Snowflake (SNOW) Q1 FY2024 Earnings Review

Snowflake’s consumption business continues to feel pressure as large enterprise customers look for ways to optimize usage. While it seemed that management had a handle on forecasting the impact of this effect when they lowered guidance back in Q4, they still underestimated the trend. With the Q1 report, they once again brought down the full year revenue target.

This had the expected effect of torpedoing the stock the day after earnings, with a post-earnings drop of 16.5%. After that, a strange thing happened. The stock began appreciating again and surpassed its pre-earnings price two weeks later. As with other software infrastructure companies, the market’s perception that demand for AI services will drive incremental usage is propping up the stock. Snowflake management added to this momentum with a few key announcements, including a preview of their upcoming Snowflake Summit conference, which will include a fireside chat with Nvidia’s CEO. This all implies that Snowflake is positioning themselves to benefit from increased AI workload demand.

In the near term, Snowflake is being directly impacted by their consumption model, which magnifies changes in customer behavior as they identify ways to reduce costs. Looking forward, the market is anticipating the moderation of these optimization headwinds, as enterprises work through the low hanging fruit of cost savings. At that point, Snowflake’s growth should return to its prior cadence driven by new cloud migration workloads and the expansion of existing ones.

Given that enterprise benefit from AI hinges on access to a consolidated, clean and secure data set, Snowflake is well-positioned to serve as a primary data source. Their positioning is further solidified as the same environment could be leveraged to run jobs that enhance the AI models. This applies to LLMs and other foundation models, as well as more traditional types of machine learning output like recommenders. Snowpark, Steamlit and other extensions that make the environment more programmable started this process. New acquisitions are bolstering the platform’s capabilities as well. Investors are looking towards announcements at the upcoming Summit user conference for more insight into Snowflake’s planned AI capabilities.

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In this post, I review the Q1 results within the context of Snowflake’s current business. I also look forward towards Snowflake’s product development roadmap as they position the platform to address the next wave of data processing use cases to support AI and ML. We will learn more from the Snowflake Summit conference the week of June 26th.

Snowflake Product Roadmap

I have written extensively about Snowflake’s product vision in the past. They aspire to become the single cloud-based data store for all of an enterprise’s data. This involves combining application and analytical workloads into one Data Cloud. While this sounds far-fetched, it is actually how data storage worked from the beginning. Analytics and transactional workloads were separated in the 90’s for performance reasons, when OLAP became its own data processing branch separate from OLTP. Given that compute and data storage capacity were expensive in fixed on-premise data centers, this separation of concerns made sense.

Elastic cloud-based infrastructure introduced the opportunity to consolidate the two functions again. To be clear, Snowflake is not inventing a new technology to efficiently store and query data to serve analytics and transactional workloads in the same process space. Rather, they are recognizing that the cloud allows them to present the same entry point for data analysts and developers. Data is still duplicated in memory and optimized for the type of query pattern the workload requires. On cloud infrastructure, Snowflake can balance these workloads efficiently, providing customers with simplicity and reduction of infrastructure management overhead.

This model provides a strong foundation for a single data store serving multiple processing engines. Looking forward, I think we can expect further platform expansion with the introduction of more workloads. The Snowflake platform has the ability to efficiently store data of all types (structured, semi-structured, unstructured). Their one-to-many access pattern is enabled by the Snowgrid technology layer. Snowgrid works across multiple cloud providers to consolidate the underlying data, manage governance policies, ensure continuity and facilitate sharing with partners.

Snowgrid encompasses many of the competitive advantages that Snowflake’s platform enjoys over similar capabilities from the hyperscalers and independents like Databricks. Those platforms address many of the same core storage/access technologies around a data warehouse/lakehouse model, but are light on the capabilities that facilitate doing anything with the data external to the organization. These include governance, data sharing, native applications and third-party data feeds through a complete marketplace. Additionally, seamlessly spanning data on multiple cloud providers is a fundamental requirement for any global enterprise.

After getting the basic platform down, the next phase of Snowflake’s evolution was grounded in building industry ecosystems around it. If enterprises can consolidate all of their data into a single cloud-based platform, then business application development and data sharing with partners can be greatly simplified and better secured. While Snowflake continues to improve their capabilities in enabling core analytics and data science workloads, future disruption revolves around two primary initiatives:

• Data Collaboration. Enabling secure data sharing between companies, with granular governance and frictionless management layered in. This effort was started in 2018 and has been the catalyst for Snowflake’s growth in data sharing and enabling industry ecosystems for customers. By providing customers with seamless mechanisms to distribute data securely to their industry partners, Snowflake is building strong network effects. These can’t be easily duplicated by competitors who are either on a single cloud (hyperscalers) or offer rudimentary solutions to data sharing that still create copies (Databricks). Strictly governed data sharing without copies will be even more critical as enterprises seek to enhance LLMs and foundation models with their proprietary data.
• Native App Development. Allow developers to build applications directly over a customer’s data set within the Snowflake environment. This represents the next driver of Snowflake’s growth. The rationale is simple. It is more expensive and less secure for an enterprise application provider to maintain their own copy of an enterprise’s data in their cloud hosting environment. Rather than trusting your CRM (or other SaaS application variant) provider to keep your data secure and pass on any cost efficiencies, why not allow CRM app developers to host their solutions within Snowflake’s environment on top of your enterprise data housed in one location? This is the crux of Snowflake’s strategy to “disrupt” application development. While early, the value proposition makes sense.

