Snowflake Inc. (Snowflake) provides a cloud-based data platform, which enables customers to consolidate data into a single source of truth to drive meaningful insights, apply artificial intelligence (AI) to solve business problems, build data applications, and share data and data products.
The company delivers the AI Data Cloud, a network where Snowflake customers, partners, developers, data providers, and data consumers can break down data silos and derive value from a growing number of data s...
Snowflake Inc. (Snowflake) provides a cloud-based data platform, which enables customers to consolidate data into a single source of truth to drive meaningful insights, apply artificial intelligence (AI) to solve business problems, build data applications, and share data and data products.
The company delivers the AI Data Cloud, a network where Snowflake customers, partners, developers, data providers, and data consumers can break down data silos and derive value from a growing number of data sets in secure, governed, and compliant ways.
The company’s platform is the innovative technology that powers the AI Data Cloud, enabling customers to consolidate data into a single source of truth to drive meaningful insights, apply AI to solve business problems, build data applications, and share data and data products. The company provides its platform through a customer-centric, consumption-based business model, only charging customers for the resources they use.
Snowflake solves the decades-old problem of data silos and data governance. Leveraging the elasticity and performance of the public cloud, the company’s platform enables customers to unify and query data to support a wide variety of use cases. It also provides frictionless and governed data access so users can securely share data inside and outside of their organizations, generally without copying or moving the underlying data. Delivered as a service, the company’s platform requires near-zero maintenance, enabling customers to focus on deriving value from their data rather than managing infrastructure.
The company’s cloud-native architecture includes three independently scalable but logically integrated layers across compute, storage, and cloud services. The compute layer provides dedicated resources to enable users to simultaneously access common data sets for many use cases with minimal latency. The storage layer ingests massive amounts and varieties of structured, semi-structured, and unstructured data to create a unified data record. The cloud services layer intelligently optimizes each use case’s performance requirements with no administration. This architecture is built on three major public clouds across 47 regional deployments around the world. These deployments are generally interconnected to deliver the AI Data Cloud, enabling a consistent, global user experience.
The company’s platform supports a wide range of product categories that enable its customers’ most important business objectives, including analytics, data engineering, AI, and applications and collaboration. The company is committed to expanding its platform’s product features and use cases and supporting developers in building their applications and businesses. Many of the company’s product features help power multiple product categories. In 2021, the company launched Snowpark for Java and Scala to allow developers to build in the language of their choice. In 2022, it added support for Python. Snowpark brings the power of Python and Java to the company’s platform and complements many use cases for its customers across several product categories, including data engineering and AI. In 2023, the company launched Snowpark Container Services, a fully managed container platform designed to facilitate the deployment, management, and scaling of containerized applications and AI models within its ecosystem. Snowpark Container Services are used in several of the company’s product categories, including AI and applications. In 2024, the company announced Snowflake Intelligence, within its AI product category, to enable its customers to create data agents, empowering business users to take actions on structured and unstructured data without the need for technical knowledge or coding skills. The company continues to invest in its Native Application program to help companies build, operate, and market applications in the AI Data Cloud by supporting developers across all stages of the application journey.
The company has an industry-vertical focus, which allows it to go to market with tailored business solutions. For example, the company has launched the AI Data Cloud for Financial Services, Advertising, Media and Entertainment, Retail & Consumer Goods, Healthcare & Life Sciences, Manufacturing, Technology, Telecom, Travel & Hospitality, and the Public Sector. Each of these brings together Snowflake’s platform capabilities with industry-specific partner solutions and datasets to drive business growth and deliver improved experiences and insights.
The company’s business benefits from powerful network effects. The AI Data Cloud will continue to grow as organizations move their siloed data from cloud-based repositories and on-premises data centers to the AI Data Cloud. The more customers adopt the company’s platform, the more data can be exchanged with other Snowflake customers, partners, data providers, and data consumers, enhancing the value of its platform for all users.
The company’s platform is used globally by organizations of all sizes across a broad range of industries. As of January 31, 2025, the company’s customers included 745 of the Forbes Global 2000, based on the 2024 Forbes Global 2000 list, and those customers contributed approximately 42% of the company’s revenue for the fiscal year ended January 31, 2025.
