Thank you, operator. Good afternoon, and thank you for joining us today to review MongoDB's second quarter fiscal 2027 financial results, which we announced in our press release issued after the close of the market today.

Joining me on the call today are CJ Desai, President and CEO of MongoDB; and Mike Berry, CFO of MongoDB. 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 EA and other business, and multi-year license revenue, and the long-term opportunity of AI, our financial guidance, and underlying assumptions, including expectations regarding profitability and operating margin, and our investments in 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 31st, 2026, filed with the SEC on September 1st, 2026.

Any forward-looking statements made on this call reflect our views only as of today. 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.

Thank you, Jess, and thanks, everyone, for joining us today. I'm pleased to share our very strong Q2 results.

Total revenue of $772 million, up 30% year-over-year, and representing the highest level of quarterly growth seen since fiscal year '24. Atlas revenue grew approximately 29% year-over-year for the fifth straight quarter, driven by large enterprise customers and building AI momentum.

EA and other had a stand-out quarter, growing 36% year-over-year due to widespread strength driven by our run anywhere capabilities. We generated a non-GAAP operating margin of 24%, driven by the strong revenue growth we delivered.

We ended the quarter with 70,600 customers, adding a record 2,900 net new customers in the period. Voyage customer count nearly doubled quarter-over-quarter, and Atlas vector search adoption continues to outpace the growth of the rest of the company, showing our strong early momentum for AI workloads.

Our core business remains strong, and our run anywhere advantage is a key differentiator for this quarter's growth across both Atlas and EA. Enterprises across financial services, healthcare, tech, are running their most demanding mission-critical workloads on MongoDB, and we are winning more workloads each quarter.

Increasingly, these same enterprises as well as AI natives are choosing our platform for AI workloads, evidenced by the adoption of Atlas vector search and Voyage embeddings. My team and I spent another quarter with the C-suite of our customers, discussing our data platform for their most pressing core, AI, and modernization needs.

Our Q2 performance is exactly why I'm confident that we are emerging as the real-time intelligent data platform for modern application in the multi-cloud and AI era. I will begin with what I'm seeing in the enterprise.

For customers that already run large part of their data estate on MongoDB, building an agent on top of that data is a natural extension because the data an agent actually needs is live, operational data, not a stale copy sitting in a warehouse. Search, vector search, and embeddings are built-in, not bolted on, rather than agents connecting to many separate systems, they connect to one platform.

We are seeing this show up across industries in a range of use cases, whether it's retrieval of internal knowledge, customer-facing chat bots and agents, or fraud and identity workflows. It is still early, but we are seeing more of this workloads reach production, such as the Financial Times, which leverages us to power AI-driven discovery, reaching millions of readers with interactive experiences at scale.

With vector search and Voyage, the Financial Times now unifies their operational data and vector embeddings on a single platform, building a hybrid full-text and semantic search solution, eliminating the complexity of syncing separate systems, and accelerating time to production. By indexing content with the high-accuracy Voyage-4 model and serving over 100,000 daily queries on the cost-efficient Voyage-4-lite model, the Financial Times has significantly cut retrieval costs with minimal performance impact.

What used to take weeks of manual index monitoring is now finished in a day. Moving on to the momentum we are seeing with frontier labs who are both customers and partners for us.

Multiple leading labs leverage Atlas for workloads that are mission critical to how they ship their products. One lab uses us for inference and chat workloads after moving away from Postgres due to performance lags and outages affecting user experience.

They migrated their chat memory system onto Atlas in just four weeks and now run at 10x faster writes than Postgres. Beyond that, labs use us for research workloads to store experimental results, evaluation data, and training artifacts for model development.

These relationships are still early, and engagement varies lab by lab, but we are energized by the traction we are seeing with them. As partners with these frontier labs, we are enabling the developers and agents building on their platforms to leverage Atlas.

Just recently, we launched a fully managed MCP server, making it easier for developers and agents to connect directly to MongoDB when they are using Claude Code, Codex, and Grok Build, as well as popular coding tools like Cursor and Devin from Cognition. This is how we stay embedded in the AI supply chain for how new applications get built.

Paul Smith, Chief Commercial Officer at Anthropic, described our technology partnership and recent integration with Claude by noting, "The best AI applications need a strong database, which is why we have long pointed to developers building on Claude to MongoDB Voyage for embeddings. More recently, demand from those developers drove MongoDB to build a new managed MCP server, which has seen fast adoption since launch, and now lets developers explore, query, and manage their MongoDB data without ever leaving Claude."

The final piece of the AI opportunity is AI natives, companies whose data layer determines whether the product can support rapid scale. Some choose us from day one; others start elsewhere, like prompt-driven development platforms, and migrate to us as they hit scaling limits and real usage arrives.

That pattern is showing up in the numbers. We added a record 2,900 net new customers this quarter, and many of them are AI natives.

