Q4 2023 Confluent Inc Earnings Call

Xing Di: Hello, everyone. Welcome to the Confluent Q4 and Fiscal Year 2023 Earnings Conference Call. I'm Xing Di from Investor Relations, and I'm joined by Jay Krebs, co-founder and CEO, and Rohan Sivaram, CFO. During today's call, management will make forward-looking statements regarding our business, operations, sales strategy, market, and product positioning, financial performance, and future prospects, including statements regarding our financial guidance for the Fiscal First Quarter of 2024 and Fiscal Year 2023. These following statements are subject to risks and uncertainties, which could cause actual results to differ materially from those anticipated by these statements. Further information on risk factors that could cause actual results to differ is included in our most recent Form 10-Q filed with the SEC. We assume no obligation to update these statements after today's call, except as required by law.

Hello, everyone and welcome to the Council in Q4 and fiscal year 2023 earnings Conference call I'm, showing the from Investor Relations and I'm joined by Jay Crafts, co founder and CEO and brought on several <unk> CFO.

During today's call management will make forward looking statements regarding our business operations sales strategy market and product positioning and financial performance and future prospects, including statements regarding our financial guidance for the fiscal first quarter of 2024 and fiscal year 2024.

These forward looking statements are subject to risks and uncertainties, which could cause actual results to differ materially from those anticipated by these statements further information on risk factors that could cause actual results to differ is included in our most recent Form 10-Q filed with the SEC.

We assume no obligation to update these statements after today's call except as required by law.

Xing Di: Unless stated otherwise, certain financial measures used on today's call are expressed on a non-GAAP basis, and all comparisons are made on a year-over-year basis. We use these non-GAAP financial measures internally to facilitate analysis of financial and business trends and for internal planning and forecasting purposes. These non-GAAP financial measures have limitations and should not be considered in isolation from or as a substitute for financial information prepared in accordance with GAAP

Unless stated otherwise certain financial measures used on today's call are expressed on a non-GAAP basis and all comparisons are made on a year over year basis. We use these non-GAAP financial measures internally to facilitate analysis of our financial and business trends and for internal planning and forecasting purposes.

These non-GAAP financial measures have limitations and should not be considered in isolation from or as a substitute for financial information prepared in accordance with GAAP.

Xing Di: A reconciliation between these GAAP and non-GAAP financial measures is included in our earnings price release and supplemental financials, which can be found on our IR website at investors.confluent.io. And with that, I'll hand the call over to Jay. Thanks, Shane. Good afternoon, everyone, and welcome to our fourth quarter earnings call. We closed fiscal year 2023 with a solid Q4, exceeding the high end of all guidelines. Total revenue grew 26% to $213 million.

A reconciliation between these GAAP and non-GAAP financial measures is included in our earnings press release, and supplemental financials, which can be found on our IR website at investors start confluent that out and with that I'll hand, the call over to Jay. Thanks, Shane Good afternoon, everyone and welcome to our fourth quarter earnings call, we closed fiscal year 2002.

23, with a solid Q4 exceeding the high end of all guided metrics total revenue grew 26% to $213 million constantly cloud revenue reached 100 million for the first time growing 46% and non-GAAP operating margin came in at five 3%, our first positive quarter, improving 27 percentage points.

Jay Krebs: Confluent cloud revenue reached $100 million for the first time, growing 46%. And non-GAAP operating margin came in at 5.3%, our first positive quarter, improving 27%. Since going public two and a half years ago, we have more than doubled our total revenue run rate and driven more than 46 percentage points in non-GAAP operating margins. These results are a testament to the power of our platform and the incredible growth of the data streaming category. Last quarter, we discussed our accelerated transition to a fully consumption-oriented go-to-market model for Confluent Cloud, including shifting our sales compensation for cloud to be based on incremental consumption and new logo acquisition, orienting our field team towards landing new customers and driving new workloads with customers, and adapting product and pricing to reduce friction for landing customers and maximize the potential for expansion.

It's growing public two and a half years ago, we have more than doubled our total revenue run rate and driven more than 46 percentage points and non-GAAP operating margin improvement. These results are a testament to the power of our platform and the incredible growth of data streaming category.

Last quarter, we discussed our accelerated transition to a fully consumption oriented go to market model for confluent class, including shifting our sales compensation for cloud to be based on incremental consumption and new logo acquisition orienting our field team towards landing, new customers and driving new workloads with customers and adapting product and pricing to reduce friction and landed customers.

<unk> maximize the potential for expansion.

Jay Krebs: As we said before, these changes are internal to our go-to-market teams and don't change our business model or revenue model or any other customer-facing aspect, all of which are already consumption-based. We've executed some of the initial changes of our consumption transformation effective January 1st, including a new compensation model and the initial rollout of new systems, metrics, and measures. Last week, I spent time with our sales and marketing teams at our sales kickoff.

As we said before these changes are internal to our go to market teams don't change our business model or revenue model or any other customer facing aspect all of which are already consumption oriented.

We've executed some of the initial changes of our consumption transformation effective January one, including a new compensation model and the initial rollout of new systems metrics and measures.

Last week I spent time with our sales and marketing teams at our sales kickoff.

Jay Krebs: The initial reaction from the team has been very positive. We will be spending the next few quarters fully adapting and optimizing our business to these changes. We believe our transition to a fully consumption-oriented business, alongside our category leadership, puts us in an excellent position to capture more of the $60 billion data streaming platform opportunity. I'd like to spend a few minutes and reflect on the increasing recognition of data streaming as a category and its potential for growth. One way of thinking about data technologies is to break them into two groups, those oriented to handling data at rest, the databases and storage systems, and those oriented to handling data in motion. These two areas have very different evolutionary paths.

The initial reaction from the team has been very positive we will be spending the next few quarters fully adapting and optimizing our business to these changes we believe our transition to a fully consumption oriented business alongside our category leadership puts us in an excellent position to capture more of the $60 billion data streaming platform opportunity in front of us.

I'd like to spend a few minutes and reflect on the increasing recognition of data streaming as a category and its potential for growth.

One way of thinking about data technologies is to break them into two groups those oriented for handling data arrest, the databases and storage systems and those oriented at handling data in motion. These two areas had very different evolutionary paths over the last several decades data at rest has become highly concentrated around the powerful infrastructure platform the database.

Jay Krebs: Over the last several decades, data at rest has become highly concentrated around a powerful infrastructure platform, the database, a $90 billion plus category. The landscape of data in motion technologies has remained highly fragmented, with technology analysts recognizing disparate technology categories, including message queues, application integration tools, data integration tools, event brokers, ETL products, iPaaS, and more. The reason for this was largely technological. Each of these product categories was defined by its technological limitations, whether latency, scale, complexity of processing, or ease of use.

A $90 billion plus category.

The landscape of data in motion technologies remains highly fragmented with technology analysts recognizing disparate technology categories, including message queues application integration tools data integration tools that brokers each L products ipass in more the reason for this was largely technological each of these product categories was to.

<unk> by its technological limits, whether latency scale complexity of processing or ease of use the potential for data streaming is to collapse. The fragmentation of data in motion technologies and create a new data platform that supersedes each of these limited precursors since.

Jay Krebs: The potential for data streaming is to collapse the fragmentation of data in motion technologies and create a new data platform that supersedes each of these limited predecessors. Since Confluent's creation, that has been our central thesis, that the data streaming platform would be a platform of similar importance and scale to databases, but acting as the central nervous system, handling all the data in motion. Now that this category has gotten to scale and usage, it's starting to get formal recognition. In December, research published by Forrester validated our thesis that data streaming platforms are a distinct category that has become a mission-critical component of the modern data stack. The Forrester Wave Streaming Data Platform's Q4 2023 recognizes streaming data as the pulse of an enterprise and names Confluent a leader.

Since confluence creation that has been our central thesis that the data streaming platform would be a data platform of similar importance and scale databases, but acting as the central nervous system and billing all the data in motion.

Now that this category has gotten to scale and usage, it's starting to get formal recognition in December research published by Forrester validated our thesis that data streaming platforms are distinct category that has become a mission critical component of the modern data stack. The Forrester wave streaming data platforms Q4, 2023 recognized as streaming data as the pulse of it <unk>.

Apprise names confluent. The leader we were also named a leader in the Forrester wave cloud data pipelines Q4, 2023, and one info world's technology of the year and the data management streaming technology categories taken together. These recognitions show us that the data streaming era is here and confluence is a clear leader.

Jay Krebs: We were also named a leader in the Forrester Wave Cloud Data Pipelines Q4 2023 and won InfoWorld's Technology of the Year in the Data Management Streaming Technology category. Taken together, these recognitions show us that the data streaming era is here, and Confluent is in a clear lead. As we've discussed before, this data streaming platform is more than just Kafka. Kafka is the data stream, a foundational layer, but it's just the start.

As we've discussed before this data streaming platform is more than just kafka kafka as the data stream foundational layer, but it's just the start to extract the full value of data and motion organizations need to connect to the systems. They have process data in real time and govern these flows of data across the enterprise each of these capabilities connectors stream.

Jay Krebs: To extract the full value of data in motion, organizations need to connect to the systems they have, process data in real time, and govern these flows of data across the enterprise. Each of these capabilities, connectors, stream processing, and governance, is on a path to become a sizable business on its own. One key aspect of our consumption transformation is that it lets our go-to-market directly drive consumption around these additional products, which can be used under the same consumption contract with no additional purchase. Today, I'd like to spend a few minutes discussing what's happening in the world of stream processing. Stream processing enables organizations to act on data as it arrives rather than wait to process it in batches at the end of the day.

Testing and governance is on a path to become a sizable business on their own.

One key aspect of our consumption transformation is it lets our go to market directly drive consumption around these additional products, which can be used under the same consumption contract with no additional purchasing friction.

Today I'd like to spend a few minutes covering what's happening in the world of stream processing stream processing enables organizations to act on data as it rives rather than waiting to process it and batch at the end of that for an airline it could be processing data from streams of flight times, whether information and customer information by itself. These streams are powerful but with stream process.

Jay Krebs: For an airline, it could be processing data from streams of flight times, weather information, and customers. By themselves, these streams are powerful, but with stream processing, these streams can be combined and enriched to drive logistics, pricing, scheduling, and cascade that information throughout the system to minimize travel disruption. For Confluent, this represents a significant growth opportunity. Today, the spend on applications around the data stream is significantly higher than on the stream itself. By making these applications easier to build and bringing that spend into our platform, we believe both adoption of our platform as well as the growth of our business will be accelerated. I'd like to spend the next few minutes addressing the question of why Confluent is uniquely positioned to succeed in stream processing with our Flink offering. There are three key reasons I'll address.

Seeing these streams can be combined and enrich to drive logistics pricing scheduling and cascade that information throughout the system to minimize travel disruptions.

For confluent this represents a significant growth opportunity.

Today, the spend on applications around the data stream is significantly higher than on the stream itself by making these applications easier to build and bringing that spend into our platform. We believe both adoption of our platform as well as the growth of our business will be accelerated I'd like to spend the next few minutes addressing the question of why confluence is uniquely positioned to succeed in stream processing.

With our flank offering there are three key reasons I'll address.

Jay Krebs: First, Flink is the emerging de facto standard. Second, the company with the stream gets the processing. And third, there is the rise of data products. Let me address each of these in turn. The first reason is perhaps the most obvious.

