Snowflake Inc (NYSE: SNOW) Part 3: Competitive Moats

Data cloud platform approach, unique technology, simplicity, ecosystem approach

Data cloud platform approach as a moat

First and foremost, Snowflake's data cloud platform approach stands as a cornerstone of its competitive moat. By offering a comprehensive solution that empowers enterprises to generate intelligence from their data while maintaining robust governance and security measures, Snowflake enables organizations to derive maximum value from their data assets. This platform-centric approach not only facilitates the deployment of cutting-edge technologies such as LLMs and other generative AI technologies but also ensures seamless integration with existing systems and workflows.

Former CEO Frank Slootman's famous statement, "AI strategy requires a data strategy," encapsulates the fundamental principle that effective utilization of artificial intelligence relies on a robust foundation of quality data. This concept underscores the essential role of data in driving advancements in AI, particularly highlighted by the emergence of generative AI technologies since late 2022. In today's landscape, characterized by the proliferation of cloud and Software-as-a-Service (SaaS) applications, data has become dispersed across numerous platforms and databases. This fragmentation presents a significant challenge, shifting the focus from managing complex on-premises data warehouses to achieving a consolidated and unified view of enterprise data. The prevalence of data silos inhibits communication, stifles innovation, and leads to the proliferation of redundant data copies, resulting in increased administrative and maintenance costs. Moreover, the fragmented nature of data introduces security and governance concerns, particularly in light of evolving regulatory requirements such as the EU's GDPR.

Addressing these challenges, Snowflake recognized the need for a transformative approach to data management. The company conceived a new SQL data warehouse purpose-built for the cloud, designed to streamline the aggregation of disparate data sources, facilitate rapid analytics, and empower organizations to derive actionable insights. Leveraging its unique SQL querying capabilities, Snowflake enables enterprises to break down data silos, integrate diverse data sets, and establish a single source of truth. This centralized approach not only enhances operational efficiency but also ensures data integrity, security, and governance compliance.

Snowflake's data platform approach excels in governance through several key features. Secure data sharing allows organizations to collaborate on data-driven decisions without compromising data integrity by securely sharing data across internal teams and external partners. Granular control over data access enables organizations to define precise permissions and privileges, ensuring that sensitive data remains accessible only to authorized personnel. Real-time auditing and compliance features empower organizations to track data access and usage, facilitating compliance reporting and auditing processes. Additionally, data masking and redaction capabilities protect sensitive information by obscuring or removing it from query results, safeguarding data privacy and confidentiality.

Snowflake's robust governance features foster trust among thousands of enterprises, particularly in the era of AI. As organizations increasingly rely on data for AI-driven insights, concerns regarding data governance, privacy, and model transparency become paramount. Snowflake's data cloud platform addresses these concerns by providing tools and capabilities to integrate with various SaaS systems, bringing CRM, marketing data, and enterprise data onto a centralized platform. By ensuring data integrity, privacy, and compliance, Snowflake instills confidence in enterprises to leverage AI and advanced analytics for strategic decision-making.

As Frank Slootman famously said, “AI strategy requires a data strategy.” In the nutshell, it means  companies need to embrace the power of data in order to leverage the evolutionary technology breakthrough in AI as witnessed by the rise of generative AI since late 2022. With the rise of cloud, and SaaS applications, the data has become spread out across too many applications and databases and the challenge has shifted to managing a complex on-prem data warehouse to having a consolidating and consistent view of what data the enterprise has. Data silo has been a pain point for many enterprises. Data and organizational silos can slow communication, hinder innovation. Multiple copies of data is created as a result which increase admin and maintenance cost. Furthermore, it creates security and governance issue particular important as there is growing regulatory requirements on data governance such as EU's GDPR. In theory, there needs to only be one copy of data and everyone can see the same thing. Seeing this pain point, Snowflake built a new SQL data warehouse from the ground up for the cloud to make enterprises easy to amass all the data, enable rapid analytics because of its unique SQL querying capabilities, and derive data-driven insights. 

