Databricks, the Data and AI company, today announced it has entered into a definitive agreement to acquire MosaicML, a leading generative AI platform. Together, Databricks and MosaicML will make generative AI accessible for every organization, enabling them to build, own and secure generative AI models with their own data. The transaction is valued at approximately USD 1.3 billion, inclusive of retention packages.
MosaicML is known for its state-of-the-art MPT large language models (LLMs). With over 3.3 million downloads of MPT-7B and the recent release of MPT-30B, MosaicML has showcased how organizations can quickly build and train their own state-of-the-art models using their data in a cost-effective way. Customers such as AI2 (Allen Institute for AI), Generally Intelligent, Hippocratic AI, Replit and Scatter Labs leverage MosaicML for a wide variety of generative AI use cases.
“Every organization should be able to benefit from the AI revolution with more control over how their data is used. Databricks and MosaicML have an incredible opportunity to democratize AI and make the Lakehouse the best place to build generative AI and LLMs,” said Ali Ghodsi, Co-Founder and CEO, Databricks. “Databricks and MosaicML’s shared vision,rooted in transparency and a history of open source contributions, will deliver value to our customers as they navigate the biggest computing revolution of our time.” Giving Organizations a Simple, Fast Way to Build, Own and Secure Models Today, virtually every organization is exploring how best to use generative AI and LLMs, and every leader is considering how they leverage these new innovations while retaining control of their most precious resource: their data. Organizations and executives want to be able to build, own and secure their own models.
The Databricks Lakehouse Platform, combined with MosaicML’s technology, will offer customers a simple, fast way to retain control, security, and ownership over their valuable data without high costs. According to MosaicML, automatic optimization of model training provides 2x-7x faster training compared to standard approaches. Combined with near linear scaling of resources, multi-billion-parameter models can be trained in hours, not days. With Databricks and MosaicML, training and using LLMs will cost thousands of dollars, not millions. Databricks’ unified Data and AI platform combined with MosaicML’s generative AI training capabilities will provide a platform robust enough to serve the world’s largest organizations and flexible enough to address a broad range of AI use cases. Databricks and MosaicML’s Shared Vision The entire MosaicML team, including MosaicML’s industry-leading research team, is expected to join Databricks after the transaction closes. MosaicML’s machine learning and neural networks specialists conduct pioneering AI research to improve model training efficiency.
The team is behind some of today’s most popular and advanced open-source foundation models, such as MPT-30B, as well as the training algorithms powering MosaicML’s products. MosaicML’s platform will be supported, scaled, and integrated over time to offer customers a seamless unified platform where they can build, own and secure their generative AI models. Databricks and MosaicML will give customers greater choice in building their own models, training models with their own unique data, and creating differentiating IP for their businesses.
“At MosaicML, we believe in a world where everyone is empowered to build and train their own models, imbued with their own opinions and viewpoints — and joining forces with Databricks will help us make that belief a reality,” said Naveen Rao, Co-Founder and CEO, MosaicML. “We started MosaicML to solve the hard engineering and research problems necessary to make large scale training more accessible to everyone. With the recent generative AI wave, this mission has taken center stage. Together with Databricks, we will tipthe scales in the favor of many — and we’ll do it as kindred spirits: researchers turned entrepreneurs sharing a similar mission. We look forward to continuing this journey together with the AI community
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