First link as the emerging de facto standard second the company with the stream gets the processing and third is the rise of data products. Let me address each of these in turn the first reason is perhaps the most obvious we believe link is simply the best technology in the space and has attracted the largest community of developers working with real time apps.

Jay Krebs: We believe Flink is simply the best technology in the space and has attracted the largest community of developers working with real-time apps. This technological superiority comes from the fact that Flink was designed to have the full processing power of a database but was designed from the ground up for streaming, addressing batch processing needs as a special case of stream processing. This affects every aspect of the design, from how storage is managed, how failover and fault tolerance works, the latency of results, and the interfaces presented to users. This is dramatically better than attempts to bolt streaming features into existing databases or batch processing. The result is our ability to offer the most complete platform and ecosystem for stream processing, one that supports SQL as well as native apps in popular programming languages, and it unifies batch and real-time processing.

This technological superiority comes with the fact that link was designed to have the full processing power of a database, but was designed from the ground up for streaming addressing batch processing needs is a special case of stream processing.

This affects every aspect of the design for cost storage is managed how fail over at fault tolerance works. The latency of results and interfaces presented users. This is dramatically better than attempts to bolt streaming features into existing databases or batch processing engines. The result is our ability to offer the most complete platform and ecosystem for stream processing one that is.

Support sequel, as well as native apps and popular programming languages, and that unifies batch and real time processing.

Jay Krebs: This platform has attracted the most vibrant community doing development in this space. The developers have spoken, and like Kafka, this is the technology that they choose when they need real-time streaming.

This platform has attracted the most vibrant community doing development in this space the developers have spoken and like Kafka. This is the technology that they choose when they need real time streaming and 2023, there were nearly 1 million unique downloads of flank and a 43% increase in open job requisitions for flink developers and like Kafka. It has proven itself.

Jay Krebs: In 2023, there were nearly one million unique downloads of Flink and a 43 percent increase in open job requisitions for Flink developers. Unlike Kafka, it has proven itself with one of the most sophisticated user bases, including companies like Apple, Capital One, Netflix, Stripe, and Uber. Perhaps what's most impressive is that Flink has attracted this broad adoption among APEX users without having significant commercial backing or go-to-market support. This is truly the best engineers taking the best technology.

With one of the most sophisticated user bases, including companies like Apple capital, one Netflix stripe Uber.

Perhaps what's most impressive is that flink has attracted this broad adoption and apex users without having significant commercial backing our go to market support. This is truly the best engineers, taking the best technology.

Jay Krebs: Our investment in Flink gives us a leadership position in the winning technology for stream processing, but our advantage isn't limited to the technology or developer community. As attractive as stream processing is, it doesn't stand alone. It is always adopted along with a stream of data that needs processing, and everyone agrees that Kafka is the standard for the stream itself.

Our investment in Flink gives us a leadership position in the winning technology in stream processing, but our advantage is not limited to the technology, our developer community as attractive as stream processing is it doesn't stand alone is always adopted along with a stream of data that needs processing, everyone agrees that kafka as the standard for the stream itself.

Jay Krebs: As the leaders in Kafka, we are in a prime position for capturing the emerging stream processing market. Indeed, this pairing is very similar to what made databases themselves successful. Databases brought together data storage with data processing into a unified product, driving a vastly simpler experience. Confluent is working towards the same goal by unifying data streaming with Kafka and stream processing via Flink. We believe the resulting data streaming platform is exactly the product for customers. This pairing is not just skin deep, either.

As the leaders in Kafka, we are in a prime position for capturing the emerging stream processing market. Indeed. This pairing is very similar to what made databases themselves successful databases brought together data storage with data processing into a unified product driving a vastly simpler experience.

Confluence is working towards the same by unifying data streaming with Kafka with stream processing via flank. We believe the resulting data streaming platform is exactly the product the customers want.

This pairing is not just skin deep either confluence can make the stream and processing layers work together as a coherent product is optimized as a single system from performance to security to Jay to discuss our ability to transactional semantics, but we think the processing layer that is unified with the underlying stream is going to be the easiest fastest and most obvious choice.

Jay Krebs: Confluent can make the stream and processing layers work together as a coherent product that is optimized as a single system, from performance to security, to data discoverability, to transactional semantics. We think the processing layer that is unified with the underlying stream is going to be the easiest, fastest, and most obvious choice for any developer. That makes Confluent's Blink offering a kind of default option when it comes to processing data in Kafka.

For any developer that makes confluence blink offering a kind of default option when it comes to processing data and Kafka.

Jay Krebs: There's a final trend that supports confluence position in stream processing, and that is the increasing role of reusable data products in modern data architecture. In classical data architecture, data largely lived in a silo, and at most, it was extracted to a single destination, the data warehouse, where it was processed to clean it up and make it usable for various reporting and analytics uses.

Theres a final trend that supports confluence position in stream processing and that is the increasing role of reusable data products in modern data architecture and classical data architecture data largely lived in a silo and at most was extracted to a single destination the data warehouse, where it was process to clean it up and make it usable for various reporting in.

Analytics use cases.

Jay Krebs: In modern data architecture, the data warehouse is no longer the single destination for data. Dozens or even hundreds of other systems feed off critical data streams. Repeating the processing that cleans up data for use dozens or hundreds of times is completely infeasible.

In modern data architecture. The data warehouse is no longer the single destination for data dozens or even hundreds of other systems feed off critical data streams.

Repeating the processing that cleans up data for use dozens or hundreds of times is completely and feasible. The result is that the processing is being pulled upstream from the destination to the source to produce high quality reusable data products that is rather than having dozens of destination systems. All tried to clean up the data instead the sources response.

Jay Krebs: The result is that the processing is being pulled upstream from the destination to the source to produce high-quality, reusable data products. That is, rather than having dozens of destination systems all try to clean up the data, the source is responsible for publishing the data in a processed, ready-to-use format to all destinations. This means the processing is happening on the stream as data enters the system rather than in the destination, and this is pulling workloads from batch processing in the destinations into stream processing at the source.

<unk> for publishing data in our processed ready to use format to all destinations. This means the processing is happening on the stream as data enters the system rather than in the destination and this is pulling workloads from batch processing and the destinations into stream processing at the source. This is why structurally we expect the bulk of stream processing won't happen and.

Jay Krebs: This is why structurally, we expect the bulk of stream processing won't happen in destination systems like databases, data warehouses, or data. We think these three reasons are each powerful enough to draw processing workloads into the data streaming platform and, put together, will make the DSP the nexus of next-gen data work. We continue to see demand from customers who are building the next wave of generative AI applications, including AI-powered procurement software, chatbots, coding platforms, and even unexpected use cases like predicting and detecting cabinetry. These organizations turn to Confluent to quickly build and scale Gen AI applications that connect their proprietary systems to LLMs so they can deliver trustworthy and contextually rich insights to their customers. We believe this represents a tremendous opportunity for Confluent as customers evolve from experimentation in the short term to production in the medium and long term.

Destination systems like databases data warehouses or data lakes.

We think these three reasons are each powerful enough to draw processing workloads into the data streaming platform and put together will make the DSP. The Nexus of next Gen data workloads.

We continue to see demand from customers, who are building. The next wave of generative AI applications, including AI powered procurement software chat bots coating platforms and even unexpected use cases like predicting in detecting cavities. These organizations turned to confluent to quickly build and scale Gen AI applications that connect their proprietary systems J L.

So they can deliver trustworthy and contextually rich insights to their customers. We believe this represents a tremendous opportunity for confluent as customers evolve from experimentation in the short term to production in the medium and long term, we continue to invest in our product and in our partner ecosystem to address the demands we see across customers.

Jay Krebs: We continue to invest in our product and in our partner ecosystem to address the demands we see across customers. Alongside Anthropic, we recently partnered with a vector database vendor, Pinecone, and their new Pinecone Serverless offering. Our integration allows customers to build retrieval augmented generation, or RAG, pipelines that allow customers to bring together the real-time state of their proprietary data sources with general-purpose AI models. OpenAI has become the poster child for GenAI.

Alongside Anthropic, we recently partnered with a vector database vendor pine cone and their new pine cones service offering our integration allows customers to build retrieval augmented generation a rag pipelines that allow customers to bring together the real time state of their proprietary data sources with general purpose AI models.

<unk> AI has become the poster child of Gen. II in Q4 open AI signed with us to improve their visibility into customer usage patterns. We are still in early stages with this customer, but we have already identified additional use cases, including ways to help reduce costs across their stack this customer and others like it continued to validate the strategic role of data <unk>.

Jay Krebs: In Q4, OpenAI signed with us to improve their visibility into customer usage patterns. We are still in the early stages with this customer, but we have already identified additional use cases, including ways to help reduce costs across their stack. This customer and others like it continue to validate the strategic role of data streaming in the generative AI landscape. Finally, I'd like to close with two more customer stories that underscore our platform advantage. Sirtis operates the largest automotive logistics company in the United States.

<unk> and the generative AI landscape.

Finally, I'd like to close with two more customer stories that underscore our platform advantage.

Our surface operates the largest automotive logistics company in the United States. It serves car manufacturers dealers rental companies and e-commerce dealers to move store Recondition title and register finished unsold vehicles. However, the data systems that supported its business and customers were old and silos, creating pricing delays supply chain.

Jay Krebs: It serves car manufacturers, dealers, rental companies, and e-commerce dealers to move, store, recondition, title, and register finished and sold vehicles. However, the data systems that supported its business and customers were old and siloed, creating pricing delays, supply chain bottlenecks, duplicate records, and customers less. So Cedars turned to Confluent Cloud for a data streaming platform to provide real-time access to data across... Connectors allow a searchist to instantly connect data to internal systems, including applications in AWS, NetSuite, Salesforce, and Snowflake, and external partners. Stream processing enables them to process data in flight and deliver it to a data warehouse, so the data is up-to-date and accessible by anyone. Stream governance allows the team to search for and tag topics so users can find the data they're looking for and know it's trustworthy.

Bottlenecks duplicate records and customers less waiting so <unk> turned to counsel and cloud for a data streaming platform to provide real time access to data across its business connectors allow us searches to instantly connect data to internal systems, including applications in AWS net suite, Salesforce and snowflake and external partners stream processing enables.

<unk> to process data in flight and deliberate to a data warehouse. So data is up to date and accessible by anyone stream governance allows the team to search and tag topics. So users can find the data they're looking for a notes trustworthy with confluence serving as its data streaming platform. Our searches has been able to open new business lines to generate tens of millions in new revenue while delivering inter.

Jay Krebs: With Confluent serving as its data streaming platform, a searchist has been able to open new business lines to generate tens of millions in new revenue while delivering internal cost and time savings, savings for the customer, and increasing its profit. We continue to see strong growth in India, particularly in the digital native segment. A fast-growing e-commerce brand is a great example.

Cost and time savings savings for the customer and increased its profit margins.

We continue to see strong growth in India, particularly on the digital native segment of fast growing ecommerce brand is a great example by matching the world of fashion has the best Technology. This company has experienced massive growth in 2023 reached tens of millions of new app users, while growing its customer base by 100% in the last 18 months previously there.

Jay Krebs: By matching the world of fashion to the best technology, this company has experienced massive growth. In 2023, it reached tens of millions of new app users while growing its customer base by 100% in the last eight years. Previously, their data platform relied on open source Kafka to power end-to-end e-commerce work, including fulfillment, Real-Time Inventories, and Order Management.

