SEC Form DEFA14A filed by Confluent Inc.

UNITED STATES

SECURITIES AND EXCHANGE COMMISSION

Washington, D.C. 20549

SCHEDULE 14A

PROXY STATEMENT PURSUANT TO SECTION 14(a) OF THE

SECURITIES EXCHANGE ACT OF 1934

Filed by the Registrant x

Filed by a Party other than the Registrant ¨

Check the appropriate box:

Confluent, Inc.

(Name of Registrant as Specified In Its Charter)

(Name of Person(s) Filing Proxy Statement if other than the Registrant)

Payment of Filing Fee (Check the appropriate box):

This Schedule 14A filing consists of the following communications relating to the proposed acquisition of Confluent, Inc., a Delaware corporation (the “Company”) by International Business Machines Corporation, a New York corporation (“Parent”), pursuant to the terms of an Agreement and Plan of Merger, dated December 7, 2025, by and among the Company, Parent and Corvo Merger Sub, Inc., a Delaware corporation and a wholly owned subsidiary of Parent. The communications below were first used or made available on December 10, 2025.

CFLT - IBM Transaction Industry Analyst Press Conference Transcript

Participants:

Elizabeth Bamonte: Hi everyone, I’m Elizabeth Bamonte, the VP of Analyst Relations at IBM, and thank you so much for joining us today. Following Monday’s announcements of IBM’s intent to acquire Confluent, we wanted to bring the analyst community together today to provide you some more context on the strategic rationale behind this acquisition. There’s a number of synergies, potential to accelerate business innovation. We’re going to address some of your feedback today and of course answer your outstanding questions.

We’re very fortunate today to be joined by Rob Thomas, our Senior Vice President of Software and the Chief Commercial Officer at IBM, and Jay Kreps, the CEO and co-founder of Confluent. First, we’re going to hear from Rob. Then, we’re going to spend most of the time taking your questions, which you can start queuing up in the chat at any time. Any questions that we don’t cover today, please know that my team and of course myself are here to make sure that we address any of them in follow-up meetings or any emails that you would like to share through. And we’re really excited to bring this to you and also receive your feedback as we continue on this journey together.

With that, I’ve got some housekeeping notes. This webinar is being recorded at the request of IBM and it will be published to our analyst portal along with the presentation slides following our webinar today. Remaining in the meeting, you are consenting to being recorded. If you do not wish to be identified or if you do not wish to be recorded, please indicate that and please leave the session. Please use the chat as a reminder to submit any of your questions. We’re going to address as many questions as possible during today’s Q&A. And again, if we don’t get to your questions today, I’m happy to personally follow up with you via email or schedule a session thereafter.

All right, and without further ado, I’m going to turn over to Rob and Jay. And again, thank you to the analyst community for being here today.

Rob Thomas: Thanks, Beth, and great to be with you all. Jay and I have probably five to ten minutes of comments here at the start. And then as Beth said, we’d love to take some questions. I’ll remind you that we’ve announced our intent to acquire them, Confluent, we haven’t actually acquired them. And so we are a little limited on what we can answer, but we will do our absolute best to hit any questions that we can.

So let me kind of start with where, where’s IBM is today and then I’ll get into strategic rationale of the deal. I think IBM is a dramatically different company than 5 years ago. Many of you have followed us throughout that. 5 years ago we were 20% software. We were doing applications. We were doing infrastructure. We were doing a bunch of different things. You fast forward to now, we’ve really repositioned IBM as a software company. Nearly 50% of the revenue’s software and we’ve really refocused the portfolio. We have divested of healthcare applications, whether things that were, I’d say, outside of our core competence and we’ve really focused back on what we think we are good at and what we think IBM’s role in the world is which is really to be an infrastructure software provider, provide the most important technology both software and hardware for how businesses run, how they operate. So I’d say we feel very good about where we are. That said, we’re always thinking about how do we keep going forward.

