth quarter and fiscal ’26. For the fourth quarter, we now expect revenue of $665 million to $670 million, which equates to 21% to 22% year-over-year growth. We expect non-GAAP income from operations to be in the range of $139 million to $143 million for an operating margin of approximately 21%.

We expect non-GAAP net income per share to be in the range of $1.44 to $1.48 based on 86.5 million diluted shares outstanding. For fiscal ’26, we now expect revenue to be in the range of $2.434 billion to $2.439 billion, an increase of $79 million from the high end of our prior guide and representing full year revenue growth of 21% to 22%. We are raising our non-GAAP income from operation expectations by $109 million at the high end and are now targeting a range of $436.4 million to $440.4 million for an operating margin of approximately 18%. We expect non-GAAP net income per share to be in the range of $4.76 and to $4.80 based on 86.7 million diluted shares outstanding. Note that the non-GAAP net income per share guidance for the fourth quarter and fiscal ’26 assumes a non-GAAP tax provision of 20%.

While we will provide detailed guidance for fiscal ’27 on our fourth quarter call, I would like to comment on how we are thinking about a few metrics as we sit here today. First, we remain committed to the long-term model presented at our Investor Day in September and continue to make great progress against all of the objectives highlighted at the event. We have seen strong margin expansion and free cash flow performance in fiscal ’26. And both of these metrics are tracking well above the long-term targets we discussed in September. As we look ahead to fiscal ’27, we will continue to make strategic investments to focus on driving growth going forward. With these planned investments and the timing of head count adds, we continue to target 100 to 200 basis points of margin expansion on average and 80% to 100% for free cash flow conversion outlined in our long-term model.

Second, our non-Atlas business is on track to exceed our prior expectations for fiscal ’26 due to the stronger performance, including greater-than-expected large multiyear deals. Given this outperformance and our current bottoms-up forecast for fiscal ’27, we currently do not expect non-Atlas multiyear transactions to provide either a meaningful headwind or tailwind to revenue in fiscal ’27. To summarize, we had another very strong quarter. We are pleased with our ability to drive both revenue growth across the business while increasing our operating profit expectations and driving meaningful free cash flow. We remain incredibly excited about the opportunity ahead, and we will continue to invest responsibly to drive long-term shareholder value.

With that, Lisa, we would now like to open the call up for questions.

Q&A Session

Operator: [Operator Instructions] And our first question of the day will be coming from the line of Sanjit Singh of Morgan Stanley.

Sanjit Singh: Fiscal year ’26 has turned out to be quite the year for MongoDB, so congrats to the team all around. CJ, I wanted to start with you since this is your first earnings call, heard you loud and clear in terms of what the goal is here to make MongoDB a foundational data platform for the AI era. In terms of making that happen in your kind of first 45 days on the job, maybe even less than that. Are there some initial things that you’re looking at some kind of things that might fit in the sort of quicker win bucket? And then longer term, what is — what are some of the changes you think that the company can make or evolve to get to that — to [ see or place ] in that sort of AI era?

Chirantan Desai: Thank you, Sanjit. Here is — this is my day 28 on the job, and I have been speaking to customers as well as our innovation team, including our Voyage AI team as well as our core database teams. The first thing I would say is the opportunity for MongoDB to be that data platform for AI workloads is very real because you need real-time operational data, you need the right context, you need to make sure that you are keeping up to date between the proprietary data of the company as in the enterprise as well as the LLM learnings that the LLM model brings to the table. And most importantly, when I think about all of that combined together, MongoDB has all the elements needed to be the right foundational platform for AI workloads.

In speaking to customers, it is still early. There are various co-pilots when it comes to productivity types of applications that are happening inside of an organization, whether it’s a bank or a health care organization or a manufacturing organization. But what I have not seen is truly AI agents running in production that fundamentally transform the business or serve customers better. There are many, many pilots still going on. When I contrast that with the AI native companies, and there is a really good fast growth at scale, AI native company that currently switched from Postgres to MongoDB because Postgres could not just scale. There is another AI company that highlighted that is using our embeddings as well as our vector database besides our operational platform.

So when I combine all this together, Sanjit, what I see is, as truly scaled agentic platforms where you can have enterprises creating agents that transform their business, MongoDB has a very important role to play. And from a low-hanging fruit standpoint, I would argue that our embedding model and reranking model is something that customers can start with today, then they can move on to our vector database and use us for also real-time operational store. So that’s how I’m thinking and some of my initial customer conversations have validated that theory.

Sanjit Singh: Understood. I know it’s early, so great to get that perspective. And then one follow-up for me, sort of a mark-to-market question. The calendar year ’24, fiscal year ’25 workload sort of improved in quality versus the prior year. I just want to get a sense of your sort of view on how the calendar year ’25 workloads are shaping up as they will unlikely be a factor in terms of thinking about growth next year? And just so in terms of the quality of the workloads this year, can you give us a sense of the quality of those workloads?

Michael Berry: Sanjit, it’s Mike. So what we’ll say there is, as we said during the prepared remarks, and we saw this in Q2 as well, what we’re really seeing is strength in the larger customers. It’s not only from new workloads, but it’s from the existing workloads. We don’t want to bifurcate between which calendar year those were added. What we’d say is that we continue to see growth in the larger customers. They are growing longer and they’re getting bigger and growing for longer, which is great. And we’re seeing that across both the United States and then broad-based in EMEA as well. And as Atlas gets bigger and bigger, all of those kind of munch together because they’re expanding, they’re adding. So what we’ll do is we’ll focus on the growth in our larger customers, especially in the U.S. and EMEA without going into each year. I hope that helped.

Operator: And our next question will be coming from the line of Matt Martino of Goldman Sachs.

Matthew Martino: Nice to see another quarter of acceleration. CJ, I appreciate you’re only a few weeks in, but I’d be curious to hear what customers are telling you is top of mind for MongoDB. What are the repeated themes in customer conversations as you take a fresh lens to the business?