Data platform relied on open source Kafka to power end to end ecommerce workflows fulfillment realtime inventories and order management, but with the Companys explosive growth can challenges scaling open source kafka, resulting in large maintenance overheads and over provisioning. So in Q4, they turned to confluent cloud with a seven figure deal to power of <unk> business services.

Jay Krebs: But with the company's explosive growth came challenges scaling open source Kafka, resulting in large maintenance overheads and overprovisioning. So in Q4, they turned to Confluent Cloud with a seven-figure deal to power six business services that previously used open source Kafka and plans to leverage our full platform, including stream governance, connectors, and stream processing, to support their ambitious growth. In closing, we're pleased with our strong finish to fiscal year 2020. We are more confident than ever that our transformation to a fully consumption-oriented business and continued innovation in our category-leading platform will serve as a catalyst for winning the $60 billion market opportunity in front of us. With that, I'll turn things over to Rob. Thanks, Jay. Good afternoon, everyone.

<unk> that previously used to open source Kafka and plans to leverage our full platform, including styrene governance connectors and stream processing to support their ambitious growth goals.

In closing, we're pleased with our strong finish to fiscal year 2023, we are more confident than ever that our transformation to a fully consumption oriented business and continued innovation in our category leading platform will serve as a catalyst for winning the $60 billion market opportunity in front of us with that I'll turn things over to Ron.

Thanks, Jay Good afternoon, everyone I will start with a brief recap of our full year results and fiscal year 2023, total revenue grew 33% to $777 million.

Rohan Sivaram: I'll start with a brief recap of our full year results. In fiscal year 2023, total revenue grew 33% to $777 million. Confluent cloud revenue grew 65% to $348.8 million, and non-gap operating margin improved 23 percentage points to end the year at negative 7.4%. This includes the fourth quarter, when we achieved our first positive non-gap operating margin of 5.3%, far exceeding the breakeven target we set a year ago. As we look back at fiscal year 2023, we are pleased to have delivered on our commitment of driving higher revenue growth while accelerating our path to positive non-gap operating margin by one year. Our ability to achieve 750 million plus revenue and positive non-gap operating margin in just nine years since the company's founding is a major accomplishment.

Cloud revenue grew 65% to $348 8 million and non-GAAP operating margin improved 23 percentage points to end the year at negative seven 4%.

This includes the fourth quarter, where we achieved our first positive non-GAAP operating margin of five 3% far exceeding the breakeven target reset a year ago.

As we look back at fiscal year 2023, we are pleased to have delivered on our commitment of driving higher revenue growth, while accelerating our path to positive non-GAAP operating margin by one year.

Our ability to achieve $750 million plus revenue and positive non-GAAP operating margin in just nine years since the company's founding is a major accomplishment.

Rohan Sivaram: It required substantial effort across every team in the company to achieve this milestone. I'm proud of our incredibly talented teams at Confluent, and I'd like to thank our employees, customers, and partners for their important contribution throughout the year. Turning to the Q4 results, key highlights include robust subscription revenue growth with our first $100 million quarter for both Confluent Cloud and Confluent Platform, record high non-gap total gross margin driven by strong unit economics of our product offerings, and our first positive quarter for both non-gap operating margin and free cash flow margin, underscoring our commitment to driving efficient growth at scale. Total revenue for the quarter grew 26% to $213.2 million

It required substantial effort across every team in the company to achieve this milestone.

I am proud of our incredibly talented teams at confluent and I'd like to thank our employees customers and partners for their important contribution throughout the years.

Turning to the Q4 results key highlights include robust subscription revenue growth with our first $100 million quarter for both confirm cloud uncomfortable platform record high non-GAAP total gross margin driven by strong unit economics of our product offerings and our first positive quarter of are both non-GAAP operating margin and free cash flow.

<unk> underscoring our commitment to driving efficient growth at scale.

Total revenue for the quarter grew 26% to $213 2 million subscription revenue grew 31% to $202 8 million.

Rohan Sivaram: Subscription revenue grew 31% to $202.8 million. Within subscription, Confluent platform revenue grew 18% to $102.8 million, representing 48% of total revenue. The strength was driven by healthy demand for Confluent platform in regulated industries. Confluent cloud revenue grew 46% to $100 million, exceeding our guidance of $97.5 million and ending the quarter at 47% of total revenue, compared to 41% of revenue a year ago and 46% last quarter. We're pleased with the healthy consumption we saw in our digital native customers, despite a still uncertain macro environment. Turning to the geographical mix of revenue, revenue from the U.S. grew 27% to $127.6 million, and revenue from outside the U.S. grew 25% to $85.5 million. Moving on to the rest of the income statement, I'll be referring to non-GAAP results unless stated otherwise. Total gross margin reached another record high of 77.5%, up 450 basis points.

Within subscription confluent platform revenue grew 18% to $102 8 million, representing 48% of total revenue the strength was driven by healthy demand for confluent platform in regulated industries.

Confluence cloud revenue grew 46% to $100 million exceeding our guidance of $97 5 million and ended the quarter at 47% of total revenue compared to 41% of revenue a year ago and 46% last quarter. We are pleased with the healthy consumption. We saw in our digital native customers. Despite the still on.

Certain macro environment.

Turning to the geographical mix of revenue revenue from the U S grew 27% to $127 6 million revenue from outside the U S grew 25% to $85 5 million.

Moving on to rest of the income statement I'll be referring to non-GAAP results unless stated otherwise total gross margin reached another record high of 77, 5% up 450 basis points subscription.

Rohan Sivaram: Subscription gross margin also reached a record high of 81.1%, up 240 basis points. Gross margin outperformance was driven by strong Confluent platform margin and the efficiency and optimization we continue to realize in our cloud offerings. Turning to profitability and cash flow, we achieved positive operating margin for the first time as a public company, improving 27 percentage points to 5.3%, representing our sixth consecutive quarter of more than 10 points and third consecutive quarter of more than 20 points in margin improvement. Our relentless focus on driving operational efficiency across the company resulted in improvement in every category of our operating expenses, with the largest improvement of 16 percentage points in sales and marketing expenses as a percentage of total revenue. Net income per share was $0.09 for Q4 using 342.4 million diluted weighted average shares outstanding. The Fully Diluted Share Count under the Treasury Stock Method was approximately $356.1 million.

Gross margin also reached a record high of 81, 1% up 240 basis points gross margin outperformance was driven by strong conference platform margin and the efficiency and optimization, we continue to realize in our cloud offering.

Turning to profitability and cash flow, we achieved positive operating margin for the first time as a public company, improving 27 percentage points to five 3%, representing our sixth consecutive quarter of more than 10 points and third consecutive quarter of more than 20 points of margin improvement.

Our relentless focus on driving operational efficiency across the company resulted in improvement in every category of our operating expenses with the largest improvement of 16 percentage points in sales and marketing expenses as a percentage of total revenue.

Net income per share was nine <unk> for Q4, using $342 4 million diluted weighted average shares outstanding fully.

Fully diluted share count under the Treasury stock method was approximately $356 1 million pre.

Rohan Sivaram: Pre-Cash Flow Margin also turned positive in the quarter, improving 21 percentage points to 3.2%. And we ended the fourth quarter with $1.9 billion in cash, cash equivalents, and marketable security. Turning now to other business metrics, in Q4, the total customer count grew 9% to approximately 4,960. Customers with 100k or more in ARR grew 21% to 1229, and customers with 1 million or more in ARR grew 24% to 158. We ended fiscal year 23 with 19 customers with 5 million or more in ARR, up from nine customers a year ago. This reflects our customers' strong confidence in standardizing on our data streaming platform, making Confluent the central nervous system of their technology stack. We believe the completion of our consumption transformation in fiscal year 24 will help accelerate the growth of our total customer count. In fact, we saw good traction in total customer count in January. While early, we believe the transformation will make it even easier for our customers and prospects to try, adopt, and expand across stream, connect, process, and govern in our product portfolio. The error rate in the quarter was slightly above 125%, exceeding our mid-term target threshold of 125%.

Free cash flow margin also turn positive in the quarter, improving 21 percentage points to three 2%.

And we ended the fourth quarter with $1 9 billion in cash cash equivalence and marketable securities.

Turning now to other business metrics in Q4 total customer count grew 9% to approximately 4960.

Customers with 100 gig or more in <unk> grew 21% to 1200, 2009 and customers with a $1 million or more in <unk> grew 24% to 158.

We ended fiscal year, 'twenty, three with 19 customers with $5 million or more in IRR up from nine customers a year ago.

This reflects our customers' strong confidence and standardizing on our data streaming platform, making confluent the central nervous system of their technology stack.

We believe the completion of our consumption transformation in fiscal year 'twenty four will help accelerate the growth of our total customer count in fact, we saw good traction in total customer count to January one early we believe the transformation will make it even easier for our customers and prospects to prior adopt and expand across stream.

Connect process and govern in our product portfolio.

And I wrote in the quarter was slightly above 125% exceeding our midterm target threshold of 125% gross retention rate remained strong and was above 90%.

Rohan Sivaram: Gross retention rate remained strong and was above 90%. As discussed last quarter, we expect NRR to be between 120% and 125% as we go through our consumption transformation this year. As we exit the transformation and start fiscal year 25, we expect NRR to revert to Q4-23 levels and exceed our midterm target threshold of 125%. RPO was $919.9 million, up 24%. Current RPO, estimated to be 64% of GDP, was $591.9 million, up 30%.

As discussed last quarter, we expect an IRR will be between 120% and 125% as we go through our consumption transformation this year.

As we exit the transformation and starting fiscal year 'twenty five we expect MLR to revert to Q4, 'twenty three levels and exceed our midterm target threshold of 125%.

Our <unk> was $919 9 million up 24% current RVO estimated to be 64% of RVO was $591 9 million up 30%.

Rohan Sivaram: As I called out last quarter, RPO-related metrics are less relevant beginning this year, given our greater focus on driving consumption for our cloud business. Next, I'm pleased to announce that we recently acquired Notable, which did not have a material impact on our financials. We closed the acquihire in Q4 of 2023 and welcomed a small team of highly talented individuals to Confluent.

As called out last quarter, our PEO related metrics are less relevant beginning this year, given our greater focus on driving consumption for our cloud business.

Next I am pleased to announce that we recently acquired notable which did not have a material impact on our financials. We close the aqua hire in Q4 of 2023 and welcomed a small team of highly talented individuals to confluent. This team focuses on developing a no code data visualization capability that simplifies navigating.

Rohan Sivaram: This team focuses on developing a no-code data visualization capability that simplifies navigation and identifies important insights. Now, I would like to discuss Confluent's position for 2024 and beyond. Driven by our TAM technology and team, we have shown in our 2023 results our success in driving efficient growth at scale. In 2024, our TAM technology and team are only getting stronger. First, our $60 billion plus TAM is underpinned by the prevalence of data streaming, as more than 150,000 organizations have built around streaming, along with long-term secular tailwinds such as cloud migration and Gen AI. Second, our technology differentiation is expanding rapidly. We have successfully evolved from a single product streaming company to the industry's only data streaming platform company. Our DSP is cloud native, complete with stream, connect, process, and govern, and available everywhere.

<unk> identifies important insights.

Now I would like to discuss confluence positioning for 2024 and beyond driven by our Tam technology and team. We have shown in our 2023 results our success in driving efficient growth at scale in.

In 2024 hour time technology and team are only getting stronger.