Both of these growth strategies provide the benefit of eliminating copying of data to another destination. For data sharing, two companies can exchange data without requiring complex APIs or rudimentary file transfer processes. More importantly, the scope of the data can be limited to just what is needed with a fixed duration. The recipient can’t “keep” a copy of the data after the partnership ends. The same benefit applies to customer data for a CRM app, employee data for HRM and every other SaaS enterprise app derivative.

While data sharing is often ignored by analysts and investors, it continues to surface as one of Snowflake’s stickiest features. The percent of customers with active data sharing relationships continues to increase. Utilization is even higher for large $1M+ customers. Surprisingly, there are still many reasons for enterprises to exchange data with partners and customers, which are often handled through inefficient processes like FTP and APIs.

With a renewed focus on maintaining control over an enterprise’s proprietary data as an input to AI model training/inference, strong governance of data is even more important. Snowflake has built granular controls into their data sharing methodology. Most importantly, data is not shared by making a copy, unlike some competitive solutions. Access to data for any partner can be immediately revoked without having to request that the partner “delete their copies”.

One Data Cloud

If Snowflake can realize their vision for a single cloud-based enterprise data store (the Data Cloud), they will unlock an enormous market opportunity. To size the opportunity, Snowflake leadership identifies a set of workloads that the data platform can address currently. Those represent the serviceable opportunity with Snowflake’s existing product set and go-to-market focus.

They size the market at $248B for this year, while projecting revenue representing just over 1% of that. The core of the market still encompasses Snowflake’s foundational workloads in analytics, data science and data sharing. They are slowly adding new workload targets, like security analytics, which they estimate as a $10B opportunity. The reasoning for this addition is straightforward – enterprises and third-party app developers can build security scanning solutions on top of Snowflake’s data cloud, taking advantage of the large scale data processing platform that Snowflake has already built.

This new workload in cybersecurity (with more workloads coming) is supported by Snowflake’s Powered By program. For cybersecurity, they already have several partners including Securonix, Hunters, Panther Labs and Lacework. The benefit to Snowflake with workloads like cybersecurity is twofold. First, these application builders generate revenue for Snowflake through their consumption of compute and storage resources. During their Investor Day in 2022, leadership revealed that 9% of their $1M customers were in the Powered By program. Second, having these capabilities available to enterprise customers provides one more reason to consolidate their data footprint onto Snowflake.

Given its growth, I would even speculate that in the future, revenue from Powered By approaches revenue from regular customer use of the Snowflake platform. This is because Powered By program participants are building their entire business on the Snowflake platform. We have already seen several sizable security vendors take this approach. During Snowday in late 2022, the SVP of Product shared that the 4 fastest growing companies from $1M to $100M in ARR are built on top of Snowflake. This could become a significant revenue driver if we consider that a typical SaaS vendor might spend 10-20% of revenue on their software infrastructure. Not all of that would go to Snowflake, but a good portion of their $10M-$20M+ in annual IT spend could.

While a $248B TAM is one of the largest in software, Snowflake leadership isn’t capping it there. They project a bigger market opportunity if they realize the full vision of the Data Cloud. The rapidly evolving opportunity to enable AI workloads hasn’t been fully sized yet. Some of this would be covered under the Data Science and ML category, but likely would grow from the $51B estimate currently. The Snowflake Summit conference and Investor Day on June 27th will provide more guidance here.

In fact, this year’s Summit conference appears that it will revolve around AI and the potential for Snowflake to provide a critical foundation for enterprises to harness it. The agenda is chock-full of AI content, including a who’s-who of luminaries from the space. The conference kicks off with a fireside chat between Snowflake CEO Frank Slootman and NVIDIA Founder and CEO Jensen Huang on Generative AI’s Impact on the Enterprise.

From the press release, here is a list of other highlights (copied from the press release):

• A Thursday keynote panel featuring Andrew Ng, Landing AI Founder & CEO; Ali Dalloul, Microsoft VP Azure AI; Jonathan Cohen, NVIDIA VP of Applied Research; and moderated by Snowflake SVP of Product Christian Kleinerman.
• Keynote presentations from Frank Slootman, Snowflake Co-Founder and President of Products, Benoit Dageville, Snowflake SVP of Product, Christian Kleinerman, unveiling the next wave of Snowflake’s product innovations including bringing 3rd party LLMs to your data, delivering 1st party LLMs as services, and creating LLM-enhanced product experiences.
• New details around how Snowflake’s recent acquisition of Neeva will enable AI-driven search and conversational experiences in enterprises.
• Technical deep dives into the latest Data Cloud advancements with generative AI and LLMs, Apache Iceberg, security & privacy, programmability, application development, clean rooms, streaming and much more.
• Dozens of partner-led sessions about leveraging generative AI within an organization’s tech stack to drive long-term business impact.
• 100+ Data Cloud Ecosystem announcements to support all aspects of an organization’s AI/ML strategies from new applications to technology integrations to services and more.

Beyond the AI opportunity, Snowflake has long recognized their advantage to move beyond the core data platform to build a robust set of data services and native applications on top of the customer’s data, keeping everything in one place. This has the benefits of lower cost, better controls and a simpler system architecture. Customers are gravitating towards these advantages, recognizing that Snowflake’s reach across all hyperscalers gives them optionality.

To track their progress in building an ecosystem of data sharing and native applications, Snowflake leadership regularly publishes a set of “Data Cloud Metrics”. These give investors a sense for their progress in data sharing, the Marketplace and the Powered by program.