Security, privacy, and governance has been top priority of Snowflake’s infrastructure since its founding. There are four features of Snowflake’s data platform approach that makes it highly conductive to governance. The first one is it secure data sharing which enables organizations to securely share data across internal teams and external partners to collaborate on data-driven decisions while without copying or moving the underlying data. The second one is its granular control over data access which allow organizations to define precise permissions and privileges for users and groups ensuring that sensitive data is accessed only by authorize personnel. The third one is its auditing and compliance features enable organizations to track data access and usage in real-time, facilitating compliance reporting and auditing processes. The final one is its data masking and redaction capabilities that allow organizations to protect sensitive information by obscuring or removing it from query results. 

The best governance features and practice creates enormous trust between thousands of enterprises and Snowflake. This trust is in particular important in the era of AI. Gen AI is powered by data across the Internet but when it comes to enterprise data there are concerns of how the governance works and what models to use. When some of the foundation models are open source, there are questions such as copyright issue on what kind of data it train on. Snowflake helps solve all the concerns by creating a data cloud platform and bringing the workloads to the data. It provides the tools such as connectors to integrate with SaaS systems such as those provided by Salesforce and ServiceNow to bring CRM, marketing data and all kinds of enterprise data on Snowflake. 

Unique technology as a moat

Snowflake's competitive edge is fortified by its distinctive technology, emphasizing cost efficiency, speed, and performance. Through its proprietary solutions, Snowflake drives immediate returns for its clients, streamlining data processing and analytics while saving time and resources. Customer migration to Snowflake is driven by its exceptional price performance, which is continuously augmented by its commitment to enhancing workload efficiency year after year (5% increase each year). One of Snowflake's distinguishing features is its time-based pricing model, which contrasts with competitors' query-based models. This model, combined with Snowflake's ability to consistently improve speed, further strengthens its value proposition and attractiveness to customers.

Supported by a commissioned study conducted by Forrester Consulting in late 2020, Snowflake showcases remarkable customer returns on investment, with an outstanding ROI of 612%. This includes a notable 50% acceleration in the speed of new product rollouts, a 75% reduction in data loading time, and overall benefits surpassing $21 million over a three-year period. These benefits notably translate into substantial time savings in product deployment, data loading processes, and IT support efforts.

Snowflake harnesses its distinctive technology platform to drive rapid innovation, a crucial advantage in the AI era. This commitment ensures Snowflake stays ahead of industry trends and customer demands. Its expansion into various data types, like transactional databases, and integration with open-source db such as Iceberg, broaden its applicability across diverse industries. Features like Snowpark and Cortex seamlessly bring Python and machine learning capabilities to the platform, empowering data scientists. Although Snowpark's revenue contribution remains in single digits, it's growing over 50%. Cortex, while yet to generate revenue, spearheaded the introduction of Arctic—a revolutionary open-source LLM—developed by the team led by CEO Sridhar Ramaswamy, demonstrating Snowflake's fast innovation speed. Streamlit and Notebook within the Snowpark line offer substantial opportunities for Snowflake to enhance its data science capabilities.

Snowflake is poised to make significant strides in AI, despite investor skepticism. With new leadership at the helm, such as CEO Sridhar Ramaswamy, Snowflake's expansion into AI/ML holds immense promise. The integration of Snowflake Cortex as a core platform layer democratizes AI by making it accessible through SQL, enabling even non-experts to leverage AI for tasks like data summarization and sentiment analysis. Snowflake Cortex is a fully managed service that facilitates the discovery, analysis, and development of AI applications in the Data Cloud, aiming to simplify AI usage for customers. It offers instant access to serverless functions, including cutting-edge LLMs like Meta AI's Llama 3 model, task-specific models, and advanced vector search capabilities. Through Snowflake Cortex, teams can expedite analytics and create context-aware LLM-powered applications within minutes. Snowflake has already introduced three LLM-powered features—Document AI (private preview), Snowflake Copilot (private preview), and Universal Search (private preview)—to enhance user productivity, showcasing its commitment to AI innovation. Universal Search facilitates faster data and app discovery across both the Snowflake account and the Marketplace. Snowflake Copilot aids developers in enhancing productivity with a text-to-SQL generator that refines queries through conversation. Document AI enables companies to extract values from various documents using a multi-modal LLM developed by Snowflake. The speed at which Cortex was developed and deployed, within just 7 months, is truly remarkable. Traditionally, the process of building an AI chatbot involved multiple steps: setting up a storage layer like S3 in AWS, utilizing a vector database such as Pinecone, collecting customer input for training data, sending data to platforms like OpenAI for training, and finally obtaining a model. However, Cortex has revolutionized this process, enabling enterprises to build AI chatbots within minutes using a single platform, thus simplifying and accelerating AI application development.

