Databricks CEO Challenges the Narrative That AI Will Kill SaaS
With AI accounting for over $1.4 billion in revenue, Databricks positions itself at the intersection of SaaS and AI infrastructure.
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Databricks on Monday disclosed that it has reached a $5.4 billion annualized revenue run rate, marking 65% year-over-year growth, with more than $1.4 billion now generated by its AI products. The figures, shared publicly by co-founder and chief executive Ali Ghodsi, are intended to counter a growing narrative in the tech industry that generative AI will erode the foundations of the software-as-a-service (SaaS) model.
According to Ghodsi, AI has not reduced demand for Databricks’ core offerings but has instead driven higher usage. While the company continues to be widely recognized as a cloud data warehouse provider—helping enterprises store and analyze large volumes of data—it increasingly positions itself as an AI-centric platform. That distinction is reflected in how private markets value the company, which recently closed a previously announced $5 billion funding round at a $134 billion valuation, alongside securing a $2 billion loan facility.
At the product level, Databricks is demonstrating how AI can complement rather than displace existing enterprise software. Ghodsi highlighted Genie, a large-language-model-powered interface that allows users to query data warehouses using natural language. Tasks that once required specialized query languages or custom reporting can now be handled conversationally, lowering technical barriers and broadening the pool of users. This shift, he said, has been a material contributor to the company’s recent growth.
The broader concern for SaaS companies, Ghodsi argues, is not that enterprises will abandon their “systems of record”—core platforms that store mission-critical business data—but that AI will make traditional user interfaces less visible. As natural language interfaces and agent-driven workflows become standard, the competitive moat created by years of user training on specific software interfaces may erode. In this scenario, products risk becoming interchangeable infrastructure rather than differentiated tools.
Databricks’ response has been to invest in AI-native infrastructure. The company recently introduced Lakebase, a database designed to work more effectively with AI agents. While still early, Ghodsi says the product’s initial revenue trajectory has exceeded that of the company’s original data warehouse at a comparable stage, suggesting potential demand for systems optimized for agent-based computing.
Despite its rapid growth and fresh capital, Databricks is not signaling near-term plans to go public. Ghodsi cited market volatility and lingering uncertainty following the 2022 downturn as reasons to prioritize balance-sheet strength over an initial public offering. With substantial capital on hand, he said, the company aims to secure a long-term runway and strategic flexibility as AI continues to reshape enterprise software.