Chirantan Desai: Absolutely, Matt. First thing I would say is that the modernization effort, whether it’s a workload that may be just running on-prem, in a large enterprise or a workload that is moving to cloud or sometimes to multiple clouds for resiliency that transformation in speaking to a large telecommunications company, a large health care company, a large tech company, and I can cite you many other examples. I was pretty overwhelmed to understand that those transformations are still going on. There is just a recent conversation I had with CTO of a large telecommunications company who said that they are moving 1,300-plus applications to another hyperscaler and trying to determine which workloads are best suited for MongoDB.

So the whole multi-cloud or a public cloud transformation is still going on. And just my intuitive sense in speaking to these customers will be going on for at least next 5 to 7 years. So that specific TAM still very much exists for MongoDB. Now these are the same set of customers, while they are trying to modernize their application stack, they are also experimenting, I would say, because I’ve not seen agents at scale that are customer facing or sometimes even employee-facing, they may have 10, 15, 20, but not that many compared to thousands of applications they run. In those AI applications area, they are experimenting sometimes with our embedding models or with our vector database or using MongoDB for real-time operational database. So that second aspect, which is still fairly early, but we are very well positioned as you think about AI workloads in enterprises and large enterprises.

And last but not the least, spending time, as you know or you may know that I spent half of my time in New York City and half of my time in Silicon Valley and speaking to my network in Silicon Valley with AI-native companies or digital-native companies, what I hear from them is that certain alternatives on relational database just do not scale because AI workloads are fundamentally around unstructured and semi-structured data. And then they decide sometimes explicitly to use MongoDB. So I put this in 3 buckets. One bucket is our core and still the cloud transformation, digital transformation, modernization, whichever term you want to use, our core will still continue to grow. As people create AI agents at scale, MongoDB has a role to play and for AI-native companies and some at scale are already using MongoDB because the alternatives in relational world just do not scale.

So those are my like 3 buckets and initial mental model on how these conversations are proceeding and what we can do for them.

Matthew Martino: Really clear. And then, Mike, just a quick follow-up for you. It was good to see the outperformance on both Atlas and non-Atlas, but with op margins now about 200 basis points shy of your midterm framework, how should we think about the philosophy around reinvestment? And any considerations around non-Atlas and the ability to expand margins as we look out into fiscal ’27?

Michael Berry: Yes. Thanks for the question, Matt. So I’m sure everyone’s focused on ’27. So what we’d say is we will guide ’27 on the next call. What we would say is, and it’s built into the guidance that you have in Q4, and I also talked about it on the prepared remarks, we are continuing to invest, and we will continue to invest. Some of the investments that we wanted to make, especially around engineering, marketing, less so, but certainly around sales capacity has been pushed into Q4. So you should expect to see OpEx continue to grow in fiscal ’27. But we also want to make sure, and that’s why Matt, we took the time to say, “Hey, we want to reorient you to what we talked to you about in September — we still expect to see margin expansion.

But you really see it in the fiscal ’26 numbers is that is coming mostly from revenue growth. That is the expectation next year. We’ll continue to grow revenue. We’re going to continue to invest in the business, but the business model will continue to drive that expansion. So you should expect to see us continue to invest, especially across those 3 areas.

Operator: And our next question will be coming from the line of Karl Keirstead of UBS.

Karl Keirstead: Okay. Great. Thank you. First of all, CJ, welcome aboard. I’m excited to work with you over the coming years. I had a question for you. So it seems as if you’re describing these good set of numbers as strength in the core, essentially even before that AI tailwind kicks in. I’d love if you could define what you think is fundamentally driving that core strength? And do you feel like it’s possible that actually Mongo is already getting an AI tailwind in the sense that there’s a heightened focus on modernizing your data in advance of AI, such that this core strength is actually AI-related?

Chirantan Desai: Karl, great to hear from you and looking forward to seeing you on Wednesday. I would say the core strength from my perspective is workloads that are — need modernization has a lot of unstructured or semi-structured data and ideally suited for MongoDB. Now when it comes to AI, could AI potentially drive more modernization efforts? That is possible but not deterministic. As in we see — as we shared in the remarks, that in the high end of the enterprise, the consumption of the workloads we acquired maybe a year ago, 1.5 years ago, that continues to move up in the right direction as our go-to-market teams are focused on the high end of the enterprise. We also saw broad-based strength in Europe. And that is pretty much to the core business like the large insurance company on the claims engine and other things that I spoke about related to policies.

So I particularly see that as, okay, is that — does that mean that if core is modern, it helps with AI workloads, absolutely, that is true because they are not mutually exclusive. And Karl, one thing I would say, this is my personal experience in building AI technologies in the past. That the AI team is typically a separate team from the core data team. And AI team relies on the core data team. And if the core data team moves slow, then AI teams get really frustrated because innovation velocity is how they measure themselves on. So my personal experience was, hey, when the core team is not agile there schemas are not flexible, it actually slows AI down. So that is definitely some facts behind your theory that it is potentially the AI revolution, which we are still in the early stages, is driving modernization in the other part of the enterprise.

Karl Keirstead: Okay. And then, Mike, for you, I think everybody on the line appreciates the more definitive guidance on Atlas for the following quarter. So thank you. I wanted to ask what’s driving that? Is it simply a function of you and just in your new — relatively new seats, wanting to be more transparent in the guidance? Or Mike, is there something actually changing in Atlas such that now that it’s at scale, it’s becoming predictable enough that it now makes more sense to give precise guidance?

