Category: In-Depth Analysis (Page 1 of 7)

These posts represent in-depth analysis of a particular company or general theme.

Cloudflare (NET) Q2 2023 Earnings Review

September 18, 2023 / poffringa

Following a disappointing Q1 earnings report highlighted by challenges in the go-to-market effort, Cloudflare demonstrated renewed momentum in Q2. The sales reorganization is progressing well. Large enterprises expanded spend, adding to multi-product subscriptions across several categories. In parallel, profitability measures continue to improve with growing operating margin and cash flow.

With a mission statement to help make a better Internet, Cloudflare’s product strategy is grounded in disruption. By owning and operating a network of data centers in 300+ cities worldwide, Cloudflare enters product categories with the assumption that they can deliver better service than incumbents through lower cost, improved performance and greater customization.

This messaging appears to be resonating with new enterprise customers. In Q2, Cloudflare landed their largest Zero Trust deal to date, pushing paid seats in a single customer deployment past 25,000. A critical requirement to land new enterprise Zero Trust deals is to have existing ones as reference customers. Several major Zero Trust customer wins in Q2 may provide the tipping point for future large enterprise adoption.

The developer platform (Act 3), is starting from a smaller base, but is experiencing rapid growth. The number of Worker applications reached 10M in Q2, quadrupling since Q3 of 2022. R2 usage accelerated to 85% sequential growth in Q2, up from an impressive 25% q/q rate in Q1. These products are showing up in new customer wins, contributing to near record additions of $100k, $500k and even $1M ACV customers.

As part of Investor Day, the leadership team showed how Cloudflare’s largest customers are also those adopting multiple product subscriptions. Similar to peers in software infrastructure that report on customers paying for 4+, 6+ and even 8+ products, Cloudflare is demonstrating that an expanding product offering can drive incremental revenue. Over the last four years, the attach rates for customers with 8+, 9+ and 10+ product subscriptions have more than doubled. These customers now contribute the majority of Cloudflare’s annual revenue.

For fast-growing start-ups establishing a software and security stack from scratch, Cloudflare’s platform appears particularly appealing. This is evidenced by the rapid uptake from many of the leading AI companies. As developer-led organizations, Cloudflare’s bottoms-up sales motion naturally resonates. A new CRO with enterprise sales experience will help Cloudflare win business where a top-down approach is needed as well.

Confluent (CFLT) Q2 2023 Earnings Review

August 21, 2023 / poffringa

After delivering favorable results in Q1, Confluent continued on largely the same trajectory in Q2. Sequential growth rates even imply stabilization around 30% annually, after almost 2 years of deceleration from Covid highs. This growth is being led by rapid adoption of Confluent Cloud, with the licensed Confluent Platform offering for on-premise deployments demonstrating surprising resiliency as well.

The other factor providing support is a sharp improvement in profitability measures, with Non-GAAP operating margin increasing by 25 points year over year from -34% to -9%. The company expects operating margin to reach break-even by Q4. Free cash flow is following a similar path. Profitability was helped by an 8% headcount reduction in January, which notably doesn’t appear to have impacted growth.

Confluent’s recent revenue outperformance has been driven by customers exceeding their commitments for Cloud service usage. While customers may be limiting the size of future contractual obligations (reflected in RPO), engineering teams are choosing to allocate more spend to Confluent as the quarter proceeds. To me, this signals the value customers are extracting from the Confluent Cloud product, as they willingly spend more than they had budgeted.

This may reflect the larger overall theme that enterprises are scrambling to improve their data processing infrastructure in anticipation of leveraging AI to create new offerings for their customers, employees and partners. While the expected benefits from AI are still unfolding and enterprises are largely in proof of concept mode, it is generally understood that effective AI requires good data.

Quality data as an input for AI and ML processing isn’t new, but the priority has been increased. This translates into more focus on consolidation, filtering, cleansing and categorization of silo’ed data stores. Data recency is also recognized as an advantage, which is creating a greater push towards real-time data distribution. It is this focus on reaching more data and delivering it in near real-time that is served by Confluent.