Fireflies, a unicorn AI-native startup, is building what it calls the number one AI assistant for work, helping people unlock the knowledge buried in their conversations. Fireflies serves more than 20 million users across a million-plus organizations, and has processed over 7 billion meeting minutes.

Fireflies chose Atlas from day one for its flexible document model over a rigid, relational schema, and today runs more than 40 microservices with Change Streams powering real-time pipelines for analytics and growth intelligence. That lean, scalable foundation has helped fuel their hyper-growth seamlessly.

We are also seeing strong traction with Voyage, our embedding and reranking models, which consistently rank at the top of independent leader boards. In August, we brought automated Voyage embeddings to Atlas for one-click vector search setup, launched Voyage Code 4, a model purpose-built for code, and shipped an upgraded reranking API, all keeping Atlas retrieval accuracy for AI ahead of the market.

Voyage traction is showing up on both ends of the market. Some of our largest existing Atlas customers are beginning to adopt Voyage for AI use cases, while a large majority of new Voyage customers are AI natives and have no prior relationship to MongoDB.

Eve is one of them, a unicorn AI native that automates legal case intake, medical chronologies, and demand letter drafting for plaintiff law firms. Eve uses Atlas embedding and reranking API powered by Voyage AI's rerank-2.5 to surface the most relevant evidence from large sets of case documents.

This improves retrieval quality directly into Eve's RAG layer, while simplifying the infrastructure needed to build and evolve these AI experiences. Turning to Enterprise Advanced.

This quarter's strength was widespread across our install base, particularly within financial services, tech, and the public sector. Two patterns in how customers are using EA stand out, and both point to why this business is strategic for us.

The first is AI in governed, self-managed environments. This quarter, we brought search and vector search to EA, closing a gap between our cloud and self-managed experiences.

Demand came in immediately and across industries from customers looking to take a consolidated approach to building AI in their own governed, self-managed environments. A major US bank shows what that looks like in practice.

EA already serves as the standardized data platform for more than 100 production applications across payments, fraud detections, document processing, customer and account services. This quarter, that bank extended that same environment to GenAI and semantic search for employee advisors, chat bots, product search, and document intelligence.

By bringing operational data, search, and vector retrieval together self-managed with EA, they keep sensitive customer and conversational data inside their own governed environment without standing up separate systems. That gives them a practical foundation to expand AI across the bank on the same platform already running their most critical operations.

The second is hybrid deployment. More and more of my customer conversations involve running across multiple clouds and self-managed environments at the same time.

For customers on both EA and Atlas, it is an "and," not an "or." For example, one of the largest cybersecurity companies runs a substantial estate across both Atlas and EA, and both parts grew meaningfully in the quarter.

Nationwide UK, the world's largest building society, is also a good example. They now run their growing speed layer application across both EA and Atlas simultaneously, giving members real-time access to account and transaction data across every digital channel, and supporting more than 24 million weekly app logins.

Splitting that workload across EA and Atlas gives Nationwide stronger operational resilience, and helps satisfy UK regulatory requirements while simplifying an estate that used to be far more fragmented. Nationwide already runs with us for faster payments, up to 3 million transactions and £1.5 billion in value on a peak day on a self-managed dual cloud EA cluster.

Bringing AI self-managed opens net new demand for us, and hybrid deployment often means that the strong EA estate opens the door to net new Atlas conversations within the same customers. EA's profitability also lets us invest more heavily in R&D and go-to-market, furthering Atlas growth and our AI roadmap.

Finally, I feel great about the leadership team driving innovation across both Atlas and EA. Ben Scofield owns core products, and Pablo Stern Plaza owns AI and emerging products.

On the go-to-market side, Ryan McBean has hit the ground running as our new CRO, giving me real confidence in our ability to capture the opportunity ahead. Before I close, I would like to remind everyone that we will be hosting our Investor Day in New York City on September 29th and our .local New York user event on September 30th.

We look forward to seeing many of you there. With that, I will turn over to Mike.

Thank you, CJ. Good afternoon, everyone.

I will walk through the second quarter fiscal '27 results, and then turn to our outlook for the third quarter and the balance of the fiscal year. As always, I will be discussing both GAAP and non-GAAP results.

As CJ noted, we had another very strong quarter and came in above all of our guidance ranges. Given this performance and the strong momentum across the business, we are rolling the beat from Q2 and raising our second half fiscal '27 guidance largely driven by strength in Atlas.

Before getting into the details, I want to highlight a few key takeaways for the quarter. First, total revenue growth accelerated to 30%, the first time we've reached that level since fiscal '24.

Second, this is the fifth consecutive quarter with Atlas growth of approximately 29%. Third, EA and other had an exceptional quarter, growing 36% year-over-year, driven by EA's growing strategic importance to many of our largest customers and early traction from our Q2 launch of search and vector search on EA.

Finally, as a result of these trends, we significantly outperformed our operating margin and EPS guidance, reflecting the strength of our operating model. Moving on to the results.