The company has acquired several companies, including Samooha, Inc., a privately-held company which developed data clean room technology; Neeva Inc. (Neeva), a privately-held internet search company which leveraged generative AI Technology; Mountain US Corporation (f/k/a Mobilize.net Corporation), a privately-held company which provided a suite of tools for efficiently migrating databases to the AI Data Cloud; LeapYear Technologies, Inc., a privately-held company which provided a differential privacy platform; Night Shift Development, Inc., a privately-held data analytics firm focused on the U.S. public sector; and Datavolo, Inc., a privately-held company that built a dataflow infrastructure to support the creation, management, and observability of multimodal data pipelines for enterprise AI.
The company sells to the U.S. government, state and local governments, foreign governments, and heavily regulated organizations directly and through its partners. A substantial majority of the company’s sales to government entities has been made indirectly through its distribution and reseller partners.
Solution
The company’s platform is built on a cloud-native architecture that leverages the massive scalability and performance of the public cloud. The company’s platform allows customers to consolidate data into a single source of truth, whether stored in Snowflake or connected from external storage like Apache Iceberg tables, to drive meaningful insights, power applications, and share data across regions and public clouds. Key elements of the company’s platform include:
Diverse data types: The company’s platform integrates and optimizes structured, semi-structured, and unstructured data, while maintaining performance and flexibility.
Massive scalability of data volumes: The company’s platform leverages the scalability and performance of the public cloud to support growing data sets without sacrificing performance.
Multiple use cases and users simultaneously: The company’s platform makes compute resources dynamically available to address the demand of as many users and use cases as needed. Because the storage layer is independent of compute, the data is centralized and simultaneously accessible by many users without compromising performance or data integrity.
Optimized price-performance: The company’s platform uses advanced optimizations to efficiently access only the data required to deliver the desired results. It delivers speed without the need for tuning or the expense of manually organizing data prior to use. Organizations can adjust their consumption to precisely match their needs, always optimizing for price-performance.
Easy to use: The company’s platform can be up and running in seconds and is priced based on a consumption-based business model, reducing hidden costs and ensuring customers pay only for what they use. Snowpark, the company’s developer framework, allows developers to interact with Snowflake through various popular programming languages, including Python. This, combined with the company’s familiar SQL-based programming model and query language, provides choice for organizations without governance tradeoffs and saves time and costs to learn new skills or hire specialized analysts or data scientists.
Delivered as a service with no overhead: The company’s platform is delivered as a service, eliminating the cost, time, and resources associated with managing underlying infrastructure. The company delivers automated platform updates regularly with minimal planned downtime, eliminating expensive and time-consuming version and patch management. This gives customers the ability to consume more data at a lower total cost of ownership compared with other solutions.
Multi-cloud and multi-region: The company’s platform is available on three major public clouds across 47 regional deployments around the world. These deployments are generally interconnected to provide a global and consistent user experience.
Seamless and secure collaboration: The company’s platform enables governed and secure sharing of live data within an organization and externally across customers and partners, generally without copying or moving the underlying data. When sharing data across regions and public clouds, its platform allows customers to easily replicate data and maintain a single source of truth. The company’s platform also enables organizations to securely share and monetize data products.
Growth Strategies
The company’s growth strategies are to innovate and advance its platform; drive growth by acquiring new customers; drive increased usage within its existing customer base; expand its global footprint; expand data content and collaboration across its global ecosystem; and grow and invest in the company’s partner network.
Platform
The company’s platform unifies data and supports a growing variety of product categories, including analytics, data engineering, AI, and applications and collaboration. Customers can leverage the company’s platform for any one of these products, but when taken together, they provide an integrated, end-to-end solution that delivers greater insights, faster data transformations, improved data sharing, and accelerated application development. Delivered as a service, the company’s platform is deployed across multiple public clouds and regions, is easy to use, and requires near-zero maintenance.
Product Categories
Organizations use the company’s platform to power the following product categories:
Analytics: The company’s platform provides reporting and analytics to improve business intelligence. For Analytics, its platform enables organizations to:
Support multiple users and activities concurrently: Enable multiple activities, such as repeatable analytics, rendering of dashboards, or ad hoc explorations, such as data science model training, with flexible compute capacity, no resource contention, and no provisioning of any infrastructure.
Generate comprehensive data insights: Run queries on structured, semi-structured, and unstructured data to capitalize on a more comprehensive view of their data to drive maximum insights.
Simplify data governance: Gain immediate insight into data and usage patterns and set policies and configurations to maximize governance.
Simplify development by uniting transactions and analytical data: Analytics includes Unistore, which, using hybrid tables, allows the company’s customers to develop lightweight transactional use cases like serving data or storing an application’s state, all within its platform.