As other software infrastructure companies are experiencing rapid drops in NRR, Confluent appears to be holding up well. While they don’t share the actual values, management reported that overall NRR is still over 130% and for Cloud it exceeds 140%. These are pretty remarkable numbers in this IT environment for a product that is approaching an $800M annual run rate.

Q2 2023 Hyperscaler Earnings Review

August 6, 2023 / poffringa

Headwinds from workload optimization may have peaked in Q2. After nearly a year of negative drag on revenue growth for the hyperscalers, it appears that cloud infrastructure customers are shifting their focus away from workload optimization and back towards the deployment of new ones. At least that is what Amazon’s CEO told us in the opening statement of their earnings release. While the hyperscalers delivered revenue growth rates that decelerated from the prior year, they are now showing an uptick sequentially from Q1.

This circumstance makes sense. After rapidly spinning up new cloud workloads during Covid, established enterprises and start-ups alike postponed the normal post-launch clean-up cycles that address right-sizing server instances, tuning queries and rationalizing data retention. In an environment of flush IT budgets and pressure to ship fast, it’s easy for engineering teams to postpone this technical debt.

As budgets contracted and digital transformation project pace slowed down in 2022, engineering teams were encouraged to shift cycles back to paying down their technical debt. With pressure to generate cost savings, a lot of that work focused on optimizing existing application workloads. Engineering teams were able to review cloud resource utilization, reset capacity allocations, tune database queries, clean up prolific logging and refactor code to run more efficiently. All normal engineering best practices.

Except that they happened all at once. As this optimization work digested the oversized backlog of Covid technical debt, it created an unusually large reduction in resource consumption over a shorter period. This translated into less revenue for the hyperscalers and software infrastructure vendors that are consumption based. A normal application workload tuning exercise can reduce resource utilization by 20-30% or more. Excessively over-provisioned capacity might even be cut by 50%, simply by downsizing server instances. The hyperscalers rightfully make this easy to execute as a consequence of elasticity, but lower resource consumption means less revenue.

While this tuning is standard practice, in the post Covid catch-up period, it created a larger than normal reduction in spend. Enterprises were increasing consumption in some areas by launching new cloud workloads and digital transformation projects (although probably at a slower rate), but the immediate drop in cost for existing workloads created a large negative headwind to revenue growth. This caused the prior cadence of sequential quarterly increases in hyperscaler spending to slow down rapidly and even go negative briefly.

The optimization catch-up cycle can only last a finite amount of time. Eventually, technical debt is worked off and Covid workloads are tuned and right-sized. The effort to capture savings has diminishing returns, or as Microsoft’s CFO said in April, that workloads can’t be optimized forever. With the latest earnings commentary from the hyperscalers, it appears the catch-up period is wrapping up. Hyperscaler spend by enterprises will be increasingly driven by new activity, with the negative drag created by post-launch optimization returning to its pre-Covid levels.

Where the steady state growth rate lands remains an open question. Data points during Covid were clearly inflated, as both large enterprises and rapidly growing start-ups threw money at their cloud migration and digital transformation projects. We can assume that this rate of new investment has slowed down from the peak, but is still robust. Over the past 12 months, negative consumption trends from optimization have obfuscated the post-Covid steady state. When optimization stabilizes, investors will get a clear idea of what the steady state growth rate will be.

Unfortunately, that growth rate won’t be easily comparable to the pre-Covid period as another force has injected a new variable, which is investment in AI. As enterprises consider their AI strategy, they are spinning up IT projects to harness their data to create digital services powered by new AI models. These efforts are generating demand for AI specific services from the hyperscalers. There would be some spillover into demand for generalized software infrastructure services as well.

As growth rates for the hyperscalers potentially level out and even inflect back upwards, investors can expect a similar pattern to follow for various software infrastructure providers. Logically, if enterprises are consuming more compute and storage resources on the hyperscalers to support their new AI-driven applications, then a similar increase in demand should materialize for adjacent software services, like databases, monitoring, delivery, orchestration and application security. Serving content at scale to users worldwide over the Internet still requires these things.