To capture data sharing activity, Snowflake reports a measure called “stable edges”. Snowflake leadership sets a high bar for considering a data sharing relationship between two companies as actively being used. In order to be considered a stable edge, the two parties must consume 40 or more credits of Snowflake usage each day over a 6 week period for the data sharing relationship. I like this measure, as it separates empty collaboration agreements from actual value creation.

In Q1, 25% of total customers had at least one stable edge. This is up from 23% in Q4 and 20% a year ago. If we apply these percentages to total customer counts in the period, we get the chart below. While total customers grew by about 29% y/y in Q1, the number of customers with at least one stable edge grew by 61%.

To me, that growth represents an important signal for the value-add of data sharing. If we assume that new customers take at least one year to get around to setting up a stable edge, then almost 32% of customers over a year old have a stable edge in place (total edges / customer count Q1 FY2023).

Facilitating these data sharing relationships represents a competitive advantage for Snowflake. They increase customer retention, generate network effects to attract new customers and drive incremental utilization as shared data sets are filtered, cleansed and combined with other third party data. This network of data sharing relationships elevates Snowflake’s value proposition for customers onto a higher plane beyond focusing on tooling for analytics and ML/AI workloads within a single company.

The other area experiencing high interest is the Snowflake Powered By program. This represents companies that have decided to build their data-driven product or service on top of Snowflake’s platform, that they then sell to their customers. For Q1 FY2023, Snowflake announced there were 425 Powered by Snowflake partners, representing 48% growth over the prior quarter’s count of 285 in Q4. For Q2, Powered By participation took another large jump forward, increasing by 35% q/q to reach 590 registrants. In Q3, Snowflake reported another 20% q/q growth, hitting 709 registrations by quarter’s end. In Q4, they reported 16% sequential growth to reach 822. 

Finally, in Q1, sequential growth accelerated sequentially to 18%, reaching 971 participants. This represents growth of 2.3x over the past year. As part of Investor Day in June 2022, leadership revealed that 9% of their $1M+ customers were in the Powered By program. Snowflake ended Q1 with 373 $1M+ customers, implying that almost 34 Powered By participants were generating more than $1M in annual product revenue.

Transitioning to AI Workloads

The future direction of large data storage and processing will be to generate ever more sophisticated intelligence, efficiency and automation from it. These represent the next steps of the evolution into AI. While the ability to apply machine learning to create self-improving algorithms and harvest richer insights from analytics have existed for a while, the new capabilities introduced by LLMs and chat-based interfaces have inspired a new push. Enterprises are scrambling to find ways to utilize the new capabilities emerging from generative AI and LLMs, even while specific use cases outside of ChatGPT interfaces are still developing.

At a high level, I think enterprises will harness these new capabilities in a couple of ways, generating incremental usage of data stores like Snowflake, along with other companies supporting a modern data infrastructure (Confluent, MongoDB, etc.).

More activity from better user interfaces. While this seems simple on the surface, I think that more efficient user interaction will drive much higher utilization of existing software services. LLMs and ChatGPT like interfaces allow humans to interact with software applications through an interface that is based on natural language. Rather than being bound to traditional GUIs with preset choices or requiring use of a scripted language (like SQL) to define tasks, chat interfaces allow users to engage software applications through text-based prompts.

As an example, Snowflake itself built a new interface internally to their traditional executive dashboards for the senior leadership team. This unlocks data to a whole new set of users. A natural language interface allows any employee to instantly become a power user. This should result in many more queries to the underlying analytics engine, driving more consumption.

As another example on the consumer side, Priceline is working with Google AI to create a virtual travel concierge as the entry point for users to plan a trip. A simple text-based instruction with some rough parameters could kick off a large number of queries to multiple application data services (flight, car, hotel, entertainment, dining, etc). This replaces complex interfaces with many combinations of drop-downs, selectors, submit buttons, etc., which are then repeated for each aspect of trip planning. The efficiency querying for all of this in one or two sentences would result in more overall usage. Not to be outmaneuvered, other travel providers like Expedia are working on similar features.

More activity from more applications. Developer co-pilots derived from training LLMs on software code allows programmers to be more productive. Anecdotally, some developers have reported productivity increases of 2-3x. Even a more conservative increase of 30-50% for a software development team would be significant. The outcome for enterprises would be the creation of more software applications (they all have a long backlog of digital transformation projects). These would require hosting, monitoring, security and data. The data generated by these new applications would flow through the same pipes and be consolidated into a central store for further processing.

More activity from enterprises running foundation models over their internal data. One major challenge with public LLMs is in the control of the enterprises’ proprietary data. Enterprises don’t want employees feeding public services with their private data, because that data becomes part of the model once it is shared. There will be a large opportunity to enable use of LLMs with enterprise specific data that maintains governance and security.

More activity from the application of foundation models to specific domains. This will likely become the largest driver of future growth for Snowflake. Recall that Snowflake has invested heavily in the past in the creation of industry verticals. These represent an ecosystem of participants in a particular industry segment, who can interact through systems of shared data and special product offerings curated for that particular vertical. As each of these verticals tries to apply AI to their domain, Snowflake will be well-positioned to offer domain-specific capabilities. They can feed foundation models to create unique offerings that possess specific industry context with the latest data.

Snowflake can help ecosystem participants securely assemble larger data sets through controlled sharing. They can extend foundation models with domain specific context and offer them to ecosystem participants. While individual enterprises will closely guard their proprietary data, they will realize that collaborating with a couple of other participants in the same sector might result in an even better AI-driven offering. We should see the emergence of tightly controlled enterprise alliances that revolve around data sharing to create better AI models for their industry that disrupt other participants. Snowflake’s sophisticated data sharing capabilities (again without making copies) will become a huge advantage here.