First our $60 billion plus Tam is underpinned by the prevalence of data streaming as more than 150000 organizations have built around streaming along with long term secular tailwind such as cloud migration and Jen AI.

Second our technology differentiation is expanding rapidly we have successfully evolved from a single product streaming company to the industry's only data streaming platform company, our DSP is cloud native.

Plead with stream connect process and govern and available everywhere. Our customers are excited about the innovation, we plan to bring to the market in 2024 as we have one of the most exciting product release cycles coming up in the history of the company starting with <unk> <unk> in Q1.

Rohan Sivaram: Our customers are excited about the innovation we plan to bring to the market in 2024, as we have one of the most exciting product release cycles coming up in the history of the company, starting with Flink GA in Q1. Finally, our team has proven ability to execute, with the latest accomplishment of delivering high revenue growth annually while improving non-gap operating margin by more than 46 points in just 10 quarters. In 2024, we have strong alignment and commitment across every function of the company to deliver on our consumption transformation. This will put us in a better position, more aligned with our customers, to address the $60 billion plus dam in front of us.

Finally, our team has proven ability to execute with the latest accomplishment of delivering higher revenue growth annually, while improving non-GAAP operating margin by more than 46 points in just 10 quarters in.

In 2024, we have strong alignment and commitment across every function of the company to deliver on our consumption transformation. This will put us in a better position more aligned with our customers to address the $60 billion plus Tam in front of us.

Rohan Sivaram: Given this backdrop, we are focused on sustaining efficient growth in 2024 by delivering our first breakeven year for both non-GAAP operating margin and free cash flow margin. Given our solid Q4 performance, we feel confident in delivering 22% total revenue growth for 2024 and eventually returning to our mid-term target growth of 30%. Turning now to our guidance. As announced on our last earnings call, we will be transitioning our revenue guidance metrics to subscription revenue beginning this quarter. To assist the investment community with transitioning to a new guidance practice, we will continue to provide total revenue guidance for the first two quarters of 2024 and for the full year 2024. We will fully transition to providing only subscription revenue guidance, beginning with Q3.

Given this backdrop, we are focused on sustaining efficient growth in 2024 by delivering our first breakeven year for both non-GAAP operating margin and free cash flow margin <unk>.

Given our solid Q4 performance, we feel confident in delivering 22% total revenue growth for 2024, and eventually returning to our midterm target growth of 30%.

Turning now to our guidance as announced on our last earnings call, we will be transitioning our revenue guidance metrics to subscription revenue beginning this quarter.

To assist the investment community with transitioning to a new guidance practice, we will continue to provide total revenue guidance for the first two quarters of 2024 and for full year 2024, we will fully transitioned to providing only subscription revenue guidance beginning with Q3.

Rohan Sivaram: For the first quarter of 2024, we expect total revenue to be in the range of $211 to $212 million, representing growth of 21% to 22%. Subscription revenue, which is our new guidance metric and consists of Confluent Cloud and Confluent Platform revenue, will be in the range of $199 to $200 million, representing growth of 24% to 25%. Non-GAAP operating margin was approximately negative 4%, representing an improvement of approximately 19 percentage points and non-GAAP net income per diluted share of approximately 0 to 2 cents. For the full year 2024, we expect total revenue to be approximately $950 million, representing growth of approximately 22%, non-GAAP operating margin to break even, representing an improvement of approximately 7 percentage points, and non-GAAP net income per diluted share of approximately $0.17. Additionally, I'd like to provide some modeling points.

For the first quarter of 'twenty 'twenty four we expect total revenue to be in the range of 211% to $112 million representing growth of 21% to 22% subscription revenue, which is a new guidance metric and consists of confluent cloud and confluent platform revenue will be in the range of $199 million to $200 million representing growth.

Of 24% to 25%.

non-GAAP operating margin at approximately negative 4% representing improvement of approximately 19 percentage points and non-GAAP net income per diluted share to be approximately zero to two cents.

For the full year 2024, we expect total revenue to be approximately $950 million representing growth of approximately 22% non-GAAP operating margin to breakeven representing improvement of approximately seven percentage points and non-GAAP net income per diluted share of approximately <unk> 17.

Additionally, I'd like to provide some modeling points, we expect confluent cloud revenue in Q1 to be approximately $105 million representing growth of approximately 43%.

Rohan Sivaram: We expect Confluent Cloud revenue in Q1 to be approximately $105 million, representing growth of approximately 43%. We expect free cash flow margin in FY24 to be break even, representing improvement of approximately 16 percentage points. Consistent with prior years, Q1 free cash flow margin will continue to show pronounced seasonality, primarily due to our corporate bonus payout, employee stock purchase program, and the holdback payment related to our MROC acquisition. However, despite these headwinds, we expect Q1 free cash flow margins to improve approximately 20 percentage points year-over-year. Finally, we are pleased with decreasing our annualized net dilution from 4.7% in FY22 to 3.5% in FY23. We expect net dilution for fiscal year 24 will be approximately 3%, in line with our midterm target.

We expect free cash flow margin in fiscal year, 'twenty four to breakeven representing improvement of approximately 16 percentage points consistent with prior years Q1 free cash flow margin will continue to show a pronounced seasonality primarily due to our corporate bonus payout employee stock purchase program and the holdback payment related to our <unk>.

Iraq acquisition.

Despite these headwinds we expect Q1 free cash flow margins to improve approximately 20 percentage points year over year.

Finally, we are pleased with decreasing our annualized net dilution from four 7% in fiscal year 'twenty two to three 5% in fiscal year 'twenty three.

We expect net dilution for fiscal year 'twenty four will be approximately 3% in line with our midterm target our goal over the long term is to bring that dilution down to under 2%.

Rohan Sivaram: Our goal over the long term is to bring net dilution down to under 2%. In summary, we are pleased with closing out the year with solid fourth quarter results. Our track record of improving non-gap operating margin is a testament to the power of our innovation engine and our commitment to driving efficient growth. Looking forward to 2024, we are focused on achieving our first positive non-gap operating margin and free cash flow margin for the full year while delivering on our top-line commitment. Now GNI will take your question. All right. Thanks, Rohan. To join the Q&A, please raise your hand. And today, our first question will come from Michael Turn with Wells Fargo, followed by Morgan Stanley. Michael, go ahead.

In summary, we're pleased with closing out the year with solid fourth quarter results. Our track record of improving non-GAAP operating margin is a testament to the power of our innovation engine and our commitment of driving efficient growth looking forward to 2024, we are focused on achieving our first positive non-GAAP operating margin.

And free cash flow margin for the full year, while delivering on our top line commitment now G&A will take your questions.

Alright, Thanks, Ron two joined the Q&A. Please raise your hand.

And today, our first question will come from Michael <unk> with Wells Fargo, followed by Morgan Stanley Michael Go ahead.

Michael Turn: Hey, thanks. Nice to bounce back here. Appreciate you taking the question. Jay, I want to go back to some of the partnerships you mentioned. You caught a few interesting companies with Anthropic, Pinecone, and then the OpenAI customer relationship. So I'm just wondering, is there commonality in terms of their needs for data streaming? Are there reasons they're landing with Confluent versus open source Kafka?

Hey, Thanks lifestyle back here I appreciate you taking the question.

Jay I wanted to go back to some of the partnerships. You mentioned you called out a few captivating companies with anthropic pine cone and then they open AI customer relationships. So I'm just wondering.

Is there a commonality in terms of their need for data streaming are there reasons theyre landing with Commvault versus open source Kafka I think this is obviously new ground for all of US. So just anything else you can provide just to help us understand what drove the useful yeah.

Jay Krebs: I think this is obviously new ground for all of us, so anything else you can provide just to help us understand what drove those is useful. Yeah. I think increasingly streaming is a critical part of the architecture for these generative AI applications. The need is very much to bring together the kind of proprietary enterprise data you would have with one of these more generic language models that kind of know about the world but doesn't know the up-to-the-second view of your business and what's happening.

Yeah, I think increasingly streaming is a critical part of the architecture for these generative AI applications that need is very much to bring together.

Proprietary enterprise data you would have with one of these more generic language models that kind of knows about the world, but doesn't know the up to the second view of your business and what's happening and so that's very much the use case, where we tend to play in that.

Jay Krebs: And so that's very much the use case where we tend to play in that. So the partnerships with vector databases like Pinecone, the models, it's very much around supporting that architecture. And our goal was really to support the integration across the best technologies in that space, and I think it was very much embraced on the other side by these companies that are trying to do that. And then OpenAI, this is an incredible technology company that I think has the potential to be Google over time in terms of the scale of their infrastructure. We're extremely happy to be part of that stack. That's great. If I could just ask a follow-on question for Rohan, it's encouraging to see the 22% guide held on to here.

So the partnerships with the vector databases like Pine cone models is very much around supporting that architecture and our goal is really to support the integration across the best technologies in that space and I think it was very much embraced on the other side by these companies that are trying to do that.

And then open AI. This is incredible technology company that I.

I think has the potential to be besides Google over time in terms of the scale of their infrastructure, we're extremely happy to be part of that stack.

That's great if I can just ask a follow on for Rohit.

It's encouraging to see the 22% guide held onto here you mentioned a number of impacts for us to consider last quarter.

Rohan Sivaram: You mentioned a number of impacts for us to consider last quarter. Any commentary you can provide just on how some of those played out in Q4 relative to what you're expecting previously is also useful. Thanks very much.

Any commentary you can provide just on how some of those played through in Q4 relative to what you're expecting.

Previously it is also useful thanks very much.

Rohan Sivaram: Thanks for the question, Michael. Yeah, of course. I mean, it all starts with our Q4 execution. We delivered subscription revenue growth of 31% and total revenue growth of 26%.

Okay.

Thanks for the question Michael.

Of course, I mean, it all starts with our Q4 execution, we delivered subscription revenue growth of 21% and total revenue growth of 26% and then a couple of milestones for the first quarter of 100 million for one continent platform in cloud and that coupled with just the green shoots we started saying and.

Rohan Sivaram: And in a couple of milestones, the first quarter of $100 million for both Confluent platform and cloud. That, coupled with just the green shoots we started seeing in the digital native segment with respect to consumption, was, I'd say one area in general, Q4 execution was solid. I think number two on the consumption transformation side, Michael, we saw good early traction with respect to where exactly we want to be. As Jay mentioned, we were at the sales kickoff last week, and the feedback was very, very positive. And generally, like one month in, we're getting just positive signals with respect to our transformation. So that's that.

On the other segment with respect to the consumption was I'd say the one area in general Q4 execution was solid.

Number two on the consumption transformation side, Michael we saw good early traction.

With respect to when exactly where we want to be as Jay mentioned, we went at sales kickoff last last week and the feedback was very very positive and.

Generally like one maintain we're getting just positive signals with respect to our transformation. So that's that and in general when you think about our guidance philosophy.

Rohan Sivaram: And in general, when you think about our guidance philosophy, if you look at our Q1 and four-year guidance, we're not assuming a huge amount of acceleration in the second half of the year. So that, I mean, when you combine all of these, it just gives us more confidence around our 2024 guidance. Very clear. Thanks very much.

If you look at our Q1 and full year guidance, we're not assuming a huge amount of acceleration in the second half of the year. So that I mean, when you combine all of these it just gives us I would say.

Our confidence around ongoing anymore right.

Very clear thanks, very much I appreciate it alright, thanks, Michael we'll go to our Sanjay Singh with Morgan Stanley and then followed by Deutsche Bank.