Arctic stands as Snowflake's latest breakthrough product, showcasing the remarkable speed at which Sridhar Ramaswamy, leveraging his expertise from Google and startup Neeva, led its development. The acquisition of Neeva (consumer search platform), though initially met with skepticism, proved strategic for Snowflake's AI venture. Neeva's prowess in machine learning, particularly in vector search and AI model optimization with limited resources, integrates seamlessly into Snowflake's AI ambitions. More than just a technology acquisition, the move was a talent acquisition act. With Sridhar Ramaswamy assuming the CEO role, he brought in a cadre of AI experts, with 90% of Neeva's team choosing to join Snowflake, a testament to Sridhar Ramaswamy's leadership and Snowflake's allure.

Arctic shines as an enterprise-focused, cost-effective, and truly open-source LLM. Using a unique Dense-MoE hybrid transformer architecture, Arctic achieves groundbreaking efficiency without sacrificing performance. Unlike conventional MoE transformers used by Mistral and Databricks, Arctic boasts 126 fine-grained experts (versus 8 experts in MoE transformer) and only 17B active parameters, setting new efficiency benchmarks. Trained in just three months with a budget under $2 million, Arctic rivals Llama 3 70B on enterprise metrics like Coding and SQL while using 17x less compute budget. Moreover, Arctic's open-source nature under an Apache 2.0 license fosters collaboration and innovation. Sharing research details, Snowflake embraces the trend of open-source AI models, aligning with Forrester's findings that highlight the growing adoption of open-source LLMs by global enterprises. Complementing Arctic, Snowflake released Arctic Embed, a family of text embedding models optimized for leading retrieval performance at a fraction of the size of comparable models. This offering provides organizations with a potent and cost-effective solution for integrating proprietary datasets with LLMs, facilitating Retrieval Augmented Generation (RAG) or semantic search services. One might wonder why Snowflake can innovate so rapidly in LLMs and Embedding models. It appears that effective techniques derived from web searching (inherited from the Neeva acquisition) are equally applicable to training text embedding models.

Streamlit, a technology acquisition that has flourished under Snowflake's wing, boasts a thriving community of over 70,000 developers. This acquisition is poised to catalyze a new wave of applications built atop Snowflake's platform, disrupting traditional SaaS models by separating code and data. With Streamlit integrated into Snowflake, developers can effortlessly create applications without the need to shuffle data or build complex APIs, as everything resides within a unified ecosystem. Streamlit (currently in public review) expedites the development of custom LLM-powered apps, empowering users to transform data, AI models, and analytical functions into interactive Python-based applications. Presently, more than 10,000 apps have been developed using Streamlit in Snowflake.

While Snowflake Notebook remains in private review, its potential impact is undeniable. Notebooks are widely adopted in the data science realm, an area where Snowflake's competitor, Databricks, excels. Snowflake Notebook provides users with a familiar Jupyter-like environment to explore data and develop machine learning applications. Visualization, an essential aspect of data analysis, is seamlessly integrated into the notebook through Streamlit visualizations. Snowflake Notebooks, a component of Snowpark, leverages Python, Java, and Scala runtimes and libraries to work with non-SQL data housed in Snowflake.

Simplicity as a moat

Snowflake's simplicity stands out as a powerful moat in the complex data landscape. By offering a managed solution that seamlessly operates across multi-cloud environments, Snowflake simplifies data management and analysis, particularly within data lakes. This approach empowers customers to leverage their data's full potential without grappling with the usual complexities associated with traditional data management solutions. As Sridhar Ramaswamy noted, Snowflake places complexity on its end while ensuring simplicity for its products.

In the realm of data management, enterprises often face a dichotomy between open-source platforms like Databricks and managed solutions such as Snowflake. Open-source platforms appeal to companies with robust engineering teams who relish delving into code intricacies, crafting bespoke extensions, and tailoring solutions to their specific needs. While this approach offers unparalleled flexibility, it typically entails heightened complexity and maintenance overhead, requiring significant investment of time and resources to set up and manage effectively.