Michael Berry: So thanks, Karl, thank you for the question. I would say it’s probably a little bit of both. One is, hey, we want to give you folks a little bit more visibility to what’s behind the guidance that we provide. That was number one. Also, as Atlas gets to be, gosh, now almost a $2 billion business, we feel better about the forecasting. The team has done a wonderful job forecasting that part as well. So when we gave the number for Q4, we want to make sure and give you the visibility. But we also have a pretty good view of what we hope it would be, understanding that, keep in mind, Q4, we want to be prudent because there are some seasonal holiday patterns that can be somewhat unpredictable, and we’ve seen that play out in the past Q4s. So I just want to note that for the guidance that we just gave.

Operator: And the next question will be coming from the line of Raimo Lenschow of Barclays.

Raimo Lenschow: CJ, all the best from me as well. I had 2 questions, one for CJ, one for Mike. CJ, on the — one of the core things in terms of adoption of Mongo will be on the developer side because they’re — at the end of the day, developers are like a big driver of like what’s getting used, et cetera. At the moment, a lot of AI is on the West Coast. What’s your thinking around like getting AI — getting developer engagement up with Mongo to kind of go against that Postgres kind of narrative that happens a lot in the valley. And then, Mike, for you, like since next year EA is not seeing benefits from all the year. Should we anchor our numbers on the ARR performance? And is that the right way to think about it?

Chirantan Desai: Thank you, Raimo. Great to hear from you. I’m going to first ask — there is a little bit of historical context in terms of your point on the West Coast. I’m going to ask, our previous CEO, Dev Ittycheria to talk about reclaim the Bay, the initiative that him and the team started, and then I’m going to specifically talk about how I think about it on the West Coast.

Dev Ittycheria: Raimo, it’s Dev here. As CJ mentioned, we’ve talked about this in previous calls, but we made a concerted effort to reinvest in the Bay Area because during COVID and post-COVID, we felt that we had neglected that region. And obviously, there was a whole new corpus of AI-native companies that were getting launched. So there’s been a real concerted effort both in terms of putting more feet on the street, putting more marketing efforts in terms of supporting that part of the world. Investing more in the start-up community and also in the venture community to get people to understand the true value proposition of MongoDB. We’ve done things like hackathons and other events in that area as well. And so the team’s really focused, dedicated to really supporting and servicing these early AI native companies, and that is starting to yield some results. And we feel really good about the progress there, but I’ll let CJ talk about what happens going forward.

Chirantan Desai: Thank you, Dev. And this is the reclaim the Bay in San Francisco on the West Coast. It is 100% true Raimo, that there is a lot of investment with AI-native companies, and we could benefit from increased mind share and being in front of them as in the developer community that you talked about, which is a super important community to us on the West Coast. So me spending personally time on the West Coast house. I do also have deep network in the West Coast community, both venture community as well as tech companies at scale. And I’ve already started leveraging that network to get their feedback. We are really excited in this quarter, as in the 4Q, we are relaunching our .local after a few years in San Francisco on January 15, where we are going to invite companies that have built on MongoDB, some great speakers on why they should build on MongoDB and show hands-on experience to the developer community in that conference on January 15.

And what I see is just speaking to many CEO founders as well as developers of smaller companies or midsized companies, all these efforts of the marketing investment that Mike and Dev originally approved is going to start yielding results as we move into the next fiscal year.

Michael Berry: And Raimo, thanks for the question. It’s Mike. On non-Atlas next year, we wanted to make sure we’ve had a lot of questions about the multiyear headwind. So thank you for the question there. We are not guiding for fiscal ’27. However, sitting here today, I would steer you more towards — if you look at the full year revenue growth of non-Atlas it’s about 4%, somewhere in that mid kind of low single digits is probably a good range to think about for next year as we sit here today.

Operator: And our next question will be coming from the line of Brad Reback of Stifel.

Brad Reback: Great. I’m not sure who this is for, but on the commentary around new customer strength within Atlas, are you seeing new customers ramp faster for net new workloads than they have been historically? And if so, why?

Chirantan Desai: Brad, my initial observation is that the team — engineering team has done a fantastic job when they launch 8.0 and all the subsequent point releases that allows Atlas to be adopted faster and remove the friction, whether you are coming via our self-serve channel or whether you are a large enterprise moving onto Atlas. So that’s one thing I would say. And I’m going to ask Dev to provide commentary as well from a context perspective.

Dev Ittycheria: Yes. I think what I’d also say is that, Brad, is that I think we’ve — the self-serve team has really removed the friction to enable customers to onboard more quickly and more easily. And given the performance — price performance gains that we’ve seen in 8 and now even better in 8.2, I think that’s really driving a lot of the traction we’re seeing in our new customers they quickly see the performance benefits and they’re scaling nicely. And so that’s allowing us to continue to acquire customers efficiently.

Michael Berry: And one last thing on that, Brad. If you look at the revenue from that, it hasn’t changed materially. It’s still, keep in mind, a pretty small number when they first onboard, so it’s not going to move the needle much. We haven’t seen much change in that cohort over the last couple of years.

Brad Reback: Great. And then, CJ, a quick follow-up for you. Philosophically, how do you think about M&A as it relates to Mongo? What types of things, if anything, you think you need to acquire?

Chirantan Desai: Brad, you know me well, and I’m a big believer in organic growth. The team, Dev and the team have laid a very strong foundation on our technology platform. I think Voyage AI in February was a brilliant acquisition, where we got unbelievable team in Palo Alto. And my goal on behalf of MongoDB is to always believe in our own teams and our technology. We participate in a large market and where it makes sense, where we can get a particular adjacent technology or a great team that can help us accelerate the road map, we would always consider that type of M&A.

Operator: And our next question will be coming from the line of Alex Zukin of Wolfe Research.

Aleksandr Zukin: CJ, maybe for you. I mean, you shared, I think, a lot of thoughts about your initial vision. You shared the 3 pillars of the core, the enterprise AI opportunity and the AI natives. I just want to maybe lean in, where do you see your particular skill set of network offering kind of not the lowest hanging fruit, but your ability to make kind of the biggest impact in, call it, the next 12 to 24 months? Like where do you really see that incremental opportunity for growth inflection?