Rohan Sivaram: Appreciate it. All right. Thanks, Michael. We'll go to Sanjit Singh with Morgan Stanley and then be followed by Deutsche.

Sanjit Singh: Yeah, just to pick up on the previous question and some of the themes last quarter, Jay, I think one of the themes that you called out last quarter is just that software development projects have slowed down throughout the course of calendar 2023. It doesn't sound like you're giving all clear signs just yet, but there seem to be some encouraging signs, like new logo acquisitions in January. In terms of what you're seeing from the customer base and sort of them sort of restarting some innovation initiatives, any update there that you can tell us as it relates to potentially driving pipeline for Confluent? Yeah, you know, I would characterize it this way. Like, you know, I think 23 was just a tight year for IT budgets kind of everywhere.

Yes.

Pick up on the previous question and some of the themes last quarter, Jay I think one of the things that you called out last quarter is just that software development projects had slowed down throughout the course of calendar 2023, it doesn't sound like you're giving me all clear signs just yet, but they are seeing seem to be some encouraging signs with like new logo acquisition in January in terms of.

What youre seeing from the customer basis or is that sort of we starting some innovation initiatives any update there that you can tell us as it relates to potentially guiding pipeline for conflict.

I would characterize it this way like I think.

<unk> three was just a tight year for it budgets kind of everywhere.

Jay Krebs: And then, you know, in the digital native space, it was, you know, extra, extra tight where there was, you know, a very significant push on optimization. So, you know, where are we now? Yeah, I would say. It's all back to where 2021 was, but there are some green shades, right?

And then in.

In the digital native space It was <unk>.

Extra extra tight where there was yield very significant push on optimization and so where are we now yes, I wouldn't say.

It's all back to where 2021 was but there is some green shoots right. There is we've definitely seen more activity.

Jay Krebs: There's, you know, we've definitely seen more activity in the digital native space, right? I think some of the optimization has been accomplished, you know, so there are projects happening there. You know, I think maybe there's kind of a normalization, you know, across both large enterprise and digital native where people are getting a little bit back to normal. You know, it's early to call that, but, you know, I would say that's the early part of what we've seen. Great, so a little bit of incremental progress. And maybe just one quick follow-up.

And the digital aerospace right I think some of the optimization has been accomplished.

Eunice projects happening there I think maybe there's kind of a normalization.

Ross, both large enterprise and digital native where people are getting a little bit back to normal.

It's early and calling up.

I would say that the early part of what we've seen.

Okay, it's a little bit of incremental progress and maybe just one quick follow up I mean from Q4 is typically a big renewal quarter for us from a software companies as you saw the renewals come up did you pick up any sort of increased.

Jay Krebs: I mean, Q4 is typically a big renewal quarter for most software companies. As you saw the renewals come up, did you pick up any sort of increased motivation by a cohort of customers to move or downgrade from paid Confluent to open source Kafka, and sort of? No, that's, you know, like, overall, the kind of gross retention rate has remained very strong, you know, as I think we called out the, you know, we track exactly when whenever we compete versus open source, whether renewal or kind of new win. And those win rates have remained very strong, in fact, actually improved in Q4 over the past year.

Increased motivation by.

Cohort of customers to move or a downgrade from a console into open source cost savings or to update them.

Yes.

Overall, the gross retention rate has remained very strong.

We called out.

We track exactly win wherever we compete versus open source, whether renewal or kind of new win.

And those win rates have remained very strong in fact actually improved in Q4 over past quarters Exelon.

Jay Krebs: Excellent, thank you. Great, thanks Sanja. We'll go to Brett Zelnick with Deutscher next, followed by RPC.

Excellent. Thank you.

Great. Thanks, Andrew we'll go to Brent <unk> with Deutsche or next followed by RBC.

Brad Alan Zelnick: Thanks very much, guys. And it's great to see a strong finish to the year. I want to follow up on Michael Turn's question around these partnerships and AI use cases, which you called out in your press release, I think, where you referenced real-time generative AI use cases, you know, as really being at the forefront right now. If we kind of go back to where we were at your analyst event in New York, I don't know if that was six or eight months ago, it feels like with these partnerships, this is really more Can you give us any perspective in terms of the types of use cases and the extent to which this is really going to materialize into demand?

Thanks, very much guys and great to see the strong finish to the year I want to follow up on Michael <unk> question around these partnerships, Jay and AI use cases, which you called out in your press release, I think you referenced realtime generative AI use cases.

As as really being at the forefront right now if we kind of go back to where we were at your analyst event in New York I don't know if that was six or eight months ago. It feels like with these partnerships.

This is really more coming into focus.

Fruition can you give us any prospective view in terms of like the types of use cases, and the extent to which this is really going to materialize into demand, which I think again, reflecting back six eight months ago was a little bit unclear as things were shifting in the world exactly you kind of knew comparable is participating but I think you left the door.

Jay Krebs: Which I think, again, reflecting back on six, eight months ago, was a little bit unclear as things were shifting in the world. Exactly. You kind of knew Confluent was participating, but I think you left the door open to exactly how you could articulate that. That would be great.

Until exactly how if you could articulate that that would be great. Yeah. Yeah. So I would say the kind of our place in that stack has played out exactly as we call. Greg we're in that kind of data supply chain for use cases around large language models I would say the predominant use case is.

Jay Krebs: Yeah. Yeah. You know, so I would say our, you know, kind of our place in that stack has played out exactly as we call it, right? We're in that kind of data supply chain for use cases around large language models. I would say the predominant use case, you know, it's a lot of language and chat stuff, you know, as you would see, very much that kind of applies, you know, to apply this language model using data about my business.

A lot of language and chat stuff as you would see.

Very much that kind of applies apply this language model using the data about my business.

Jay Krebs: You know, that's the broad version of it. That could be around augmenting internal employees and making them effective. That could be something customer-facing. That could be kind of a back-end data processing task.

The broad version of it that could be around augmenting internal employers are making them effective that can be something customer facing that could be out of our back end data processing task.

Jay Krebs: You know, we see that across a variety of disciplines, whether it's, you know, I call out some of them, but everything from retail to tech companies to financial services. So, you know, I think that's happening. Where are we at in that cycle? You know, I would say it's still early.

We see that across a variety of disciplines, whether it's.

Some of them, but you know everything from kind of retail to tech companies to financial services. So.

I think thats happening where are we at in that cycle I would say, it's still early or theres more experiments in production applications and obviously, we're kind of a production data layer. So that's where we come into play, but I think it's definitely promising and kind of adds to the set of use cases that we have there to drive adoption of this new architecture around data center.

Jay Krebs: There are more experiments than production applications, and obviously, we're, you know, kind of a production data layer. So that's where we come into play. But I think it's definitely promising.

Jay Krebs: It kind of adds to the set of use cases that we have that drive the adoption of this, you know, new architecture around data. Great. And maybe just a quick follow-up for Rohan.

Great and maybe just a quick follow up for ROE Han Thanks, Jay.

Brad Alan Zelnick: Thanks, Jay. You know, great job on the quarter, a better Q1 guide than we were modeling, but would I be wrong to assume, and just what I think I'm hearing from you, is that you're feeling better about the environment and growth opportunity versus a quarter ago, given you've nudged up your 2024 guidance, but you're still keeping the margin guide for flat, making me wonder, are you hiring more into the opportunity you see ahead? Or is there maybe dilution from notable?

Great job on the quarter better Q1 guide than we were modeling, but would I be wrong to assume and just what I think I'm hearing from you is that you are feeling better about the environment and growth opportunity versus a quarter ago, given you've nudged up your 2024 guidance, but you are still keeping the margin guide for flat, making me wonder are you hiring more.

And to the opportunity you see ahead or is there maybe dilution from notable what should we not to nitpick, but what should we be thinking about here.

Rohan Sivaram: What should we, not to nitpick, but what should we be thinking about here? Yeah, thanks, Brad. Thanks for the question. On the top line side, like I mentioned, three things: strong Q4 performance, our consumption transformation from start to finish exactly as we expected it to be, and just some green shoots on digital native. That's driving our slight increase in dollar terms and increased confidence in our 24 guide. On the margin side, we have a guide of seven percentage points improvement year over year. That's holding to what we said a year ago, so I wouldn't call out anything specific.

Yes, thanks, Brian Thanks for the question when you.

On the topline side like I mentioned <unk> strong Q4 performance our consumption transformation after the start and exactly how we expected it to be and just some green shoots foundational nature, that's driving our slight increase in dollar terms and.

And increased confidence in our 24 guidance on the margin side.

We have a guide on seven percentage point improvement year over year.

That's holding to what we said a year back so I wouldn't call out anything specific.

Rohan Sivaram: I don't call out that it does not have any material impact on our financials, and it's included in the guidance. But in general, as we head into 2024, we will hire in critical areas of the business, and we will overall make sure that we are driving durable growth and doing that efficiently in a thoughtful manner. Great. Thanks very much.

I'll call out that not alone does not have any material impact on our financials and it's included in our guidance, but in general as we head into 2024, we will hire in critical areas of the business and to overall make sure that we are driving durable growth and.

And doing that efficiently in a thoughtful manner.

Matthew George Hedberg: Nice job, guys. All right, thanks, Brad. We'll take a question from Matt Hedberg on RPC, followed by Needham.

Right. Thanks, very much nice job guys.

Thanks, Brad we'll take a question from Matt Hedberg with RBC, followed by Needham Matt.

Matthew George Hedberg: Great. Thanks, guys. I'll offer my congratulations as well.

Great. Thanks, guys I'll offer my congrats as well.

Jay Krebs: Following up on Brad's question a bit and focusing on the TAM for data streaming, do you have a sense for the percentage of workload, Jay, or workload or apps that customers typically see as needing real-time data versus where, you know, maybe batches find it? Yeah. Yeah, it's a great question.

Following up on Brad's question, a bit and focusing on the Tam for data streaming do you have a sense for the percentage of workload, Jay that or workload or apps that customers typically see as needing real time data versus where maybe batches find yes.

Yes, it's a great question I mean, the key observation is I think that.

Jay Krebs: I mean, the key observation is that, you know, everybody wants data to be up-to-date, and they want things to sync with the business. The question is, how critical is that, right? Is that something that you must have at all costs?

Everybody wants data to be up to date and they want things in sync with the business. The question is how critical is that right is that something that you must have at all costs is that something you would like to have.

Jay Krebs: Is that something you would like to have? And, you know, I would say there are two changes there. First, increasingly, you know, use cases do need that. As systems become more part of the operational stack of companies, you know, as more of the use of data is driving action, not just, you know, reporting, I think that does require things to be much more in sync with the current state of the world. And so I think there's a trend overall in that direction. And then, secondly, you know, the cost of real-time, the cost of streaming, and the difficulty of it are very much coming in line with batch computing. There's no reason this should be any harder.

And I would say Theres two changes are first increasingly use cases do you need that as systems become more part of the operational stack of companies you guys more of the use of data is driving action not just reported I think that does require things to be much more in sync with the current state of the world.

I think theres a trend overall in that direction and then secondly.

The cost of real time, the cost of streaming and the difficulty of it is very much coming in line with with batch commuting Theres. No reason this should be harder just newer right and that takes time to for sure. So yeah.