Data Engineering: The company’s platform enables organizations to efficiently build and manage streaming and batch data pipelines in SQL or Python for downstream consumers like data science teams, analytics teams, and business applications. For Data Engineering, the company’s platform enables organizations to:
Drive faster decision making: Ingest data and transform it in real time to help ensure access to up-to-date information to drive better business outcomes.
Dynamically meet peak business demands: Meet fluctuating business demands by instantly scaling resources up and down.
Build a modern scalable data lake / lakehouse in the cloud: Consolidate data into one centralized place with the scalability, security, and power of the cloud to enable real-time analytics on all data. Store structured and unstructured data in one place where data teams can integrate different tools and platforms on a single, shared dataset. Customers can rely on this centralized data repository to address a variety of use cases.
Enact better governance and security to enable broader data access: Simplify data governance and provide rich security and controls to help ensure data is managed and accessed according to regulatory and corporate requirements.
AI: The company’s unified data and AI platform enables organizations to build and deploy large language models (LLMs), ML models, and other AI functionality to:
Transform unstructured data into insights. Efficiently and securely run natural language processing tasks (such as summarization, translation, and categorization) on unstructured data at scale using Document AI and Cortex LLM Functions.
Develop conversational assistants on enterprise data: Interact with data via conversational applications that can answer ad hoc user queries by combining language models in Cortex AI with real-time structured data retrieval using Cortex Analyst and unstructured data retrieval using Cortex Search.
Build and deploy LLMs, ML and embedding models: Train and deploy ML models, fine-tune embedding, and language models customized with proprietary data to deliver results tailored to a specific industry or organization using the Snowflake ML development suite of services and Snowpark Container Services, a graphics processing unit (GPU)-powered, managed compute service.
Applications: The company’s platform can power new applications, as well as enable existing applications with capabilities for AI, reporting, and analytics. For Applications, the company’s platform enables organizations to:
Develop analytical AI applications: Build AI applications with the company’s platform serving as the analytical and AI engine to provide massive scalability and insights with minimal operational overhead.
Embed Snowflake into existing applications: Feed data and analytics directly into business applications in the context of daily workstreams.
Develop and distribute Snowflake-native applications: Build, scale, and deploy applications that run securely within the boundary of the end customers’ Snowflake accounts with Snowflake’s Native Application Framework.
Collaboration: The company’s platform enables organizations to securely share, monetize, and acquire live data, applications, and AI products. For Collaboration, the company’s platform enables organizations to:
Securely share live data: Build an internal marketplace for employees across all parts of the organization to access, share, and analyze live data and also access and share AI products.
Acquire data sets to enrich analytics: Leverage public and commercially available data sets on the Snowflake Marketplace to enrich insights, augment analysis, and train AI models.
Monetize new data products and applications. List data, applications and AI products on the Snowflake Marketplace and tap into new monetization streams.
Invite external parties to access governed data: Invite customers, suppliers, and partners to securely access their data, streamline operations, and increase transparency.
Easy data replication: The company’s platform allows for easy replication of data, accounts, policies, and pipelines for multiple users across multiple public cloud providers and regions without compromising data integrity and governance, enabling its customers and their users to rely on a single source of truth and achieve cross-cloud business continuity.
Enable data clean rooms: The company’s platform enables data clean rooms, allowing organizations to design their own collaborative data environment in a privacy-compliant manner.
Architecture
The company’s platform was built from the ground up to take advantage of the cloud, and is built on an innovative multi-cluster, shared data architecture. It consists of three independently scalable layers deployed and generally connected globally across public clouds and regions:
Centralized storage. The storage layer is based on scalable cloud storage and can manage structured, semi-structured, and unstructured data. It can be grown independently of compute resources, allowing for maximum scalability and elasticity, and ensures a single, persistent copy of the data. The stored data is automatically partitioned, and metadata is extracted during loading to enable efficient processing.
Multi-cluster compute. The compute layer is designed to capitalize on the instant elasticity and performance of the public cloud. Compute clusters can be spun up and down easily within seconds, enabling the company’s platform to retrieve the optimal data required from the storage layer to answer queries and transform data with optimized price-performance. This functionality allows a multitude of users and use cases to operate on a single copy of the data.
Cloud services. The cloud services layer acts as the brain of the platform ensuring the different components work in unison to deliver a consistent user-friendly customer experience. It performs a variety of tasks, including security operations, system monitoring, query optimization, and metadata and state tracking throughout the platform.
