몽고DB(MDB) 2027 회계연도 2분기 실적 발표 콜: 매출 30% 증가, 가이던스 상향

핵심 요약

• 몽고DB(MongoDB)는 2027 회계연도 2분기 매출이 전년 동기 대비 30% 증가한 7억 7,200만 달러를 기록해 2024 회계연도 이후 가장 높은 분기 성장률을 나타냈다고 발표했습니다.
• 아틀라스(Atlas) 매출은 5분기 연속 약 29% 성장했습니다. 북미 대기업 및 연간 반복 매출(ARR) 10만 달러 이상 고객에 힘입어 아틀라스는 전년 동기 대비 역대 최대인 1억 2,700만 달러의 매출 증가를 기록했습니다.
• 엔터프라이즈 어드밴스드(EA) 및 기타 부문도 전반적으로 견조한 실적을 거두었습니다. 경영진은 자체 관리형 AI 워크로드를 위한 검색 및 벡터 검색(Vector Search)의 초기 도입을 포함해 금융 서비스, 기술 및 공공 부문의 수요를 강조했습니다.
• 비GAAP 기준 영업이익은 1억 8,600만 달러를 기록해 전년 동기 15%에서 24%로 영업이익률이 상승했습니다. 잉여현금흐름은 1억 3,800만 달러로 약 두 배 증가했습니다.
• 몽고DB는 전분기 대비 역대 최대인 2,900곳의 고객사를 추가하며 총 고객사 7만 600곳으로 이번 분기를 마감했습니다. 보야지(Voyage) 고객 수는 2분기 연속 전분기 대비 약 두 배 증가했습니다.
• 경영진은 2027 회계연도 매출 전망치를 29억 9,000만~30억 3,000만 달러로 상향 조정했으며, 올해 아틀라스의 성장률을 약 27%로 예상하고 있습니다.

핵심 재무 실적

몽고DB는 또한 3분기 연속 GAAP 기준 EPS 흑자를 기록했습니다. 경영진의 연간 전망은 2027 회계연도 내내 GAAP 기준 EPS 흑자를 유지하는 것을 전제로 합니다.

사업 및 운영 실적

아틀라스의 연환산 매출(ARR)은 거의 23억 달러에 달했습니다. 성장은 북미 지역과 ARR 10만 달러 이상을 창출하는 고객군을 중심으로 대기업 고객이 지속적으로 견인했습니다.

몽고DB의 ARR 10만 달러 이상 고객 수는 전년 동기 대비 17% 증가한 약 3,000곳에 달했습니다. 이 그룹의 아틀라스 고객 중 48%가 최소 2개 이상의 플랫폼 기능을 사용했으며, 이는 전년 동기의 42%에서 상승한 수준입니다. 경영진은 이러한 증가의 상당 부분이 벡터 검색 및 텍스트 검색 도입에 기인한 것으로 보았습니다.

AI 관련 수요는 아직 초기 단계이지만 기존 대기업, 프론티어 랩, AI 네이티브 기업 전반으로 확대되었습니다. 경영진은 고객 대응용 에이전트, 내부 지식 검색, 챗봇, 사기 및 신원 확인 워크플로우, 추론, 대화 메모리, 연구 데이터를 주요 활용 사례로 언급했습니다.

보야지 고객 수는 2분기 연속 전분기 대비 약 두 배 증가했습니다. 신규 보야지 고객의 다수는 기존에 몽고DB와 거래 관계가 없던 곳으로, 경영진은 이들이 아틀라스로 유입되는 잠재적 통로(funnel) 역할을 할 것으로 보고 있습니다. 유입은 주로 클로드(Claude)와 코덱스(Codex)를 필두로 한 코딩 에이전트를 통해 이루어졌습니다.

EA는 아틀라스, 프라이빗 인프라, 멀티 클라우드에 걸친 AI 및 하이브리드 배포 시 자체 관리 및 거버넌스 환경을 요구하는 고객들로부터 이점을 얻었습니다. EA 및 기타 ARR은 약 11% 성장하여 3분기 연속 두 자릿수 ARR 성장을 기록했습니다. 경영진은 EA의 성장이 아틀라스를 희생시켜 이루어진 것이 아니라고 강조하며 두 제품을 대체재가 아닌 보완재로 설명했습니다.

