Databricks said it will acquire MosaicML, which is a generative AI platform specializing in large language models (LLMs), for $1.3 billion.
The news lands as data platforms such as Snowflake, Databricks and MongoDB race to provide ways for enterprises to build their own generative AI models while keeping corporate data secure. The data platform game is focused on fast training of LLMs and models with strong data governance.
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MosaicML is best known for its MPT LLMs. For instance, MosaicML has more than 3.3 million downloads of MPT-7B and MPT-30B LLMs.
Databricks will take MosaicML and integrate its models into Databricks Lakehouse. According to Databricks, MosaicML will enable customers to train LLMs in hours not days and for "thousands of dollars, not millions."
Constellation Research analyst Doug Henschen said:
“Databricks has spent the last few years building up the house side of its Lakehouse platform, but the company’s beginnings were as a data science platform. It can’t afford to lose its distinction and differentiation as a platform for data science, so the acquisition of MosaicML makes complete sense. What’s more, it’s a good fit in terms of company culture and location.”
The $1.3 billion price tag is inclusive of retention packages. Databricks said it expects the entire MosaicML team to join the company. Retaining MosaicML's team will be critical as Databricks integrates and scales the combined platform.
In a blog post MosaicML said it started talking to Databricks about partnerships, but it became clear the effort would scale better combined. MosaicML said:
“Generative AI is at an inflection point. Will the future rely mostly on large generic models owned by a few? Or will we witness a true Cambrian explosion of custom AI models that are built by many developers and companies from every corner of the world? MosaicML’s expertise in generative AI software infrastructure, model training, and model deployment, combined with Databricks’ customer reach and engineering capacity, will allow us to tip the scales in the favor of the many.”
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