On the other hand, managed solutions like Snowflake present an alternative for organizations seeking a streamlined, plug-and-play approach to data management. Snowflake's proprietary model provides a comprehensive, pre-configured solution that is finely tuned out-of-the-box, minimizing the need for extensive customization. While users retain access to the processing engine's functionality, the user interface shields them from the underlying complexities, presenting a simplified and intuitive experience. This managed approach is particularly attractive to companies lacking the inclination or resources to navigate the intricacies of data infrastructure management. By offering a turnkey solution that meets their needs without requiring deep technical expertise, Snowflake empowers organizations to focus on deriving value from their data, rather than grappling with the complexities of system configuration and maintenance.

Snowflake's multi-cloud strategy streamlines the management of company data, offering a simplified perspective across various cloud environments. Customers operating within a single cloud region can effortlessly expand into other cloud platforms like GCP, AWS, or Azure with Snowflake. The platform facilitates straightforward scheduled data replication between these cloud accounts, ensuring seamless data movement and accessibility. What sets Snowflake apart is its consistent user interface across all cloud accounts. This uniformity eliminates the need for extensive retraining when transitioning between different cloud-native data services, reducing the learning curve for users. Snowflake's objective is to automate the data flow process, abstracting complexities related to data location, latency, metadata management, bandwidth considerations, query responsiveness, and more.

Ecosystem approach as a moat

Snowflake's ecosystem approach, reminiscent of Salesforce's playbook, further strengthens its competitive position. By fostering a vibrant ecosystem of partners and developers, Snowflake enhances its platform's capabilities and extends its reach into new markets and use cases. Firstly, Snowflake's data sharing capabilities boast a robust network effect, augmenting the platform's value proposition by creating a virtual cycle of data-driven innovation and collaboration among its users. Secondly, the Native Application Framework brings applications closer to the data, streamlining product development cycles by mitigating legal, governance, and security complexities, and ensuring data integrity and governance are inherently embedded within the developer platform. Thirdly, Snowflake's venture arm invests in early-stage startups who build on its Native Application Framework and companies within the modern data stack ecosystem, fostering partnerships and consolidating Snowflake's position as the core computational and analytical layer. Lastly, strategic partnerships with major SaaS companies like Salesforce and ServiceNow enable seamless data flow across systems, enhancing interoperability and facilitating smoother operations for users.

Data Sharing. Snowflake’s data sharing capabilities drive a strong network effect, wherein the platform's value grows with each new adoption. As more customers embrace Snowflake, the exchange of data among users, partners, data providers, and consumers amplifies, enriching the platform's utility for all stakeholders. This sharing enables customers to access and leverage each other's data or data products, enhancing data science and machine learning endeavors by broadening the dataset scope. Presently, 27% of Snowflake's total customers engage in data sharing, with over 2.4k data listings available in the marketplace, which has doubled compared to 2 years ago. As the Data Cloud expands through widespread adoption, the benefits of increased data availability multiply, particularly in the era of AI, where industry data holds significant relevance. For instance, FedEx could harness Snowflake's data sharing functionalities alongside LLMs to merge 1st and 2nd party data into a unified platform, empowering the development of an AI chatbot application to elevate customer service. By seamlessly integrating its internal shipment data with relevant insights from trusted partners like customs agencies and transportation hubs, FedEx's AI chatbot gains access to real-time information on shipment status, delivery routes, and service inquiries, enabling personalized and accurate customer assistance across various touchpoints.

Furthermore, data sharing emerges as a pivotal differentiator for Snowflake, fostering new customer adoption, particularly as Snowflake becomes the industry standard in certain sectors. Some Snowflake customers are even influencing their vendors to adopt Snowflake, recognizing the benefits of accessing data through its robust data sharing mechanisms. Looking ahead to the era of gen AI, Snowflake is primed to offer domain-specific capabilities by leveraging its data sharing infrastructure. By feeding foundation models with the latest public industry-specific data, Snowflake facilitates the creation of tailored offerings with specific contextual relevance. While enterprises safeguard their proprietary data, collaborative efforts among sector peers can yield disruptive AI models and superior AI-driven solutions tailored to their industries. In this landscape, Snowflake's advanced data sharing capabilities stand out as a significant advantage.