회사의 2분기 말 아틀라스 고객 수는 전년 동기 5만 8,500곳에서 증가한 6만 9,300곳을 기록했습니다. 총 고객 수는 5만 9,900곳에서 7만 600곳으로 증가했습니다.

경영진 가이던스

2027 회계연도 아틀라스 성장률 전망치는 이전 전망의 중간값보다 300bp 상향되었습니다. 경영진은 하반기 매출 증가가 주로 아틀라스의 견조한 실적에 의해 견인되었다고 밝혔습니다.

회사는 이제 2027 회계연도 영업이익률이 이전 범위 상단보다 100bp 높은 약 250bp 확대될 것으로 예상하고 있습니다. 가이던스 상단 기준으로 경영진은 23%의 매출 성장률과 21%의 영업이익률, 즉 '44의 법칙(Rule of 44)' 달성을 목표로 하고 있습니다.

연간 잉여현금흐름 전환율은 몽고DB의 장기 목표 범위인 80%~100%의 상단에 도달할 것으로 예상됩니다. 회사는 AI, 핵심 데이터베이스 기능, 제품 엔지니어링, 매출 목표를 부여받는 영업 인력, 마케팅 및 개발자 인지도에 계속 투자할 계획입니다.

위험 요소 및 주시 포인트

• 경영진은 고무적인 도입 신호에도 불구하고 AI의 재무적 기여도는 여전히 미미하다고 밝혔습니다.
• 3분기는 올해 연간 기준 아틀라스의 전년 동기 대비 비교 기저가 가장 높은 분기입니다. 4분기 사용량 역시 연말 연시 계절성과 9월 및 10월에 형성된 사용량 기준선의 영향을 받을 수 있습니다.
• 다년간 계약의 예측이 어렵기 때문에 EA 매출은 분기별로 변동할 수 있습니다. 따라서 경영진은 개별 분기보다는 연간 성장에 집중할 것을 조언했습니다.
• 보야지가 몽고DB의 제품이라는 점에 대한 인지도가 일부 고객 사이에서 여전히 제한적이어서, 아틀라스로의 교차 판매 속도에 영향을 미칠 수 있습니다.
• 아틀라스가 사용량 기반 모델이기 때문에 경영진은 당장 다가오는 분기 이후의 가이던스에 대해 계속해서 신중한 접근 방식을 적용하고 있습니다.

애널리스트 Q&A 하이라이트

아틀라스 전망: 경영진은 5분기 연속 약 29% 성장을 달성한 점, 전년 동기 대비 아틀라스 매출이 역대 최대인 1억 2,700만 달러 증가한 점, 순 ARR 확장률이 상승한 점, 기업의 지속적인 사용량 유지를 자신감의 근거로 제시했습니다. 경영진은 다음 분기 이후의 가이던스는 신중하게 유지하고 있음을 재확인했습니다.

EA 대 아틀라스: 경영진은 더 강력해진 EA 수요가 아틀라스를 대체하고 있다는 시각을 일축했습니다. 고객들은 복원력, 규제 준수, 데이터 주권 및 하이브리드 멀티 클라우드 요구 사항을 충족하기 위해 두 제품을 함께 사용하는 경우가 늘고 있습니다. EA에서의 검색 및 벡터 검색 기능은 기존 고객과의 아틀라스 도입 관련 논의를 새롭게 유발할 수도 있습니다.

AI 워크로드 패턴: 초기 도입 중 가장 강력한 사례는 확장성, 성능 및 실시간 운영 데이터를 필요로 하는 고객 대응용 애플리케이션과 에이전트입니다. 내부 지식 검색 및 임직원용 챗봇도 새롭게 부상하고 있는 반면, 소규모 부서 단위의 실험은 상대적으로 파급력이 적었습니다.

보야지 전환 기회: 일부 보야지 사용자는 이미 아틀라스 고객으로 전환되었으나, 경영진은 보야지 AI(Voyage AI) 인수 후 약 18개월이 지난 현재 교차 판매는 여전히 초기 단계라고 밝혔습니다. 자동화된 임베딩과 아틀라스와의 긴밀한 연동을 통해 구축 시간을 단축할 계획입니다.