Total revenue in the second quarter was $772 million, representing 30% year-over-year growth, compared to 24% growth in the year-ago quarter. Turning to our product breakdown.

Atlas revenue grew approximately 29% year-over-year and exceeded our guidance by approximately 300 basis points. Consumption was strong, resulting in a third straight beat consistent with our guidance framework.

This is the sixth straight quarter of year-over-year dollar growth in Atlas, adding a record $127 million in the quarter. Our main growth driver this quarter continued to be strength in North America and our largest customers, particularly those in the $100,000-plus ARR cohort, consistent with the broader up-market momentum we have discussed in recent quarters.

This continued strength is reflected in our total company net ARR expansion rate, which increased to 122% for the quarter, compared to 119% a year ago and 121% last quarter. The quarter-over-quarter increase in net ARR expansion rate was driven by strength in both Atlas and EA.

We also continue to see momentum in the AI native cohort and across AI signals, including adoption of vector search, new Voyage customers, and a continued increase in clusters connecting through MCP. Turning to EA and other revenue.

We saw very strong results, with revenue growing approximately 30% year-over-year, our strongest quarter in three years. We saw early demand for the search and vector search capabilities we launched on EA in Q2, adding retrieval capabilities that enhance our ability to support AI workloads.

This strength was broad-based, reflecting momentum across a number of deals rather than any single transaction, with particular strength in financial services, public sector, and technology. This continued momentum highlights the strategic importance of EA, as customers continue to expand their self-managed footprints to support both traditional and AI applications.

EA and other ARR, which normalizes for the impact of duration, grew approximately 11% year-over-year, the third consecutive quarter of double-digit ARR growth. Moving down the P&L, total non-GAAP gross margin was 75.9%, up approximately 210 basis points year-over-year, and subscription gross margin was 78.3%, up approximately 70 basis points year-over-year.

The increase in subscription gross margin was primarily driven by the higher EA revenue mix in Q2. Moving to profitability, we are excited that Q2 marks our third consecutive quarter of GAAP EPS profitability, and our full-year guidance incorporates our expectation to be GAAP EPS profitable for fiscal '27.

Non-GAAP income from operations was $186 million for an operating margin of 24%, compared to 15% in the year-ago period. We continue to be very pleased with our operating margin results, which benefited from the strong revenue performance this quarter.

Second quarter non-GAAP net income was $163 million or $1.90 per share, based on 85.8 million fully diluted shares outstanding. This compares to net income of $87 million or $1.00 per share on 87.1 million fully diluted shares outstanding in the year-ago period.

Our remaining performance obligations, which we define as obligations for contracts with a duration greater than 12 months, ended the quarter at $1.52 billion, representing year-over-year growth of 91%, with the current portion growing 73%. We had a very strong quarter for new customers, adding approximately 2,900 customers sequentially, bringing our total customer count to 70,600, up from 59,900 in the year-ago period.

Growth continues to be driven primarily by Atlas, which had 69,300 customers at the end of the second quarter, compared to 58,500 in the year-ago period. Within Atlas, Voyage customers roughly doubled quarter-over-quarter for the second consecutive quarter, continuing the encouraging signs of the demand for our AI embedding capabilities.

We continue to feel good about the momentum we are seeing with new customers and would remind you that this metric will fluctuate from quarter to quarter. We ended the quarter with nearly 3,000 customers with at least $100,000 in ARR, representing 17% year-over-year growth.

Revenue growth from this cohort continues to be strong and outpace total company revenue growth, consistent with our move up market. We also continue to see strong Atlas performance adoption.

Of our Atlas customers generating at least $100,000 in ARR, 48% are leveraging two or more features on our platform, which is up from 42% in the year-ago quarter, driven largely by vector and tech search adoption. Turning to the balance sheet and cash flow.

We ended the second quarter with $2.4 billion in cash, cash equivalents, and short-term investments. During the quarter, we allocated $100 million towards share repurchases and $59 million to settle taxes on employee RSUs.

Operating cash flow was $142 million, compared to $72 million in the year-ago period, and free cash flow was $138 million, compared to $70 million a year ago. We remain committed to driving meaningful and durable cash flow.

Through the first half of fiscal '27, we have generated $344 million in operating cash flow and $335 million in free cash flow. Now, I'd like to share some of the assumptions driving our third quarter outlook and provide some additional detail into how we're thinking about the rest of fiscal '27.

As I mentioned earlier, we continue to be pleased with the strong and consistent Atlas growth. Our growth to date has been driven primarily by continued strength with our largest enterprise customers, and we expect that to continue in the second half of fiscal '27.

Based on this continued momentum, we expect Atlas growth of approximately 26% in Q3, and we are raising our full-year growth expectation to approximately 27%, an increase of 300 basis points from the midpoint of our prior guidance. Our second half guidance raise for total revenue is primarily driven by the strength we are seeing in Atlas.