Chirantan Desai: I would say, Alex, and — you are aware of the enterprises and the customer obsession I have and the relationships that I have formed over many, many years with technology leaders at large companies. So, from my perspective, there are 2 areas where I can benefit our go-to-market teams immensely. Number one is Fortune 500, where MongoDB can still penetrate even at a higher rate than it is penetrating today, both within the existing accounts as well as the new accounts we get. So that’s Fortune 500. And then I was with our sales teams in Europe, and there are many customers that they are targeting, including existing customers, large banks, manufacturing companies and so on, where they’re trying to expand where my personal relationships with those technology buyers can help.

So that’s bucket number one is make no mistake, high end of the enterprise as in Fortune 500 and Global 2000. Number two, on the other extreme would be AI-native companies, lived in Silicon Valley for a very long time. I understand where venture community is investing, folks who are creating, whether it’s domain-specific AI companies or foundational companies have relationships there as well across [ 101, 280 and 237 ], and that’s where I also plan to — I would say, plant the seeds in a correct fashion so that over time, that becomes a meaningful business for MongoDB if we are the underlying infrastructure for those companies. So those are the 2 extremes that I’m going to spend personally a lot of time on.

Aleksandr Zukin: Excellent. And you mentioned Voyage AI the acquisition this year being kind of a crown jewel in the portfolio. Maybe just help us understand with the AI native, specifically the opportunities there, are those starting — are you guys landing with Voyage? Are you landing with Atlas? Are you landing with both now at a more kind of constant pace? Help us understand kind of that incremental differentiator.

Chirantan Desai: Yes. I would say one example, and this in my remarks, I shared that there is a super high growth AI company that is doing very, very well and will become a very large company. I have absolutely no doubts about that. They were not able to scale with Postgres and few other technologies, Redis and so on that they were using, and they moved completely to MongoDB and seeing that week-over-week and month-over-month growth is super inspiring. And I spoke to the hyperscaler where this workload is running and they are seeing the same that, wow, this company is doing really well. So that’s built on MongoDB because Postgres had scaling issue. The other extreme, I spoke to a fairly successful AI native company that is doing decent ARR, growing very fast.

And when I said, hey, have you considered MongoDB to the founder, CEO, who is very technical. And he said, CJ, we didn’t, we built our own vector database and so on. And while I was speaking to him Alex, about 10 days ago, he basically said, once he looked at the portfolio, he said, let me start with embeddings first. So we are going to try. Of course, we have to prove it to him why our embeddings improves his accuracy on search and so on and improve the performance. So he said, let’s start with embedding models first from Voyage AI once that works CJ, I’m willing to replace my vector DB that we have homegrown created it with MongoDB and oh, by the way, if that works well, eventually, I’m willing to swap out my operational database as well and use MongoDB.

So in those kind of scenarios where they are already on a certain track we can land with Voyage AI embeddings. And I’m also seeing in a very large customer of MongoDB, I spoke to somebody who is running the AI initiatives, and they love the Voyage AI embeddings and reranking model, and they’ve already approved it for 2 big workloads. So we can absolutely land with that is the short answer.

Aleksandr Zukin: Sounds like a beautiful synergy.

Operator: And the last question for the day will be coming from the line of Ryan MacWilliams of Wells Fargo.

Ryan MacWilliams: The consumer app development environment is getting stronger as new iOS app development has surged multiyear highs. We think it’s due to agentic coding, And I know it’s early but on the enterprise side, are you seeing stronger product velocity from your customers in building their enterprise applications?

Chirantan Desai: I’m going to ask Dev to provide his opinion, and then I’ll provide mine.

Dev Ittycheria: Yes, I think what we’re seeing is we’re clearly seeing a lot of, I would say, prototyping and iteration. I would say the enterprise requirements still have a pretty strong and stringent requirements around security and durability and performance. So while there’s a big difference between coming out with the prototype and having a production-grade system that an enterprise can truly rely on trust. And so there is still a lot of work required to make those applications enterprise class. But clearly, with the advent of [ cogen ] tools, the rate and pace of software development is only going to increase. And as I think we said in the past, that’s one of the big reasons why we think AI is a tailwind. It’s just that the ability to produce more software, [indiscernible] more database and more and more strategies has been encapsulated in software. So from that point of view, we think that’s all good news for us.

Chirantan Desai: Yes. And the only thing I’ll add on is, when I speak to customers who I’ve been speaking for a long time, in regulated industries, which is financial services, which is health care, which is public sector, the requirement for an AI agent to be in production versus prototype are vastly different, and they are looking for governance, auditability, this and that, while the innovation and the need for the speed is very high. So I have not seen — like customers will tell me, CJ I have 10 agents in production, 15 agents in production. And when I really asked them, I say, are they really customer-facing? Can they be audited on the probabilistic outcome they derive? The answer is, oh, we are still working through that.

That doesn’t mean that it will not happen soon, but it will never happen. But I still feel we are fairly early. And even the environment on which they are building agents, they are telling me they try one, it doesn’t work, they move on to the next one. So the churn for some of these AI companies that deliver these tools is also very real. And that’s why I’m very encouraged by the MongoDB opportunity. We have the platform for operational data. We have the best vector database and we have the embedding models where they can comfortably at enterprise scale, build a real AI agent using MongoDB platform.

Ryan MacWilliams: Excellent. Really appreciate that detail. And then for Mike, on the Atlas 4Q growth guidance. I appreciate the color there. Just a quick clarification, on this 4Q Atlas guidance, should we expect results closer to the pin or a guidance philosophy consistent with your historical precedent?