Opens up a new set of services to offer in the Snowflake Marketplace. While providers in the Snowflake Marketplace have been focusing on selling curated data sets, AI models provide a whole new layer of services that Snowflake can offer through the Marketplace. As we saw with Q1’s Data Cloud Metrics, sequential growth of Marketplace offerings is slowing down. I’m not surprised, as there are likely only so many generic demographic, weather, financial, etc. datasets that can be sold. Sophisticated, contextually-aware AI models distributed through the Marketplace could provide a new growth vector for vendors.

For all of these benefits, you see the core outcome summarized as “more activity”. That is why AI could represent a new catalyst for data service providers at all layers of the stack, like Confluent for pipelines, MongoDB for the transactional data and of course Snowflake for the consolidated data cloud.

Software and data service providers would like to power as many of the steps in the AI value chain as possible. Foundation models are becoming ever more available through open source, the public domain and commercial offerings with API interfaces. The generic steps of providing the data inputs (structured and unstructured), training, adaptation and inference could be powered by a single platform. This platform would provide the foundation for an ever-increasing number of domain specific, AI-enhanced tasks that are incorporated into enterprise application functions. For Snowflake, they could enable several of these steps, if not power the whole platform at some point.

For a more clear view of how modern data infrastructure providers could benefit from the increased use of newer foundational models by enterprises, we can refer to a diagram provided by Confluent at their Investor Day. With traditional machine learning processes, the primary focus by enterprises was on performing custom training and feature engineering. These models were created by loading large enterprise data sets through a batch function from a data lake or data warehouse (or lakehouse). Once the base enterprise model was established, inference would customize results for each request. Inference generated some data access, but not at the same volume as the original model construction.

With Generative AI, LLMs and other foundation models, a third party often provides the generic model pre-trained with public data. The enterprise then applies much heavier inference to inject its contextual data into the generic model. This inference can be applied in real-time for every user interaction. Given the scope of functions and data addressed from a chat-based interface, the volume of data accessed in real-time to deliver a customized response for each user (based on their history) could actually be much larger and broader in scope than what was required to build a standard ML model under the prior method.

Generative 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.

Generative AI is powered by data. That’s how models train and become progressively more interesting and relevant. Models have been primarily trained with internet and public data, and we believe enterprises will benefit from customizing this technology with their own data. Snowflake manages a vast and growing universe of public and proprietary data.

Snowflake Q1 FY2024 Earnings Call

This all implies that a cloud-based data store like Snowflake that houses all of an enterprises’ data would be very useful for adding user-specific context to any pre-trained model. As this data is often requested in near real-time, overall consumption of Snowflake storage and compute resources would logically increase for the customer. This also highlights the need for Unistore, Snowflake’s solution to support transactional workloads.

Going back to the examples of travel companies like Priceline and Expedia updating the user interface with a natural language prompt, the model needs access to user data in near real-time in order to address all possible queries. For example, a customer who wants to check the status of their flights or make a change to some aspect of their trip would require that the model be aware (through inference) of their specific history. A pre-trained model with data loaded once in a batch manner would not be as useful in this context.

At Snowflake’s upcoming Summit conference, we will learn more about their plans to address AI use cases. Highlighting the event, is the announcement of a fireside chat between Snowflake CEO, Frank Slootman and NVIDIA Founder and CEO, Jensen Huang on “Generative AI’s Impact on the Enterprise.” This will likely outline the large opportunity for Snowflake to leverage their existing enterprise relationships to drive growth from the rush to deliver new generative AI digital experiences.

In the near term, their Snowpark development environment is experiencing increased usage. In his opening remarks, Snowflake’s CEO shared that in Q1 more than 800 customers used Snowpark for the first time. About 30% of all customers are now using Snowpark on at least a weekly basis, up from 20% in the prior quarter. Consumption of Snowpark has increased nearly 70% q/q.

Recent acquisitions, like Applica, are enabling new AI capabilities for Snowflake. As part of Q1 earnings, they also announced the acquisition of Neeva, which brings search capabilities layered over generative AI that allow users to query data more efficiently. Snowflake plans to infuse their core search capabilities across the Data Cloud. Neeva brings more human capital with a strong search background as well.

Applica’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 a reference table 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 assistants that can be shared across users.

We announced our intent to acquire Neeva, 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 Neeva will increase our opportunity to allow nontechnical users to extract value from their data.

Snowflake Q1 FY2024 Earnings CAll

The key for Snowflake will be to extend their reach beyond being the data source for these new AI-driven experiences to providing the environment to also perform a larger share of the overall AI workflow. We will hear more about all these plans at Summit.

Competitive Positioning

I have written extensively in the past about Snowflake’s competitive advantages. While they cooperate with the hyperscalers to various degrees, they also compete with hyperscaler ambitions to control all data for an enterprise within their environment. Where the hyperscaler solutions overlap with Snowflake’s in many ways, the primary competitive differentiation for Snowflake has to do with the benefits they derive from being independent from the hyperscalers.

Being a neutral third party allows Snowflake to focus on the features built around the core data processing engine that generate network effects for customers. These include the most robust data sharing capabilities and comprehensive industry-specific ecosystems. Additionally, an extensive data marketplace and soon library of native apps make Snowflake the center for an enterprise to secure all their data.