The strength in Atlas is highlighted by the sixth straight quarter of increasing revenue dollar growth year-over-year, strong net ARR expansion rate, increasing multi-product penetration, and early signs of adoption of AI workloads. We discussed over the last several quarters that as Atlas has gotten larger, it has become more predictable and less sensitive to revenue movements by any individual customer or cohort.

This can be seen in the consistency of the results we have delivered over the last three quarters, where we have seen approximately 200 to 300 basis points of outperformance relative to our initial guidance. We use the same guidance framework for our Q3 outlook, understanding that Q3 is our toughest compare of the year for Atlas.

For EA and other, given the strength we saw in the first half, including the demand we are seeing for the search and vector search capabilities we launched on EA in Q2, we are raising our full-year expectations for EA and other revenue to approximately 11% growth in fiscal '27, up from our prior guidance of mid single-digit growth. This is the first time in three years EA and other is projected to grow at a double-digit rate for the full year.

Our second half guide is consistent with what we shared last quarter. We continue to expect EA and other revenue to be approximately flat in the second half, with growth in the mid single digits in the third quarter.

Because multi-year deals are inherently hard to predict, we will continue to be prudent in how we guide the EA business. We are excited about the growth we are seeing in EA, and would encourage you to focus on the full-year growth rather than any single quarter, since performance will naturally move around period to period.

Turning to profitability. You can see in the first-half fiscal '27 results the leverage in the business model and the ability to drive incremental profitability while still investing in growth initiatives, specifically engineering and product innovation.

We remain committed to driving both revenue growth and improved profitability. We now expect to expand operating margin by approximately 250 basis points in fiscal '27, 100 basis points higher than the high end of our previous range.

We will achieve this expansion while continuing to invest in key growth initiatives across both products and go-to-market. Our product investment remains focused on enhancing our AI and core database capabilities, including on EA, and you will hear more about our new product innovations at our upcoming Investor Day.

On the go-to-market side, we are investing in accelerating adoption of new product innovations, and continuing to focus on our highest growth opportunities by geography and customer segment. We will also continue to invest in quota-carrying headcount, marketing programs, and developer awareness.

On cash flow, given our strong first-half performance, we now expect full-year free cash flow to conversion to be at the upper end of our long-term target range of 80% to 100%. Now, let's shift to how this translates to guidance for the third quarter and fiscal '27.

To reiterate, the second-half raise is being driven mainly by strength in Atlas. For the third quarter, we expect total revenue of $756 million to $761 million, representing 20% to 21% year-over-year growth.

We expect non-GAAP income from operations of $152 million to $156 million for an operating margin of approximately 20.5% at the high end of guidance. We expect non-GAAP net income per share of $1.57 to $1.61 based on 87.1 million diluted shares outstanding.

For fiscal '27, we now expect total revenue of $2.99 billion to $3.03 billion, representing full-year growth of 21% to 23%, which would be the second straight year of total revenue acceleration at the high end of guidance. We expect non-GAAP income from operations of $616 million to $636 million for an operating margin of approximately 21% at the high end of guidance.

With the combination of 23% revenue growth and 21% operating margin, we are targeting a Rule of 44 performance at the high end of our fiscal '27 outlook. We expect non-GAAP net income per share of $6.39 to $6.58 based on 86.4 million diluted shares outstanding.

In closing, I want to thank the entire MongoDB team for another quarter of strong execution. We are pleased with our results, confident in the durability of our growth, and remain focused on driving long-term shareholder value as we continue to invest responsibly in the business.

Last, but not least, we look forward to seeing many of you later this month at our Investor Day. You can find more information on how to register for the live event or listen to the livestream on our IR website.

With that, operator, let's open it up for questions.

Perfect. Thank you.

Congrats on a great quarter. The question I had was on Atlas.

If I listen to your guidance comments, the strength driven by Atlas, what are the factors that you are considering there? What's driving your confidence?

Thank you.

Thanks for the question, Raimo. It's Mike.

As we talked about, we feel very good about the Atlas business. What we look at is this was the fifth straight quarter of approximately 29% year-over-year growth, very consistent.

We've increased the full-year guidance by 300 basis points from the previous guide. That is also buttressed by a record net new $127 million net new Atlas dollars, as well as the increase in the net ARR expansion rate.

Now Atlas is almost a $2.3 billion run rate. As we look forward, we continue to expect really good growth from our larger enterprise customers, especially in the US.

We've started to see some benefit from AI, even though it's small, but we are excited about the momentum. We do expect consumption to continue to be consistent with what we've seen during the first half of the year.

Thank you.

Thank you.

Hey, guys. Thanks for taking the question.

I guess, maybe, CJ, if I look at the business, right, on the first half, clearly Atlas is accelerating, subscription revenue growth is accelerating. It felt like Q2, maybe there was a slight decel on Atlas.

The guide for the rest of the year, particularly Q4, implies a pretty meaningful deceleration in Atlas. I understand conservatism, but if kind of early and rolling down the hill with some of the AI natives and labs, what are some of the dynamics?