Michael Berry: Yes. So thanks. I don’t want to go into the golf analogy. And besides Ryan, you know I like hockey analogies better. What I would say is that, hey, we are — we feel really good about Atlas. It’s had a great year so far. We feel good about it going into Q4, we remain excited about the growth. That being said, we are being prudent for Q4 as a holiday — as the seasonal holiday patterns, hey, they can be somewhat unpredictable and we’ve seen that play out in the past Q4s. So what I would say is, hey, we just need to be prudent as we enter the holiday season.

Operator: Thank you. That does conclude today’s Q&A session. I would like to go ahead and turn the call back over to MongoDB’s President and CEO, CJ Desai, please go ahead.

Chirantan Desai: Thank you, Lisa. In summary, we delivered an exceptional third quarter, highlighted by accelerating Atlas growth, robust customer additions and significant operating margin outperformance. We are raising our revenue and operating income guidance for the fourth quarter and full fiscal year 2026 and reiterating our commitment to the long-term financial model we outlined at Investor Day. Our results underscore that MongoDB’s core business is firing on all cylinders even before any meaningful AI tailwinds. At the same time, we are uniquely positioned to become the generational modern data platform for the AI era, all while driving durable, efficient growth. Thank you, everyone, for joining, and thank you for listening.

Operator: This does conclude today’s conference call. You may all disconnect.

Operator: [Operator Instructions] And our first question of the day will be coming from the line of Sanjit Singh of Morgan Stanley.

Sanjit Singh: Fiscal year ’26 has turned out to be quite the year for MongoDB, so congrats to the team all around. CJ, I wanted to start with you since this is your first earnings call, heard you loud and clear in terms of what the goal is here to make MongoDB a foundational data platform for the AI era. In terms of making that happen in your kind of first 45 days on the job, maybe even less than that. Are there some initial things that you’re looking at some kind of things that might fit in the sort of quicker win bucket? And then longer term, what is — what are some of the changes you think that the company can make or evolve to get to that — to [ see or place ] in that sort of AI era?

Chirantan Desai: Thank you, Sanjit. Here is — this is my day 28 on the job, and I have been speaking to customers as well as our innovation team, including our Voyage AI team as well as our core database teams. The first thing I would say is the opportunity for MongoDB to be that data platform for AI workloads is very real because you need real-time operational data, you need the right context, you need to make sure that you are keeping up to date between the proprietary data of the company as in the enterprise as well as the LLM learnings that the LLM model brings to the table. And most importantly, when I think about all of that combined together, MongoDB has all the elements needed to be the right foundational platform for AI workloads.

In speaking to customers, it is still early. There are various co-pilots when it comes to productivity types of applications that are happening inside of an organization, whether it’s a bank or a health care organization or a manufacturing organization. But what I have not seen is truly AI agents running in production that fundamentally transform the business or serve customers better. There are many, many pilots still going on. When I contrast that with the AI native companies, and there is a really good fast growth at scale, AI native company that currently switched from Postgres to MongoDB because Postgres could not just scale. There is another AI company that highlighted that is using our embeddings as well as our vector database besides our operational platform.

So when I combine all this together, Sanjit, what I see is, as truly scaled agentic platforms where you can have enterprises creating agents that transform their business, MongoDB has a very important role to play. And from a low-hanging fruit standpoint, I would argue that our embedding model and reranking model is something that customers can start with today, then they can move on to our vector database and use us for also real-time operational store. So that’s how I’m thinking and some of my initial customer conversations have validated that theory.

Sanjit Singh: Understood. I know it’s early, so great to get that perspective. And then one follow-up for me, sort of a mark-to-market question. The calendar year ’24, fiscal year ’25 workload sort of improved in quality versus the prior year. I just want to get a sense of your sort of view on how the calendar year ’25 workloads are shaping up as they will unlikely be a factor in terms of thinking about growth next year? And just so in terms of the quality of the workloads this year, can you give us a sense of the quality of those workloads?

Michael Berry: Sanjit, it’s Mike. So what we’ll say there is, as we said during the prepared remarks, and we saw this in Q2 as well, what we’re really seeing is strength in the larger customers. It’s not only from new workloads, but it’s from the existing workloads. We don’t want to bifurcate between which calendar year those were added. What we’d say is that we continue to see growth in the larger customers. They are growing longer and they’re getting bigger and growing for longer, which is great. And we’re seeing that across both the United States and then broad-based in EMEA as well. And as Atlas gets bigger and bigger, all of those kind of munch together because they’re expanding, they’re adding. So what we’ll do is we’ll focus on the growth in our larger customers, especially in the U.S. and EMEA without going into each year. I hope that helped.

Operator: And our next question will be coming from the line of Matt Martino of Goldman Sachs.

Matthew Martino: Nice to see another quarter of acceleration. CJ, I appreciate you’re only a few weeks in, but I’d be curious to hear what customers are telling you is top of mind for MongoDB. What are the repeated themes in customer conversations as you take a fresh lens to the business?

Chirantan Desai: Absolutely, Matt. First thing I would say is that the modernization effort, whether it’s a workload that may be just running on-prem, in a large enterprise or a workload that is moving to cloud or sometimes to multiple clouds for resiliency that transformation in speaking to a large telecommunications company, a large health care company, a large tech company, and I can cite you many other examples. I was pretty overwhelmed to understand that those transformations are still going on. There is just a recent conversation I had with CTO of a large telecommunications company who said that they are moving 1,300-plus applications to another hyperscaler and trying to determine which workloads are best suited for MongoDB.

So the whole multi-cloud or a public cloud transformation is still going on. And just my intuitive sense in speaking to these customers will be going on for at least next 5 to 7 years. So that specific TAM still very much exists for MongoDB. Now these are the same set of customers, while they are trying to modernize their application stack, they are also experimenting, I would say, because I’ve not seen agents at scale that are customer facing or sometimes even employee-facing, they may have 10, 15, 20, but not that many compared to thousands of applications they run. In those AI applications area, they are experimenting sometimes with our embedding models or with our vector database or using MongoDB for real-time operational database. So that second aspect, which is still fairly early, but we are very well positioned as you think about AI workloads in enterprises and large enterprises.

