Snowflake's Mission: Demolishing Data Limitations in the Era of Enterprise AI

Welcome to the AI Infrastructure Unicorns series. They provide the hardware, software, and services necessary for Generative AI companies but even if GenAI will someday become extinct, these infrastructure builders won’t stay without the job as they serve a much bigger industry of AI/ML models in general.

Introduction

A few days ago, Snowflake made headlines with their Arctic LLM, an enterprise-grade model that is ready to rival DBRX, an LLM from one of their main competitors, Databricks. Snowflake Arctic offers top-tier intelligence at low training costs under $2 million, utilizing a Dense-MoE Hybrid architecture with 17B active parameters out of 480B total. It’s available under an Apache 2.0 license, promoting open access and collaboration. We’ve recently published a profile on Databricks (read here) as part of our AI Infra series and though Snowflake can’t be called a unicorn anymore since their IPO in 2022, we decided to tell their story as well. With Arctic, they demonstrated that they play high stakes and want to join the generative AI narrative, delivering its potential to enterprises. 

Let's explore Snowflake’s history, its struggles after the IPO, the CEO-for-the-task strategy, the tech specs for Arctic, and what makes it special, as well as the future prospects for Snowflake.

Today we will cover:

1. How it all started – two architects and a startup founder in the cloud

2. Let’s add some fairy dust – AI comes in

3. Arctic – what’s special about it?

4. Mission – demolish any and all limits to data users

5. Financial situation and current struggles

6. Conclusion

How it all started

Snowflake was born in 2012 out of a shared frustration with traditional data warehousing solutions and a vision to redefine data analytics. The goal was to create a new data warehouse that could unite all users, data, and workloads in a single cloud service. Benoit Dageville, Thierry Cruanes, and Marcin Żukowski brought significant expertise in data warehousing to the table. Dageville and Cruanes had been architects at Oracle for over a decade. And only Żukowski had startup experience as a founder, having sold Vectorwise to Ingres Corp. in December 2010. Unusually, the founders decided not to appoint any of themselves as CEO. Instead, after raising seed and Series A rounds from Sutter Hill and working in stealth mode for two years, they launched in 2014 with a product, a Series B round, and their first appointed CEO, former Microsoft and Juniper Networks executive Bob Muglia. This ‘CEO-for-the-task’ approach would become a hallmark of Snowflake.

Their first product, the Elastic Data Warehouse launched on Amazon Web Services (AWS), set the stage for rapid evolution and expansion. By 2015, Snowflake was gaining traction, evidenced by a $45 million Series C funding round and a first-place win at the Strata + Hadoop World Startup Showcase.

The momentum continued with the introduction of new features like Snowpipe for continuous data ingestion and innovative data sharing capabilities, bolstering customer confidence and investment. Snowflake’s platform soon expanded beyond AWS to include Microsoft Azure, further broadening its market reach.

In 2019, with an IPO in mind, the founders decided to invite Frank Slootman as CEO, given his experience leading two companies through successful IPOs: Data Domain and ServiceNow. Under Slootman’s leadership, Snowflake broadened its product offerings, venturing into Google Cloud and launching the Snowflake Data Marketplace and Snowgrid.

He didn't let them down during the company's IPO in 2020. It was a landmark event, raising $3.4 billion and valuing Snowflake (SNOW) at $33 billion, making it the largest-ever IPO for a software company at the time. The stock finished its first day of trading above $250 per share (from $120 initial set). 

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