Databricks acquires LLM pioneer MosaicML for $1.3B

Databricks has announced its definitive agreement to acquire MosaicML, a pioneer in large language models (LLMs), in a strategic move to democratize generative AI for organizations. The acquisition, valued at approximately $1.3 billion, including retention packages, highlights Databricks’ commitment to making AI accessible and solidifies its Lakehouse platform as a leading environment for building generative AI and LLMs.

MosaicML, known for its advanced MPT large language models, has garnered recognition for enabling organizations to construct and train state-of-the-art models efficiently and cost-effectively using their own data. By joining forces with Databricks, MosaicML aims to fulfill its mission of empowering everyone to build and train their own models while preserving control and ownership.

Through this acquisition, Databricks and MosaicML intend to provide organizations with a simple and rapid method to develop, own, and secure their models. The integration of Databricks’ Lakehouse Platform with MosaicML’s technology will allow customers to maintain control, security, and ownership of their valuable data without incurring high costs. MosaicML’s automatic optimization of model training enables faster training times, and the scalability of resources allows for the training of multi-billion-parameter models within hours, significantly reducing the cost of utilizing LLMs.

The unified Data and AI platform of Databricks, combined with MosaicML’s generative AI training capabilities, will create a robust and flexible platform capable of serving large organizations and addressing various AI use cases. The acquisition will see the entire MosaicML team, including its esteemed research team, joining Databricks.

The completion of the acquisition is subject to customary closing conditions, including regulatory clearances. Once finalized, the MosaicML platform will be progressively supported, scaled, and integrated into Databricks’ unified platform, empowering customers to build, own, and secure their generative AI models using their unique data and fostering differentiating intellectual property for their businesses.