Databricks’ $134B Valuation Puts the Enterprise AI Data War in Focus

Databricks has become one of the clearest signals of where enterprise AI spending is heading. Not only toward models, but toward the data platforms needed to make those models useful inside large businesses.

The company recently closed a funding round of more than $7 billion, including $5 billion in equity financing at a $134 billion valuation, according to a May 26 Inc. profile by David H. Freedman. That came about five months after Databricks completed a $1 billion raise at a valuation above $100 billion.

The scale of the valuation reflects more than investor appetite for AI. Databricks and Snowflake are now locked in a less visible but consequential fight to define how enterprises prepare, govern, and operationalize data for AI. While OpenAI, Anthropic, and Google dominate the model conversation, Databricks and Snowflake are competing for a layer closer to business execution: the platform where corporate data becomes usable for analytics, automation, and agentic AI.

Why should ERP leaders pay attention? Because AI adoption will not be decided by models alone. It will depend on whether companies can make structured and unstructured data usable across finance, supply chain, HR, commerce, customer operations, and industry-specific processes. Databricks’ rise shows how much value the market is placing on that foundation.

Early Data Bet Meets the AI Moment

Databricks was founded in 2013 by researchers from the University of California, Berkeley’s AMPLab, who had developed Apache Spark, an open-source data-processing engine designed to help structure and analyze raw data. The original premise was that Spark could help organizations beyond technology giants work with unstructured data at scale.

That problem has only become more urgent. Most organizational data is unstructured, including text, images, documents, and other information that does not sit neatly in spreadsheets or traditional database fields. Unstructured data is also critical to building and running AI systems.

Databricks’ early market challenge was timing. Many companies were not yet ready for large AI projects and lacked the engineering talent to use big data tools effectively. That changed after the release of ChatGPT in late 2022, when enterprises began looking for ways to use their own data in AI systems. Databricks had already spent years building around the problem that suddenly became central to enterprise AI strategy.

The company expanded beyond unstructured data into structured data as well, creating what it calls the Data Lakehouse: a single platform intended to handle multiple types of data. A Microsoft agreement to bundle Databricks products with Azure helped the company pass a $100 million annual revenue run rate by the end of 2018. By this February, Databricks said its annual revenue run rate had reached $5.4 billion, with fourth-quarter revenue up more than 65% year over year.

Tomasz Tunguz, a general partner at Theory Ventures, told Inc. the acceleration was unusual at Databricks’ scale. “Usually when a company grows this big, you see its growth rate slowing down,” he said. “It’s exceptional that Databricks’ growth has been accelerating as it scales.”

Snowflake’s Lead Meets the AI Shift

Snowflake entered the race from the opposite direction. Founded in 2012, it built its lead around structured enterprise data in the cloud, making data easier to access for SaaS applications and business intelligence tools. By the time Snowflake went public in September 2020, its annual revenue run rate exceeded $500 million, its year-over-year growth rate was 174%, and the IPO brought in $3.4 billion at a $70 billion valuation.

That structured-data strength became a vulnerability when enterprise AI demand shifted attention to unstructured data. Inc. describes Snowflake as having established itself as the dominant company in enterprise data management before Databricks became a serious threat, particularly because Snowflake’s software had not been optimized for unstructured data or AI tools.

Snowflake has worked to close those gaps. The company has pushed into open-source databases, unstructured data, and AI, while appointing former Google executive Sridhar Ramaswamy as CEO to accelerate its AI capabilities. Snowflake executive vice president of product Christian Kleinerman told Inc. the company continues to emphasize simplicity, ease of use, governance, security, and privacy.

Snowflake is still growing. Wolfe Research’s Alex Zukin told Inc. the company is growing about 30% annually and its growth rate has been accelerating, a rare pattern for a publicly held, multibillion-dollar software company. Snowflake’s stock, however, had fallen 35% for the year through early May as investors pulled back from SaaS-adjacent companies.

Acquisition Race Shows Where AI Competition Is Going

The Databricks-Snowflake “rivalry” is increasingly playing out through acquisitions. Both companies are buying AI and data-management startups to add capabilities faster than they could build them internally.

Databricks has acquired multiple AI companies, including MosaicML, which it bought for about $1.3 billion. MosaicML brought technology for training and customizing generative AI models for individual businesses. Databricks also acquired Arcion, Neon, and Mooncake Labs, which Inc. describes as helping make high-speed transaction-processing data available on the fly.

Another major acquisition was Tabular, of which technology allowed databases to work with software from other database vendors. Per Inc., Snowflake had been trying to acquire Tabular before Databricks stepped in with a much higher offer and bought it for more than $1 billion.

Snowflake has been active as well. It acquired Neeva, a startup building its own large language model, for roughly $185 million. The deal also brought in Ramaswamy, who had been leading Neeva before becoming Snowflake’s CEO. Snowflake has also partnered with OpenAI and other large language model vendors.

The acquisition activity shows the fight is no longer only about storing or querying data. It is about who can provide the data infrastructure, model customization, governance, and transaction-level connectivity required for enterprise AI and agents to operate inside real business systems.

Agentic AI Raises the Stakes

Both Databricks and Snowflake are now eyeing agentic AI, where autonomous agents handle more complex enterprise tasks. Agentic AI cannot function on disconnected or poorly governed data. It requires current operational context, access to structured and unstructured information, and the ability to interact safely with business processes. That puts data platforms into a more strategic position inside the enterprise architecture.

Databricks CTO of Neural Networks Hanlin Tang told Inc. companies are already deriving real value from agentic AI, adding he would not have said that a year earlier.

One customer example in the Inc. profile is Albertsons, which uses Databricks-enabled AI systems to optimize promotional spend. Karthik Iyer, group vice president and transformation leader for merchandising technology at Albertsons Companies, said calculations that previously took days and produced inconsistent results now return answers in seconds with consistent output.

That example is especially relevant for ERP and enterprise application leaders. Promotional planning, inventory, pricing, and supply chain decisions sit close to core operational systems. If AI data platforms can change the speed and consistency of those decisions, the impact will extend beyond analytics into planning and execution.

Sponsor Industry‑Grade Research

Editor’s note: Read the full Inc. Magazine profile here: https://www.inc.com/david-h-freedman/databricks-took-the-lead-racing-toward-a-massive-ipo/91340390