Is it possible that EA is flipping some deals to Atlas like happened in Q4 of last year? Or what's kind of the dynamic that maybe we're not seeing?

Okay. Alex, thank you.

Let me address. There were quite a few questions in there.

I would say first, to see consistent 29% growth in Atlas now, as Mike outlined, it is extremely encouraging. That execution, whether it's in the enterprise or with AI native cohort, is overall very encouraging for us.

Like you called out, we have seen the acceleration in the first half compared to what we guided in beginning of March. That's number one.

Number two, I want to be very clear that the growth of EA, self-managed MongoDB, is not coming at expense of Atlas. Atlas actually continues to grow.

We are, Alex, meeting customers where they are. When I originally joined, and I outlined in the first earnings call, is that customers asked us that we want to run for this large, massive workloads that they run on MongoDB EA, "CJ, we want to get this AI ready, and hence, the team should build search and vector search on it," because these kind of workloads, for a variety of reasons, whether it's data sovereignty, whether they don't want to move it to public cloud for other reasons, will run in our self-managed environment.

We did that. We delivered that on June 30th.

We saw that that was received really well in our customer base. As Mike called out, this was a widespread strength on our self-managed MongoDB, and it was not concentrated in a single customer and across industries.

Point number one is that I feel very good about Atlas consumption trends in the first half going into the second half. Number two, EA growth that we are seeing from a self-managed perspective, whether they are running in neo clouds, whether they are running on-prem in their colos, whether some customers run EA in a public cloud in certain regions around the world, feel very good that that is not coming at expense of Atlas.

The couple of examples that I highlighted, we are actually seeing that from operational resilience perspective, some of the large banks or government customers have said that this is a strength of MongoDB data platform versus one over the other from Atlas on EA perspective. Now, in terms of guidance, I'll let Mike comment on it.

We raised the guidance by 300 basis points for the year on Atlas. Where we started in March, and now we are at 27% growth.

We are always going to be prudent about it. For Q4 specifically, it is still in consumption dynamics that is still ways away from our perspective.

We need to see how things play out in the month of September, in month of October, which becomes the baseline, then the holidays are coming in Q4, which does impact our consumption. We are trying to be prudent in how we guide, and I am optimistic on what I am seeing both from the core cohort perspective on Atlas as well as what we are seeing on the AI native side.

Mike, do you want to comment on the guidance on Atlas?

Yeah. Thank you.

Great answer, CJ. I just want to underline what he said, is our guidance philosophy, Alex, has not changed in terms of how we guided the rest of the year will always be prudent more than a quarter out, and that's what's reflected in the guidance.

I also want to address your comment about Q4 and just be super clear on the call. Hey, there were no large bundled deals in the quarter.

There was none of that Q4 dynamic this quarter.

Great. Thanks for taking my question.

CJ, for you, this is the second quarter you've highlighted strong momentum in Voyage customer count. I think you made an interesting comment in the prepared remarks, where a variety of Voyage customers are net new to MongoDB.

Seems like a great top of funnel to win some hyper-growth workloads among AI natives. How would you characterize the success in converting some of those customers to a broader platform sale thus far?

Thanks.

Yeah. Matt, I would approach this in two buckets, okay?

Bucket number one, we are really, really energized by the new customer count for MongoDB that is coming via Voyage. You are absolutely correct, and that's why those remarks were made explicitly, that many of them are actually not MongoDB customers, okay?

That is absolutely true. The acquisition, as was done in February of 2025.

We are only 18 months into the acquisition. Between the Voyage team that is making sure that we are best-in-class embedding model, when you look at external data benchmarks and so on, but most importantly, there are some customers who come in as Voyage customers and they become Atlas customers.

It is still early because we just started making sure that we can now cross-sell, up-sell, whatever the right term you want to use. I see this as a massive opportunity for Atlas long term, that we are getting this Voyage customers.

Almost always, when I look at the names of the kind of customers we are getting, whether they are in San Francisco Bay Area, whether they are large enterprise, whether they are in London or Tel Aviv or Seattle, they tend to be driven by AI workloads. When the team did analysis on where is the referral for Voyage is coming, as you would have imagined, most of this referral is coming via coding agents, number one, Claude, and number two, Codex is driving most of the referral traffic for Voyage.

We have multiple things happening. Coding agents love Voyage.

They're recommending us and we are getting this new customer cohort. Second, is that becomes top of the funnel, like you said, that we will cross-sell, up-sell.

Our alliance team have a plan for Atlas customers. Number three, almost always, these are AI workloads.

Overall, early, but super encouraging.

Great. Maybe I'll direct this one to CJ.

CJ, you said that MongoDB is seeing some early momentum with AI workloads. As all of us try to monitor the timing and magnitude of the pending AI pull-through to Mongo, I'm just wondering if you could elaborate on what kind of AI use cases or workloads have you found have the greatest pull-through to Mongo?

