Snowflake Inc.

Good afternoon. Thank you for attending today's Snowflake Q1 fiscal year 24 earnings conference call. My name is Cole, and I'll be your moderator for today's call. All lines will be muted during the presentation portion of the call with an opportunity for questions and answers at the end. If you would like to ask a question, please press star 1 on your telephone keypad. I'd now like to pass the conference over to our host, Jimmy Sexton. Please go ahead.

Good afternoon, and thank you for joining us. us on Snowflake's Q1 fiscal 2024 earnings call. With me in Bozeman, Montana are Frank Slootman, our chairman and chief executive officer, Mike Scarpelli, our chief financial officer, and Christian Kleinerman, our senior vice president of product, who will join us for the Q&A session. During today's call, we will review our financial results for the first quarter fiscal 2024 and discuss our guidance for the second quarter and full year fiscal 2024. During today's call, we will make forward-looking statements, including statements related to the expected performance of our business, future financial results, strategy, products and features, long-term growth, our stock repurchase program, and overall future prospects. These statements are subject to risks and uncertainties, which could cause them to differ materially from actual results. Information concerning those risks is available in our earnings press release distributed after market closed today and in our SEC filings, including our most recently filed Form 10-K for the fiscal year ended January 31, 2023, and the Form 10-Q for the quarter ended April 30, 2023, that we will file with the SEC. We caution you to not place undue reliance on forward-looking statements and undertake no duty or obligation to update any forward-looking statements as a result of new information, future events, or changes in our expectations. We'd also like to point out that on today's call, we will report both GAAP and non-GAAP results. We use these non-GAAP financial measures internally for financial and operational decision-making purposes and as a means to evaluate period-to-period comparisons. Non-GAAP financial measures are presented in addition to and not as a substitute for financial measures calculated in accordance with GAAP. To see the reconciliations of these non-GAAP financial measures, please refer to our earnings press release distributed earlier today and our investor presentation, which are posted at investors.snowflake.com. A replay of today's call will also be posted on the website. With that, I would now like to turn the call over to Frank.

Thanks, Jimmy. Welcome, everybody, listening to today's earnings announcement. Snowflake's product revenue grew 50% in Q1 of fiscal year 2024, totaling $590 million. The net revenue retention rate reached 151% and remaining performance obligations came in at 3.4 billion, up 31% year on year. Non-GAAP adjusted free cash flow was 287 million, up 58% year over year. We are, however, operating in an unsettled demand environment, and we see this reflected in consumption patterns across the board. While enthusiasm for Snowflake is high, enterprises are preoccupied with costs in response to their own uncertainties. We proactively work with customers to optimize their environments. This may well continue near-term, but cycles like this eventually run their course. Our conviction in the long-term opportunity remains unchanged. Degenerative AI, with its chat style of interaction, has captured the imagination of society at large. It will bring disruption, productivity, as well as obsolescence to tasks and entire industries alike. Degenerative AI is powered by data. That's how models train and become progressively more interesting and relevant. Models have primarily been trained with Internet and public data, and we believe enterprises will benefit from customizing this technology with their own data. A Snowflake manages a vast and growing universe of public and proprietary data. The data cloud's role in advancing this trend becomes pronounced. AI's focus on large language models and textual data, both structured and unstructured, will lead to rapid proliferation of model types and specializations. Some models will be broadly capable, but shallow in functions. Others will be deep, specialized, and impactful in their specific realm. For years, we focused on the extensibility of our platform via Snowpark, making Snowflake ideally suited for rapid adoption of new and interesting language models as they become available. AI is also not limited to textual data. Equally far-reaching will be seen with audio, video, and other modalities. The Snowflake mission is to steadily demolish any and all limits to data, users, workloads, applications, and new forms of intelligence. We will therefore continue to see us add, evolve, and expand our functions and feature sets. Our goal is for all the world's data to find its way to Snowflake and not encounter any limitations in terms of use and purpose. From our perspective, machine learning, data science, and AI are workloads that we enable with increased capability, continuous performance, and efficiency improvements. Data has gravitational pull, and given the vast universe of data Snowflake already manages, it's no surprise that interest in these capabilities is escalating while its uses are still evolving. Data science, machine learning, and AI use cases on Snowflake are growing every day. In Q1, more than 1,500 customers leveraged Snowflake for one of these workloads, up 91% year over year. A large U.S. financial institution uses Snowflake for model training. Facing memory constraints with their prior solution, they chose to move feature engineering workloads to Snowflake. With Snowflake, they can fully ingest all data, replacing a sampling approach, which left models less predictive and long-running. Snowflake enables machine learning for a broad spectrum of user types, not just programmers. For analysts, we have introduced and preview ML-powered SQL extensions, such as anomaly detection, top insights, and time series forecasting. SQL proficient users can now leverage powerful machine learning extensions without the need to master the underlying data science. For data scientists and engineers, Snowpark is our platform for programmability. New here is a PyTorch data loader and an MLflow plugin, both in private preview. PyTorch is a popular framework for machine learning, and MLflow helps manage the lifecycle and operations of machine learning. Snowflake had an early start in support of language models through last year's acquisition of APLICA, now in private preview. APLICA's language model solves a real business challenge, understanding unstructured data. Users can turn documents such as invoices or legal contracts into structured properties. These documents are now referenceable for analytics, data science, and AI, something that is quite challenging in today's environment. Streamlit is the framework of choice for data scientists to create applications and experiences for AI and ML. Over 1,500 LLM-powered Streamlit apps have already been built. GPT-Lab is one example. GPT-Lab offers pre-trained AI assistance that can be shared across users. We announced our intent to acquire Neva, a next generation search technology powered by language models. Engaging with data through natural language is becoming popular with advancements in AI. This will enable Snowflake users and application developers to build rich search-enabled and conversational experiences. We believe Neva will increase our opportunity to allow non-technical users to extract value from their data. More broadly, Snowflake continues to enable industries and workloads. In Q1, more than 800 customers engaged with Snowpark for the first time. Approximately 30% of all customers are now using Snowpark on at least a weekly basis, up from 20% at the end of last quarter. Snowpark consumption is up nearly 70% quarter over quarter. The Snowflake connector for ServiceNow is in public preview. Customers can access ServiceNow data inside of the data cloud without needing to manually integrate APIs or third-party tools. ServiceNow data is significant because it holds a wealth of IT and security data. The connector is the first so-called native app built by Snowflake. Native apps, which are in private preview, run inside the Snowflake governance perimeter make use of common services today developers waste time convincing customers to expose their data with native apps developers can focus on their core interests application development they offer security and deployment concerns to snowflake during the quarter we also launched a manufacturing data cloud which focuses on supply chain management as a data problem supply chain management is one of the few remaining realms in enterprise software that have struggled to platform itself. Supply chains are all somewhat unique, and the data siloing problem prevents supply chain visibility essential to managing it. With the manufacturing cloud, Snowflake continues to evolve from being a data cloud to also being an operational hub for large enterprises and institutions. We also announced that Blue Yonder, one of the largest software companies in supply chain management, will fully re-platform onto Snowflake. Blue Yonder is a key participant in both the manufacturing and the retail data clouds. They are the first major supply chain provider to make this commitment to creating the end-to-end supply chain platform on Snowflake. Supply chain management is a network discipline, as the chains are typically comprised of numerous different entities. We therefore expect significant network effects from this strategic alliance with Blue Yonder. Our summit conference in June will feature more significant product announcements, and we look forward to seeing you there. With that, we'll turn the call over to Mike.