Jay Krebs: It's just newer, right? And that takes time to mature. So yeah, you know, we felt like, hey, without making a lot of, you know, really big assumptions, if you look at kind of what's the portion of workloads that the average enterprise would have on this streaming platform, you know, I would say about a third, maybe a third live in this kind of operational database world where you're doing the quick lookups and serving the interactive web apps, maybe a third are in the And maybe a third are in that, you know, stream processing space.

We felt like Hey.

Without making a lot of.

Really big assumptions, if you look at kind of whats the portion of workloads that the average enterprise would have on the streaming platform I would say about a third maybe a third live in this kind of operational database world, where youre doing the quick look ups and serving the interactive web apps, maybe a third or in the kind of.

<unk> world kind of backend dashed up it really.

So he doesn't need to move out of that.

Kind of offline processing, and maybe a third or in that stream processing space I think thats. The end state that we are aiming for if you look at companies that are a little more technologically advanced and have been at this for a while that's where they are if you look at companies who are just starting in the space of a few things that they've done so the.

Jay Krebs: I think that's the end state that we're aiming for. If you look at companies that are a little more technologically advanced and have been at this for a while, that's where they are. You know, if you look at companies who are just starting in this space, they just have a few things, right, that they've done. So the assumption is that those, you know, those newcomers will be able to progress. What enables that is making this technology easy and approachable, which is, of course, you know, the direction of all our investment. Great. And then maybe just a quick follow-up.

<unk> is that those those newcomers will be able to progress what enables that is making the technology easy and approachable, which is of course the <unk>.

Direction of our investments.

Great and then maybe just a quick follow up obviously good execution here in Q4, and Ron noted does that new customer as a increase in January which is good to hear have you heard any more just general feedback from the sales force on these changes and did you notice any abnormal sales rep attrition.

Jay Krebs: Obviously, good execution here in Q4, and Rohan noted that new customer ads increased in January, which is good to hear. Have you just heard any more general feedback from the sales force on these changes, and did you notice any abnormal sales repetition? Yeah, yeah, it's a great question.

Yeah, Yeah. It's a great question. So like when we were thinking about the risks involved in this consumption transformation that was definitely one of our potential risks was like hey, there's a big change for the sales team.

Jay Krebs: So, when we were thinking about the risks involved in this consumption transformation, that was definitely one of our potential risks. It was like, hey, this is a big change for the sales team. But, what we've seen so far, I think, has been very promising. So, first of all, people understood why we were doing it. They felt like it was coming more in line with some of the pure companies, and, you know, those companies have done it successfully. So I think there's enough kind in the water that, you know, this makes sense. I think it's in line with what we see from customers, like what customers want to do. So I think it made sense to people. I had a lot of conversations with, you know, bag-carrying sales reps and sales leaders at our sales kickoff last week. And, you know, I was expecting a more mixed set of feedback. Usually, if you make big changes, you get a little bit of everything. On the whole, I thought it was extremely positive.

What we've seen so far I think has been very promising so first of all people understood why we were doing it.

They felt like it was coming more in line with some of the peer companies.

Those companies have done it successfully so I think theres enough kind of in the water that you notice makes sense I think it's in line with what we see from customers like where customers want to do.

It made sense to people that got a lot of conversations with.

<unk>.

Bag carrying sales reps sales leaders at our sales kickoff last week and I was expecting a more mixed set of feedback is really if you make big changes you get a little bit of everything on the whole I thought it was extremely positive.

Jay Krebs: So that, you know, that's been good. And then, yeah, attrition, you know, we that was one of our concerns overall. But you know, that's not been an issue.

So that's been good and then attrition we.

That was one of our concerns overall.

Jay Krebs: You know, attrition is under what we modeled for the year and in line with historical norms for years when we haven't had this change. So, that's very positive. Great to hear, congrats guys. All right, thanks, Matt. We'll go to Mike Sikos with Needham Next, followed by William Blair.

<unk> not been an issue with attrition is under what we modeled for the year and in line with historical norms for years, where we hadn't had this change so that's very positive.

Great to hear congrats guys.

Alright, Thanks, Matt will go to Mike <unk> with Needham next followed by William Blair.

Mike Sikos: Hey, thanks for taking the questions, guys. And I just wanted to pick up where Matt left off, just because I know that there's so much focus on this go-to-market transformation that you guys have been talking about. We're probably going to get this question, but I just want to get it in a public forum here.

Thanks for taking the questions guys and I just wanted to pick up where Matt left off just because I know that there is so much focus on this go to market transformation that you guys have been talking about.

We're probably going to get this question I just want to get it in a public forum here, but the concerns if we wanted to play Devil's advocate is it part of the Q4 strength was driven by.

Rohan Sivaram: But the concern, if we wanted to play devil's advocate, is that part of the Q4 strength was driven by, let's say, sales reps really trying to jam some of these contracts in under the old incentive structure. Can you just parse that out while we have everyone here to hear, I guess, what you saw on that front? Yeah, I'm happy to do that. First, it's important to understand that there's no change for the Confluent platform, the licensed software offering. So there's no particular, I mean, sales reps always want to get something done in the current year if they can, but there's no particular need to jam it through in 23 versus 24 on the Confluent platform side. On the Confluent Cloud side, it's very important to understand that the revenue actually comes with the consumption. And so what you're seeing has nothing to do with the kind of deals closing. That would show up in RPO, but the revenue represents just the increase in consumption, as you would expect. So yeah, I don't think there was a... You know, always people want to close deals in the, you know, as soon as they can.

Let's say sales reps really trying to jam some of these contracts and under the old incentive structure can you just parse that out well, while we have everyone here to here, but I guess, what you saw on that front.

Im happy to do that I mean first it's important to understand that there's no change for Commvault platform. The license software offering so theres no sales.

Sales Rep has always wanted to get something done in the current year. They can sure Theres no particular need to jam it through in 'twenty three versus 24 on the confluent platform side.

Uncomfortable and cloud side, it's very important to understand that the revenue actually comes with the consumption and.

And so what you're seeing has nothing to do with that.

As you kind of deals closing that would show up in <unk>, but the revenue represents just the increase in consumption as you would expect so so yes, I don't think there was a.

Always people want to close deals in the suite.

Rohan Sivaram: But I don't think there was a, you know, huge transition or kind of pull forward. Great, and then a bit of a two-parter here, one to close out the sales and another just to the broader platform. The first question is more just a financial check here, but for Rohan, maybe you could give us a comment, but I think last quarter the company had alluded to maybe 200 to 300 bits of an operating margin headwind based on the upfront expense recognition for Confluent Cloud with this incentive structure. Can you confirm that that's still the case when we think about this guidance here for the year? And then the second part, again, this is coming back to you, Jay, but can you talk about the importance of Flink and where I'm going with this? I believe Flink actually generalizes the processing, right? It can handle both streaming and batch processing.

As soon as they can but I don't think there was a huge transition or kind of a pull forward.

Great and then a bit of a two parter here wanted to close out the sales and another just to the broader platform.

First more more just the financial checking here, but for <unk> and maybe you could give us a comment but I think last quarter. The company had alluded to maybe 200 to 300 bps of an operating margin headwind.

Just on the upfront expense recognition for <unk> cloud with this incentive structure can you confirm that that's still the case when we think about this guidance here for the year and then the second part again this.

This is coming back to you Jay but can you talk about the importance of flink.

And where I'm going with this is I believe a frank actually generalize the processing quite it can handle both streaming and batch processing.

Mike Sikos: Again, for those folks who don't have the technical chops, myself included, but what is the importance of that generalization when we think about the potential that Flink has for your platform? Yeah. Do you want to go first, Rohan?

Again for those folks who don't have the technical charts myself included but what is the importance of that generalization. When we think about the potential of <unk> for your platform, yes, Greg during their first Ron yes.

Rohan Sivaram: Yeah, I'll go first. Mike, you're right. What we said last quarter was around just how commission is recognized, and that had a two to three hundred basis point headwind on our operating margins. That still holds true.

<unk>.

Mike you're right.

<unk> said last quarter was around just how commission is recognized and that had a two to 300 basis point headwind to our operating margins that still holds good and Thats back impact has actually been incorporated into our guidance that we shared so of the seven seven percentage points improvement that we are talking.

Jay Krebs: And that impact has actually been incorporated into our guide that we shared. So the seven percentage points improvement that we are talking about year over year takes into account that dynamic that's happening. So to just confirm what you said, it's true. Thank you for that. Yeah, and then on the Flink side, that's exactly right. You know, when people think about streaming, one of the mistakes I think they often make is to think of it as kind of a niche, right? What's actually happened in the world to make this area successful is that it's really kind of a generalization of the batch systems. And that's actually true in the Kafka layer, where data is streamed, but it's actually stored, you know, permanently, if you like, as well.

About year over year takes into account that dynamic that's happening. So can you just confirm what you said.

Thank you for that.

And then on the Flink side, that's exactly right.

When people think about streaming one of the mistakes I think they often make us to think of it as kind of a.

Niche right.

What's actually happened in the world to make this area successful is its really kind of a generalization of the batch systems and that's actually true and the Kafka layer, where data is streamed but it is actually stored.

Permanently if you like as well and it's true in the <unk> data is processed in real time, but can also be reprocessed and batch. So unit has actually a very sophisticated batch processing engine as well and it's a bit into the <unk>, but like that ability to provide something that's a generalization it's actually key to the earlier point.

Jay Krebs: And it's true in the Flink layer, where data is processed, you know, in real time, but can also be reprocessed in batch. So, you know, it has a very sophisticated batch processing engine as well. And, you know, it's a bit into the tech weeds, but that ability to provide something that's a generalization is actually key to the earlier point of, you know, what workloads would move, why would they move? You want something that is, you know, as capable as fully featured can handle the full set of workloads but now does them, you know, continuously in sync with the business. Terrific. Thank you for that. I'll have to nerd out with you on that another time, but I appreciate it. Thank you, guys. All right. Thanks, Maya. We'll go to Jason Ader with William Blair next, followed by Golan Sachs and Chapin.

Of.

What workloads would move why would they move you want something that is as capable as fully featured can handle the full set of workloads, but now does them continuously in sync with the business.

Terrific. Thank you for that they'll have to nerd out with you on that other times, but I. Appreciate it. Thank you guys alright, Thanks, Mike who will go to Jason Ader with William Blair next followed by Goldman Sachs.

Jason Yes.

Thanks, Shane and good afternoon, guys first question for you just.

Jason Ader: Yeah, thanks, Shane. Good afternoon, guys. First question for you, just beyond the comp model shift that you guys have undertaken here. I just wanted to get some more detail on some of the organizational changes. I know you have a new CRO.

Beyond the comp model shifts that you guys have undertaken here.

I just wanted to get some.

More detail on some of the organizational changes I know you have a new CRO.

Jay Krebs: Can you just talk about how sales ops is changing, account coverage, sales engineering, customer success, partner engagement, just sort of a little bit of a lay of the land in terms of how the sales organization looks today versus what it looked like a year or two ago? Yeah, you know, so the field operations overall remain under Erica Schultz. There are some changes, you know, within that that kind of line up to this consumption change, right? Changes a little bit in how the sales engineers operate since the distinction between kinds of land and expand is now a little bit different in a consumption world. You know, changes in some of the organizations in the Americas and elsewhere as well. Okay, and so it's more of a tweak, is that fair, versus an overhaul? Yeah, yeah, I mean, you know, I don't know where the line is between one and the other.

Can you just talk about how sales ops is changing account coverage sales engineering and customer success partner engagement, just sort of a a little bit of a lay of the land in terms of how the sales organization looks today versus what it looked like a year or two ago.

