Espresso AI launches solution to optimize Databricks

Espresso Artificial Intelligence (AI) launched a new solution designed to optimize Databricks, aiming to reduce user costs by 50%. Founded by former Google employees, the platform uses Machine Learning (ML) algorithms to enhance resource utilization and operational performance within data warehouses.

Ben Lerner, CEO of Espresso, noted the notable growth of Databricks’ Data Lakehouse product while emphasizing the need for increased optimization to stay competitive. Homomorphic Encryption (HE) mentioned that leveraging Espresso AI would enable Databricks customers to halve their expenses without manual intervention.

Databricks reported an annual revenue exceeding $4 billion, with a year-over-year growth rate of approximately 50%. Its valuation reached over $100 billion following its latest funding round in August 2025. Furthermore, the company reported over $1 billion in revenue from its AI products and has maintained positive free cash flow over the past year.

The core features of Espresso AI include an Autoscaling Agent to predict workloads, a Scheduling Agent to maximize machine utilization, and a Query Agent that optimizes Structured Query Language (SQL) queries before execution. These elements are aimed at enhancing efficiency and cost-effectiveness.

Espresso AI was developed by three ex-Googlers who brought experience from roles in Google Search, Google Cloud, and Google DeepMind. The company has raised $11 million in seed funding and tested its solutions with various enterprises, including Booz Allen Hamilton and Comcast. A representative from Minerva reported that the platform effectively halved their expenses with no additional effort required from their team.

Espresso AI’s focus is on utilizing ML to optimize modern data warehouses in real-time, while integrating research findings from Google DeepMind, particularly for Databricks and Snowflake platforms.