And in our view, and we shared this in our investor presentation back in February, we did sense that we had a little bit of a gap in data. We have a good data business with warehouses, databases, but the world is constantly evolving. And so the thesis on Confluent kind of started with a view that there’s two major problems in the world. One is most of the data today is still batch and therefore it takes time. Data is not really in a usable form and it’s fairly static. So that was one issue. Second is as agents become more predominant in enterprise architectures, most of the communication of agents is point-to-point, meaning agent to agent. And there was there’s no, I’d say, unifying way to log events, log actions under an agentic architecture. And that’s what brought us to Confluent. And we think that together we can create what we’re calling a smart data platform which is real-time data capability, connectors, transport, processing of data underneath an agentic architecture. And we think this can be a huge catalyst for what IBM is doing in data today and also a huge growth opportunity for Confluent.

I would almost view this as back when we did Red Hat, OpenShift was a very small business, but we thought we were at an inflection point in the market. At that stage, it was in containers and we’ve been able to really accelerate IBM and Red Hat together, the adoption of containers. I would say the same analogy holds here for real-time data. That’s why when we’re looking at total addressable market, we think this is $100 billion plus in terms of the near-term opportunity of what we can do together. We’ll realize a lot of synergy on go to market. We intend to invest more in what Confluent is doing from an R&D perspective. And so we think this is a great fit to IBM and I’d say it has a great connectivity to what we did with Red Hat more recently with HashiCorp. I’ll kind of get into how we’re thinking about the whole IBM portfolio in a moment. But with that, let me hand it to Jay to kind of recap for everybody what is Confluent, why has it been so successful, and then I’ll come back with a few more thoughts on IBM. So Jay.

Jay Kreps: Yeah. Yeah, you know, happy to give a little bit of an introduction. You know, this is a technology area I’ve worked in for now, over 11 years, even before Confluent existed, starting with some of the open source technologies that myself and my co-founders helped to build. Then over the life of Confluent as we kind of brought that out to a larger customer base. I think the easiest way to understand this area is if you kind of divide up the things we do with data, a lot of the use cases around data are around data storage, right. Are databases and storage systems, you know, where does data go to sit. And Confluent in a sense is about how data flows between all those things, how all the parts of a company come together. And you know, of course, there’s been other technologies.

Rob Thomas: I’m sorry, Jay, real quick. Can you go to the next slide, please?

Jay Kreps: Yeah.

Rob Thomas: Sorry, Jay.

Jay Kreps: There we go. Yeah. So, how do all the parts of a company come together? How, you know, if you are a global retailer and you are selling things, there’s probably a hundred different systems that need to react or take into account or take action when something sells, right? How does that happen? Does that happen only at the end of the day when everything is reconciled, or is this something that can be directly part of the operation of a business? You know, if you’re a car manufacturer, you have the production of cars, cars driving all around the world. If you are a bank, you have transactions occurring all the time. You know, how can all of this data flow across all the parts of the organization? How can you react and respond to this? How can you do it in a way that’s smart? You know, how can you bring intelligence to bear? You know, create the context data for modern AI systems and agents.

You know, this is this is ultimately the domain that Confluent operates in. And the idea is really take, you know, a lot of the sophisticated computer science that’s been applied to data storage and databases and apply it to the flow of data and do that at very large scale. And this started with an open-source project called Kafka. Confluent has brought a whole set of capabilities around that. You know, how data can flow as real-time streams across an organization, but also how you can process those streams continuously. How you can react to them. So in my retail example, you might think of that stream of sales being generated by the retailer. You could think of maintaining inventory continuously as things sell across e-commerce, different stores in different parts of the world, other channel partners. And you could imagine, you know, reacting, responding, restocking, making pricing decisions all in reaction to that all continuously throughout the day as part of running the business. And this applies not just in retail, but in, you know, virtually every industry across the larger economy. The open-source technology has become incredibly popular. And you know, I think open source is an area that IBM understands very well. And Confluent has gotten to scale quite quickly. You know, reaching over a billion dollars in last 12 months revenue, 6,000 customers in broad adoption across the Fortune 500. So, you know, that that’s kind of a 30 second overview of Confluent, the area that we sit and happy to go a little deeper on that.

Rob Thomas: So, I’ll just add to this. I mean, I’ve gotten to know Jay a good bit and his team. We really admire what they’ve been able to accomplish. And a lot of our thought process here as we do in most acquisitions is I think there’s often as much we can learn from the company that we acquire as vice versa. And one example would be, I think what Confluent has built in terms of a consumption business model, how they’ve used that to design their go to market, not to mention what they’re doing in product. I think it’s incredibly impressive and it is part of how we think about realizing synergy is just how do we as IBM become better based on the companies that we bring into IBM.