And last but not the least, spending time, as you know or you may know that I spent half of my time in New York City and half of my time in Silicon Valley and speaking to my network in Silicon Valley with AI-native companies or digital-native companies, what I hear from them is that certain alternatives on relational database just do not scale because AI workloads are fundamentally around unstructured and semi-structured data. And then they decide sometimes explicitly to use MongoDB. So I put this in 3 buckets. One bucket is our core and still the cloud transformation, digital transformation, modernization, whichever term you want to use, our core will still continue to grow. As people create AI agents at scale, MongoDB has a role to play and for AI-native companies and some at scale are already using MongoDB because the alternatives in relational world just do not scale.

So those are my like 3 buckets and initial mental model on how these conversations are proceeding and what we can do for them.

Matthew Martino: Really clear. And then, Mike, just a quick follow-up for you. It was good to see the outperformance on both Atlas and non-Atlas, but with op margins now about 200 basis points shy of your midterm framework, how should we think about the philosophy around reinvestment? And any considerations around non-Atlas and the ability to expand margins as we look out into fiscal ’27?

Michael Berry: Yes. Thanks for the question, Matt. So I’m sure everyone’s focused on ’27. So what we’d say is we will guide ’27 on the next call. What we would say is, and it’s built into the guidance that you have in Q4, and I also talked about it on the prepared remarks, we are continuing to invest, and we will continue to invest. Some of the investments that we wanted to make, especially around engineering, marketing, less so, but certainly around sales capacity has been pushed into Q4. So you should expect to see OpEx continue to grow in fiscal ’27. But we also want to make sure, and that’s why Matt, we took the time to say, “Hey, we want to reorient you to what we talked to you about in September — we still expect to see margin expansion.

But you really see it in the fiscal ’26 numbers is that is coming mostly from revenue growth. That is the expectation next year. We’ll continue to grow revenue. We’re going to continue to invest in the business, but the business model will continue to drive that expansion. So you should expect to see us continue to invest, especially across those 3 areas.

Operator: And our next question will be coming from the line of Karl Keirstead of UBS.

Karl Keirstead: Okay. Great. Thank you. First of all, CJ, welcome aboard. I’m excited to work with you over the coming years. I had a question for you. So it seems as if you’re describing these good set of numbers as strength in the core, essentially even before that AI tailwind kicks in. I’d love if you could define what you think is fundamentally driving that core strength? And do you feel like it’s possible that actually Mongo is already getting an AI tailwind in the sense that there’s a heightened focus on modernizing your data in advance of AI, such that this core strength is actually AI-related?

Chirantan Desai: Karl, great to hear from you and looking forward to seeing you on Wednesday. I would say the core strength from my perspective is workloads that are — need modernization has a lot of unstructured or semi-structured data and ideally suited for MongoDB. Now when it comes to AI, could AI potentially drive more modernization efforts? That is possible but not deterministic. As in we see — as we shared in the remarks, that in the high end of the enterprise, the consumption of the workloads we acquired maybe a year ago, 1.5 years ago, that continues to move up in the right direction as our go-to-market teams are focused on the high end of the enterprise. We also saw broad-based strength in Europe. And that is pretty much to the core business like the large insurance company on the claims engine and other things that I spoke about related to policies.

So I particularly see that as, okay, is that — does that mean that if core is modern, it helps with AI workloads, absolutely, that is true because they are not mutually exclusive. And Karl, one thing I would say, this is my personal experience in building AI technologies in the past. That the AI team is typically a separate team from the core data team. And AI team relies on the core data team. And if the core data team moves slow, then AI teams get really frustrated because innovation velocity is how they measure themselves on. So my personal experience was, hey, when the core team is not agile there schemas are not flexible, it actually slows AI down. So that is definitely some facts behind your theory that it is potentially the AI revolution, which we are still in the early stages, is driving modernization in the other part of the enterprise.

Karl Keirstead: Okay. And then, Mike, for you, I think everybody on the line appreciates the more definitive guidance on Atlas for the following quarter. So thank you. I wanted to ask what’s driving that? Is it simply a function of you and just in your new — relatively new seats, wanting to be more transparent in the guidance? Or Mike, is there something actually changing in Atlas such that now that it’s at scale, it’s becoming predictable enough that it now makes more sense to give precise guidance?

Michael Berry: So thanks, Karl, thank you for the question. I would say it’s probably a little bit of both. One is, hey, we want to give you folks a little bit more visibility to what’s behind the guidance that we provide. That was number one. Also, as Atlas gets to be, gosh, now almost a $2 billion business, we feel better about the forecasting. The team has done a wonderful job forecasting that part as well. So when we gave the number for Q4, we want to make sure and give you the visibility. But we also have a pretty good view of what we hope it would be, understanding that, keep in mind, Q4, we want to be prudent because there are some seasonal holiday patterns that can be somewhat unpredictable, and we’ve seen that play out in the past Q4s. So I just want to note that for the guidance that we just gave.

Operator: And the next question will be coming from the line of Raimo Lenschow of Barclays.

Raimo Lenschow: CJ, all the best from me as well. I had 2 questions, one for CJ, one for Mike. CJ, on the — one of the core things in terms of adoption of Mongo will be on the developer side because they’re — at the end of the day, developers are like a big driver of like what’s getting used, et cetera. At the moment, a lot of AI is on the West Coast. What’s your thinking around like getting AI — getting developer engagement up with Mongo to kind of go against that Postgres kind of narrative that happens a lot in the valley. And then, Mike, for you, like since next year EA is not seeing benefits from all the year. Should we anchor our numbers on the ARR performance? And is that the right way to think about it?