프론티어 랩에서의 추론: 몽고DB는 한 연구소가 지난 달력 연도 말에 시작한 추론 워크로드에서 아틀라스의 가동 시간 및 성능 향상을 확인한 후, 2분기 동안 여러 추가 워크로드로 확장했다고 밝혔습니다.

실적 발표 컨퍼런스 콜 전문

전체 실적 발표 컨퍼런스 콜 녹취록

경영진 발표

Operator

Hello, and welcome to MongoDB's Second Quarter Fiscal '27 Earnings Call. [Operator Instructions]

I would now like to hand the conference over to Jess Lubert, Vice President of Investor Relations. You may begin.

Jess Lubert

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 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 assumption growth, the impact of EA and other business and multiyear 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 31, 2026, filed with the SEC on September 1, 2026. 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 thanks, everyone, for joining us today. I am 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 standout 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, health care, 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 a large part of their data estate on MongoDB, building an agent on top of that data is a natural extension because the data and 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, so 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 chatbots and agents or fraud and identity workflows. It is still early, but we are seeing more of these 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 Symantec search solution, eliminating the complexity of sinking separate systems and accelerating time to production. By indexing content with a high accuracy Voyage for model and serving 100,000 daily queries on the cost-efficient voice for light model, the Financial Times has significantly cut attributable cost 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 post guess due to performance legs and outages affecting user experience. They migrated their chat memory system on to Atlas in just 4 weeks and now run at 10x faster REITs than [ post guess ]. Beyond that, labs uses 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 Cloud Code, Codex and [ Grabill ] as well as popular coding tools like Cursor and Devon 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 native. Companies whose data layer determines whether the product can support rapid scale. Some choose us from day 1, others start elsewhere like pro-driven development platforms and migrate to us as they hit scaling limits and real usage arise. 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 start-up is building what it caused the #1 AI assistant for work, helping people unlock the knowledge, but it in their conversations. [ Fireflies ] serves more than 20 million users across 1 million-plus organizations and has processed over 7 billion meeting minutes. [ Fireflies ] choose Atlas from day 1 for its flexible document model over a rigid relational schema and totally runs today runs more than 40 micro services with change streams powering real-time pipelines for analytics and growth intelligence. That lean, scalable foundation has helped their -- fuel their hyper growth seamlessly.

We are also seeing strong traction with Voyage, our embedding and rebanking models, which consistently rank at the top of independent leaderboards. In August, we brought Automated Voyage Embeddings to Atlas for [ 1 click vector ] search setup, launched [ voice Cohort 4 ], a model purpose-built for Core and shipped an upgraded reranking API, all keeping Atlas retrieval accuracy for AI ahead of the market. Voice traction is showing up on both ends of the market. Some of our largest existing Atlas customers are beginning to adopt Voyage for the AI use cases, while a large majority of new voyage customers are AI native and have no prior relationship to MongoDB.

EU is one of them. Our Unicorn AI native that automates legal case intake, medical chronologis and demand letter drafting for plaintiff law firms. [ Eves ] Atlas embedding and reranking API part by Voyage AI [indiscernible] ranked 2.5 to surface the most relevant evidence from large sets of case documents. This improves retrial quality directly into use rag layer while simplifying the infrastructure needed to build and evolve these AI experience.

Turning to Enterprise Advanced. This quarter's strength was widespread across our installed base particularly within financial services, tech and the public sector. 2 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 environment. 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 govern, self-managed environment. A major U.S. 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 [ Symantec Search ] for employee advisers chatbots, product search and document intelligence. By bringing operational data, Search and Vector retrievable together, self-managed with EA they keep sensitive customer and conversational data inside their own government environment without sending up separate systems. That gives them a practical foundation to expand AI and 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 U.K., 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 a nationwide stronger operational resilience and help satisfy U.K. 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 [ GBP 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 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 road map.