If we can go to the next slide, just to give you a bit more when I talk about a smart data platform, obviously it’s the real-time capabilities, the event engine that that Jay was touching on. To the credit of the Confluent team, they’ve then gone further into things like adopting Flink for processing. And you connect that to what IBM has in terms of the install base around MQ series or APIs. We think we can build a platform together that I would say will uniquely solve the data needs that applications will have in the agentic era. And I don’t think anybody else is really trying to solve this problem certainly from a hybrid cloud and multicloud perspective. So I think that positions us well and then we go one more slide.

So what does this mean in the context of IBM? I would I would kind of start with the bottom. We think we’ve built the best portfolio for hybrid cloud. Whether it’s what we’re doing with Red Hat, automation capabilities that we layered on top of that with HashiCorp, organic innovation like Concert. We think we’ve built the best hybrid multicloud platform in the industry. So that’s kind of the bottom. Then if you think about the top with watsonx Orchestrate, I think we probably have the greatest IBM organic innovation in maybe the last 20 years. We’ll see. Time will tell. We have to prove that every day. But I would say the customer adoption which you can see in our book of business for Orchestrate has been dramatic. And so to me, we have a really good play at the top of the stack around watsonx Orchestrate agents and playing in this world where there’s going to be, you know, a billion new applications. And we have a great play at the bottom. I think the key thing to add to this is the middle here which is what Confluent is the default leader in, which is around real-time data access and how that connects applications to the infrastructure. So that’s really the why now, what compelled us to do something. So, as I said, super excited about this. Would love to open it up now to any questions that you all have. And as I said, we’ll answer as many as we can at this stage.

Elizabeth Bamonte: Great. Thank you, Rob. And thank you, team. I’ve got a couple of questions coming in. The first one is from Anurag at TechAisle. For years, Rob, you’ve championed the AI ladder framework: collect, organize, analyze, and infuse. With Confluent expected to provide the central nervous system for data in motion. Does this signal a fundamental architectural shift in which real-time streams rather than static data warehouses must become the primary collect layer to make agentic AI actually viable for the enterprise?

Rob Thomas: I think Anurag that’s certainly, I’d say that’s part of the bet in our thesis. Which is that merely persisting or storing data and then accessing it retroactively or in a batch form, I’d say I’m skeptical that’s going to deliver what AI needs if you look at it over the next decade. I think, look, it’s still incumbent upon us to prove together that real-time data is the answer for that. But my instinct is that it is because I think we’ve gotten as far as we can get with static repositories. I think to really solve the AI problem, we need we need a new approach.

Elizabeth Bamonte: Okay.

Jay Kreps: Yeah. And I would just add on to that. You know, if you think about what was the AI problem 5 years ago. You know, by and large the action was in fitting models offline. Get a data science team, get a bunch of data into your data lake, do some model building, you know, take this built model and find some way to plug it into the operation of the company. If you think about what’s happening with Gen AI, the model building part is actually no longer what the average enterprise needs to go do. You know, that is a problem that OpenAI or Anthropic is doing. So the problem is no longer, hey, stick everything in a data lake and do model building, right? The problem is how can I create a live version of context about my organization that I can feed into an LLM that an agent can make decisions with so that it has context of the business so that that can take action in the business. And if that data is out of sync, I think it’s just very hard to have that be enabled to do anything. And so, you know, a lot of the capabilities around collecting, transforming, all the things that were just mentioned, equally important. You just have to be able to do that in real time so that the data is in sync so that the actions the agent is taking are the right ones and that that’s certainly an area where, you know, I think Confluent can help plan.

Elizabeth Bamonte: I’m going to go to a question from Abashek from Everest. Um because it kind of builds on this last question. Which assumptions about how real-time data will live and flow in future enterprise architectures led you to acquire Confluent or that decision? Based on those same assumptions, what capabilities do you believe IBM still needs to build to complete that future data foundation?

