Databricks and Microsoft extend Azure partnership through 2030s with custom chip focus

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Databricks and Microsoft are expanding their strategic partnership through the 2030s, with the data analytics firm committing to increased use of Microsoft Azure and Azure Cobalt custom chips. The deal marks a significant win for Microsoft's cloud business as enterprise AI adoption accelerates, with Databricks valued at $188 billion and serving over 20,000 organizations globally.

Databricks Microsoft Partnership Strengthens Cloud Infrastructure Commitment

Databricks announced an expanded long-term partnership with Microsoft that extends through the 2030s, signaling a major commitment to Microsoft Azure infrastructure and custom silicon

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. The San Francisco-based firm, recently valued at $188 billion in a funding round expected to close this summer, will significantly increase its use of the Azure platform to run core business operations and analytics while building its unified data lakehouse on the cloud infrastructure

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Source: SiliconANGLE

Source: SiliconANGLE

The deal represents a sizeable win for Microsoft's cloud business as enterprise AI adoption accelerates across industries. Databricks' platform helps users ingest, analyze and build AI applications using complex data from various sources, serving over 20,000 organizations globally, including 70% of Fortune 500 companies

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Azure Cobalt and Arm-Based Processors Drive Performance Gains

A key component of the partnership involves Databricks expanding its use of Azure Cobalt, Microsoft's Arm-based processors designed for data-intensive workloads and agentic AI workloads

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. Databricks currently uses Cobalt 100 processors and plans to adopt Cobalt 200, which Microsoft claims delivers up to 50% better performance and provides memory encryption by default

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"With Databricks deepening its investment in Azure Databricks and Azure Cobalt-powered infrastructure, customers will benefit from greater performance, efficiency, and scale for their most demanding workloads," said Judson Althoff, CEO of Microsoft's Commercial Business . The move toward Azure custom chips reflects a broader industry trend where cloud providers develop specialized silicon to optimize performance and cost control for AI applications.

Enterprise AI Integration Across Microsoft Product Suite

Under the partnership, Microsoft will continue integrating Databricks' AI capabilities across its product ecosystem, including Microsoft 365, Teams, Copilot, Power BI, OneLake, Purview and Microsoft Foundry

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. The integrations will include Databricks' conversational analytics tool Genie, an AI assistant that connects users and agents with an organization's proprietary data and business context.

Genie Ontology provides the business concepts and relationships needed to interpret that data, while Unity AI Gateway gives companies a centralized way to manage models, agents, usage and costs with proper security and governance

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. The companies are targeting a persistent challenge in enterprise AI: although organizations possess large amounts of operational data, they often struggle to connect models and agents to trusted business knowledge while maintaining appropriate controls.

Market Implications and Customer Adoption

The partnership addresses what Judson Althoff describes as the defining characteristic of next-generation enterprise AI: "how effectively organizations turn their unique knowledge into intelligence"

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. Azure Databricks is already deployed by thousands of organizations, including Banco Bradesco SA, Electrolux AB, Sumitomo Mitsui Banking Corp., Unilever PLC and Major League Baseball's Cincinnati Reds

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Source: ET

Source: ET

For Microsoft Azure, securing a deeper commitment from one of the most valuable private companies represents a strategic advantage in the competitive cloud market. As enterprises accelerate AI deployments, the ability to offer integrated data platforms with governance frameworks becomes increasingly critical. The extended timeline through the 2030s suggests both companies expect sustained growth in enterprise AI adoption and are positioning for long-term market leadership in helping organizations operationalize AI with their own data assets.

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