The hyperscalers still make money from consumption of Snowflake resources by customers. This is because Snowflake is hosted on each of the hyperscalers. Usage of compute and storage generates revenue for the underlying infrastructure provider. This provides an additional incentive for the hyperscalers to support Snowflake’s business – it’s easy money without the overhead of supporting a product.

On the other hand, the hyperscalers recognize that they can make more money by owning the entire customer software stack. As enterprise gravity is heaviest at the data layer, the hyperscalers appear the most competitive with independent providers to control access to large enterprise data sets. This posturing relative to Snowflake has traditionally been strongest with Google Cloud Platform, followed by Microsoft Azure. AWS has has the opposite approach, actively collaborating with Snowflake to win more enterprise business overall.

As Snowflake is rapidly evolving their platform to address new use cases for AI, the hyperscalers are as well. Microsoft made some recent announcements, including the introduction of Microsoft Fabric as a new end-to-end data and analytics platform. Google is rolling out new ways to connect applications and share data across cloud providers. Yet, these efforts still represent either repackaging of existing services or rely on server infrastructure to be maintained by the customer or another partner.

More importantly, at a high level, they don’t address the risks of relying on making copies of data between partners or the implied lock-in. As an independent provider, Snowflake’s solution works seamlessly across all hyperscalers, without requiring extra hardware to be provisioned to proxy requests between providers. For data sharing between partners, there are no copies to track down if the relationship ends. The creation of shared AI models between industry partners could be facilitated by Snowflake’s Clean Rooms, exposing the minimal amount of proprietary data. Made available through a Native App within the Snowflake platform, that data wouldn’t even need to leave the environment.

With that said about Microsoft Azure and GCP maintaining an arm’s length relationship with Snowflake, on the Q1 earnings call, management actually mentioned that Snowflake’s adoption rate on Azure had been improving.

Yeah, you know, just Microsoft relationship has been growing faster than the other two cloud platforms that we support. It’s been very clear from the top Microsoft that they’re viewing Azure as a platform, not as a sort of a single integrated proprietary Microsoft stack. And they’ve said over and over that we’re about choice, we’re about innovation. And, yes, we will compete with Microsoft from day one, and that will — and we’ve been very successful in that regard for a whole bunch of different reasons.

But people keep on coming, and that’s — and we expect that. And I think that’s sort of a net benefit for the world at large as they get better and better, you know, products and they get, you know, more choice. The good news is that I think the relationship is relatively mature, meaning that, you know, when there is friction or people who are not following the rules, we have good established processes for addressing and resolving that. And that’s incredibly important, right, as we sort of get out of the juvenile state, where things are dysfunctional at the field level.

So, I have no reason to believe that that will not continue, you know, in that manner. So, I think Azure will continue to grow and grow faster than the other platforms.

Snowflake q1 FY2024 Earnings Call

Based on the CEO’s comments, it appears that Microsoft is backing away a bit from their “all in one” product strategy and opening up to collaboration with third party providers. This is similar to the transition that AWS went through over the past 5 years, where they previously deliberately competed with independent providers, even hosting open source offerings as their own product (MongoDB, Elasticsearch, Kafka, etc).

Since then, AWS realized the incremental value of offering their enterprise customers the option to use third party software services on their platform. This resulted in a better outcome than disenfranchising them by pushing an internal product. AWS appreciated that third party solutions built on their infrastructure still generated revenue from the underlying storage, compute and network services. It appears that Microsoft Azure may be arriving at a similar conclusion.

Databricks

Snowflake’s other large independent competitor, Databricks, isn’t standing still either. They too have a user conference scheduled for the end of June (perhaps deliberately). Given the two companies have major conferences during the same week in two different locations, I imagine competition for speakers was fierce. Investors can compare the featured speakers from Databricks and Snowflake and draw their own conclusions. Snowflake landed Jenson, but Databricks has Satya and Eric Schmidt.

Databricks has been moving forward in pursuing the AI opportunity as well. Back in April, they announced the release of Dolly 2.0, which they claimed was the “first open source, instruction-following LLM, fine-tuned on a human-generated instruction dataset licensed for research and commercial use.” With the release, they delivered a dataset with 15,000 human-generated prompt and response pairs designed for instruction tuning of large language models. 

These are meant to provide customers with a demonstration of how the Databricks platform can incorporate LLMs for enterprise-specific tasks, representing a starting point for summarization and content generation.  They expect users to use this example to bootstrap even more powerful language models. I am sure we will learn much more at the Data + AI Summit in a week.

On the financial side, investors received an update on Databricks’ growth trajectory from an interview with their CEO by Bloomberg on June 13th. In it, the CEO revealed that Databricks reached $1B in annual revenue for their fiscal year ended in January 2023, which grew by 60% over the prior year. He further claimed that makes them “the fastest-growing software company according to our records.”

I was a little perplexed by this comment. Snowflake ended their most recent fiscal year in January 2023 as well. They reported over $2B in revenue for the prior 12 months, growing by 69% annually. So, Snowflake finished the same fiscal period with 2x the revenue and a higher growth rate. Also, consider that at Databricks’ scale, Snowflake was growing revenue even more quickly. They ended FY2022 with $1.22B in revenue, increasing by 106% annually.

Granted, the annual growth rate for Snowflake is decreasing rapidly this year, but we also don’t now how Databricks’ quarterly performance has been progressing in this challenging macro environment. I highlight these comparisons because investor sentiment associated with Databricks would imply a much higher growth rate, given the perception of how disruptive their offering should be for Snowflake. I would have estimated 100% or more annual growth at that scale. In the Bloomberg interview, Databricks’ CEO did highlight that their data warehouse product passed $100M in ARR in April.