Rob Thomas: I would say we looked at a few things. As I alluded to before, we have an incredible install base with MQ series. But most of the implementations in MQ are really just messaging. It’s not truly in an event engine. And so, as I look at those clients, their applications rely on MQ, like they don’t run without them. But there’s probably more that would enrich those applications and I would say kind of cement IBM as the application architecture working with them. I think that creates a pretty big need for what we’re doing here. In terms of what else we do in data going forward, I’d say time will tell on that. I mean, we acquired MAT a year ago around data lineage. I think the world of data and AI governance is still relatively untapped and so we kind of thought about lineage as being a key capability. But I think in time we’ll probably have a better sense of this. But I think if our thesis is right in that organizations start to move towards real-time data across multicloud, I think that probably opens up a lot of other opportunities.

Elizabeth Bamonte: Jay, maybe you can comment. I mean, you guys have thought a lot about where Confluent goes as a next step, even independent of IBM. Maybe you could hit on some of that.

Jay Kreps: Yeah. Yeah, I’m happy to talk about that. You know, where we started was really just help companies adopt streams of data at scale across an organization and then we’ve grown out from that. Once you have those streams, that’s kind of the nexus of the flow of data across an organization. It’s a very strategic position to be in with a lot of capabilities you can add. One of the most important that Rob alluded to earlier is the real-time processing of data, the governance of that data flow, some of the AI capabilities that can be brought around it, the connectivity into all the different systems that organizations would have to capture real-time streams. You know, those are some of the things we’ve brought to our platform and then in combination, how can that grow? Well, I think there’s a lot of ideas, right? Like, you know, IBM has a incredibly rich product portfolio. So, there’s a lot of things that can plug in here. We’re obviously in the early days of exploring that. But I think that there’s a clear belief that this is, you know, when you think about how organizations harness data there there’s this very clear move towards real time. You know, maybe it was good enough to throw everything into a data lake or warehouse and kind of look at it tomorrow in some reports. You know, there’s a point in time where that was good enough and if you think about what applications need to do now, what organizations are struggling with, it’s really act on that data. Like actually have a feedback loop that is in software that takes action on it. That fundamentally requires this shift towards continuous real-time view of data across different systems. And that’s an area where Confluent’s uniquely positioned.

Elizabeth Bamonte: Thank you. I’ve got another question from David Mener at ISG related to the streaming data question. Do you ever see streaming data becoming the default data processing model rather than data at rest?

Rob Thomas: Jay, maybe you start and then I can add.

Jay Kreps: Well, I’m obviously biased. So, yeah, I mean, look, you know, if you look at what’s happening, it’s not that the, you know, the batch data stores are long-term stores. It’s not that that’s going away, right? It’s not that people don’t need databases, but you know, what people have learned is, um, you know, a lot of what a business does is inherently continuous. You know, in my retail example, sales are happening all the time. There’s a whole set of activities of the business that are happening all the time. So being able to work with data continuously is a really core capability. And it’s been held back largely by technology, largely by it being too difficult, too complicated, too, you know, inefficient, cost prohibitive, not correct. And those are the set of problems that have been solved in in, you know, over the last however many years in really building reliable large-scale streaming, making it transactionally correct, enabling kind of rich complicated processing, making that something that’s simple to harness. Now suddenly it’s like, you know, it’s not, you know, do I need to do this continuously, but why wouldn’t I? Most software outside of that runs continuously. In some sense, this batch stuff is sort of a relic of an older year. You know, I mean it it’s something that kind of comes out of technology, not out of the world. Businesses are not inherently batch processes. And so I think that’s the direction of travel. Now look, anything in the world of enterprise software, it’s not like the new thing comes and the old thing magically disappears. It always runs alongside. So it’s not like any of that older stuff is, you know, overnight going away. But I think if you look at the direction of travel, you know, where which area is going to have greater needs in organization, it is the real-time use of data. And if you look at the capabilities now that are, you know, possible in that stack, it’s incredibly broad and in many case is a superset of what you could do in a batch system. And I think that’s a very exciting thing.

Rob Thomas: I would just add, I think rarely are things binary in technology. Meaning I don’t think our thesis is that, hey, data at rest goes away. I wouldn’t say that at all. I think this is more about market expansion. And if there truly are going to be a billion new applications based on generative AI, that will probably require a different type of a data layer than just simply writing that against existing warehouses and lakehouses. So I think actually warehouses and lakehouses have a long way to run. But I think this is actually about a bigger market given what’s happening in AI.