Chirantan Desai: Thank you, Raimo. Great to hear from you. I’m going to first ask — there is a little bit of historical context in terms of your point on the West Coast. I’m going to ask, our previous CEO, Dev Ittycheria to talk about reclaim the Bay, the initiative that him and the team started, and then I’m going to specifically talk about how I think about it on the West Coast.

Dev Ittycheria: Raimo, it’s Dev here. As CJ mentioned, we’ve talked about this in previous calls, but we made a concerted effort to reinvest in the Bay Area because during COVID and post-COVID, we felt that we had neglected that region. And obviously, there was a whole new corpus of AI-native companies that were getting launched. So there’s been a real concerted effort both in terms of putting more feet on the street, putting more marketing efforts in terms of supporting that part of the world. Investing more in the start-up community and also in the venture community to get people to understand the true value proposition of MongoDB. We’ve done things like hackathons and other events in that area as well. And so the team’s really focused, dedicated to really supporting and servicing these early AI native companies, and that is starting to yield some results. And we feel really good about the progress there, but I’ll let CJ talk about what happens going forward.

Chirantan Desai: Thank you, Dev. And this is the reclaim the Bay in San Francisco on the West Coast. It is 100% true Raimo, that there is a lot of investment with AI-native companies, and we could benefit from increased mind share and being in front of them as in the developer community that you talked about, which is a super important community to us on the West Coast. So me spending personally time on the West Coast house. I do also have deep network in the West Coast community, both venture community as well as tech companies at scale. And I’ve already started leveraging that network to get their feedback. We are really excited in this quarter, as in the 4Q, we are relaunching our .local after a few years in San Francisco on January 15, where we are going to invite companies that have built on MongoDB, some great speakers on why they should build on MongoDB and show hands-on experience to the developer community in that conference on January 15.

And what I see is just speaking to many CEO founders as well as developers of smaller companies or midsized companies, all these efforts of the marketing investment that Mike and Dev originally approved is going to start yielding results as we move into the next fiscal year.

Michael Berry: And Raimo, thanks for the question. It’s Mike. On non-Atlas next year, we wanted to make sure we’ve had a lot of questions about the multiyear headwind. So thank you for the question there. We are not guiding for fiscal ’27. However, sitting here today, I would steer you more towards — if you look at the full year revenue growth of non-Atlas it’s about 4%, somewhere in that mid kind of low single digits is probably a good range to think about for next year as we sit here today.

Operator: And our next question will be coming from the line of Brad Reback of Stifel.

Brad Reback: Great. I’m not sure who this is for, but on the commentary around new customer strength within Atlas, are you seeing new customers ramp faster for net new workloads than they have been historically? And if so, why?

Chirantan Desai: Brad, my initial observation is that the team — engineering team has done a fantastic job when they launch 8.0 and all the subsequent point releases that allows Atlas to be adopted faster and remove the friction, whether you are coming via our self-serve channel or whether you are a large enterprise moving onto Atlas. So that’s one thing I would say. And I’m going to ask Dev to provide commentary as well from a context perspective.

Dev Ittycheria: Yes. I think what I’d also say is that, Brad, is that I think we’ve — the self-serve team has really removed the friction to enable customers to onboard more quickly and more easily. And given the performance — price performance gains that we’ve seen in 8 and now even better in 8.2, I think that’s really driving a lot of the traction we’re seeing in our new customers they quickly see the performance benefits and they’re scaling nicely. And so that’s allowing us to continue to acquire customers efficiently.

Michael Berry: And one last thing on that, Brad. If you look at the revenue from that, it hasn’t changed materially. It’s still, keep in mind, a pretty small number when they first onboard, so it’s not going to move the needle much. We haven’t seen much change in that cohort over the last couple of years.

Brad Reback: Great. And then, CJ, a quick follow-up for you. Philosophically, how do you think about M&A as it relates to Mongo? What types of things, if anything, you think you need to acquire?

Chirantan Desai: Brad, you know me well, and I’m a big believer in organic growth. The team, Dev and the team have laid a very strong foundation on our technology platform. I think Voyage AI in February was a brilliant acquisition, where we got unbelievable team in Palo Alto. And my goal on behalf of MongoDB is to always believe in our own teams and our technology. We participate in a large market and where it makes sense, where we can get a particular adjacent technology or a great team that can help us accelerate the road map, we would always consider that type of M&A.

Operator: And our next question will be coming from the line of Alex Zukin of Wolfe Research.

Aleksandr Zukin: CJ, maybe for you. I mean, you shared, I think, a lot of thoughts about your initial vision. You shared the 3 pillars of the core, the enterprise AI opportunity and the AI natives. I just want to maybe lean in, where do you see your particular skill set of network offering kind of not the lowest hanging fruit, but your ability to make kind of the biggest impact in, call it, the next 12 to 24 months? Like where do you really see that incremental opportunity for growth inflection?

Chirantan Desai: I would say, Alex, and — you are aware of the enterprises and the customer obsession I have and the relationships that I have formed over many, many years with technology leaders at large companies. So, from my perspective, there are 2 areas where I can benefit our go-to-market teams immensely. Number one is Fortune 500, where MongoDB can still penetrate even at a higher rate than it is penetrating today, both within the existing accounts as well as the new accounts we get. So that’s Fortune 500. And then I was with our sales teams in Europe, and there are many customers that they are targeting, including existing customers, large banks, manufacturing companies and so on, where they’re trying to expand where my personal relationships with those technology buyers can help.

So that’s bucket number one is make no mistake, high end of the enterprise as in Fortune 500 and Global 2000. Number two, on the other extreme would be AI-native companies, lived in Silicon Valley for a very long time. I understand where venture community is investing, folks who are creating, whether it’s domain-specific AI companies or foundational companies have relationships there as well across [ 101, 280 and 237 ], and that’s where I also plan to — I would say, plant the seeds in a correct fashion so that over time, that becomes a meaningful business for MongoDB if we are the underlying infrastructure for those companies. So those are the 2 extremes that I’m going to spend personally a lot of time on.

