This entity focuses on advancing AI through rigorous science, particularly in the areas of Large Language Models (LLMs) and generative AI. They develop and release open-source technologies like DBRX and MPT models, alongside tools for efficient deep learning training and evaluation. The work aims to provide high-quality, commercially usable models and performance optimizations for the AI community.
Funding
$1B raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
WIFounders
Product
Problem
Enterprises often manage data across disparate storage systems, rely on separate tools for ETL, warehousing, and machine‑learning, and face high operational overhead when scaling AI workloads. This fragmentation leads to longer time‑to‑insight, inconsistent governance, and costly, inefficient model training and inference.
Solution
The platform provides a unified lakehouse architecture that consolidates data ingestion, storage, governance, analytics, and AI into a single managed service. It combines open‑source components such as Delta Lake, Unity Catalog, and the Mosaic AI stack to deliver reliable, secure data management and high‑performance compute across any public cloud. Built‑in pipelines automate batch and streaming ETL, while integrated MLflow, Composer, LLM Foundry, and DBRX enable rapid development, fine‑tuning, and deployment of large language models and generative AI at scale. The service exposes serverless SQL, interactive notebooks, and API‑first interfaces, allowing data engineers, scientists, and application developers to collaborate on end‑to‑end data‑to‑AI workflows without moving data between silos.
Target Audience
Primary customers are large enterprises and data‑driven organizations that need a single platform for data engineering, analytics, and AI, including data engineers, data scientists, ML engineers, and application developers. The solution also serves technology‑focused startups seeking scalable lakehouse infrastructure.
Features
- Delta Lake storage with ACID transactions and schema enforcement for reliable data lakes.
- Unity Catalog unified data governance, fine‑grained access control, and lineage across all assets.
- Delta Sharing zero‑copy data exchange for secure collaboration with external partners.
- Serverless Databricks SQL warehouse delivering auto‑scaling, cost‑optimized analytics queries.
- Data Engineering runtime supporting batch, streaming, and orchestration via Apache Spark and native job scheduling.
- Mosaic AI stack: Composer training library, LLM Foundry for efficient large‑model training/fine‑tuning, DBRX and MPT open‑source LLMs, and Mosaic Diffusion for text‑to‑image generation.
- Integrated MLflow model registry, experiment tracking, and model serving with FP8 quantization for low‑latency inference on GPUs such as NVIDIA H100.
- Multi‑cloud deployment (AWS, Azure, GCP, SAP) with unified UI, REST/SQL APIs, and IDE integrations for VS Code, PyCharm, and Jupyter.
- Enterprise‑grade security: end‑to‑end encryption, role‑based access, and compliance‑ready audit logs.