Elizabeth Bamonte: Thank you Rob. Thank you Jay. A question from Sheeva Lava at IDC. How might this fit into IBM’s other integration and messaging capabilities including StreamSets?

Rob Thomas: We’re going to obviously continue to build this into the kind of the platform that we described. I want I don’t want to go too far on specific integration plans. But I would say like all things in technology, StreamSets has a unique place in the world that’s actually quite different than what Confluent does in its core business. And MQ is quite different than both of them. Like if you look at them, I think every client of MQ also owns Confluent and StreamSets as an example. So I think this is more about how use cases are implemented. And maybe we get further into that as we go.

Elizabeth Bamonte: I’ve got a question and probably take this have this question and maybe one for one more. Sunchit from Greyhound Research. Are you planning technical pre-integration pre-integrations across Terraform, OpenShift, Kafka, Stream Governance for common deployment patterns, especially in regulated industries? And will customers see native data streaming services embedded into Watinx and CloudPak offerings? Or will Confluent remain externally modular?

Jay Kreps: I’m not sure we can answer much of any of that. We’re certainly not going to do anything pre-close. Just I want to be clear on that. We’re separate companies. We’ll continue to operate as separate companies. But I’d say time will tell on all those.

Elizabeth Bamonte: Okay.

Rob Thomas: Yeah, that that’s exactly right. And I would say one of the nice things is OpenShift is such a common platform. Terraform is such a common platform. In many of these cases, we already have really good integration. And so, you know, it’s exciting to amplify some of that. But these technologies are very often deployed together and work together.

Sarbjit from Stackpane. I strongly believe that real-time data will give more agency to agents. Is that how you see it?

Jay Kreps: Well, I’m biased, but I do.

Rob Thomas: I would say definitely yes. Look, agents will need to be able to interact with each other beyond just point-to-point or what you can kind of get today. So, I think you’re hitting on a big part of the thesis here. Actually, Jay, somebody on Jay’s team wrote a great blog post on this maybe six months ago that was talking about the role that streaming data plays in agents. We could probably link to that. Sorry, Jay, you were going to say something.

Jay Kreps: No, that’s exactly right. And you can kind of see why, right? Like, you know, if you’re trying to build an agent, this context data to, you know, make decisions on, that’s kind of the key ingredient that each enterprise has to figure out and bring to bear. You know, you can do some of that by just trying to hook this up to the systems out there in your organization. But as you do that, those systems are often not built to structure the data in a way that’s actually useful for the agent. So you end up having to do some processing, pre-processing, materialization of new data sets. But when you do that, you have to do it continuously so that that data is up to date, right? If you don’t do that, the agents literally making decisions off the wrong information. So like the most simple example and perhaps overused, right? Support agent trying to answer questions to the customer. Most frustrating and possible experiences, you interact with a company, something goes wrong, it’s not what you expect. You start interacting with support and it doesn’t know anything that happened in the last day, right? It’s just a complete fail. Virtually every example like that where the agent is going to take some action, interact with customers, it has to have a view of what’s happening in the business in sync. And you have to have the ability to curate and process data for that. And that that’s what drives the real-time requirement. You know, I think that is a very different thing from what we would have thought of, you know, kind of being data driven in the past that might be a more kind of offline BIO oriented activity. And so, I think that’s, you know, technically what drives it, but totally agree with the sentiment.

Elizabeth Bamonte: Great. I have one last question and I know we’ve got so much engagement in the chat, so I’m really grateful and please know that I will work with my team to make sure we address all of all of them to our best ability. But I want to close on question from Phil Hassie. What will be the first thing that a Confluent client recognizes that IBM has made a difference in their ability to optimize their data?

Jay Kreps: I think it’s hard to project to something post-close. I would maybe frame it a different way. I think one of the biggest synergy opportunities is IBM’s global distribution and reach. Confluent built an incredible company on their own. But we think we can introduce Confluent to countries and companies around the world that it was going to be take a long time for them to get to on their own. So I almost view this more as we’re just scratching the surface. Confluent has 42% of the Fortune 500 if I’m not mistaken. So, we’re kind of just scratching the surface of the adoption of real time. And I think that’s going to be the most important thing in the first year or two.

Rob Thomas: Yeah, I totally agree.