This architecture is built on three major public clouds across 47 regional deployments around the world. These deployments are generally interconnected through the company’s Snowgrid technology to deliver the AI Data Cloud, enabling a global and consistent user experience.
Technology
Innovation is at the core of the company’s culture. The company has developed innovative technology across its platform, including managed service, storage, query capabilities, compute model, data sharing, global infrastructure, and integrated security.
Managed Service
High availability: Within a region, all components of the company’s platform are distributed over multiple data centers to ensure high availability. Hardware and software problems are automatically detected and addressed by the system, with full transparency to the company’s customers.
Transactions: The company’s platform supports full ACID compliant transactional integrity, so that data remains consistent even when its platform is concurrently used by many users and use cases.
Data availability and recovery: The company’s platform provides customers the ability to replicate data across various deployments, create point-in-time consistent snapshots of data, and view or recover deleted or changed data over a configured period. This allows customers to avoid difficult trade-offs between high recovery times, data loss, or downtime.
Storage
Columnar data: The company’s platform stores data in a proprietary columnar representation, which optimizes the performance of analytical and reporting queries. It also provides high compression ratios, resulting in economic benefits for customers. The company also enables customer choice by allowing customers to leverage its platform for data stored in Parquet and Apache Iceberg tables in customer-managed external storage.
Micro-partitioning: The company’s platform automatically partitions all data it stores without the need for user specification or configuration. It creates small files called ‘micro partitions’ based on size, enabling optimizations in query processing to retrieve only the data relevant for user queries, simplifying user administration and enhancing performance.
Metadata: When data is ingested or accessed through interoperable storage, the company’s platform automatically extracts and stores metadata to speed up query processing. It does so by collecting data distribution information for all columns in every micro-partition.
Semi-structured and unstructured data: In addition to structured, relational data, the company’s platform supports semi-structured data, including JSON, Avro, and Parquet, and unstructured data, including PDF documents, screenshots, recordings, and images. Data in these formats can be ingested and queried with performance comparable to a relational, structured representation.
Query Capabilities: The company’s platform is engineered to query petabytes of data. It implements support for a large subset of the ANSI SQL standard for read operations and data modification operations. The company’s platform provides additional features, including:
Time travel: The company’s platform keeps track of all changes happening to a table, which enables customers to query previous versions based on their preferences. Customers can query as of a relative point in time or as of an absolute point in time. This has a broad array of use cases for customers, including error recovery, time-based analysis, and data quality checks.
Cloning: The company’s architecture enables it to offer zero-copy cloning, an operation by which entire tables, schemas, or databases can be duplicated—or cloned—without having to copy or duplicate the underlying data. The company’s platform leverages the separation between cloud services and storage to be able to track independent clones of objects sharing the same physical copy of the underlying data. This enables a variety of customer use cases, such as making copies of production data for data scientists, creating custom snapshots in time, or testing data pipelines.
Compute Model: The company’s platform offers a variety of capabilities to operate on data, from ingestion to transformation, as well as rich query and analysis. The company’s compute services are primarily presented to users in one of two models, either through explicit specification of compute clusters or through a number of serverless features.
Compute Clusters: The company’s platform exposes compute clusters as a core concept. The company’s customers can create as few or as many compute clusters as they want and specify compute capacity at tiered levels. These clusters can be configured to run only when needed, with cluster instantiation operations typically completed in seconds. Compute clusters can also be configured as a multi-cluster warehouse in which the company’s platform can automatically add and remove additional instances of a given cluster to address variations in query demands. This gives it the ability to offer extremely high levels of concurrency with a simple configuration specification. The company also offers warehouse recommendations for workloads that has large memory requirements, such as ML use cases.
Serverless features: The company offers a number of additional services that automatically provide the capacity its customers require. For example, the company’s data ingestion service automatically ingests data from cloud storage and allocates compute capacity based on the amount of data ingested; its clustering service continuously rearranges the physical layout of data to ensure conformity with clustering key specifications, improving performance; its materialized views service propagates changes from underlying tables to views that has materialized subsets or summaries; its replication service moves data between regions or clouds; its search optimization service analyzes changes in data, maintains information that speeds up lookup queries, and accelerates queries performing lookups of specific values; and its query acceleration service automatically offloads parts of eligible queries to shared, flexible compute clusters to handle high-burst workloads.
Data Sharing: In the company’s platform, data sharing within any given region is defined through access control and not through data movement. As such, the data consumer sees no latency relative to updates from the data provider and incurs no cost to move or transform data to make it usable. Based on the same technology principles, the company’s platform enables data clean rooms.