Yes.

The field operations overall remains under Erica Schultz there are some changes within that that kind of lineup to this consumption change right.

It changes a little bit and how the sales engineers operate.

The distinction between kind of land and expand is now a little bit different than a consumption world.

Changes in some of the organizations in the Americas and elsewhere as well.

Okay. So it's.

So it's more of a tweak that fare versus overhaul.

Yes, yes.

I don't know where the line between one and the other is but on the whole I feel like we've got a lot of continuity in that team that people kind of driving this are the same people who kind of setup. The set of changes that we executed in 2003. So I think theres been a lot of continuity and how we've thought about the kind of set of adjustments, we would need to make okay. And then one quick one for <unk>.

Jay Krebs: But, you know, on the whole, I feel like we've had a lot of continuity in that team, that the people kind of driving this are the same people who kind of set up the set of changes that we executed in 23. So I think there's been a lot of continuity in how we think about the kind of set of adjustments we made. And one quick one for Rohan. Can you talk about the shape of the quarterly revenues in 2024 and any rough estimates on the impact of Flink and the timing of the impact from Flink? I'll start with the second part, Jason.

Johan.

Yeah.

Can you talk about the just the shape of the quarterly revenues.

2024, and any rough cut on impact of flank and timing of impact from Frank.

I'll start with the second part Jason with respect to fling as as we mentioned earlier, we expect to achieve falling in Q1 and with any kind of infrastructure product. It takes a couple of quarters for our customers to stock build applications on user so as we've said before.

Rohan Sivaram: With respect to Flink, as we mentioned earlier, we expect to GA Flink in Q1. And with any kind of infrastructure product, it takes a couple of quarters for customers to start building applications and using it. So as we said before, very consistent with what we said before, we expect revenue material revenue contributions from Flink to happen in fiscal year 25. So that's on the fling part.

Very consistent with what we've said before we expect revenue material revenue contributions from flying to happen in fiscal year 'twenty five.

So that's that's on the plane art on the shape.

Rohan Sivaram: On the shape, I'll just call out a couple of things. I'm not gonna guide or provide color commentary on every quarter, but in general, I think one thing I can give you some additional color is around the shape of the cloud business for 2024. In general, cloud as a percentage of total revenue for 2024, we expect it to be in the range of 50 to 51%, which is kind of in line with where the estimates are. And of course, from a first half versus second half, what I've shared earlier is that as a result of the consumption transformation, we're expecting second half growth rates to be slightly more elevated than first half. But as you can see from the guide, it's not a huge acceleration in that number. Hope that helps. Yeah Very helpful, thank you. Thanks, Jason. I will go to Kash Rangan with Goldman, followed by J.P. Morgan.

I'll just call out a couple of payments are not going to guide or provide color commentary on every quarter, but in general.

One thing I can give you some additional color is around the shape of the cloud business for 2024 in general cloud as a percentage of total revenue for 2024, we expect to be in the range of 50% to 51%, which is kind of in line with Rand estimates are and of course from a first half versus second half.

What I've shared earlier is as a result of the consumption transformation, we're expecting second half growth rates to be slightly more elevated than first half, but as you can see from the right. It's not a huge acceleration in that number of that helps yet very helpful. Thank you.

Thanks, Jason I'll go to cash Rangan with Goldman followed by J P Morgan cash.

Kasthuri Gopalan Rangan: Kash. Yeah, thank you very much. Good to connect with you guys. As you brought up the new consumption model and how salespeople are going to get compensated. What has been the customer feedback? Does it change anything about the way the customer is going to be dealing with Confluent, regardless of the way you compensate your salespeople? And secondly, Jay, as you go into 2024, we're hearing a lot more about Flink. The message is that Confluent is an elegant technology platform. It's pretty complicated, but it gets even more complicated as we get into the discussion of where Flink fits in versus Kafka.

Yes, yes. Thank you very much I'll get to connect with you guys.

So as you as you brought the new consumption.

Model and our salespeople are going to get compensated what has been the customer feedback does it change anything about the way the customer is going to be dealing with Costco.

Regardless of where you compensate your salespeople and secondly, Jane as you go into 2024 hour work hearing a lot more flank.

The message compound is an elegant technology platform is pretty complicated.

It gets even more complicated as we get into a discussion of where <unk> fits in versus Kafka.

Jay Krebs: How are we to navigate this complexity in the technology portfolio versus the complexity in the way we're going to be compensating salespeople? What are the tools and techniques you're giving your go-to-market organization to navigate and get through this? Complications.

Are we how are we to navigate this complexity in the technology portfolio versus the complexity in the.

And the way.

Kind of a compensating salespeople.

The tools and techniques, you're giving your go to market organization to navigate and get through this.

Scott Yes.

Jay Krebs: Thank you. Yeah, yeah, on the first question, you know, there's nothing immediate that changes the interaction with customers, or at least not in a way that you would immediately notice, right? So the, you know, the payment model, the kind of business model, you know, that has all been consumption-oriented since prior to the IPO, and so you might not notice anything if you're a customer. But hopefully, you notice that our field team is, you know, more helpful in finding the applications that are critical to your success, you know, making sure that those get to production as quickly as possible. Hopefully, they were doing that already, but the, you know, the consumption incentive kind of directly drives that behavior, but it wouldn't be something where we have to, you know, send you some notice of something changing; it really is internal to our operations that the change is most apparent.

Yeah on the first question.

There is nothing immediate to changes in the interaction with customers or at least not in the way that that you would immediately notice spread so the.

The payments model that kind of business model that has all been consumption oriented.

Since prior to the IPO and so you might not notice anything if you are a customer hopefully you noticed that our field team is more helpful. In finding the applications that are critical to your success, making sure that those get to production as quickly as possible.

Hopefully they were doing that already but the consumption of incentives directly drives that behavior, but it wouldn't be something where we have to send you. Some notice of something changing really is internal to our operations. The changes most apparent.

Jay Krebs: On your second point, you know, I think you will hear about complexity around Kafka or around Flink. I think it's actually very important to separate out the operational complexity of trying to build a big in-house data system that you self-manage from actually using one of these cloud services. The interface to Kafka, you know, is a very simple kind of read and write data streams. The interface to Flink is, you know, just SQL as people are used to this kind of common language of databases or in other common programming languages, very simple, you know, similar constructs.

On your second point I think you will hear about complexity around kafka or around link I think it's actually very important to separate out the operational complexity of trying to build a big in house data system that you self manage from actually using one of these cloud services the interface to <unk>.

<unk> is a very simple kind of written write data streams. The interface for link is just sequel as people are used to this kind of a common language databases or in other common programming languages very simple similar contracts. So that developer interfaces not complicated if you want to stand up and build an in house data platform and run.

Jay Krebs: So that developer interface is not complicated. If you want to stand up and build an in-house data platform and run it yourself off open source, yeah, there's a lot of rocket science involved in that. And so, you know, when we think about how that affects our sales model, well, that's part of what we're bringing, right? The value of these cloud platforms, in particular, is taking all that away. You can just depend on this as a service. And, you know, I do think that that's where, you know, call it 90% of the complexity of this new stuff lies. And that's always been true. Like running databases is hard, running data systems of all kinds is hard.

It yourself off the open source, yes, theres a lot of rocket science involved in that and so when we think about how does that affect our sales model.

Part of what we're bringing right the value of these cloud platforms. In particular has taken all that away even just depend on this as a service and I do think that that's where.

Call it 90% of the complexity in this new stuff lives and Thats always been true like running databases as hard running data systems of all kinds is hard on this stuff is no different from that.

Jay Krebs: And this stuff is no different. All right. Thanks, Kash. We'll take our next question from Pinjalim Bora with J.P. Morgan, followed by Misuhu. Thank you, everybody.

Alright, Thanks, Kash, we'll take our next question from pendulum Bora with J P. Morgan followed by Mr. Hu.

Can you elaborate.

Pinjalim Bora: Thanks. Thanks, guys. And thanks for being the questions.

Thanks, Thanks, Dave.

Jay Krebs: Congratulations on the quarter. I want to ask you about the global market changes. I think I heard you have already put kind of the initial changes in place. Maybe go a little bit deeper.

Thanks for taking the question congrats on the quarter I want to ask you a little go to market changes I think I heard you have already booked kind of the initial changes in place maybe go a little deeper what has been rolled out what is remaining.

Jay Krebs: What has been ruled out? What is remaining? What about the sales enablement side? Because it seems like the conversations for the sales reps also change, looking at more use case driven versus. Contracts, and maybe talk about what is being rolled out. Yeah, yeah, yeah, the kind of set of things that need to change at a high level, you know, the compensation structure changes, what we track in Salesforce changes; we're now tracking the individual application workloads, not just the high-level contracts progressing. That's intentional; that lets us really explicitly drive that.

What about sales enablement side, because it seems like the conversation for the sales reps also changes a bit.

More use case driven versus committed contracts and maybe talk about what is rolled out and what are the remaining yes, yes, yes.

Set of things that need to change at a high level.

The compensation structure changes.

What we track.

And sales force changes, we're now tracking the individual application workloads, not just the kind of high level contracts progressing.

Thats intentional that.

Lets us really explicitly drive that and then yes, there is a bit of a different motion and enablement around that and so what have we done we've.

Jay Krebs: And then yeah, there is a bit of a different motion and enablement around that. And so, you know, what we've done, we've rolled out these new systems, we've changed the compensation, we're kind of managing and driving this, we've run through the enablement, that was obviously a big focus at our sales kickoff. So, you know, does that mean everything's done, you know, mission accomplished? Well, no, now we have to go drive it successfully. Like any new thing, you have to put all the parts together, turn it into a car, and drive the car.

It rolled out these new systems, we have changed the compensation, we're kind of managing and driving this we've run through the enablement of that was obviously a big focus in our sales kickoff.

So does that mean everything's done mission accomplished we'll know now we have to go drive it successfully like any new thing you got to put all the parts together turned it into a car and drive the car. So that last bit is kind of the key focus this quarter and next quarter is really make sure we nail that debt.

Jay Krebs: So, you know, that last bit is kind of the, you know, key focus this quarter and next quarter is really making sure we nail that, that, you know, all of this works well in every territory for every rep, everywhere in the world. That's obviously our focus. But in terms of like, well, how do we feel relative to the last quarter as we talked about this? Well, obviously, a number of things have de-risked, right?

All of this works well in every territory for every rep.

Everywhere in the world.

That's obviously our focus but in terms of like what how do we feel relative to last quarter as we talked about this while obviously a number of things that de risk strategy, we rolled out this compensation program.

Jay Krebs: Like we rolled out this compensation program, you know. I think it was successful; people understood it, they felt they could make money on this plan. We got the systems and tools built to run the business, you know. So a lot of progress has been made, but there's obviously still a lot of work to do. You're understood, Heather. And Jay, I want to ask you about Flink. Is it possible to understand what portion of your customer base today already uses Flink? version of LingQ, or maybe using Kafka Streams on it.

It was successful people understood. They felt they could make money on this plan we.

<unk> got the systems and tools built to run the business a lot of progress has been made but there's obviously still a lot of work to do.

Yes understood.

That's helpful and one day, one off tail off link.

Is it possible to understand what portion of your customers customer base today.

Users can even open source version of sling or maybe using kafka screens on AWS.