Insights from a Pair of Data Summits

July 23, 2023 / poffringa

AI has crashed onto the investing stage in 2023, driving significant stock price gains for several companies. Some, like Nvidia and Microsoft have already projected a direct revenue benefit as part of recent earnings reports. Others have indicated they expect AI to drive demand tailwinds going forward as part of management commentary.

Eventually, most software service and infrastructure providers should benefit from increased demand, as AI services proliferate and contribute to all areas of the economy. Because many AI services are delivered through Internet-based applications, the same guardrails of security, delivery, monitoring and operational data storage will be needed. This is in addition to the increased consumption of data services to collect, prep, distribute and process the inputs for various AI models.

AI-driven expert systems and co-pilots will raise the productivity of information workers. Enterprises will need fewer of them to accomplish the same amount of work. This will free up budget to increase spend on AI software services, similar to the efficiencies gained from the proliferation of SaaS tools over the last decade that helped internal business teams automate most aspects of their operations.

Software development teams, in particular, will experience a significant boost in output per employee. Enterprises will be able to clear their application backlogs more quickly, increasing the demand for hosting infrastructure and services. At steady state, fewer developers will be needed, supporting a shift of IT budget from salaries to software.

As data is the largest ingredient to these enterprise AI development efforts, software vendors providing data processing and infrastructure services stand to benefit. AI has further elevated the value of data, incentivizing enterprise IT leadership to review and accelerate efforts to improve their data collection, processing and storage infrastructure. Every silo’ed data store is now viewed as a valuable input for fine-tuning an even more sophisticated AI model.

In the realm of big data processing, enterprises need a place to consolidate, clean and secure all of their corporate data. Given that more data makes better AI, enterprise data teams need to ensure that every source is tapped. They are scrambling to combine a modernized data stack with an AI toolkit, so that they can rapidly, efficiently and securely harness AI capabilities to launch new application services for their customers, partners and employees.

At the center of these efforts are the big data solution providers. These include legacy on-premise data warehouses, cloud-based data platforms and of course, the hyperscalers. Among these, Snowflake and Databricks are well-positioned, representing the fastest growing modern data platforms that can operate across all three of the hyperscalers. While the hyperscalers will win their share of business, enterprise data team leadership often expresses a preference for an independent data platform where feasible.

Fortunately for investors, Snowflake and Databricks held their annual user conferences recently. Perhaps it was intentional that they fell within the same week – at least they staggered the events between the first and second half. Both companies made major product and partnership announcements, leading to many comparisons between the two and speculation about changes in relative product positioning.

The market for the combination of big data and AI processing will be enormous, with some projections reaching the hundreds of billions of dollars in annual spend. While the Snowflake and Databricks platforms are clearly converging in feature set scope, they still retain different approaches based on their historical user types. Such a large market will likely support multiple winners.

MongoDB (MDB) Q1 FY2024 Earnings Review

July 2, 2023 / poffringa

After guiding for a sequential 4% drop in revenue for Q1, MongoDB delivered a strong beat. More importantly, their preliminary estimate for Q2 revenue would achieve a reacceleration of annual growth if they outperform at the same level as Q1. The revenue projection for Q2 even leapfrogged past the analyst consensus for Q3. While the market expected some conservatism, this level of outperformance caught investors by surprise, with the stock surging 28% the next day.

Equally impressive were improvements in profitability. In the past, MongoDB has been discounted for poor operating leverage. The transition to 2023 has brought record levels of operating income and FCF, closing the gap with peers in the software infrastructure space. This also led to a significant beat and raise on EPS, which we don’t often see with high growth SaaS companies.

Even customer activity notched records. Both total customer additions and those with spend over $100k in ARR represented all-time quarterly highs. Of new customers, over 200 companies are categorized in the burgeoning AI industry, providing another catalyst as these start-ups are landing new capital at levels on par with the Covid beneficiaries of 2020-2021.

