Main content

Confluent share price rallies as it beats full year guidance

Derek du Preez Profile picture for user ddpreez February 8, 2024
Confluent CEO Jay Kreps managed market expectations last quarter by lowering the ‘data-in-motion’ vendor’s guidance - which the company has exceeded.

An image of Jay Kreps, Confluent CEO, on stage
(Jay Kreps)

Confluent’s share price was up more than 20 per cent in extended trading yesterday, as the ‘data-in-motion’ company exceeded previous guidance for its full-year earnings. Confluent provides a commercial offering data streaming and stream processing technology, which it sees as becoming as critical to enterprises in the future as database and storage technology has been in recent history. 

The market was cool on Confluent’s third quarter earnings back in November, as CEO Jay Kreps lowered the company’s full year revenue guidance from a range of $767-772 million down to $758-769 million. The stock market is often about managing expectations, but it seems that this softening of guidance wasn’t necessary as Confluent today announced full year earnings of $777 million, up 33 per cent year over year, beating even the high end of what it previously expected. 

Kreps said: 

We closed fiscal year 2023 with a solid Q4, exceeding the high end of our guided metrics. These results are a testament to the power of our platform and the incredible growth of the data streaming category.

The key numbers released this week include: 

  • Fourth quarter total revenue of $213 million, up 26% year over year; fiscal year 2023 total revenue of $777 million, up 33% year over year

  • Fourth quarter subscription revenue of $203 million, up 31% year over year; fiscal year 2023 subscription revenue of $729 million, up 36% year over year

  • Fourth quarter Confluent Cloud revenue of $100 million, up 46% year over year; fiscal year 2023 Confluent Cloud revenue of $349 million, up 65% year over year

  • 1,229 customers with $100,000 or greater in ARR, up 21% year over year

Last quarter Kreps discussed how Confluent was restructuring its go-to-market strategy around Confluent Cloud, which it believes will help accelerate the company’s growth. Commenting on the progress of this, Kreps said: 

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 in landing customers and maximize the potential for expansion.

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 oriented. We've executed some of the initial changes of our consumption transformation effective January 1, 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. 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.

A whole new category

Kreps is keen to position data streaming as a whole new technology category that is going to underpin enterprises’ data platforms for years to come, rather than just being a complementary data tool for fringe use cases. He sees data streaming - and stream processing - as technology categories that will become as important as databases and storage. The key differentiator is that Confluent focuses on data that is live, moving and in action. He said:  

One way of thinking about data technologies is to break them into two groups; those oriented for handling Data at Rest, 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 a powerful infrastructure platform, the database, a $90 billion plus category. The landscape of data in motion technologies remained highly fragmented, with technology analysts recognizing disparate technology categories including message queues, application integration tools, data integration tools, event brokers, ETL products, and more.

The reason for this was largely technological. Each of these product categories was defined 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 Confluent’s creation, that has been our central thesis that the data streaming platform would be a data platform of similar importance and scale to databases, but acting as the central nervous system handling all the data in motion.

However, Kreps is also keen to dispel the notion that data streaming platforms - and Confluent for that matter - are just focused on Apache Kafka (an open source tool that started inside LinkedIn and kick started the whole idea of ‘data in motion’ as a technology enabler). 

Kreps describes Kafka as the foundational layer, but to get the full value out of data in motion, buyers need to connect to the systems they’ve already got in place, process data in real-time and govern these flows of data across the enterprise. Confluent sees connectors, stream processing and governance as a path to becoming a sizable business in their own right. 

On the stream processing side, Confluent acquired Immerok just over a year ago, which has developed a cloud-native, fully managed Apache Fink service - allows organizations to combine, develop and reshape their data streams with other data from across the organization. For instance, Kreps explained: 

Stream processing enables organizations to act on data as it arrives, rather than waiting to process it in batch at the end of the day. For an airline, it could be processing data from streams of flight times, weather information, and customer information. 

By itself, 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 disruptions.

And Kreps sees big opportunities for Confluent going forward with the introduction of stream processing to its portfolio. He said: 

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.

Everyone agrees that Kafka is the standard for the stream itself. 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 by unifying data streaming with Kafka with stream processing via Flink. We believe the resulting data streaming platform is exactly the product the customers want. 

This pairing is not just skin deep, either. 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. And 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. 

My take

Lots of work to be done still, but lots of opportunity ahead. As organizations seek out additional data opportunities with Artificial Intelligence, and as they begin to rethink their data strategies, it’s likely that Confluent will become more strategic with those use cases too. Confluent’s challenge is that those data at rest technologies are deeply embedded in the history of many organizations and it will take some time to pull buyers towards a ‘live data’ environment. But from the use cases we’ve seen, there’s a clear advantage to be gained from ‘data in motion’. Looks like an ambitious year ahead for Confluent.


A grey colored placeholder image