Below, we do another overview of Confluent’s product and an update following the Q4 earnings report. Here are two resources we recommend reading from our premium site for more information on the company.
Confluent Product Overview and Q3 Earnings
Big Data, Analytics and the Importance of ML
We believe open source with enterprise-grade features will become a key market moving forward as it solves for the downside of open source such as a lack of technical support. In Kafka’s case, the downside are things like a lack of data verification and having to manually connect to various data warehouses and other platforms to import/export data. Confluent also makes the argument that multi-cloud and hybrid cloud architectures are best served with a supported enterprise version for multi-tenancy security and data residency.
Notably, from my perspective, we are not betting on Confluent being used over the open-source version of Kafka in a direct competition, rather we are betting that Kafka will increase in importance. In this case, if Kafka continues to grow, Confluent will take a percentage of this market share should more enterprises prefer a managed version of Kafka. 70% of the Fortune 500 use Kafka and 80% of the Fortune 100. According to this site it has a 12.5% market share.
Kafka is popular because of its high-performance real-time data streaming capabilities for mission critical applications. It is distributed and fault-tolerant, which means if one component fails, the system will still work. It can also scale to hundreds of clusters and billions of messages.
As discussed in our original write-up, Kafka was developed at LinkedIN to process the large number of messages per second the social media company handles. The framework enables event streaming, which helps messaging and data integration. There is high scalability with a publish/subscribe model that allows applications to share and create data in a serverless and microservices architecture. What Kafka solves for is the ingestion of events data in real-time with low latency with continuous read/write. If data remains at rest and/or in a mainframe environment, then companies cannot be truly data-driven. Kafka on the other hand can scale from a billion messages per day to a trillion messages per day.
Machine Learning and Kafka
Confluent opens up the amount of data that can integrated. The thesis is the increase in the number of companies that will need real-time data processing and real-time data analytics due to the increase in software driven architectures. The idea is that “data in motion” will replace data at rest, or batch data processing from traditional databases. This is also important for the real-time data streams that machine learning requires.
Kafka is more than a messaging system as discussed in this article and is used for business applications, streaming ETL middleware, real-time analytics and edge/hybrid use cases for the framework.
Here are some examples of how Kafka can be used outside of messaging systems:
- Fraud detection through a machine learning pipeline for Paypal’s billions of messages
- Data correlation in real-time for Lyft for matching maps, estimated time of arrival and cost calculations
- Unity uses Confluent to be internally data-driven across R&D and cloud-services, plus to help drive the monetization network by rewarding players for watching ads and incorporating banner ads
- Continuous calculations for betting platforms
- Drug discovery that is automated and scalable
Machine learning requires model training from historic data and also model deployment for scoring and predictions. Training can be done with batch yet scoring is partial towards real-time data. ML-powered applications run inferences on large volumes of data to return predictions very quickly (milliseconds). Rather than use Remote Procedure Calls (RPC) and frameworks like gRPC, some companies use a Kafka streaming model.
Here is how the company states the problem that Confluent seeks to solve:
“By becoming more software driven, more businesses will rely on real-time data. Confluent believes that data in rest is not able to meet the current and future demands of software-driven businesses. Daily batch processing and static real-time queries or “point-in-time” queries with stored data lead to an unnecessarily large and tangled architecture that is not capable of data flow between applications.”
Enterprise-grade Features
As with Spark and other open-source projects, there is a marketplace for making the frameworks easier to use. Confluent Kafka opens up the amount of data that can be integrated, for example, to combine transactional data (orders, inventory) with sentiment-driven data (likes, page clicks). This helps with predictive analytics and also machine learning because the “data flow” allows for algorithms to work as they are intended to.
In order for data to be in motion, Confluent’s platform connects data from many different sources. The company has over 50 fully managed connecters with Big Data and Analytics from Azure, Amazon/AWS, Google and Databricks. Without these connectors offered by Confluent, integrations between systems on an open-source framework can take months and also require intensive resources to manage.
Confluent is attempting to stave off competitors through “completeness of product” which touches on our multi-cloud and hybrid cloud discussion. We’ve discussed hybrid for a few years, yet our most recent write-up was here and here on Datadog. The recent write-up is worth a read if you want to know exactly why agnostic, best-of-breed products are sometimes outpacing Big Tech when it comes to cloud services and products. Datadog is the best example of a product where customers are avoiding vendor lock-in.
The completeness of product goes beyond multi-cloud and hybrid as Confluent is attempting to hold off competitors through data security and data governance, as well. Because data is often an organization’s most prized asset, it often has internal processes for compliance. There is often external, geographic compliance required by governments and industry agencies, as well, for global companies.
In order for completeness of product to work, Confluent needs to have a large geographic footprint. The company has added eight more regions for Confluent Cloud with an emphasis on APAC. There is also a new partnership with Alibaba Cloud. This can help offer differentiation for multinationals who have operations in China.