MongoDB has emerged as a hot stock once again, with its valuation multiple now pressing up against the top of its peer group. The stock has more than doubled in 2023 and reached its 52-week high recently. MongoDB is well-positioned to capitalize on tailwinds from AI, as enterprises revamp their data infrastructure to deliver new insights and services from their proprietary data sets. With new product announcements at MongoDB.local in June, MongoDB is further supporting the case for consolidation of application workloads onto its developer data platform.

Snowflake (SNOW) Q1 FY2024 Earnings Review

June 19, 2023 / poffringa

Snowflake’s consumption business continues to feel pressure as large enterprise customers look for ways to optimize usage. While it seemed that management had a handle on forecasting the impact of this effect when they lowered guidance back in Q4, they still underestimated the trend. With the Q1 report, they once again brought down the full year revenue target.

This had the expected effect of torpedoing the stock the day after earnings, with a post-earnings drop of 16.5%. After that, a strange thing happened. The stock began appreciating again and surpassed its pre-earnings price two weeks later. As with other software infrastructure companies, the market’s perception that demand for AI services will drive incremental usage is propping up the stock. Snowflake management added to this momentum with a few key announcements, including a preview of their upcoming Snowflake Summit conference, which will include a fireside chat with Nvidia’s CEO. This all implies that Snowflake is positioning themselves to benefit from increased AI workload demand.

In the near term, Snowflake is being directly impacted by their consumption model, which magnifies changes in customer behavior as they identify ways to reduce costs. Looking forward, the market is anticipating the moderation of these optimization headwinds, as enterprises work through the low hanging fruit of cost savings. At that point, Snowflake’s growth should return to its prior cadence driven by new cloud migration workloads and the expansion of existing ones.

Given that enterprise benefit from AI hinges on access to a consolidated, clean and secure data set, Snowflake is well-positioned to serve as a primary data source. Their positioning is further solidified as the same environment could be leveraged to run jobs that enhance the AI models. This applies to LLMs and other foundation models, as well as more traditional types of machine learning output like recommenders. Snowpark, Steamlit and other extensions that make the environment more programmable started this process. New acquisitions are bolstering the platform’s capabilities as well. Investors are looking towards announcements at the upcoming Summit user conference for more insight into Snowflake’s planned AI capabilities.

Datadog (DDOG) Q1 2023 Earnings Review

June 4, 2023 / poffringa

Datadog stock surged 45% over the month of May, following their earnings report on May 4th. The results aligned with the common theme of “better than expected”, shared with several other software companies reporting results recently. This outperformance appears to have set a baseline across the software sector, with upward momentum building as more companies report results. A new tailwind has been excitement around the potential for AI to drive an incremental demand cycle for software and security infrastructure.

While AI holds promise, it will require several quarters or even years to play out. In the meantime, enterprise IT spend moderation, workload optimization and deal scrutiny have blunted the continuing secular trends of digital transformation and cloud migration. The market is eagerly trying to anticipate when optimization headwinds might abate, which could drive a re-acceleration of growth. AI’s impact on software infrastructure, if it materializes, would be through more consumption of supporting services as additional applications and digital experiences are brought online.

Datadog is navigating these same trade-offs. Over the last few quarters, their results have been impacted by the slowdown in cloud migration, workload optimization and even spend reduction in products with variable consumption like log retention. To account for these factors, management set 2023 revenue guidance conservatively, projecting just 24% annual growth this year, down from 63% in 2022.

While this represents a huge deceleration in growth, the market is looking for signs that revenue growth in the 20% range may represent the bottom. That explains why a slight beat to earnings estimates is generating an outsized reaction. Datadog stock jumped over 14% the day after the earnings report. One side effect of the revenue growth slowdown has been an increase in profitability. Datadog, and other software companies, began moderating staffing and other operational costs in anticipation of a slowdown. These reductions, compounded by revenue outperformance, are driving higher operating margins.

In at least one positive sign around the demand environment, software companies are still reporting “record customer pipelines”, with new customer additions roughly tracking with prior quarters, albeit on the lower end. The challenge has been in extracting larger contracts from those existing customers in the near term.