Databricks’ growth will be something to watch, along with their continued competitive jockeying with Snowflake. However, given Snowflake’s larger scale and significant FCF generation, they can afford to funnel more money towards R&D and S&M than Databricks. At some point, that should overcome any start-up disruptive advantage that Databricks may have enjoyed.

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Q1 Earnings Results

Following Snowflake’s earnings report on May 24th, the stock dropped 16.5%. This was a reaction to weak forward revenue guidance that missed expectations both for Q2 and the full year. However, over the following three weeks, the stock regained that drop, reaching a recent peak near $191 on June 15th, which was about 7.8% higher than the pre-earnings price of $177.14. In fact, this represents the ATH for 2023 and is close to the 52 week high.

Looking at the prior 12 months, we see that SNOW stock has traded in a range between about $120 and a peak of just about $200 around September of 2022. Its historical ATH price is over $400, hit back in November of 2021. It also peaked at a P/S ratio over 100 at that point, but with a much higher growth rate. Looking forward, if the macro environment improves, interest rates moderate and Snowflake’s revenue growth picks up a bit, we may revisit that price over the next couple of years.

As I will discuss, an investment in Snowflake is based on two assumptions. The first assumes that revenue growth will stabilize at some point and potentially re-accelerate slightly. Revenue growth rates have been decreasing quickly over the past 12 months and could drop into the low 30% range through 2023. This has been driven by headwinds from customer usage optimization and delays in ramping up new workloads. Because Snowflake operates on a consumption model, the impact on revenue of these dynamics is immediate.

The most acute problem has been when large customers make changes that lower their overall consumption, causing spend to move backwards. This is clearly a big impact on revenue growth, where the customer isn’t just slowing down spend, but is actually making it sequentially smaller. For a high growth company, this effect is a killer, as it quickly counteracts any benefit from other customers that are expanding their usage.

As an example, Snowflake leadership cited a few large customers who reduced the amount of data being retained, specifically citing at least one that cut back from 5 years of historical data to 3 years. This is a common, but aggressive, cost reduction tactic. The trade-off is less visibility for historical queries, but is likely acceptable for a temporary period. Over time, the data set can be built back up. For Snowflake, this immediately reduces revenue from storage of that data, as well as making large data queries run faster. Both effects result in incrementally less usage, which means less revenue from resource consumption.

And the third optimization is the one that we really saw in a few of our largest customers, with them just wanting to really change their storage retention policies. Like one customer went from five to three years, and it’s a massive petabytes and petabytes of data.

And so, we lose that storage revenue. But on top of that, now your queries run quicker because you’re querying less amounts of data. And we are seeing more customers wanting to do that. And I spoke to some of the hyperscalers, I won’t say which one, and they confirm they’re seeing retention policies change within their customers, wanting to archive more older data.

Snowflake Q1 FY2024 earnings call

At some point, though, these negative headwinds will abate. Enterprises can only optimize usage to a point. In the prior example, while reducing historical data retention from 5 years to 3 years represented a reasonable trade-off, cutting retention further from 3 years to 1 year is very unlikely. When these negative growth trends dissipate, enterprises will return to their normal expansion cadence and may start embracing recent product additions. At the same time, new customers will be starting their cloud migrations. These could combine to re-accelerate growth. This would likely manifest as a large step-up in sequential quarterly increases.

The second assumption behind the Snowflake investment thesis is that the company will eventually reach its product vision to become the central repository for all of an enterprise customers’ data. This represents a huge TAM and could propel Snowflake’s annual revenue well beyond their $10B target. Snowflake management has already set their TAM at $248B, which I suspect will expand again as they add new AI-focused product offerings.

With that set-up, let’s look at the specifics of their Q1 financial performance and then wrap up with some take-aways for the investment plan.

Revenue

Snowflake’s sequential revenue growth slowed down substantially starting in Q4 and continuing into Q1. For the year prior from Q3 FY2022 through Q3 FY2023 (ending October 2022), sequential revenue growth was consistently over 10%, even approaching 20% in some quarters. In Q4, product revenue growth dropped out of hypergrowth, to hit 6.1% sequentially. For Q1, product revenue sequential growth ticked up slightly to 6.3%. Annualized, this would yield about 28% growth. While representing a huge slowdown, compared to other software infrastructure companies in this environment, it is fairly inline.

The problem has been the trajectory of the descent and SNOW’s valuation. A drop from the 10%+ range to 6% sequential growth is obviously large. It does appear that sequential growth may have stabilized, given this is the second quarter with product revenue above 6%. The Q2 guide even implies a little acceleration, if we assume a couple point beat over the 5.5% preliminary estimate. This could take Q2 sequential product revenue growth up to the 8-9% range, which is necessary to hit the revised full year guidance.

While the improvement in sequential revenue growth is nice to see, a P/S ratio of 26 represents one of the higher valuations in software infrastructure. This has a lot of growth baked into it, with the assumption that Snowflake will become a much, much larger company in the future. While the future is bright, the current valuation multiple doesn’t align well with the revenue growth rate. The trailing FCF multiple of about 100 provides some support. Annualizing Q1’s FCF run rate brings the P/FCF ratio down to 53.