Native App Framework. The concept of running applications directly on customers’ data within the Snowflake environment holds the potential to disrupt the traditional SaaS model, where the customer’s data is typically retained in the SaaS vendor’s environment. This conventional approach often entails duplicating data and significant overhead in managing data pipelines, leading to potential security vulnerabilities in the event of a data breach.  Snowflake’s vision is to upend this model by bringing applications to the data rather than the data to the applications. With the Native App Framework, Snowflake advocates for migrating a portion of these SaaS applications onto the Data Cloud to operate directly on the customer’s data instance within Snowflake. This framework empowers developers to build, deploy, and operate applications directly in the Snowflake Marketplace, accessible to all Snowflake customers for monetization. For example, Capital One Software leverages Snowflake’s Native App Framework to develop and deploy their Slingshot application natively in the Data Cloud, providing tools for monitoring and optimizing operational costs associated with Snowflake utilization. This Marketplace for developers to showcase and monetize their applications has the potential to spark a new generation of SaaS applications, tailored to operate seamlessly within Snowflake, offering software vendors a cost advantage by eliminating the need to charge customers for managing their data overhead. 

Within just a year of introducing the Native App Framework for public review, Snowflake has now made it generally available in AWS and Azure, boasting over 2500 data products and applications already published in the marketplace. These data products cater to a wide array of business needs, ranging from comprehensive 360-degree customer views to location geocoding and machine learning solutions. Among these offerings, one of the most popular data products hails from the location intelligence company Mapbox, renowned for its 4 million developers on its platform. Users can extract valuable insights from geospatial data using simple SQL statements, enabling use cases such as optimizing delivery routes and strategically locating warehouses to minimize distances for maximum customer reach. Although still in the early stages of revenue generation, there is a notable trend of a growing number of applications embracing native app, ultimately contributing to another network effect that complements Snowflake's existing data sharing network.

Snowflake Ventures. Snowflake Ventures' mission is to support growth-stage companies that prioritize data mobilization, deliver value to Snowflake's customers, and expand opportunities within the Data Cloud ecosystem. Snowflake has launched the Powered by Snowflake funding program, allocating up to $100 million to support startups developing Snowflake Native Apps. Additionally, they organize the Startup Challenge for early-stage startups, attracting thousands of participants, with the winner receiving potential investments of up to $1 million. One notable winner, Maxa, is on a mission to revolutionize access and utilization of ERP data. In the realm of AI, Snowflake has invested in Mistral (LLM), Landing AI (visual AI), and Reka (LLM), underlining their dedication to helping customers derive value from enterprise data through LLMs and AI technologies. Furthermore, in the modern data stack domain, Snowflake has strategically invested in Matillion (ETL), DataOps.live (data orchestration), dbt Labs (data transformation), Tecton (data science tools), and DataRobot (ML platform). This strategic approach underscores Snowflake's commitment to empowering organizations to leverage the capabilities of the Data Cloud.

Strategic Partnership. In the era of dominant SaaS business models and the ascendance of cloud computing, companies like Salesforce, Adobe, and ServiceNow wield significant influence in sales, marketing, and workflow automation. Establishing strategic partnerships with these industry leaders is paramount. Snowflake enhances its ecosystem by developing connectors that enable seamless integration of third-party applications and database systems with its platform. These connectors offer immediate access to up-to-date data without the need for manual integration against API endpoints. Data is automatically refreshed according to user-defined frequencies within the Snowflake account, supporting both the initial load of historical data and incremental changes. Notably, Snowflake recently deepened its integration partnership with Salesforce, enabling bidirectional zero-ETL data sharing between Snowflake and Salesforce. Moreover, ServiceNow has opted to migrate its data ecosystem to the Snowflake Data Cloud, with Snowflake providing a dedicated connector for ServiceNow to facilitate automated data ingestion. These strategic alliances underscore Snowflake's commitment to enabling seamless data integration and empowering organizations to leverage the full potential of their data across diverse platforms and ecosystems.