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Databricks completes $10B funding round, raises $5.25B in debt - SiliconANGLE

Jan 22, 2025 - siliconangle.com
Databricks Inc. has completed a $10 billion funding round and raised an additional $5.25 billion in debt, led by JPMorgan Chase with participation from Barclays, Citi, and others. The Series J funding round, initially rumored in November, includes investments from Thrive Capital, Qatar Investment Authority, Temasek, Macquarie Capital, and Meta Platforms Inc. The investment from Meta aims to enhance collaboration on AI, particularly with Meta's Llama language models. Databricks, known for its cloud-based data platform, plans to use the funds to develop new AI products, make acquisitions, and expand its international operations. The company also aims to provide liquidity for employees and has postponed plans for a public offering until at least the second half of 2025.

Databricks' platform supports a data lakehouse architecture and offers features for AI model customization and prompt tuning using open-source tools like DSPy. The integration of Meta's Llama models allows customers to perform tasks such as summarization and translation without deploying large language models manually. CEO Ali Ghodsi emphasized the importance of data intelligence in leveraging generative AI's potential and achieving business goals. The recent acquisition of MosaicML Inc. for $1.3 billion has further expanded Databricks' AI capabilities, with thousands of customers now running Llama models on its platform.

Key takeaways:

  • Databricks has completed a $10 billion funding round and raised $5.25 billion in debt, with JPMorgan Chase leading the debt deal.
  • The funding round included participation from Thrive Capital, Qatar Investment Authority, Temasek, Macquarie Capital, and Meta Platforms Inc.
  • Databricks plans to use the funds to develop new AI products, make acquisitions, and expand its international operations.
  • The company has integrated Meta's Llama models into its platform, enabling enterprises to fine-tune AI models and automate prompt tuning using DSPy.
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