Elizabeth Bamonte: All right. With that, we will close our webinar. I just want to give a big thanks to you, Rob, and Jay for your participation today. I want to thank our analyst community for your ongoing engagement and your active participation today. Thank you so much. We will absolutely be sending the recording and the materials from today and also following up with all of you who didn’t get your questions addressed. But again, thank you so much on behalf of IBM Analyst Relations and IBM in general. Thank you so much everyone.

Rob Thomas: Thanks all.

Jay Kreps: Thank you.

Additional Information and Where to Find It

This communication may be deemed to be solicitation material in respect of the proposed acquisition of Confluent, Inc. (the “Company”) by International Business Machines Corporation (“Parent”) pursuant to the Agreement and Plan of Merger, dated as of December 7, 2025, by and among the Company, Parent and Corvo Merger Sub, Inc. The Company intends to file a preliminary and definitive proxy statement with the U.S. Securities and Exchange Commission (the “SEC”) with respect to a special meeting of stockholders to be held in connection with the proposed acquisition. After filing the definitive proxy statement (the “Proxy Statement”) with the SEC, the Company will mail the Proxy Statement and a proxy card to each stockholder of the Company entitled to vote at the special meeting. The Proxy Statement will contain important information about the proposed transaction and related matters. BEFORE MAKING ANY VOTING OR INVESTMENT DECISION, THE COMPANY’S STOCKHOLDERS AND INVESTORS ARE URGED TO READ THE PROXY STATEMENT (INCLUDING ANY AMENDMENTS OR SUPPLEMENTS THERETO) IN ITS ENTIRETY WHEN IT BECOMES AVAILABLE AND ANY OTHER DOCUMENTS FILED BY THE COMPANY WITH THE SEC RELATING TO THE PROPOSED ACQUISITION OR INCORPORATED BY REFERENCE THEREIN BECAUSE THEY WILL CONTAIN IMPORTANT INFORMATION ABOUT THE PROPOSED ACQUISITION. Investors and stockholders of the Company may obtain a free copy of the preliminary and definitive versions of the proxy statement once filed, as well as other relevant filings containing information about the Company and the proposed acquisition, including materials that are incorporated by reference into the Proxy Statement, without charge, at the SEC’s website (https://www.sec.gov) or from the Company by going to the Company’s Investor Relations Page on its website (https://www.confluent.io).

Participants in the Solicitation

The Company and its directors, and certain of its executive officers, consisting of Lara Caimi, Jonathan Chadwick, Alyssa Henry, Matthew Miller, Neha Narkhede, Greg Schott, Eric Vishria, Michelangelo Volpi, who are the non-employee members of the Board of Directors of the Company (the “Board”), and Jay Kreps, Chief Executive Officer and Chairman of the Board, Rohan Sivaram, Chief Financial Officer, and Ryan Mac Ban, Chief Revenue Officer, may be deemed to be participants in the solicitation of proxies from the Company’s stockholders in connection with the proposed acquisition. Information regarding the Company’s directors and certain of its executive officers, including a description of their direct or indirect interests, by security holdings or otherwise, can be found under the captions “Security Ownership of Certain Beneficial Owners and Management,” “Executive Compensation,” and “Director Compensation” contained in the Company’s definitive proxy statement on Schedule 14A for the Company’s 2025 annual meeting of stockholders, which was filed with the SEC on April 23, 2025. To the extent holdings of the Company’s securities by its directors or executive officers have changed since the applicable “as of” date described in its 2025 proxy statement, such changes have been or will be reflected on Initial Statements of Beneficial Ownership on Form 3 or Statements of Beneficial Ownership on Form 4 filed with the SEC, including (i) the Form 4s filed by Ms. Narkhede on May 6, 2025, June 4, 2025, June 12, 2025, September 11, 2025, October 31, 2025, November 5, 2025 and December 3, 2025; (ii) the Form 4s filed by Mr. Sivaram on May 22, 2025, June 4, 2025, June 9, 2025, August 22, 2025, September 10, 2025, October 31, 2025, November 24, 2025 and December 3, 2025; (iii) the Form 4s filed by Mr. Kreps on May 19, 2025, May 22, 2025, June 9, 2025, August 18, 2025, August 22, 2025, September 8, 2025, November 17, 2025 and November 24, 2025; (iv) the Form 4 filed by Mr. Chadwick on April 4, 2025 and June 12, 2025; (v) the Form 3 filed by Mr. Ban on May 16, 2025 and the Form 4s filed by Mr. Ban on May 22, 2025, June 24, 2025, August 22, 2025, September 24, 2025 and November 24, 2025; (vi) the Form 4s filed by Mr. Vishria on May 21, 2025, June 9, 2025, June 12, 2025, September 2, 2025 and October 31, 2025; (vii) the Form 4 filed by Mr. Volpi on June 9, 2025; (viii) the Form 4 filed by Ms. Caimi on June 12, 2025; (ix) the Form 4 filed by Mr. Schott on June 12, 2025; and (x) the Form 4 filed by Ms. Henry on June 12, 2025. Additional information regarding the identity of potential participants, and their direct or indirect interests, by security holdings or otherwise, will be included in the definitive proxy statement relating to the proposed acquisition when it is filed with the SEC. These documents (when available) may be obtained free of charge from the SEC’s website at www.sec.gov and the Company’s website at https://www.confluent.io.