Snowflake delivered $623.6M of total revenue in Q1, which was up 47.7% annually and 5.9% sequentially. This beat the analyst estimate for $608.7M, which would have been 44.1% growth. Product revenue was $590.1M, up slightly more at 49.6% annually and 6.3% sequentially. In Q4, Snowflake guided for product revenue in a range of $568M – $573M for 44%-45% annual growth and 2.7% sequentially at the midpoint. Leadership doesn’t guide to total revenue, but analysts calculate it from guidance for product revenue.

Looking forward to Q2, Snowflake guided for product revenue in a range of $620M-$625M, representing annual growth of 33%-34% and 5.5% sequentially. Analysts were looking for total revenue growth of 38.0%, with product revenue estimated at $646.3M for 38.6% annual growth. Snowflake obviously came in short of this.

Even with the underperformance relative to analyst estimates, we could see a slight acceleration in sequential growth for actual Q2 results. With the 5.5% guide, if we assume a similar beat of 3.4% sequentially from Q1, then Q2 sequential growth could tick up to 8.8% (from 6.3% in Q1).

The small beat in Q1 and lower guide for Q2 caused Snowflake leadership to lower the full year FY2024 guidance. This is the second time Snowflake has reduced annual guidance. As investors will recall, leadership shared a preliminary, optimistic full year target for 47% growth in product revenue as part of the Q3 earnings call. Then, in Q4, they lowered that to 39.5% growth. For Q1, they lowered it yet again to a target for $2.6B, which would represent growth of 34% annually.

Snowflake leadership attributed the need for reduction to a slowdown in customer consumption starting in mid-April and continuing through May. They are now assuming this level of consumption does not improve through the year. Hopefully, this represents a conservative baseline, which Snowflake can beat. However, two subsequent reductions in full year guidance leave analysts and investors with little confidence.

As an aside, management might consider dispensing with the full year guide at this point. Given the variability of their consumption model, it seems that projecting revenue up to 12 months out just results in disappointment. The hyperscalers and other large software providers generally project forward results for the next quarter. Another large consumption business, Twilio, eliminated their full year guide a couple years ago for the same reason.

Shifting to other growth metrics, RPO was $3.41B at the end of Q1, which is up 30.6% annually. However, it was down 6.9% sequentially from Q4’s ending value of $3.66B. Current RPO was 57% of total RPO, which is $1.94B. That is up 40.6% from a year ago, where CRPO made up 53% of total RPO and was $1.38B.

On a sequential basis, CRPO decreased by a little better 3.5% below Q4’s value. Snowflake does experience some seasonality on RPO generation. A year ago, in the transition from Q4 to Q1, total RPO decreased by 1.4% sequentially and current RPO remained about the same. To have both total RPO and current RPO decrease by that much sequentially from Q4 implies that customers are further reducing forward commitments. While in the past customers would front-load larger commitments, in the current environment, they prefer to consume existing commitments until they have to extend their contract obligation.

Profitability

Snowflake’s profitability followed revenue growth, with a bright spot around FCF generation. Non-GAAP gross margin reached 77% in Q1, up from 75% in Q4. This was attributed to improved efficiency and volume discounts from the hyperscalers.

Non-GAAP operating income was $32.6M, representing an operating margin of 5.2%. Management had projected break-even operating margin coming out of Q4, so this represents a nice beat. Management attributed the continued improvement in operating margin to economies of scale and leveraging large customer relationships. It’s generally much cheaper in terms of sales resources to expand an existing customer versus landing a new one.

Adjusted free cash flow was $287M, which increased 58% y/y and represents an adjusted FCF margin of 46%. Actual FCF was $283M for a FCF margin of 45%. Snowflake leadership commented that FCF in Q1 benefited from some advance payments. In Q4, the adjusted FCF margin was 37% and was 43% in the year ago quarter. Q1 delivered the highest FCF margin in over a year. On a Rule of 40 basis using FCF, Snowflake would score over 90.

Looking forward, the Q2 operating margin target was set at 2%. This is lower than the 5% just delivered in Q1, but higher than the prior quarter’s preliminary estimate. If we assume the same-sized beat as Q1, then Q2 actual operating margin could reach 7%.

For the full year, management made a couple of small adjustments to the profitability estimates set in the Q4 guidance. They maintained product gross margin at 76%. After setting preliminary guidance for Non-GAAP operating margin at 6% in Q1, they lowered the full year target to 5%. However, they raised the full year adjusted free cash flow margin target from 25% to 26% . These all represent minor adjustments.

While some software infrastructure peers announced layoffs this year, Snowflake has continued to invest in headcount. This is in spite of their slowing revenue growth rates. I think this reflects management’s ongoing confidence in the growth potential.

In Q1, they added 426 total employees, representing 7.2% growth sequentially and 38.4% annually. The department receiving the largest number of new hires was R&D, with 234 headcount added in the quarter, up 17.0% sequentially. The second largest department recipient was S&M with 128 additions for 4.7% sequential growth.

On the earnings call, management confirmed these trends. Looking forward, they will continue to prioritize hiring in product and engineering. For S&M, they will invest where they experience sufficient ROI. Overall, they have slowed the hiring plan for the year and expect to add about 1,000 employees in FY2024, which includes contributions from acquisitions. On the earnings call, management highlighted the acquisition of Neeva. That will add 40 employees to the company in FY2024, which are primarily in the R&D group.

Snowflake’s CFO also provided an update on the share repurchase program announced in the Q4 report. Snowflake continues to have a strong cash position with $5B in cash, cash equivalents, and short-term and long-term investments. They used approximately $192M to repurchase approximately 1.4M shares to date at an average price of $136. They plan to continue to repurchase shares opportunistically with FCF, up to the $2.6B authorized.