Aleksandr Zukin: Excellent. And you mentioned Voyage AI the acquisition this year being kind of a crown jewel in the portfolio. Maybe just help us understand with the AI native, specifically the opportunities there, are those starting — are you guys landing with Voyage? Are you landing with Atlas? Are you landing with both now at a more kind of constant pace? Help us understand kind of that incremental differentiator.

Chirantan Desai: Yes. I would say one example, and this in my remarks, I shared that there is a super high growth AI company that is doing very, very well and will become a very large company. I have absolutely no doubts about that. They were not able to scale with Postgres and few other technologies, Redis and so on that they were using, and they moved completely to MongoDB and seeing that week-over-week and month-over-month growth is super inspiring. And I spoke to the hyperscaler where this workload is running and they are seeing the same that, wow, this company is doing really well. So that’s built on MongoDB because Postgres had scaling issue. The other extreme, I spoke to a fairly successful AI native company that is doing decent ARR, growing very fast.

And when I said, hey, have you considered MongoDB to the founder, CEO, who is very technical. And he said, CJ, we didn’t, we built our own vector database and so on. And while I was speaking to him Alex, about 10 days ago, he basically said, once he looked at the portfolio, he said, let me start with embeddings first. So we are going to try. Of course, we have to prove it to him why our embeddings improves his accuracy on search and so on and improve the performance. So he said, let’s start with embedding models first from Voyage AI once that works CJ, I’m willing to replace my vector DB that we have homegrown created it with MongoDB and oh, by the way, if that works well, eventually, I’m willing to swap out my operational database as well and use MongoDB.

So in those kind of scenarios where they are already on a certain track we can land with Voyage AI embeddings. And I’m also seeing in a very large customer of MongoDB, I spoke to somebody who is running the AI initiatives, and they love the Voyage AI embeddings and reranking model, and they’ve already approved it for 2 big workloads. So we can absolutely land with that is the short answer.

Aleksandr Zukin: Sounds like a beautiful synergy.

Operator: And the last question for the day will be coming from the line of Ryan MacWilliams of Wells Fargo.

Ryan MacWilliams: The consumer app development environment is getting stronger as new iOS app development has surged multiyear highs. We think it’s due to agentic coding, And I know it’s early but on the enterprise side, are you seeing stronger product velocity from your customers in building their enterprise applications?

Chirantan Desai: I’m going to ask Dev to provide his opinion, and then I’ll provide mine.

Dev Ittycheria: Yes, I think what we’re seeing is we’re clearly seeing a lot of, I would say, prototyping and iteration. I would say the enterprise requirements still have a pretty strong and stringent requirements around security and durability and performance. So while there’s a big difference between coming out with the prototype and having a production-grade system that an enterprise can truly rely on trust. And so there is still a lot of work required to make those applications enterprise class. But clearly, with the advent of [ cogen ] tools, the rate and pace of software development is only going to increase. And as I think we said in the past, that’s one of the big reasons why we think AI is a tailwind. It’s just that the ability to produce more software, [indiscernible] more database and more and more strategies has been encapsulated in software. So from that point of view, we think that’s all good news for us.

Chirantan Desai: Yes. And the only thing I’ll add on is, when I speak to customers who I’ve been speaking for a long time, in regulated industries, which is financial services, which is health care, which is public sector, the requirement for an AI agent to be in production versus prototype are vastly different, and they are looking for governance, auditability, this and that, while the innovation and the need for the speed is very high. So I have not seen — like customers will tell me, CJ I have 10 agents in production, 15 agents in production. And when I really asked them, I say, are they really customer-facing? Can they be audited on the probabilistic outcome they derive? The answer is, oh, we are still working through that.

That doesn’t mean that it will not happen soon, but it will never happen. But I still feel we are fairly early. And even the environment on which they are building agents, they are telling me they try one, it doesn’t work, they move on to the next one. So the churn for some of these AI companies that deliver these tools is also very real. And that’s why I’m very encouraged by the MongoDB opportunity. We have the platform for operational data. We have the best vector database and we have the embedding models where they can comfortably at enterprise scale, build a real AI agent using MongoDB platform.

Ryan MacWilliams: Excellent. Really appreciate that detail. And then for Mike, on the Atlas 4Q growth guidance. I appreciate the color there. Just a quick clarification, on this 4Q Atlas guidance, should we expect results closer to the pin or a guidance philosophy consistent with your historical precedent?

Michael Berry: Yes. So thanks. I don’t want to go into the golf analogy. And besides Ryan, you know I like hockey analogies better. What I would say is that, hey, we are — we feel really good about Atlas. It’s had a great year so far. We feel good about it going into Q4, we remain excited about the growth. That being said, we are being prudent for Q4 as a holiday — as the seasonal holiday patterns, hey, they can be somewhat unpredictable and we’ve seen that play out in the past Q4s. So what I would say is, hey, we just need to be prudent as we enter the holiday season.

Operator: Thank you. That does conclude today’s Q&A session. I would like to go ahead and turn the call back over to MongoDB’s President and CEO, CJ Desai, please go ahead.

Chirantan Desai: Thank you, Lisa. In summary, we delivered an exceptional third quarter, highlighted by accelerating Atlas growth, robust customer additions and significant operating margin outperformance. We are raising our revenue and operating income guidance for the fourth quarter and full fiscal year 2026 and reiterating our commitment to the long-term financial model we outlined at Investor Day. Our results underscore that MongoDB’s core business is firing on all cylinders even before any meaningful AI tailwinds. At the same time, we are uniquely positioned to become the generational modern data platform for the AI era, all while driving durable, efficient growth. Thank you, everyone, for joining, and thank you for listening.

Operator: This does conclude today’s conference call. You may all disconnect.