Finally, I feel great about the leadership team driving innovation across both Atlas and EA. Ben Cefalo owns core products, and Pablo Stern-Plaza owns AI and emerging products. And on the go-to-market side, Ryan Mac Ban 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 29, and our [ Dorlocal ] New York user event on September 30. We look forward to seeing many of you there.

With that, I will turn it over to Mike.

Michael Berry

Thank you, CJ, and 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. And 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 upmarket 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 3 years. We saw early demand for the Search and Vector Search capabilities we launched on EA Q2, adding retrievable 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 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 outpaced 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 2 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 and 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 multiproduct penetration and early signs of adoption of AI workloads. We discussed over the last several quarters in 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 3 quarters where we have seen approximately 200 to 300 basis points of outperformance relative to our initial guidance. We used 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 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 3 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 multiyear 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 how this translates to guidance for the third quarter and fiscal '27. To reiterate, this 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 the 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 live stream on our IR website.

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

Operator

[Operator Instructions] Our first question comes from the line of Raimo Lenschow with Barclays.

질의응답

Raimo Lenschow

Perfect. Congrats on the great quarter. The question I had was on Atlas. If I look at your -- if I listen to your guidance comments, the strength driven by Atlas. What are the factors that you're considering there? And what's driving your confidence?

Michael Berry

Thanks for the question, Raimo. It's Mike. So as we talked about, we feel very good about the Atlas business. And 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. And 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 and now Atlas is almost a $2.3 billion run rate. So as we look forward, we continue to expect really good growth from our larger enterprise customers, especially in the U.S. We started to see some benefit from AI even though it's small, but we are excited about the momentum and we do expect consumption to continue to be consistent with what we've seen during the first half of the year.

Operator

Our next question comes from the line of Alex Zukin with Wolfe Research.

Aleksandr Zukin

I guess maybe, CJ, if I look at the business, right, on the first half, clearly, Atlas accelerating subscription revenue growth is accelerating but it felt like 2Q, maybe it was a slight decel on Atlas, the guide for the rest of the year, particularly Q4 implies a pretty meaningful deceleration in Atlas. And I understand conservatism, but if we're 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. What's kind of the dynamic that maybe we're not seeing?

Chirantan Desai

Okay. So Alex, thank you. Let me address there are quite a few questions in there. I would say, first, to see consistent 29% growth in Atlas now, as Mike outlined, is extremely encouraging and that execution, whether it's in the enterprise or with AI-native cohort is overall very encouraging for us. And like you called out, we have seen the acceleration in the first half compared to what we guided in the beginning of March. So 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, and 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 these 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 solvent, whether they don't want to move it to public cloud for other reasons, will run in our self-managed environment. So we did that, and we delivered that on June 30, and we saw that, that was received really well in our customer base.

And as Mike called out this was a widespread stregth on our self-managed MongoDB, and it was not concentrated in a single customer and across industries. So 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 colors, whether there are sometimes 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. And a couple of examples that I highlighted, we are actually seeing that from an 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. But we raised the guidance by 300 basis points for the year on Atlas. You know where we started in March, and now we are at 27% growth. We are always going to be prudent about it. And for Q4 specifically, it is still in conduction dynamics that is still weighs 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. So we are trying to be prudent in how we guide and I am optimistic on what I'm seeing, both from the cohort perspective on Atlas as well as what we are seeing on the AI native side? And Mike, do you want to comment on the guidance on Atlas?

Michael Berry

Yes. 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, we'll 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.

Operator

[Operator Instructions] Our next question comes from the line of Matt Martino with Goldman Sachs.

Matthew Martino

CJ, for you, this is the second quarter you've highlighted strong momentum in Voyage customer count. And I think you made an interesting comment in the prepared remarks, where a variety of Voyage customers are net new to MongoDB. It seems like a great funnel to win some hypergrowth workloads among AI natives? How would you characterize the success in converting some of those customers to a broader platform sale thus far?

