IBM Acquires Confluent for $11 Billion to Bolster Real-Time AI Data Capabilities

IBM has completed its acquisition of Confluent in a deal valued at approximately $11 billion, as it moves to strengthen its position in real-time data infrastructure for enterprise AI.

Under the terms of the agreement, IBM acquired all outstanding Confluent shares for $31 per share in cash, underscoring the strategic importance of data streaming in scaling artificial intelligence deployments.

The acquisition brings Confluent’s data streaming platform—used by more than 6,500 enterprises, including 40% of Fortune 500 companies—into IBM’s portfolio, creating a combined offering focused on delivering continuous, real-time data to AI systems.

The deal targets a critical challenge facing enterprises: while AI adoption is accelerating, many organizations remain constrained by fragmented data systems where information is delayed or siloed, limiting the effectiveness of AI models and automation.

Rob Thomas said the integration will enable businesses to move trusted data across systems in real time, allowing AI models and agents to act on current information rather than outdated datasets.

Confluent’s platform is built on Apache Kafka, a widely adopted technology for managing real-time data flows across enterprise environments.

The platform is already embedded in operations across industries. Companies such as Michelin, L'Oréal, BMW Group and Ticketmaster use it to stream data across supply chains, manufacturing systems and customer platforms.

Jay Kreps said joining IBM will allow the company to scale its mission of making real-time data streaming foundational to enterprise operations.

The acquisition comes as demand grows for infrastructure that can support AI systems operating in real time. According to International Data Corporation, more than one billion new applications could emerge by 2028, driven by AI and automation.

IBM said the combined platform will provide a unified data layer across on-premises and hybrid cloud environments, enabling AI models, agents and automated workflows to operate with the speed and context required for production use.

The deal reflects a broader shift in enterprise technology, where real-time data streaming is becoming central to scaling AI beyond experimentation into full-scale deployment.