Jay Krebs: Yeah, yeah, yeah, it's a pretty high overlap, you know, like any open source stat, it's a little, you know, there's, it's an inexact science tracking the usage of open source things. But yeah, we had given some adoption stats for Flink. It's smaller than Kafka is, but on a very similar growth trajectory.

<unk>.

Yeah.

Pretty high overlap.

Like any open source that its a little.

There is.

It's an inexact science tracking their usage of open source things.

But yes, we had given some adoption stats for flink, it's smaller than Kafka has been on a very similar growth trajectory I think it was a few earnings calls ago, we kind of plot it out.

Jay Krebs: You know, I think it was a few earnings calls ago. We kind of plotted out the relative adoption of those two projects. So, yeah, it's certainly double digit percentages of the customer base already uses. Thanks, Pinjalim. We'll take our next question from Gregg Moskowitz with Misuhu, followed by TD Cowan.

Relative adoption of this of those two projects. So yes, it's certainly double digit percentages of the customer base already uses open source plant.

Understood. Thank you.

Thanks opinion, and we will take our next question from Gregg Moscowitz with Mizuho, followed by TD Cowen Greg.

Gregg Steven Moskowitz: Okay, thank you for taking the questions. Your net new logos were lighter than the typical Q4. Did the self-service activity really slow down? And did the upcoming go-to-market transition to consumption factor into that? Also, it sounded like you had a nice bounce back in net new logos in January. And so any additional color there would be helpful as well. Yeah, I would say this one is, you know, that's an accurate observation. I would say this one is mostly mechanical.

Greg.

Thank you for taking the questions.

Net new logos were lighter than the typical Q4 to the self service activity really slowed down and enter the upcoming go to market transition to consumption factor into that also it sounded like you had a nice bounce back in net new logos in January and so any additional color.

Color there would be that would be helpful as well.

Yes, I would say this one is.

That's an accurate observation I would say this one is mostly mechanical and this is one where the consumption changes did have an impact in Q4.

Jay Krebs: And this is one where the consumption changes did have an impact in Q4. So one of the changes we made in Q4, you know, we incented just kind of the bookings for cloud or platform. You know, starting in Q1, starting in January, we're directly incentivizing both land, you know, new logos and expand the kind of consumption off of it. So yeah, if you were going to close a very small new customer in, you know, late December, you might want to do it in January because you get paid on it.

So one of the changes we made in.

In Q4, we incentives just kind of the bookings for cloud our platform.

Starting in Q1, starting in January were directly incentive both land, new logos and expand the kind of consumption off of it. So yes. If you were going to close a very small new customer in late December.

You might want to do in January because you get paid on it and say, yes, we did see a bit of a shift there we're off to a good start as we noted in the script and in January and then if we think about the trajectory over the rest of the year, we do think theres an opportunity to really drive.

Jay Krebs: And so, yeah, we did see a bit of a shift there. We're off to a good start, as we noted in the script in January. And then, you know, if we think about the trajectory over the rest of the year, we do think there's an opportunity to really drive, you know, you know, a higher velocity land of customers. And that's part of our goal with this consumption transformation. So that's one of the areas we're going to be watching to, you know, try and see some significant growth there over the course. Thanks, Jay.

A higher velocity land of customers and Thats part of our goal with this consumption transformation. So that's one of the areas, we're going to be watching to try and see some significant growth there over the course of the year.

Rohan Sivaram: And then, Rohan, your subscription gross margins continue to impress, and in fact, your total gross margins are now well above your prior or current, I should say, long-term guidance. Is there anything sort of one-time in nature that's contained within gross margins today, or are you unlocking more efficiency than you maybe had expected previously? Now, Gregg, that's the right observation.

Very helpful. Thanks, Jay and real heightened figure subscription gross margins continue to impress and in fact youre.

Total gross margins are now well above your prior or current I should say long term guidance is there anything sort of onetime in nature. That's contained within gross margin today or are you on locking more efficiency than you maybe had expected previously.

<unk> sorry.

Alright observation when you look at our gross margins, we had record gross margins for our total gross margins as well as subscription gross margins and when you just kind of double clicking into it the dynamics are as cloud mix over time increases that's a headwind to gross margins and during the same time, our engineering and product teams have done.

Rohan Sivaram: When you look at our gross margins, we had record gross margins for total gross margins as well as subscription gross margins. And when you just kind of double-click into it, the dynamics are that as cloud mix over time increases, that's a headwind to gross margins. And at the same time, our engineering and product teams have done an incredible job of making sure we're improving the efficiency with which we are delivering our products to our customers. And that's one driver.

Incredible job in making sure they're improving the efficiency with which we done enabling our products to our customers.

Rohan Sivaram: As we look ahead, I think there's an opportunity for us to drive a higher multi-tenant mix in our product, which is obviously going to be a tailwind to gross margins. So looking forward, we expect to be in the, I would say, zip code of 75 plus percent, which is our long-term target gross margin. With some puts and takes, the tailwind is going to be more efficiencies coming through multi-tenant, and the headwind is going to be, you know, as cloud becomes a bigger piece of the mix. So we expect them to balance out and be in this range that you see right now. Terrific. Thank you. Thanks, Gray. Our final question today will come from Derek Wood with TD Cowen. Derek

That's one driver as we look ahead I think there's an opportunity for us to drive a higher <unk> mix in our <unk> product, which is obviously going to be a tailwind to gross margins. So looking forward, we expect to be in.

I would say a ZIP code of 75 plus percent, which is our long term target gross margins with some puts and takes that tailwind is going to be a more efficiencies coming from multi tenant and the headwind is going to be as cloud becomes a bigger piece of the mix. So we expect them to offset and be in this range and that you see right now.

Terrific. Thank you.

Thanks, Greg our final question today will come from Derrick Wood with TD Cowen Derek.

Derek Wood: Great. Thanks. First, for you, Jay, I think you guys in the past have talked about Confluent Cloud being 60% cheaper than self-managed open source. And we know there are over 100,000 companies using open source Kafka.

Great. Thanks first.

For you Jay.

I think you guys in the past I've talked about <unk> cloud, 60% cheaper than <unk>.

Self managed to open source and we know there's over 100000 companies using open source Kafka.

Jay Krebs: You think in this environment where there's more focus on spend efficiency, maybe you could see some kind of rising interest on the cloud side. So I guess as you move into 2024, are there any things you'd flag that you're doing to help drive more open source conversion? Yeah, yeah, it's a great observation. Yeah, there are two key things. So I think the observation is right. There's like excellent TCO.

You take in this environment, where there's more focus on spend efficiency maybe.

Maybe you could see some kind of.

Rising interest on on the cloud side. So I guess as you move into 2024 are there any.

Thanks, you'd flagged that youre doing to help drive more more open source converging.

Yeah, Yeah. It's a good observation, yes. There is two key things. So I think the observation is right theres like excellent Tcl. The two things that we think are critical to get right to raise the volume of conversions. One is via this consumption transformation one of the things that happened in 2003 was I do think a lot.

Jay Krebs: The two things that we think are critical to get right to, you know, raise the volume of conversions. One is, you know, this consumption transformation. One of the things that happened in 23 was, I do think a lot of organizations tamped down on kind of bottom-up purchasing.

Sort of organizations Tamped down on kind of bottom up purchasing so the interplay between product led growth and the sales team suddenly becomes very very important and that's a key aspect of how we've designed our plan for this year and how we've designed the consumption model for confluence is make sure that we have the right.

Jay Krebs: So the interplay between product-led growth and the sales team suddenly becomes very, very important. And that's a key aspect of how we've kind of designed our plan for this year and how we've designed the consumption model for Confluent is to make sure that we have the right, you know, setup to land customers on our product, convert them over, and kind of take them all the way, you know, with a little bit of help from the field team. And I think that's actually incredibly, you know, incredibly important in the infrastructure space if, you know, if you want to be able to attract volumes of customers. So that's the first change. The second change on our side is, you know, reducing the kind of friction of adoption. So that's technological. Just how easy is it to get from here to there?

Setup to land customers in our product.

Convert them over and kind of take them all the way with a little bit of help from the field team and I think that's actually incredibly incredibly important in the infrastructure space.

Want to be able to land volumes as customers. So that's the first change the second change on our side as well.

Reducing the friction of adoption so.

Thats technological just how easy is it to get from here to there like there may be some savings, but if im also reducing my team do I have the people to actually make the change or make the switch part of that is just doing everything we can to make our product a drop in replacement, making it as easy to switch, making that kind of starting costas as appealing as possible all of that matters a lot too.

Jay Krebs: There may be some savings, but if I'm also reducing my team, do I have, you know, the people to actually make the change or make the switch? You know, part of that is just, you know, doing everything we can to make our product a drop-in replacement, making it as easy to switch, making that kind of starting cost as appealing as possible. You know, all of that matters a lot to make the, you know, not just the payoff, but the payoff time, really appealing.

To make the.

Not just the payoff with the payoff time really appealing.

Derek Wood: Great. And Rohan, this was the biggest cloud revenue upside quarter you've delivered in nearly two years. Can you just parse out the puts and takes?

Great and ROE on this.

The biggest cloud revenue upside quarter, you delivered in nearly two years can you just parse out the puts and takes that you see notable improvements in consumption in the quarter or are you overly conservative in your guide, perhaps with respect to the two large customers you flagged last quarter in terms of headwinds can you just double.

Derek Wood: Did you see notable improvements in consumption in the quarter? Were you overly conservative in your guide, perhaps with respect to the two large customers you flagged last quarter in terms of headwinds? Can you just double-click on the outperformance in the quarter and what that's telling you around consumption trends heading into 24? Yeah, I mean, of course, we were pleased with the results from Confluent Cloud.

Click on the <unk>.

Outperformance in the quarter and what that's telling you around consumption trends heading into 'twenty four.

No I mean of course.

Pleased with the results from ongoing cloud <unk> grew 46% and the mill.

Rohan Sivaram: We grew 46%. And the momentum with which we are exiting Q4 is also showing up in our Q1 guide, which is north of 40% as well, Derek, right? So when you look at those puts and takes, the first piece I spoke about in my prepared remarks was just the green shoots around the digital native segment. That's a positive as we head into next year. And outside of that, when you look at our broader customer base, in general, we felt that consumption came in line with our expectations and was nothing unusual. And obviously, that's probably the driver of consumption.

The momentum with which we are exiting Q4 is also showing up in our Q1 guide which is north of 40% is derived Eric right. So when you look at the puts and takes the first piece.

I spoke about during the prepared remarks was just the green shoots around digital native segment.

That's a positive as we head into next year and outside of that when you look at our broader customer base in general we felt the consumption came in line with our expectations and nothing unusual and obviously.

Rohan Sivaram: And as we enter next year, we were optimistic with respect to, as you can see in our Q1 guide. Thanks, well done. Thanks, Derek. That concludes today's earnings call. Thanks again for joining us, everyone. We appreciate it. Take care. Winslow, Brad Zelnick, Kasthuri Rangan, Matthew Hedberg, Gregg Moskowitz, Pinjalim Bora, Tyler Radke, Bradley Sills, Rudy Kessinger, Bradley Sills, Robbie Owens, Confluent. Goodbye.

That's probably the drivers of consumption and as we enter next year.

We're optimistic with respect to as you can see in our Q1 guidance.

Thanks, Rob.

Thanks, Derek that concludes today's earnings call. Thanks, again for joining US everyone. We appreciate it take care.

Yeah.

Hum.

Hum.

Goodbye.