Chirantan Desai

Yes. So Matt, I would approach this in 2 buckets, okay? Bucket number one, we are really, really energized by the new customer count for MongoDB that is coming via Voyage. And you are absolutely correct, and that's why those remarks were made explicitly that many of them are actually not MongoDB customers, okay? So that is absolutely true. And the acquisition, as you know, was done in February of 2025. So 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, but it is still early because we just started making sure that we can now cross-sell upsell, whatever the right term you want to use. But I see this as a massive opportunity for Atlas long term that we are getting this Voyage customers and 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're large enterprise, whether they are in London or Tel Aviv or Seattle, they tend to be driven by AI workloads. And when the team did analysis on where is the referral for our Voyage is coming, as you would have imagined, most of this referral is coming via coding agents.

Number one, codex, and -- sorry, Claude and #2 Codex is driving most of the referral traffic for Voyage. So we have like multiple things happening. Coding agents [indiscernible] 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, upsell our [indiscernible] team have a plan for Atlas customers, and number three, almost always, these are AI workloads. So overall, early but super encouraging.

Operator

Our next question comes from the line of Karl Keirstead with UBS.

Karl Keirstead

Okay. 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 flag customer-facing enterprise workloads. And 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.

Chirantan Desai

Absolutely. So 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 always almost always database platform that was used for customer-facing workloads, whether it's say, insurance claims, health care 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're a wealth manager at a bank, and there are lots and lots of knowledge-based articles that you want to vectorize, use our embeddings and then use as a chatbot for folks that are doing wealth management and talking to clients real time. That is 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-based 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 embedding make the vectorization better. That will be another large enterprise example where we are seeing use cases.

But 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 [indiscernible] kind of a thing that appeals to a couple of hundred employees we are not seeing MongoDB being used because they are like, hey, this is too big. MongoDB, of course, gives us scale and all these other functionalities. So that's number one.

And 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 EU or whether it was Fireflies which are agents in production. But when you look at these agents in production, we have used 11 labs 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, and that's where you're using it. So these are the 2 vectors that I'm seeing in enterprises are agents going into production where it makes sense on Atlas, okay, let's do that.

And 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 year 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. So that would be my overall summary on where we are seeing, and I'm going to have Mike comment anything additional, if you need it.

Michael Berry

No. Great answer. Thank you.

Operator

Our next question comes from the line of Sanjit Singh with Morgan Stanley.

Sanjit Singh

I appreciate you taking the question. CJ so I wanted to focus on Enterprise Advance? 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 25%. And 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 one on the time line on when you get these customers on the new AI features what the ultimate, if you want to call it, an upgrade or migration to Atlas, what that timing would look like? That's the first part of the EA question.

The second part of the EA question is which cohorts are implemental using it. So you mentioned kind of the large enterprises, the financial institutions [indiscernible] not the pricing. But do you see an opportunity -- I think you guys had hinted that neo cloud using EA as well. Do you -- is there a world where the AI have started to use EA because maybe under a theme of like data sovereignty or some other reason on why they become adopters of EA as well. So that's my few question of EA.

Chirantan Desai

Sounds good. So I'm going to up-level this a little bit, Sanjit. And we are a very customer-driven company. And the reason we invested in EA road map 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 a Voyage available in a self-managed type of an environment. that was very customer-driven, and we are meeting customers where they are, okay? So that's my number one thing.

Second thing, as Mike shared, 3 quarters of double-digit EA growth gives me optimism that now we have 2 growth drivers, Atlas and EA. And as I shared, not coming at expense of each other because that's a very important thing. Sometimes customers will say, I need to do this for operational resiliency. Sometimes customers will say, I don't see this workload moving to Atlas but given what you're 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. So 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 be very specific, in the second quarter, we have a customer in public sector who decided to then expand their usage of our self-managed MongoDB as an EA. But in addition, we are currently working with them because they see some benefits of MongoDB core base. 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."

So from my standpoint, having now 2 growth drivers on behalf of MongoDB Corporation for both Atlas and EA is very, very encouraging. Now in terms of the time line, 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 like months, but it's weeks on the way we have release 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. So that would be my overall perspective.

And then the last thing I would say is that specifically in banking and health care, what I'm also seeing is, "Hey, CJ, we are going to use Atlas, but we are going to potentially fail over to EA because of our resiliency that you guys provide, which is definitely world-class and our big advantage." So 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.

Operator

Our next question comes from the line of Ryan MacWilliams with Wells Fargo.

