Microsoft Corporation (MSFT.US) and Databricks extend strategic partnership to the 2030s, jointly promoting the large-scale implementation of enterprise AI.

On July 23rd, Microsoft Corporation (MSFT.US) and the data and artificial intelligence company Databricks announced an expansion of their strategic partnership lasting over a decade, extending the collaboration until the 2030s, aimed at helping enterprises build and deploy enterprise-grade AI applications based on their own business data. According to the agreement, Databricks will further deepen its use of Microsoft Corporation's Azure cloud platform, with its core business operations and unified lakehouse architecture running on Azure Databricks, and adopting Microsoft Corporation's next-generation Azure Cobalt infrastructure based on Arm architecture to enhance performance and energy efficiency. Azure Databricks is a solution jointly developed by both parties, enabling the unified integration of data, analytics, and AI on the Azure cloud platform. On the infrastructure level, Databricks will expand its use of Microsoft Corporation's in-house Arm architecture chip Azure Cobalt. The company is currently running workloads on the Cobalt 100 processor and plans to further adopt Cobalt 200 Microsoft Corporation claims that this chip can provide up to a 50% performance improvement compared to its predecessor, and enables memory encryption by default. This move indicates that, in the context of increasing demand for AI computing power, both parties are optimizing performance for data-intensive and AI workloads through customized chip solutions. Both companies stated that Microsoft Corporation will continue to integrate Databricks' data and AI platform capabilities into its full product line, including features such as the AI collaborator Genie embedded directly into customer workflows. Databricks Co-founder and CEO Ali Ghodsi said, "By deeply integrating Databricks Genie and Unity AI Gateway into Microsoft Corporation's product ecosystem, we are helping enterprises unify their data assets and embed AI in business knowledge. This enables customers to fully realize the value of intelligent models and assets, while effectively controlling costs and ensuring governance compliance."