Snowflake Inc.

Good evening, and thank you for attending today's Snowflake Q1 fiscal 2023 conference call. My name is Don Yeo, and I will 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 would now like to pass the conference over to our host, Jimmy Sexton.

Jimmy, please. Good afternoon, and thank you for joining us on Snowflake's Q1 fiscal 2023 earnings call. With me in Bozeman, Montana, are Frank Sloodman, our Chairman and Chief Executive Officer, Mike Scarpelli, our Chief Financial Officer, and Christian Kleinemann, 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 of fiscal 2023 and discuss our guidance for the second quarter and full year fiscal 2023. 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, 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 31st, 2022, and the Form 10-Q for the quarter ended April 22nd, 2022, that we will file with the SEC. We caution you to not lose any 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 Augustine's 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, and good afternoon, everybody. Product revenue grew 84% year-on-year to $394 million. Remaining performance obligations grew 82% year-on-year to $2.6 billion. And in the quarter, we added 16 global 2,000 customers. We closed the quarter with a record $181 million of non-GAAP adjusted free cash flow, pairing high growth with improving unit economics and operational efficiency. Historically, enterprises have struggled to report yesterday's news in a timely manner. Data had to be transformed and transported to systems that produced reports and dashboards that made sense out of transactional and operational data. There wasn't enough time and money in the day to ingest ever larger volumes of data, and perform long-running processes to make the data consumable and analytics ready. The public cloud infrastructure, coupled with cloud-native data platforms like Snowflake, broke the back of these laboring systems with tremendous scale, performance, and economics. While these have been incredible advances, they are only scratching the surface of what is possible. Goals are evolving to the full-scale mobilization of data in the service of the enterprise mission. Data can do a lot more than authoritatively report what happened historically. We sometimes refer to our innovation of traditional data warehousing workloads at the end of the beginning because so much more is possible. Data science can discover and describe data relationships and therefore also predict them and optimize courses of action. To enable machine learning and data science workloads, Snowflake has been investing for years now in the tooling for these workload types. This includes a wide variety of data programmability options, as well as the Snowpark language runtime capabilities in support of Java, Scala, and Python. More generally, the Snowflake strategic focus is to enable every single workload type that needs to access governed data. The core idea behind the Snowflake data cloud is to enable work to come to the data and stop bringing data to the work. Prior to the data clouds, data was copied, transferred, and replicated to be used wherever it was needed. That has led to rivers of data moving 24-7, causing operational complexity, costs, and governance risks. The Snowflake data clouds holds the promise to bring that undesirable legacy to an end. Data stays put on the data cloud, and our workload enablement ensures that customers can have their needs met in terms of data engineering, data warehousing, data lake, data science, data analytics, and data application development. We recently announced several product development milestones. Our acquisition of Streamlit closed. With Streamlit, we are enabling developers to build apps using their favorite tools and with simplified data access and governance. We're making great progress on our integration plans. Support for unstructured data. is now generally available. Examples include securely sharing PDF documents in Snowflake Data Marketplace and storing medical images to extract data from them. We also entered a public preview for programming of unstructured data from within Snowpark. Snowflake Data Marketplace monetization is now in public preview. This allows companies to easily publish a variety of data sets that then become available for purchase by other Snowflake users. Our summit conference in June will feature our most significant product announcements in four years. We look forward to discussing more innovations with you then. Use cases are often industry-specific applications of Snowflake. In the quarter, we announced the introduction of our Healthcare and Life Sciences Data Cloud and the Retail Data Cloud. The Healthcare and Life Sciences Data Cloud helps customers deliver improved patient outcomes and accelerate clinical research and time to market. One of our pharmaceutical customers estimates that using Snowflake will improve their time to market for a new drug by three years. The retail data cloud empowers retailers, manufacturers, and consumer packaged goods vendors to access new data and seamlessly collaborate across the retail industry. Customers such as 8451 and Albertsons are leveraging Snowflake to optimize operations for businesses across the sector. We also enable new use cases through partner enablements. We recently announced new partnerships with Dell Technologies and Pure Storage. With Dell, joint customers will be able to use on-premise data stored on Dell object storage with the Snowflake data cloud while keeping their data local or seamlessly copying it to public clouds. With Pure Storage, joint customers will be able to work with data stored locally on Pure Storage FlashBlade. As we enable more workload types and use cases, the opportunity for data sharing grows. In Q1, the number of stable edges grew 122% year on year. 20% of our growing customer base has at least one stable edge, up from 15% a year ago. Snowflakes data marketplace listings grew 22% quarter over quarter, now with more than 1,350 data listings from over 260 providers. Our Snowflake Data Marketplace fuels our rich application development ecosystem and Powered by Snowflake program. Today, there are over 425 Powered by Snowflake partners, representing 48% quarter-over-quarter growth. Over the past three years, we have achieved high growth while greatly improving unit economics, operating efficiency, and cash flow. The company has a fortified balance sheet with $5 billion plus in cash, cash equivalents, and investments in zero debt. We have the ability to continue to grow at scale, generate cash, and invest accordingly. We will continue to do so. Snowflake is not a growth at all cost company, and we only invest with defined expectations in terms of return and business impact. Research and development investments must lead to innovation and differentiation. Sales and marketing investments must lead to productive growth. And G&A investment is focused on system and process efficiency. Our strategic focus on continued growth informs all of our investments, coupled with improving free cash flow generation. With that, I will now turn the call over to Mike.