Global Infrastructure
Replication: The company’s platform enables customers to replicate data from one region or public cloud to another region or public cloud while maintaining transactional integrity, either at the granularity of a database or an account.
Business continuity: The company’s platform enables failing over and failing back a database and redirecting clients transparently across regions or public clouds. This provides an integrated and global disaster recovery capability.
Global listings for sharing: The company’s platform enables a listing to be published globally to access consumers across regions or public clouds.
Built-in Security: The company built its platform with security as a core shared responsibility between it and its customers. The company’s platform provides a number of configurable capabilities for customers to confidently use its platform while preserving the security requirements of their organizations, including:
Authentication: The company’s platform supports a number of authentication capabilities, including federated authentication with a variety of identity providers, as well as support for multi-factor authentication.
Access control: The company’s platform provides a fine-grained, customer-configurable security model based on role-based access control. It provides granular privileges on system objects and actions.
Data encryption: The company’s platform encrypts all customer data uploaded to its platform, both at rest and in transit over untrusted networks, and simplifies operations by providing automatic re-keying of data. It also supports customer-managed keys, where an additional layer of encryption is provided by keys controlled by customers, giving them the ability to control access to the data.
Sales and Marketing
The company sells its platform primarily through its direct sales team, which consists of field sales and inside sales professionals segmented by customer industry, size, and region. The company’s direct sales team is primarily focused on new customer acquisitions and driving increased use of its platform by existing customers. The breadth of the company’s platform allows it to engage at every level of an organization, including data analysts and data engineers through the company’s self-service model and senior executives through its direct sales team. The substantial majority of the company’s global sales and marketing efforts are carried out by teams located in North America. Outside of North America, the company has dedicated direct sales teams for the EMEA and APJ regions for organizations of all sizes.
Many organizations initially adopt the company’s platform through a self-service trial on its website. The company deploys a range of marketing strategies to drive traffic to its website and usage of the company’s platform. The company’s marketing team combines the creation of inbound demand with direct marketing, business development, and efforts targeted at business and technology leaders.
Partnerships
The company’s partnership strategy is focused on delivering complete end-to-end solutions for its customers, driving general awareness of the company’s platform, and broadening its distribution and reach to new customers. The company’s Snowflake Partner Network is a global program that manages its business relationships with a broad-based network of companies. The company’s partnerships consist of channel partners, system integrators, data providers, applications, AI solutions, and other technology partners. Collectively, these partners help it source leads, execute transactions, and deliver training, implementation, and business value for its customers. The company’s system integrator partners help make the adoption of and migration to its platform easier by providing implementations, value-added professional services, managed services, and resale services. The company’s technology partners provide strategic value to its customers by providing software tools, such as data loading, business intelligence, AI, data governance, and security, as well as data sets and applications on the Snowflake Marketplace, to augment the capabilities of its platform. The company continues to invest in formal alliances with the leading consulting, data management, and implementation service providers to help its customers migrate their legacy database solutions to the cloud. Additionally, with Snowflake Ventures, Snowflake is investing in key partners that are innovating with the AI Data Cloud, and the company has over 1,100 partners in its Powered by Snowflake Start Up program, many of whom are building next-generation data-intensive applications.
Competition
The company’s competition includes large, well-established, public cloud providers that generally compete in all its markets, including Amazon Web Services (AWS), Microsoft Azure (Azure), and Google Cloud Platform (GCP).
Seasonality
Historically, the company has received a higher volume of orders from new and existing customers in the fourth fiscal quarter of each year. As a result, the company has historically seen higher net cash provided by operating activities and non-GAAP free cash flow in the first and fourth fiscal quarters of each year, and its sequential growth in remaining performance obligations has historically been highest in the fourth fiscal quarter of each year. In addition, while historically revenue has been higher in the company’s fourth fiscal quarter, it is also the most negatively impacted by reduced holiday consumption (for the year ended January 2025).
Intellectual Property
As of January 31, 2025, the company held more than 900 issued U.S. patents and had more than 400 U.S. patent applications pending. The company also held more than 200 issued patents in foreign jurisdictions. As of January 31, 2025, the company held more than 35 registered trademarks in the United States and also held more than 640 registered or protected trademarks in foreign jurisdictions.
History
The company was founded in 2012. It was incorporated in the state of Delaware in 2012. The company was formerly known as Snowflake Computing, Inc. and changed its name to Snowflake Inc. in 2019.
