IBM acquiring Confluent to boost AI development capabilities

Complementary capabilities

Agents are now the focus of most enterprises' AI development initiatives.

Confluent is a leader in data streaming, and with agentic AI, streaming data is becoming the thing people really want. They want not only static data, but they want to have streaming, live data and analyze real-time data. … It's a gap for most vendors, so the fact that IBM made this acquisition is a big deal.

Agents are AI applications that can be trained with reasoning capabilities and contextual awareness, which enable them to act autonomously. For example, agents can take on tasks such as surfacing insights that lead to informed decisions and performing business processes that improve an enterprise's overall efficiency.

Without real-time data, however, agents are limited. Without platforms such as Confluent Cloud feeding them streaming data, they are unable to respond to immediate needs such as questions about where an order is in the supply chain or regarding an enterprise's current inventory.

IBM already provides hybrid and multi-cloud data management capabilities that enable customers to prepare data. In addition, with Watsonx, IBM provides agent orchestration capabilities. What IBM doesn't have is the connective tissue that links the two. Confluent will provide that, according to Rob Thomas, IBM's senior vice president of software and chief commercial officer.

"The missing piece to the puzzle was connecting agents and other applications to the infrastructure, which is the role that Confluent plays," he said. "We thought of this as a significant piece of what clients will need to implement and be successful with enterprise AI."

Like Thomas, Sanjeev Mohan, founder and principal of analyst firm SanjMo noted that, from IBM's perspective, the acquisition's value is that Confluent provides capabilities IBM didn't previously possess.

"This is a significant strategic deal for IBM as it modernizes its data platform to serve real-time agentic AI workloads," he said. "Confluent fills a critical gap in IBM's portfolio -- real-time data streaming at enterprise scale."

Meanwhile, just as Confluent's capabilities complement IBM from a technological perspective, the data management capabilities that will surround Confluent complement its capabilities, Mohan continued.

Particularly, once integrated into IBM's ecosystem, Confluent will benefit from connections to IBM's data lakehouse through watsonx.data and Red Hat OpenShift.

"This combination fills gaps on both sides," Mohan said.

Beyond technological integration, the deal benefits Confluent by providing economic stability, according to Catanzano.

Confluent's stock price hit a high over $93 per share in November 2021, but dropped to the $15-range in August before rebounding slightly over the past few months. Meanwhile, though broad ecosystems for analytics and data management that featured integrated tools from various specialists were common before interest in AI development surged over the past few years, now there's a trend toward simplification.

Rather than use data ingestion and integration tools from one vendor, vectorization capabilities from another, governance and observability from still another -- among other data management capabilities -- some enterprises now favor an end-to-end platform from a single provider. In addition to AWS, Google Cloud and Microsoft, vendors such as Databricks, Snowflake and Teradata also provide complete data platforms.

IBM is similarly expanding its breadth.

"Confluent has had a rough ride lately," Catanzano said. "Its stock price has been up and down, and a lot of people didn't see its value. They're a niche player at this point with what they do, while the market is moving toward a full data platform, so I think they see where the market is going."

While the acquisition removes Confluent from the volatility and scrutiny of the public markets and makes the vendor's niche capabilities one aspect of a broader data platform, perhaps the biggest benefit of the acquisition will be the exposure that comes with being part of a multi-national corporation, according to Kreps, Confluent's CEO.

Confluent claims about 6,500 customers, which represents a fraction of the total number of worldwide Kafka deployments. IBM's global sales and marketing teams, along with existing customer relationships, could dramatically increase Confluent's base.

"This is an opportunity to accelerate, to get out to more customers faster," Kreps said. "The role we want to have in organizations is to act as a central nervous system for the flow of data. That's obviously a very strategic position, and to get into that position, you need to have the right senior connectivity. For those reasons, this is a great opportunity for us to accelerate."

While seemingly beneficial to both IBM and Confluent, mergers and acquisitions come with inherent risk. Potential pitfalls, among others, include integration problems -- both cultural and technological -- and miscalculated synergies.

One potential problem with IBM's acquisition of Confluent has to do with the perception that Confluent is a cutting-edge vendor built on open-source technology, while IBM is a giant corporation that was founded in 1911, according to Mohan.

"Confluent built its brand as a cloud-native, open-source alternative," he said. "Some customers chose Confluent specifically to avoid hyperscaler lock-in with AWS, Microsoft or Google Cloud. Now they're owned by a legacy vendor."

A developing trend

Just as IBM's purchase of Confluent to aid AI development isn't the tech giant's first major acquisition of the year, the deal isn't the first of the year featuring a hyperscaler buying a niche data management vendor.

In May, Salesforce reached an agreement to acquire data integration specialist Informatica for $8 billion in a deal that ultimately closed in November. In addition, in October, data integration vendor Fivetran and data transformation specialist DBT Labs agreed to merge, bringing together complementary capabilities that aid AI development.

While two acquisitions and a merger don't make a trend, they could signal the start of a consolidation wave, according to Mohan.

AI initiatives are expensive, he noted. As a result, some enterprises are less likely to spend the overhead required to integrate tools from various vendors. In addition, tech giants are adding more managed services such as data integration and streaming data.

"Independent vendors are getting squeezed from two directions," Mohan said. "Staying independent requires massive R&D investment to keep pace with both threats simultaneously."

Catanzano likewise noted that there is movement away from data ecosystems featuring tools from various vendors toward using end-to-end data platforms.

"What's happening is that the big companies feel like they need to own every component now, from data ingestion all the way through AI, analytics and streaming data," he said. "Before, it was, 'We're good with an ecosystem,' and now we're seeing platform plays."

With the acquisitions of DataStax and Confluent, IBM is one of those aiming to be the sole provider for its customers, Catanzano continued.

"You can see the pattern," he said. "It's a whole platform strategy."

Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than 25 years of experience. He covers analytics and data management.