Databricks Aims to Own AI Workflows

This shift shows Databricks is trying to change the basis of competition from storing and querying data to building AI systems on top of that data. Snowflake won the cloud warehouse era by making SQL analytics easy for broad enterprise teams. Databricks is pushing the fight upstream and downstream, from data prep and model training to governance, so customers can keep data, permissions, models, and app building inside one stack.

• The clearest product move was buying MosaicML for $1.3B in 2023, which gave Databricks a native way to help enterprises train and tune their own models on private data instead of stopping at analytics dashboards. That made Databricks look less like a Spark company and more like an AI infrastructure vendor.
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• Unity Catalog is the practical wedge. It lets a company define who can see which tables, columns, and files across Databricks workloads, which matters more in AI because the same sensitive data used for BI can also feed model training, retrieval, and agent workflows. Governance becomes a buying trigger, not back office plumbing.
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• Snowflake is responding from the opposite starting point. It began with the warehouse buyer and has added Cortex and agent products on top, while Databricks began with engineers and data scientists, then pushed into SQL and BI. In large enterprises both often coexist, but the control point is moving toward whichever platform owns AI workflows around the data.
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Going forward, the winner is likely to be the platform that turns raw enterprise data into governed AI outputs with the fewest extra tools. Databricks has momentum because its AI push gives it more products to sell into the same data estate, while Snowflake will keep forcing it to make those AI workflows simpler for mainstream enterprise teams.