Espresso AI, a company founded by former Google experts from DeepMind and Google Cloud, has launched a new solution designed to transform Databricks into an "agentic lakehouse." This LLM-driven platform aims to tackle cost inefficiency head-on, leveraging advanced AI to automate optimization and reportedly reduce Databricks costs by up to 50% without manual intervention.
Espresso AI launches a solution to turn Databricks into an "agentic lakehouse."
The platform uses AI agents to automate optimization and reduce costs by up to 50%.
It was founded by ex-Googlers with backgrounds in DeepMind and Google Search.
Core features include an Autoscaling Agent, a Scheduling Agent, and a Query Agent.
The solution addresses low warehouse utilization, which often wastes half of a user's bill.
A 6-month beta showed success with enterprises, cutting one company's bill in half.
The launch positions Espresso AI as a key enabler for Databricks as it experiences explosive growth and aims to compete fiercely with rivals like Snowflake. “Databricks is seeing explosive growth with their Data Lakehouse product,” said Ben Lerner, CEO of Espresso. “But if they want to catch up with Snowflake adoption they’ll need to be as optimized and cost efficient as possible. By leveraging Espresso AI, Databricks customers can cut their bill in half and see their efficiency skyrocket without any manual effort.” This addresses a critical pain point, as the average Databricks user's warehouse utilization is between 40% and 60%, meaning a significant portion of spending is wasted on idle resources.
The platform's efficiency is driven by three core AI agents that work in concert. The Autoscaling Agent is trained on a customer's unique metadata to predict and adapt to workload spikes and fluctuations. The Scheduling Agent intelligently routes queries to existing machines with extra capacity instead of provisioning new ones, maximizing the use of paid-for resources. Finally, the Query Agent optimizes every piece of SQL before execution, leading to improved performance and lower costs across the board.
Founded by ex-Googlers Ben Lerner, Alex Kouzemtchenko, and Juri Ganitkevitch, Espresso AI has raised $11 million in seed funding. The company has validated its approach through a six-month beta with strong enterprise interest. “Espresso AI cut our bill in half with no lift from our side,” said Nataliia Mykytento, Head of Engineering at Minerva. “They were instrumental in reducing costs that were growing too fast for comfort.” By applying advanced machine learning to data infrastructure, Espresso AI provides a hands-free solution for enterprises to control cloud data costs and enhance operational efficiency.
Espresso AI uses machine learning to optimize modern data warehouses in real time. Founded by ex-Googlers, their platform applies research from DeepMind to significantly reduce Databricks and Snowflake costs.
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including Block, Comcast, Condé Nast, Rivian, Shell and over 60% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to take control of their data and put it to work with AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake, MLflow, and Unity Catalog.
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