Customer Activity

Revenue growth pressure reflected clearly in Q1 customer activity for Snowflake. While a few software infrastructure peers saw sustained growth in customer additions and large customer counts, Snowflake experienced a bit of contraction.

Snowflake added 317 customers in Q1 to reach 8,167 total. This was up 4.0% sequentially and 28.9% annually. Q1 represented the slowest growth rate in total customer additions over the past 3 years. In the prior quarter, Snowflake added 536 customers for sequential growth of 7.4%. However, there is seasonality to the Q1 customer addition rate, which historically decreases from Q4 to Q1. A year ago, the sequential growth rate from Q4 to Q1 dropped from 9.9% to 6.1%, adding only 361 new customers in Q1 FY2023. Still, adding fewer customers this quarter than the year ago number represents a more pronounced slowdown.

Growth of $1M product revenue customers performed better. Snowflake added 43 of these large customers in Q1, which matched the number of additions from Q4. Further, this number significantly exceeded the 22 added a year ago in Q1 FY2023.

While growth in $1M+ customers is steady on an absolute basis, the sequential growth rates have been ticking down. This is impacting the dollar-based net revenue retention rate (DBNRR rate), which dropped another 7% in Q1 to reach 151%. Although an absolute DBNRR rate of 151% is best-in-class, it has fallen by 23% over the past year.

If maintained, this high DBNRR rate would provide a lot of support for elevated revenue growth. On the surface, it reflects an increase in spend by existing customers of 51% on a y/y basis. With revenue growth projected to be 34% for the full year of FY2024, we can expect the DBNRR rate to decrease further from here. The rate of descent will likely slow down and could bottom within the next year.

Investment Plan

Following the accelerated investment in cloud infrastructure during Covid and the current macro pressures on IT budgets, software service providers continue to experience headwinds from enterprises looking to constrain their spending. This has translated into the theme of optimization, where any opportunity to reduce cost is considered, even if that temporarily lowers the effectiveness of the solution. This mode has impacted all spending categories to some extent. Even security companies have reported contract delays and portioning out commitments.

Snowflake has not escaped this gravity. This pressure started in the second half of 2022. In Q1, they continued to experience headwinds from customer optimization, where enterprises are still looking for mechanisms to lower costs. Reducing data retention was the latest trick, applying a double whammy of cutting storage revenue and reducing compute consumption as queries run faster across less data.

However, these optimization tweaks can only go so far and will come to an end at some point. Further, as interest rate increases stabilize, enterprises will likely loosen budget constraints a little. These changes would return growth to the “normal” tailwinds of digital transformation and cloud migration. I don’t expect a large acceleration from prior spending levels, just the removal of these ongoing negative adjustments. Added to a normalization could be a new, secular tailwind from AI, which could drive up Snowflake consumption in a few ways.

First, new natural language interfaces would expand the user base and make complex queries easier to create. Where the Snowflake user audience was previously limited to analysts familiar with a query language, a natural language prompt allows them to retrieve large amounts of information through a simple question (or series of iterative questions). In fact, I imagine these queries will be so easy to run that Snowflake’s existing alerts for large consumption jobs will become even more critical.

Second, increased productivity from AI-powered developer co-pilots will result in more enterprise application releases. That will drive up IT budget allocation for hosting, monitoring, security and data storage. As development becomes more efficient, we might even see a shift of IT budget from developer salaries towards increased investment in infrastructure. This trend could increase the overall investment for cloud hosting and data services from enterprises.

Finally, every enterprise has the opportunity to customize publicly available foundation models with their own proprietary data to create contextual competitive advantage. This makes the consolidation, cleansing and organization of all their data even more of a priority. Snowflake is well-positioned to become the data source for these AI workloads. Additionally, their investment in data sharing, native apps, governance and industry ecosystems places Snowflake as a unique enabler of strategic AI model collaborations between enterprises.

We will find out more about Snowflake’s product aspirations at their upcoming Summit conference the week of June 26th, which may feature some big announcements including something brewing with Nvidia. Competitors, of course, are not standing still and are announcing their own new capabilities to harness the opportunity stemming from AI. Similar to the rush of investment towards cloud infrastructure over the past decade, we may see a repeat effect where multiple providers in each category benefit.

While the valuation remains high, I think Snowflake maintains the ability to generate durable revenue growth with improving cash flow margins as they march towards their $10B product revenue target. The market appears to be aligned with this view, at least so far, as SNOW stock now exceeds its pre-earnings price. It is still well below the ATH peak of $400 from late 2021.

As I already have a large allocation of my portfolio in SNOW and the stock price has recovered from the post-earnings dip, I don’t plan to add to my holdings. I will continue to monitor Snowflake’s progress, particularly as they position the company to capitalize on new demand from AI workloads. The Summit user conference may provide another catalyst for growth.

Further Reading

• Peer analyst Muji over at Hhhypergrowth published an update on Snowflake’s Q1 results. Additionally, he offers thorough coverage of past product announcements and customer events. These are very useful supplements to my posts, usually providing additional insight on product development.
• During Summit, Snowflake will hold an Investor Day. Interested investors can register for the event and view it virtually.

NOTE: This article does not represent investment advice and is solely the author’s opinion for managing his own investment portfolio. Readers are expected to perform their own due diligence before making investment decisions. Please see the Disclaimer for more detail.