Competitors:
Regarding direct competitors, one example is Amazon MSK which offers a competing managed streaming service. This competitor is a good option for developers provisioning a Kafka cluster and a new streaming platform may not be needed in this case.
Rather than re-architect Kafka to be cloud-native, Amazon MSK cloud-enabled it as provisioned infrastructure. This means Confluent is stronger than MSK with scaling elastically by offering elastic quotas, which eliminates the need to size clusters for spikes. It’s also stronger on multi-tenancy security. Amazon MSK also does not offer Kafka Connect or Kafka Streams.
For more enterprise uses where Kafka Connect or Kafka Streams is required, then Confluent is more likely to be used to save development time and learning curve in writing Kafka Connects sinks and source.
Blockchain and Metaverse Potential
We’ve written at length about Confluent’s core use. However, there is a blockchain potential with Confluent with one case study right now with Dapper Labs.
“These are steps that attracted Dapper Labs. They're one of the most innovative NFT companies delivering fun and games on the blockchain. They have a number of decentralized apps, but one that's risen dramatically in popularity is called NBA Top Shot. To date, there have been over 10 million digital collectible transactions and Confluent is at the center of their data streaming architecture to facilitate these purchases. Dapper chose us to run their mission critical workloads because of the scalability and security of our cloud solution.”
There’s also a case for 5G networks needing data in motion. Here’s what was said about Dish on the call:
“A significant customer for both AWS and us is DISH Network. With their new 5G smart network, DISH is transforming how people and enterprises leverage data. They deployed Confluent Cloud over AWS to connect their network systems and customers with real-time data. This means that Confluent is a key part of their network's data backbone, starting with fault management and network resiliency functions to ensure network availability, and our enhanced collaboration with AWS is making it easier for customers like DISH to unlock data in motion everywhere.”
Confluent Q4 Overview
Confluent has been accelerating in revenue for four consecutive quarters and also across other key metrics.
The company reported fiscal year 2020 revenue growth of 58% year-over-year and fiscal year 2021 revenue growth of 64% year-over-year. Confluent Cloud revenue growth for fiscal year 2020 was 117% compared to FY2021 revenue growth of 200% year-over-year.
If we look at Q4, total revenue is outpacing the fiscal year growth for 2021 and also outpaced Q3. Revenue growth for Q4 was at 71% — the highest growth rate from publicly available information which dates back two years to Q1 2020.
Cloud revenue did decelerate on a sequential basis, however, the company stated Q4 is often seasonal due to engineers being out of the office and on vacations. We will see if this picks back up in Q1. Regardless, on an annual basis there was a significant improvement. Notably, if we look at 2020 cloud revenue, we can see it’s lumpy at times with Q3 2020 being the weakest and Q2 2020 being the strongest.
In regards to “sandbagging” which is essentially the company guiding low and blowing out the guidance, which has happened a few times now, the company has a lot of moving pieces in terms of business model and likely wants to win trust with institutions. We are not opposed to this even if it means the price action was somewhat severe after the earnings report due to the guidance. What we are more concerned with is that Confluent continues to raise and beat, and that the underlying key metrics help us to substantiate the company’s longer-term strength.
Bradley stated the following in our last write-up and got pretty close to the revenue growth that Confluent actually reported:
Looking forward, management guided that Q4 revenue will rise 55% YoY $109 million, which would mark a deacceleration from the most recent growth rate of 67% YoY growth. However, this estimate is likely conservative, as management guided that Q3 sales would grow 46% YoY to $90 million and actual Q3 sales grew 67% YoY to $103 million. If we assume that Confluent beats it guide by a similar amount in Q4 as it did in Q3 ($13 million), then Q4 sales growth will accelerate to 73% YoY (this is merely an observation – no guarantees).If we assume that Confluent beats it guide by a similar amount in Q4 as it did in Q3 ($13 million), then Q4 sales growth will accelerate to 73% YoY (this is merely an observation – no guarantees).
Most notably, the company is reporting high remaining performance obligations growth of 91% year-over-year. This is higher than the 75% year-over-year we saw in Q3.
Bradley discussed this in our last write-up:
Confluent also states that RPO is an important metric to monitor in order to measure the health of the sales pipeline. In Confluent’s first conference call as a public company (Q2), CFO Steffan Tomlinson explained that:
“Given the various revenue components and billing terms in our model, remaining performance obligations or RPO and current RPO rather than billings, are important metrics to measure the health of the business. RPO provides insight into the organic momentum of our business as it represents contractually committed revenue to be recognized in the future regardless of billing terms and variability in cloud consumption pattern”. RPO provides insight into the organic momentum of our business as it represents contractually committed revenue to be recognized in the future regardless of billing terms and variability in cloud consumption pattern”
Financials Deep Dive
By Bradley Cipriano
A slight blemish during the quarter was Confluent’s customer growth, which lagged the growth in sales. Customers increased 65% YoY to 3,470, which lagged the 71% YoY growth in total sales. This drove subscription revenue per customer up 4% YoY to $31,000/customer, implying the recent acceleration in sales was driven by higher spending rather than customer growth.