I was intrigued by a comment that Mike made intra-quarter, where he flagged customer-facing enterprise workloads. I know in your prepared remarks, you mentioned customer support use cases, but perhaps you could elaborate a little bit on specific use cases that have the most powerful pull-through so we can all watch for those.

Thank you.

Absolutely. What I have seen, and this is across many conversations, Karl, is I'm just going to first look at the bucket of enterprise, okay?

Enterprises as in whether you want to say Global 2000 or Fortune 500 or Fortune 100. When I look at those, MongoDB was almost always database platform that was used for customer-facing workload, whether it's a insurance claims, healthcare policies, whether it's credit card transactions and getting the past data for the end users.

MongoDB always shines when it is a massive workload that is customer-facing. What I'm seeing is initially, say, you are a wealth manager at a bank, and there are lots and lots of knowledge base articles that you want to vectorize, use our embeddings, and then use as a chat bot for folks that are doing wealth management and talking to clients real time.

That is a one very specific example where a particular large bank is using MongoDB. There are also other examples where because there are lots and lots of documents, employee-facing use cases where knowledge base articles so that employees can leverage, do a search because now search is fully integrated into the operational data, and documents get loaded, and then embeddings make the vectorization better, that would be another large enterprise example where we are seeing use cases.

The clarity that I got was that it was almost always, "Hey, we want to use MongoDB where the scale matters on the agents that we are trying to create for our customer-facing activities," whatever the customer-facing activities are. We are not seeing early traction with, "Hey, I created a co-pilot kind of a thing that appeals to couple of hundred employees."

We are not seeing MongoDB being used, because they're like, "Hey, this is too big. MongoDB, of course, gives us scale and all this other functionality."

That's number one. Karl, the other thing I would say is when you look at AI natives, including I'm going to put frontier labs in there, but you look at the example that I shared, which was on Eve or whether it was Fireflies, which are agents in production, but when you look at these agents in production, we have used ElevenLabs in the past and others, these are millions of agents in production that are doing something that is customer-facing, and they would say, "We want to use MongoDB for scale, performance, and of course, run anywhere."

That's why they are using it. These are the two vectors that I'm seeing.

In enterprises, are agents going into production where it makes sense on Atlas, okay, let's do that. On EA, that I touched on in my prepared remarks, make no mistake, as these regulated industries are trying to get their operational data AI ready was also the reason EA growth was driven across the industry, including tech, where customer says, "Hey, I'm building an AI agent for my tech," whatever technology platform uses MongoDB or technology company, and we saw the growth there.

That would be my overall summary on where we are seeing. I'm going to have Mike comment anything additional if we needed.

No.

Great answer. Thank you.

Appreciate you taking the questions. CJ, I want to focus on Enterprise Advanced.

I think under your tenure, the EA growth profile has definitely been uplifted, while Atlas growth has to your point sustained at a very attractive rate at 29%. Some of the things that we've been hearing from customers is that if Mongo wants these customers to ultimately get to Atlas, right, advancing EA's capabilities with search and vector search and potentially Voyage as well, that's really important.

Do you have a perspective on, one, the timeline on when you get these customers on the new AI features, what the ultimate, if you want to call it an upgrade or a migration to Atlas, what that timing could look like? That's the first part of the EA question.

The second part of the EA question is which cohorts are incrementally using it? You mentioned kind of the large enterprises, the financial institutions, not surprising.

Do you see an opportunity, I think you guys had hinted at neo clouds using EA as well, is there a world where the AI natives start to use EA because maybe under the theme of data sovereignty or some other reason on why they become adopters of EA as well? Those are my two questions on EA.

Thank you very much.

Sounds good. I'm going to up-level this a little bit, Sanjit, and we are a very customer-driven company.

The reason we invested in EA roadmap that we outlined, and it is nice to see it's working out, is that customers said to us, many, many customers even in my early days, that you must invest in EA, and if EA gets to being AI-ready with search, vector search, and so on, they are asking, "Hey, can we also make Voyage available in a self-managed type of an environment?" That was very customer-driven, and we are meeting customers where they are, okay?

That's my number one thing. Second thing, as Mike shared, three quarters of double-digit EA growth gives me optimism that now we have two growth drivers, Atlas and EA, and as I shared, not coming at expense of each other, because that's a very important thing.

Sometimes customer will say, "I need to do this for operational resiliency." Sometimes customer will say, "I don't see this workload moving to Atlas, but given what you are doing, and I'm seeing it on search and vector search being unified, here is a new workload that we want to try it on Atlas."

Our momentum on EA is also driving potential additional use cases that a bank or a public sector organization, a government organization is using Atlas. I'll be very specific.

