MongoDB, Inc.

Good day, and thank you for standing by. Welcome to the MongoDB second quarter fiscal year 2024 earnings conference call. At this time, all participants are in a listen-only mode. After the speaker's presentation, there will be a question and answer session. To ask a question during the session, you will need to press star 11 on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press star 11 again. Please be advised that today's conference is being recorded. I would now like to turn the conference over to your speaker for today, Mr. Brian Dinu. Please go ahead, sir. The floor is yours.

Thank you, Lisa. Good afternoon, and thank you for joining us today to review MongoDB's second quarter fiscal 2024 financial results, which we announced in our press release issued after the close of the market today. Joining me on the call today are Dave Itacheria, President and CEO of MongoDB, and Michael Gordon, MongoDB's COO and CFO. During this call, we will make four looking statements, including statements related to our market and future growth opportunities, the benefits of our product platform, our competitive landscape, customer behaviors, our financial guidance, and our planned investments. These statements are subject to a variety of risks and uncertainties, including the results of operations and financial conditions that could cause actual results to differ materially from our expectations. For a discussion of the material risks and uncertainties that could affect our actual results, Please refer to the risk described in our quarterly report on Form 10-Q for the quarter ended April 30th, 2023, filed with the SEC on June 2nd, 2023. Any forward-looking statements made in this call reflect our views only as of today, and we undertake no obligation to update them except as required by law. Additionally, we will discuss non-GAAP financial measures in 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 measure. With that, I'd like to turn the call over to Dave.

