Confluent delivers solid Q2 numbers as Confluent Cloud gains momentum

Confluent - the company behind the concept of ‘data in motion’ and which provides a commercial offering of Apache Kafka - has delivered a strong set of Q2 results, building on the momentum it outlined earlier this year. The company’s share price was trending upwards on the news, which saw Confluent’s revenues and high-paying customer base grow significantly over the past year. 

Commenting on the results, co-founder and CEO of Confluent, Jay Kreps, said: 

Confluent Cloud continues to increase as an overall mix of our business and is seeing rapid adoption across our customer base as reflected by strong consumption trends. We're especially proud of our performance given the uncertain macro environment we're currently operating in.

The key figures from Q2 2022 are: 

• Total revenue of $139 million, up 58% year over year

• Confluent Cloud revenue of $47 million, up 139% year over year

• Remaining performance obligations of $591 million, up 81% year over year

• 857 customers with $100,000 or greater in ARR, up 39% year over year

It’s worth noting that last quarter Confluent said that it had 791 customers with $100,000 or greater in ARR, which indicates that this number has grown by 66 in the last three months (not an insignificant amount). This quarter also exceeded the high end of Confluent’s guidance. 

Kreps went on to explain why he believes that Confluent continues to see strong demand, despite the economic headwinds at play. He argued that the reasons are twofold. Firstly, he said that Confluent sits in the enterprise operational stack, which powers applications that service critical business functions and customer experiences. He explained: 

Given this criticality, it can't be switched off without a complete disruption to the operations of the business.

Secondly, Kreps points to cost savings for buyers, deriving from the use of Confluent Cloud. He added: 

Using Confluent Cloud has significant TCO advantages compared to trying to build out internal teams of engineers to attempt to build internal services around open source.

But Confluent Cloud is not just a matter of putting Kafka in the cloud. In building Confluent Cloud, we rethought virtually every layer of the stack from how data is routed over the network, how it is processed, where it is placed and how it is stored. This deep engineering investment is necessary to provide a truly cloud-native service that can meet the needs of the most mission-critical use cases and can help customers truly step back from the operations of the service and focus on their applications.

To achieve this, over the last 5 years, we've poured more than 3 million engineering hours into Confluent Cloud. Today, it represents a 10x better Kafka service with a deep competitive moat of hard technology. By making a service that is 10x better than open source Kafka, Confluent lets organizations avoid investments in low-level operations, monitoring and scaling and be able to instead rely on a service that can scale elastically with their needs.

This is what drives the substantial cost savings customers see when they adopt our service. As we shared last quarter, a recent Forrester study identified TCO savings of more than $2.5 million for businesses that use Confluent, translating to an ROI of 257% in less than 6 months.

Reducing friction

Interestingly, Confluent is also thinking through how it can foster its relationship with developers and attract more customers to try Confluent Cloud, without committing to long-term payments. Kreps said that the early-stage of this journey is critical for customer acquisition and often starts with developers experimenting with pilots and proof of concepts, so that they can simply learn the technology. 

Kreps explained: 

At this stage, it's critical for the onboarding process to be low friction. So a developer can instantly gain full access to the power of our platform with minimal disruption. To make this process even easier for developers, I'm very pleased that towards the end of our first quarter, we removed the requirement of entering credit card information for the free trial of our product.

This paywall removal is a strategic move that aligns well with our customer growth go-to-market model, allowing us to reduce the friction for developers to test our product, grow usage and progress to the production stage. And we are already seeing strong returns at the top of our funnel, as evidenced by the accelerating growth in Q2 sign-ups, which are up more than 130% year-over-year and up more than 50% sequentially.

Kreps said that the removal of the paywall has been successful in increasing sign-ups, but has also created some short-term noise in its total customer count metric. Customers who would have incurred small amounts of spend had previously counted as customers in their initial trial phase will now just show up as sign-ups, not paying customers, which has impacted Confluent’s Q2 customer count groups. But it’s worth noting that MongoDB, and others, have pursued a similar strategy with its cloud offering, which was very successful in the long run. 

Kreps finished by saying: 

This means that the change has eliminated a large chunk of pre production customers paying us an average of less than a few hundred dollars per quarter, creating a reset of our pay-as-you-go customer count. 

Reset of customer count aside, it's unquestionably the right strategy for our business as our customers can now test drive Confluent risk-free. And for us, the reduction in developer risk and friction drives easier land and ultimately more paying customers as the larger cohort of trials leads to sticky production applications that grow and expand at scale.

My take

It’s clear the Confluent’s cloud strategy is paying off and that it is gaining traction in the market in the right areas (an increase in higher paying customers, for example). It’s move to remove friction for developers is a smart one - we’ve seen this pay off for other vendors, where customers are allowed to start small, try out the technology, and then eventually scale up significantly.