In the second quarter, we have a customer in public sector who decided to expand their usage of our self-managed MongoDB as in EA, but in addition, we are currently working with them, because they see some benefits of MongoDB codebase, and they're like, "CJ, if you guys are going to manage it and provide security patches and all other things," they currently have a pilot for Atlas in the government cloud that they are running. From my standpoint, having now two growth drivers on behalf of MongoDB Corporation for both Atlas and EA is very, very encouraging.

Now, in terms of the timeline, one thing is that when we introduced this functionality for search and vector search, which was driven by the AI demand, we are charging our customers extra for that feature set that we are providing in EA, so that's number one. Number two, in terms of time to value, Sanjit, I would say the time to value is pretty fast.

It's not months, but it's weeks on the way we have released these features for our customers. It's just that they are self-managing versus when we manage in Atlas.

Like what we saw on my Financial Times use case that I shared, that the time to value for them to leverage vector search and embedding was in weeks, not in months and years, to get AI ready for searches and others that happen. That would be my overall perspective.

Then the last thing, I would say is that specifically in banking and healthcare, what I'm also seeing is, "Hey, CJ, we are going to use Atlas, but we are going to potentially failover to EA because of op resiliency that you guys provide, which is definitely world-class and our big advantage." That's the summary that I see this as a durable growth driver for MongoDB Corporation.

It is driven based on customer demand, and the customer demand is, "We want to run anywhere. Sometimes we'll self-manage; sometimes it's MongoDB-managed."

Hey, thanks for taking the question. Two-part question here.

First one for CJ. Are you seeing customers come back to you ahead of their scheduled renewal and renew at a higher rate compared to a year ago?

Are they truing up sooner or are they seeing consumption trends improve more strongly? Then for Mike, I know your guidance philosophy hasn't changed, but given less history with quarterly Atlas guides, getting some questions on the implied 4Q Atlas guide.

Could you just help us with some inputs into that guide and how we should think about it as investors? Thanks.

The first thing, as Mike outlined, our NRR was very strong and high, and that was true across both Atlas and EA. In terms of retention rate, and what I'm seeing even the dynamics on, hey, a customer may want to optimize the workload with our customer success teams and all that, the trends are very healthy and improving, which is a great testimonial to our 8.0 release last year, that customers feel very good about price performance and how their consumption is growing.

Now, there are some customers who have outlined to me, the large ones, that, "Our consumption is growing faster than we thought it would, and CJ, can we have a conversation on if we continue to grow at this rate, should we re-look at the contract?" That's not happening a lot.

This is onesies and twosies, maximum single digits, but not widespread. That's encouraging, meaning we are not getting the pushback as the consumption increases, that we want to renegotiate the contract or the commits, and so on.

That's how I would answer that. In terms of, I will state what I stated before, which was Alex's question, is I feel very good about Atlas business, the durability of that business, the innovation we are driving.

Of course, we're not going to guide based on Mike's framework on how we're going to guide for Q3. We are always going to be prudent about Q4, but we raised, that's why, 27% guide for the year on Atlas.

Mike?

Yep. Thank you, CJ.

Ryan, to that point, the guidance methodology and philosophy has stayed consistent all year, and we want to stay with that, which is when we guide for the current quarter, I will call it, or the first quarter out, we always want to stay within that, "Hey, you should look at that 200 to 300 basis point range we provided." Again, hopefully, consumption comes in better, we finish at the upper end of that.

We will always, Ryan, be prudent on the out quarters. It is a consumption business.

I know it only seems like not that long, one more quarter out, but we want to be prudent. Hopefully, then we execute well in Q3 and we're able to increase that guide when we get to Q4.

Thanks very much for taking the question. CJ, the question for you is really about sort of attach rates on Voyage and Vector, and I'm kind of curious when you land a new customer with these products, are they coming in and experimenting first and then scaling quickly?

I'm just kind of curious, obviously landing a new customer with them is great, but getting them to scale and getting the ARR to be more meaningful from a total company perspective is where you want to go. I'm just kind of curious how fast those products can go from something that's maybe piloted in the department to being thought of as strategic or company-wide as something like Atlas or EA.

Thanks.

Of course. I will touch on both.

Atlas is completely consumption driven as you're fully well aware. When I see some of the large customers, like these are a few Fortune 100 customers, they did all their testing.

They're like, "Okay, we do search in this other siloed system or we were trying to do vector search from some early-stage startup that provides a vector functionality." This large bank told me that based on their testing, they believe that vector should be integrated fully in the operational data layer, and MongoDB doing that was seen as a huge advantage.

The time to value there was few weeks, and then we would have a dedicated search node and so on, which drives the consumption. Even a large media company, which became one of our biggest vector search customer, that was driven by an agent trying to do the semantic query and figuring it out, okay, if this is an operational data layer, then it just works.

We are seeing that even in AI-native cohort, that vector being part of the database is received really, really well. One of the examples I shared last quarter, ElevenLabs, which continues to scale nicely with MongoDB, they see that as a huge advantage of vector being embedded.