Thank you, Brian, and thank you to everyone for joining us today. I am pleased to report that we had another exceptional quarter as we continue to execute well despite challenging market conditions. I will start by reviewing our second quarter results before giving you a broader company update. We generated revenue of $424 million, a 40% year-over-year increase and above the high end of our guidance. Atlas revenue grew 38% year-over-year, representing 63% of revenue, and is now a $1 billion plus revenue run rate product. We generated non-GAAP operating income of $79 million for a record 19% non-GAAP operating margin. And we had another solid quarter of customer growth, ending the quarter with over 45,000 customers. Overall, we delivered an exceptional Q2. We had a healthy quarter of new business acquisition, led by continued strength in new workload acquisition within our existing customers. From a new logo perspective, we added 1,900 new customers in the quarter. Our direct sales team had another strong quarter of enterprise customer additions. Finally, our enterprise advanced and other non-Atlas business significantly exceeded our expectations, another indication of our strong product market fit and the appeal of our run anywhere strategy. Moving on to Atlas consumption trends, the quarter played out slightly better than our expectations. Michael will discuss consumption trends in more detail. Finally, retention rates remain strong in Q2, reinforcing the mission criticality of our platform, even in a difficult spending environment. As we've told you in the past, our market is different from most other software markets because the unit of competition is a workload, not a customer. We start a customer relationship by acquiring the first workload, and we grow from there, acquiring incremental workloads over time. Over the last few years, we have oriented our entire company around winning more workloads. Starting with product, at our New York user conference held in June, we made a number of product announcements that will position us to capture more workloads faster. We introduced Atlas Stream Processing, which enables developers to work with streaming data to build sophisticated event-driven applications. The flexibility of the document model and the power of the MongoDB query language provide a compelling and differentiated way to process streaming data compared to alternative approaches. Our early access program is meaningfully oversubscribed as customers realize they can use a familiar and easy approach to work with streaming data and immediately see value. We announced the general availability of Relational Migrator, which makes it easier for customers to migrate their existing relational applications to MongoDB. We are seeing increased adoption across industries and geographies. For example, a leading international retailer was able to leverage Relational Migrator to dramatically accelerate their migration off Oracle. We also announced Atlas Vector Search, which enables developers to store, index, and query vector embeddings. Instead of having to bolt on vector search functionality separately, adding yet another point solution and creating a more fragmented developer experience, developers can aggregate and process the vectorized data they need to build AI applications, while also using MongoDB to aggregate and process data and metadata. We're seeing significant interest in our vector search offering from large and sophisticated enterprise customers. even though it's still only in preview. As one example, a large global management consulting firm is using Atlas Vector Search for an internal research application that allows consultants to semantically search over 1.5 million expert interview transcripts. Over time, AI functionality will make developers more productive to the use of code generation and code assist tools that enable them to build more applications faster. Developers will also be able to enrich applications with compelling AI experiences by enabling integration with either proprietary or open source large language models to deliver more impact. Now, instead of data being used only by data scientists to drive insights, data can be used by developers to build smarter applications that truly transform a business. These AI applications will be exceptionally demanding requiring a truly modern operational data platform like MongoDB. In fact, we believe MongoDB has even stronger competitive advantage in the world of AI. First, the document model's inherent flexibility and versatility renders it a natural fit for AI applications. Developers can easily manage and process various data types all in one place. Second, AI applications require high performance, parallel computations, and the ability to scale data processing on an ever-growing base of data. MongoDB supports its features with features like sharding and auto-scaling. Lastly, it is important to remember that AI applications have the same demands as any other type of application. Transactional guarantees, security and privacy requirements, text search, in-app analytics, and more. Our developer data platform gives developers a unified solution to build smarter AI applications. We are seeing these applications developed across a wide variety of customer types and use cases. For example, Observe.ai is an AI startup that leverages 40 billion parameter LLM to provide customers with intelligence and coaching that maximize performance of their frontline support and sales teams. Observe.ai processes and run models on millions of support touchpoints daily to generate insights for their customers. Most of this rich unstructured data is stored in MongoDB. Observe.ai chose to build on MongoDB because we enabled them to quickly innovate, scale to handle large and unpredictable workloads, and meet their security requirements of their largest enterprise customers. On the other end of the spectrum is one of the leading industrial equipment suppliers in North America. This company relies on Atlas and Atlas Device Sync to deploy AI models at the edge. To their field teams, mobile devices to better manage and predict inventory in areas with poor physical network connectivity They chose MongoDB because of our ability to efficiently handle large quantities of distributed data and to seamlessly integrate between network edge and their backend systems. As much as we innovate our products, we also continuously innovate on how we engage with our customers. We are highly focused on reducing friction in the sales process so we can acquire more workloads quickly and cost-effectively, given the large size of our market opportunity. Historically, the most significant source of friction has been negotiating with customers to secure an upfront Atlas commitment, since it can be hard for customers to forecast consumption growth for a new workload. Given our high retention rates and the underlying consumption growth, several years ago, we began reducing the importance of upfront commitments in our go-to-market process to accelerate workload acquisition. This year, we took additional steps in that direction. For example, we no longer incentivize reps to sign customers to one-year commitments. Obviously, this has short-term impacts on our cash flow but positions us better for the longer term by accelerating workload acquisition. We are pleased with the impact these changes have had in the business in the first half of the year. Specifically, new workload acquisition has accelerated, especially within existing customers. We believe that our efforts to reduce friction are resulting in more efficient growth, and we'll always look for ways to improve our go-to-market approach to make it even easier for customers to bring new workloads onto our platforms. Now, I'd like to spend a few minutes reviewing the adoption trends of MongoDB across our customer base. Customers across industries, including Renault, Hootsuite, and Ford, are running mission-critical projects in MongoDB Atlas, leveraging the full power of our developer data platform. One of the 2023 MongoDB North American Innovation Award winners is Ford. With a focus on innovation, quality, and customer satisfaction, Ford is a leader in the automotive industry and a household name around the world. Ford is committed to developing advanced technologies that enhance the safety, performance, and sustainability of its vehicles. Their Data Explorer and Transportation Mobility Cloud applications aggregate customer vehicle data from 24 different sources at a volume ranging up to 15 terabytes. Since migrating to MongoDB Atlas from their previous solution, Ford has seen a 50% performance improvement and faster rewrite times. Cathay Pacific, Foot Locker and Market Access are examples of customers turning to MongoDB to free up the developer's time for innovation while achieving significant cost savings. Cathay Pacific, Hong Kong's home airline carrier, operating in more than 60 destinations worldwide, turned to MongoDB on their journey to become one of the first airlines to create a truly paperless flight deck. Flight Folder, their application built on MongoDB, consolidates dozens of different information sources into one place and includes a digital refueling feature that helps crews become much more efficient with fueling strategies, saving significant flight time and costs. Since the flight folder launch, Cathay Pacific has completed more than 340,000 flights with full digital integration in the flight deck. In addition to the greatly improved flight crew experience, flight times have been reduced and the digital refueling has saved eight minutes on the ground on average. All these efficiencies have helped the company avoid the release of 15,000 tons of carbon and save an estimated $12.5 million. PowerLedger, Wells Fargo, and System 1 are among customers turning to MongoDB to modernize existing applications. System 1, a customer acquisition marketing company, acquired MapQuest in 2019. At the time of the acquisition, MapQuest had a fragmented architecture that mixed disparate data-persistent technologies with third-party services. System 1 selected Atlas as a key piece of MapQuest's architecture transformation and has realized estimated cost reductions of 75% and performance improvements of 20% over its prior relational database solution. MapQuest is planning a number of future projects that will use Atlas Search and time series collections to improve the user experience and create a feedback loop on location-based relevancy in different cities. In summary, I'm incredibly stoked with our second quarter results. Our ability to win new workloads remains strong, and our Run Anywhere strategy is resonating with customers. While it's early days on AI, we continue to see evidence that MongoDB will be a platform of choice for AI applications, just like we are for other modern and demanding applications. We continue to invest to maximize our long-term potential. With that, here's Michael.