IBM is reportedly closing in on a deal to buy data software company Confluent for roughly $11 billion. The Wall Street Journal broke the news on Sunday, citing unnamed sources, which immediately sent confluent stock soaring more than 20% in after-hours trading.
If the deal goes through, it would value the company well above the current confluent market cap, which sat around $8 billion before the reports surfaced. While IBM stock saw a slight dip following the news, the move signals that the tech giant is willing to pay a high price to secure the infrastructure needed for modern data processing.
A turnaround for shareholders
The potential IBM Confluent deal comes after a difficult year for the data company. Before this rally, the confluent stock price was down about 17% year-to-date, hurt by broader market skepticism regarding AI software valuations. Investors who held on through that volatility are now looking at a substantial payout if the confluent acquisition is finalized.
The deal could be announced as early as Monday, though sources caution that talks could still fall apart.
What does Confluent do?
For those looking at the $11 billion price tag and asking what does Confluent do, the answer lies in how modern applications handle data. Founded by the creators of Apache Kafka, Confluent built a platform that processes data in real-time.
Unlike traditional databases that store information to be looked at later, Confluent handles “data in motion.” This allows businesses to process things like bank transactions, inventory changes, and customer interactions the moment they happen. This capability is essential for companies trying to build AI applications that rely on live data rather than old records.
IBM’s infrastructure push
Buying Confluent would be IBM’s second infrastructure purchase this year, following its $6.4 billion purchase of HashiCorp. By adding Confluent’s real-time streaming capabilities to its portfolio, IBM secures a critical piece of the backend plumbing that enterprises need to run hybrid cloud and AI workloads.