Generally, growth from new customers is more sustainable and higher quality relative to growth from increased spending. However, DBNRR remained robust at over 130%, signaling that customers are increasing their spend over time.
It is odd that customer spending increased but cloud growth deaccelerated during the quarter. Since cloud is a usage-based revenue model, increased spending should have driven cloud outperformance. However, cloud spending slowed from 245% YoY growth in Q3 to 211% in Q4. On the Q4 call, management explained that cloud was impacted by seasonality due to relatively lower spending over the holidays which lead to slightly slower rates of usage. While this may be true, it doesn’t explain the YoY deacceleration, as this trend would have existed in the year-ago quarter. Nevertheless, there is inherent variability in a usage-based model so investors should not expect an acceleration in sales every quarter.
Given the slowdown in customer growth and slight deceleration in cloud sales, the Street may be concerned that Confluent’s growth may be somewhat cannibalistic. This would explain the sell-off in its stock following otherwise strong results which reported a beat and raise. Investors may be wondering if cloud growth is coming at the expense of platform growth, or vice versa?
CEO-Founder Jay Kreps discussed this concern on the call and stated that the company is growing both in the cloud and in hybrid environments. He said that “we don't really view this as kind of a transition where we're just shifting from platform to cloud and just kind of swapping out customers from one product to the other. Effectively, we have to have kind of an outpost in each environment a customer is in. So, we expect to continue to see growth in Confluent Platform throughout this, and we think that's not a bad thing. That's a good thing.” CFO Steffan Tomlinson added that “what our customers are telling us is, by and large, they're running hybrid environments”.
A common issue with ramping cloud sales is that sales in other parts of the business stagnant, but we do not believe this is the case. For example, Confluent’s financial results remain high quality which suggests that cloud/platform sales are not cannibalistic.
For example, net deferred revenue (deferred revenue less accounts receivables) increased 105% YoY to $109 million, or 31% of TTM subscription sales. This was an improvement from the 26% and 23% level in Q4 2020 and Q4 2019, respectively. The rise in net deferred revenue relative to subscription sales signals that the company is receiving relatively more cash upfront, improving the quality of topline growth. If sales were cannibalistic, we would have likely seen a reduction in cash receipts and/or a deacceleration in growth. Instead, cash improved and sales accelerated.
Furthermore, RPO also increased 91% YoY to $501 million, an acceleration from the 75% and 72% YoY growth rates in Q3 and Q2, respectively. While we need the 10K to fully assess the quality of RPO, total RPO represents 92% of management’s NTM guide, up from 81% in Q3. This improves the quality of forward sales and suggests that there is conservatism in management’s forward guide.
However, we do note that cash support for RPO declined slightly during the quarter. Total deferred revenue-to-RPO fell from 52% in Q3 to 49% in Q4. This trend is likely driven by the rise of cloud bookings, since cloud is a usage-based model and new cloud customers are typically on pay-as-you-go plans, which are billed in arrears. On the Q4 call, CEO-Founder Jay Kreps explained that cloud accounted for 50% of ACV bookings in Q4, highlighting how cloud will be the majority of revenues going forward. As customers become more familiar with Confluent’s products, they will likely increase their commitments and convert from pay-as-you-go customers to larger customers that pay upfront. As a result, we view the slight decline in upfront cash receipts as a natural progression for the firm and not a major concern at this time.
Cash Levels and Stock Based Compensation
Confluent recently raised nearly $1 billion in cash following a convertible debt offering in December. Following this raise, the company has over $2 billion in cash, which is well above its current cash burn of ~$108 million (based on TTM free cash flow). The company is focused on growth, so investors should be prepared for continued losses and cash outflows. On the Q4 call, management highlighted that their near-term priorities are to continue to invest in innovation and to expand its geographic footprint, signaling that growth is being prioritized over near-term profitability.
Nevertheless, given Confluent’s relatively large cash balance, we likely should not expect an equity raise in the near term. However, the company will still be dependent on capital markets until it is sustainably cash flow positive. Looking forward, the Street expects EBITDA (a proxy for cash flows) to remain negative through at least FY2023, suggesting that Confluent will remain reliant on capital markets for the next few years. Importantly, there are signs of improvement, as free cash flow margin improved from -30% in the prior year to -22% in the current quarter.
Furthermore, Confluent has relatively high levels of stock-based compensation (SBC), which subsidizes cash used for working capital but dilutes shareholders. Stock-based compensation has trended near 48% of quarterly sales for the last two quarters and was 40% of TTM sales. This is relatively high and ranks in the top 10 for cloud (shown below), but is a function of Confluent recently going public (which frontloads SBC). We expect SBC to decline as a percentage of sale going forward as it laps the IPO and topline growth outpaces expenses.