Ryan MacWilliams

2-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? Like are they truing up sooner? Or are they seeing the consumption trends improve more strongly?

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

Chirantan Desai

Yes. So the first thing, as Mike outlined, our NRR was very strong and high, and that was true across both Atlas and EA. So 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 relook at the contract. But that's not happening a lot. This is like onesies and twosies, maximum single digits but not widespread. So 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. So that's how I would answer that.

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

Michael Berry

Yes. So thank you, CJ. So Ryan, to that point, we've -- 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. And 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.

Operator

Our next question comes from the line of Kirk Materne with Evercore ISI.

S. Kirk Materne

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 does 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.

Chirantan Desai

Yes, of course. So I'll touch on both. Atlas is completely consumption driven, as you are fully well aware. So when I see some of the large customers like these are a few Fortune 100 customers, the ask, they did all their testing. They're like, okay, we do search in this other siloed system or we are trying to do Vector Search from some -- our [indiscernible] start-up 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. And 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 cementer query and figuring it out, okay, if this is an operational data layer, then it just works. And we are seeing that even in AI-native cohort, that vector being part of the database is received really, really well. And one of the examples I shared last quarter, 11 labs, which continues to scale nicely with MongoDB, they see that as a huge advantage of vector being embedded. And we are doing the same thing now in EA.

Now on embedding, 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. And this customer told me, "Oh, we love Voyage. We are using Voyage." And I said, "That is a MongoDB product." And they're like, "Oh, we did not know that." Okay, then we should now look at Atlas because you have Atlas Auto embeddings. So 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.

Operator

Thank you. Ladies and gentlemen, due to the interest of time, we'll take 2 more questions. Our next question will come from the line of Tyler Radke with Citi.

Tyler Radke

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? And then Mike, just on EA clearly, a big outperformance this quarter. I think you had some of the new capabilities released from a GA perspective in July. So 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 for CA?

Chirantan Desai

Yes. So Tyler, we were very specific on comments because we want to be extremely transparent with you. So with one of the labs, they started towards the later half of last calendar year with one of the workloads that was running in France 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 rights compared to post [indiscernible] that's what they were using originally. They started then moving just recently in Q2, a few other workloads for inference on Atlas.

So we had one inference workload that started last year in November, December time frame. And then they moved another couple of workloads for inference or there's some other products that they have created. And I want to say this is August or around June, July time frame. So we are seeing -- they told me like straight up. This is the 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. So that's what I would say that we started with one in France, some of the other workloads, and then we got some additional just in Q2.

Michael Berry

And Tyler, this is Mike. On your question on EA, great question. Thank you for that. So the last 3 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, the hard part here is estimating the multiyear 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 4 quarters are any history, hopefully, some of those do come in as multiyear yields or larger than we expected. So certainly, we want to make sure that especially for EA we're prudent on the guide. But as CJ talked about, we feel really good about the progress there. We think it can be and now it can be a durable growth driver. Hopefully, we can do better than we guided.

Operator

Our last question comes from the line of Koji Ikeda with Bank of America.

Koji Ikeda

I wanted to ask about EA and really around Atlas in the total business, too. And so 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. And EA sounds really, really good, too. And CJ, I think you mentioned that EA is not coming at the expense of Atlas, but 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 3 to 5 years?

Michael Berry

Koji, it's Mike. So let me take that. So 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 but we do expect EA to be a more durable growth driver. So 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, and we feel very good that it is an and, not an or. So 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.

Chirantan Desai

And Koji, I would say from a technical perspective, this run anywhere of resilience, hybrid multi-cloud, these are different terms that customers use with us. And what we are seeing is like 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 are saying, "Hey, can we run EA in that Neo Cloud, which goes to our run anywhere and driving demand." We offer database as 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, all 3 or 4 customers is what very encouraging for these to be durable growth drivers.

Operator

Ladies and gentlemen, at this time, I would like to turn the call back to management for closing remarks.

Chirantan Desai

Thank you very much, operator. So 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 start-ups, 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.

Operator

Ladies and gentlemen, that concludes today's conference call. Thank you for your participation. You may now disconnect.