We are doing the same thing now in EA. Now, on embeddings, we are making it easier, like I shared in my remarks, to make sure that we have auto-embeddings in Atlas, how it works, how does it work in the cloud, and that time, what I am right now in all the customer conversations seeing, is that still the awareness is low that Voyage is actually coming from MongoDB.

This customer told me, "Oh, we love Voyage. We are using Voyage."

I said, "You know that is a MongoDB product," and they are like, "Oh, we did not know that. Then we should now look at Atlas, because you have Atlas auto-embeddings."

It varies, but search, vector search, I would say the time to value is not that long, because the moving pieces as in the moving systems are fewer, and that's why it works.

Thank you. CJ, you talked about some inference workloads at AI labs.

Can you just elaborate, were those new this quarter? How do you see sort of the sizing of those workloads compared to some other kind of large workloads across traditional companies?

Then, Mike, just on EA, clearly big outperformance this quarter. I think you had some of the new capabilities released from a GA perspective in July, I guess what gives you the confidence that there's not even more upside in the second half, given that most of the raise in the second half was Atlas versus EA?

Thanks.

Tyler, we were very specific on comments because we want to be extremely transparent with you. With one of the labs, they started towards the later half of last calendar year with one of the workloads that was running inference on MongoDB and used us as a memory layer.

With that lab, our team, what they saw on the Atlas performance for that specific inference, then they said, "Wow, Atlas is performing really well across reads and writes compared to Postgres," that's what they were using originally. They started then moving, just recently in Q2, few other workloads for inference on Atlas.

We had one inference workload that started last year in November-December timeframe, and then they moved another couple of workloads for inference for their some other products that they have created in, I want to say, this is August, around June-July timeframe. We are seeing they told me straight up, this is their technology team, that Atlas has taken all the pain away from an uptime perspective, performance perspective.

We don't even think about it, and we are now, as we create new products, we want to run inference on it. That's what I would say, that we started with one inference, some of the other workloads, and then we got some additional just in Q2.

Tyler, this is Mike, on your questions on EA. Great question.

Thank you for that. The last three quarters, we've seen ARR growth, as CJ talked about, in double-digit.

We did increase the full-year guide from mid single digit to 11% for the full year. Just like us, hey, the hard part here is estimating the multi-year deals.

We will always be prudent for all of our sake to make sure that we don't lean over until we see those deals land. If the last four quarters are any history, hopefully, some of those do come in as multi-year deals or larger than we expected.

Certainly, we want to make sure that especially for EA, we're prudent on the guide. As CJ talked about, we feel really good about the progress there.

We think it can be and know it can be a durable growth driver. Hopefully, we can do better than we guided.

Hey, guys. Thanks so much for squeezing me in.

I wanted to ask about EA and really around Atlas and the total business, too. Clearly in the prepared remarks, and your answers to all these questions, AI is definitely sounds like it's becoming a driver for the total business.

EA sounds really, really good, too. CJ, I think you mentioned that EA is not coming at the expense of Atlas.

How should we be thinking about just Atlas and EA, and any sort of change ultimately to how Atlas could become, or the revenue mix from Atlas to total revenue over the next three to five years? Thank you.

Hey, Koji, it's Mike. Let me take that.

We will talk more, obviously, as we guide next year. We'll have a financial session at Investor Day.

If you take a look at this year's full-year guidance with Atlas at 27% and EA now at 11%, if Atlas is around 74% now, that certainly should continue to increase as a percent. We do expect EA to be a more durable growth driver.

To that extent, it should continue to increase, probably not at the rate we thought before, because as you talked about, the AI push is both in Atlas and in EA. We feel very good that it is an and, not an or.

We expect Atlas to continue as a percent of total revenue, but certainly EA also contributing much more than we thought when we started the year.

Yeah. Koji, I would say from a technical perspective, this run anywhere, op resiliency, hybrid, multi-cloud, these are different terms that customers use with us.

What we are seeing is specifically, there was a question on neo clouds and others. Yes.

What happens is when somebody wants to run in neo cloud because of the capacity issues in a public cloud that they may have, they're saying, "Hey, can we run EA in that neo cloud," which goes to our run anywhere, and driving demand. We offer database-as-a-service in some of the other neo clouds, which also goes to EA bucket line, and that's why we are very clear that EA doesn't come at expense of Atlas, and just seeing that broad-based strength versus just one particular customer or three or four customers is what very encouraging for these two to be durable growth drivers.

Thank you very much, operator. In summary, we delivered a strong second quarter with broad-based strength across Atlas, EA, and AI workloads.

What's notable is the breadth of the demand: frontier labs, global banks, public sector, fast-scaling startups, some expanding what they already run with us, others coming to us new. We are seeing AI workloads land on MongoDB across all of them.

That's why we raised our outlook for the second half, and why we are confident we can keep expanding operating margin while we invest. MongoDB is emerging as the real-time intelligent data platform of choice, and I have never felt better about our position with our customers.

Thank you very much.