Forward Looking Statements

This communication contains “forward-looking statements” within the meaning of the “safe harbor” provisions of the United States Private Securities Litigation Reform Act of 1995. All statements other than statements of historical fact are statements that could be deemed “forward-looking statements”, including all statements regarding the intent, belief or current expectation of the companies and members of their senior management teams. Words such as “may,” “will,” “could,” “would,” “should,” “expect,” “plan,” “anticipate,” “intend,” “believe,” “estimate,” “predict,” “project,” “potential,” “continue,” “target,” variations of such words, and similar expressions are intended to identify such forward-looking statements, although not all forward-looking statements contain these identifying words.

These forward-looking statements include, but are not limited to, statements regarding the benefits of and timeline for closing the Company’s proposed transaction with Parent. These statements are based on various assumptions, whether or not identified in this communication, and on the current expectations of the Company’s management and are not predictions of actual performance. These forward-looking statements are provided for illustrative purposes only and are not intended to serve as, and must not be relied on by any investor as, a guarantee, an assurance, a prediction or a definitive statement of fact or probability. Actual events and circumstances are difficult or impossible to predict and may differ from assumptions. Many actual events and circumstances are beyond the control of the Company. These forward-looking statements are subject to a number of risks and uncertainties, including the timing, receipt and terms and conditions of any required governmental and regulatory approvals of the proposed transaction that could delay the consummation of the proposed transaction or cause the parties to abandon the proposed transaction; the occurrence of any event, change or other circumstances that could give rise to the termination of the merger agreement entered into in connection with the proposed transaction; the possibility that the Company’s stockholders may not approve the proposed transaction; the risk that the parties to the merger agreement may not be able to satisfy the conditions to the proposed transaction in a timely manner or at all; risks related to disruption of management time from ongoing business operations due to the proposed transaction; the risk that any announcements relating to the proposed transaction could have adverse effects on the market price of the common stock of the Company; the risk of any unexpected costs or expenses resulting from the proposed transaction; the risk of any litigation relating to the proposed transaction; and the risk that the proposed transaction and its announcement could have an adverse effect on the ability of the Company to retain and hire key personnel and to maintain relationships with customers, vendors, partners, employees, stockholders and other business relationships and on its operating results and business generally.

Further information on factors that could cause actual results to differ materially from the results anticipated by the forward-looking statements is included in the Company’s Annual Report on Form 10-K for the fiscal year ended December 31, 2024, Quarterly Reports on Form 10-Q, Current Reports on Form 8-K, the Proxy Statement and other filings made by the Company from time to time with the Securities and Exchange Commission. These filings, when available, are available on the investor relations section of the Company’s website (https://www.confluent.io) or on the SEC’s website (https://www.sec.gov). If any of these risks materialize or any of these assumptions prove incorrect, actual results could differ materially from the results implied by these forward-looking statements. There may be additional risks that the Company presently does not know of or that the Company currently believes are immaterial that could also cause actual results to differ from those contained in the forward-looking statements. The forward-looking statements included in this communication are made only as of the date hereof. The Company assumes no obligation and does not intend to update these forward-looking statements, except as required by law.