In Datadog’s case, their most important business metric, in my opinion, has been resilient. That metric is the growth in customers adopting multiple Datadog products. As the Datadog team keeps expanding the platform offering into new areas like security and developer experience, it’s critical that customers continue to add these product subscriptions to their contracts. If they weren’t, then Datadog’s outsized growth potential would be significantly limited. For Q1 at least, growth in customers subscribing to 2 or more, 4 or more and 6 or more products progressed almost linearly. Further, management shared anecdotes of customer contract renewals with subscriptions of 10 or more products, topping out at 14 for a large FinTech company in a 7-figure upsell.

Confluent (CFLT) Q1 2023 Earnings Review

May 26, 2023 / poffringa

Following their Q1 earnings report on May 3rd, Confluent stock jumped by 16%. Since then, CFLT has continued appreciating, recently passing their previous high for 2023. While the report itself was pretty good (but not outstanding), the market appears to be anticipating more growth to come. Perceived AI tailwinds are likely at play. In order to capitalize on the potential advantages from advanced insights and new proprietary AI models, enterprises need access to all their data in one place. It should be filtered, consistent and recent. As the leading independent provider of data streaming, and soon stream processing capabilities, Confluent is well positioned to address this demand.

Fortunately, Confluent doesn’t have to convince most enterprises of the value of real-time data streaming. Over 75% of the Fortune 500 already use Apache Kafka at some level to accomplish this. Confluent’s task is to demonstrate that their data streaming platform, which offers many enhanced capabilities over self-managed open source Kafka, is worth the incremental cost. While this may have been a more difficult sell in the corporate data center, Confluent Cloud provides enterprises with a managed solution on their hyperscaler of choice, eliminating the need to maintain a large team of operations engineers with Kafka expertise.

Additionally, stream processing tools allow data engineers to filter, transform and aggregate data in flight, front-loading processing before it arrives at its destination. The most popular open source solution for stream processing is Apache Flink. With their acquisition of Immerok in January, Confluent is now integrating a managed Flink stream processing toolset into their core Cloud platform. Management expects that revenue from stream processing could eventually match that of core data streaming, effectively doubling their TAM.

While their Q1 results continued to reflect the pressure of elongated deal cycles and enhanced scrutiny, Confluent managed to deliver subscription revenue growth over 40% y/y. For the remainder of 2023, they maintained revenue targets at conservative levels. Profitability measures dipped in Q1 due to a couple of one-time charges, but are tracking towards break-even operating margin in Q4. That will represent another 2000 bps of annual improvement. Long term, the Confluent management team sees FCF margin reaching 25%.

Revenue growth is primarily being driven by expansion of Confluent’s largest customers, with continued increases in $1M+ commitments and even anecdotal references to $5M-$10M of annual spend. Confluent’s NRR rate remains above 130% overall, with NRR for just Cloud well above that. Confluent needs to keep the new customer pipeline flowing, as focus has shifted to Kafka migrations and workload expansions within existing customers. Confluent’s revenue target for this year represents about 1% of their calculated $100B addressable market, leaving plenty of room for future expansion.

Cloudflare (NET) Q1 2023 Earnings Review

May 14, 2023 / poffringa

After setting an optimistic target for full year 2023 revenue growth in early February, Cloudflare management was forced to reset guidance lower after sales cycles unexpectedly elongated in March. While this can be attributed to exogenous effects like banking turmoil and macro conditions, investors are left guessing whether the Q1 results were a one-time speed bump or a precursor to further deceleration in growth.

This dilemma would be difficult to reconcile had we been left with just the Q1 report. That earnings call conflated a few contributors to the underperformance, including longer customer sales cycles and a restructuring of the GTM team. Fortunately, Cloudflare leadership scheduled an Investor Day a week later, which provided a much clearer picture of the performance drivers and a better understanding of Cloudflare’s plan to address these challenges.

Stepping back, Cloudflare’s history and future growth are grounded in disruption. An investment in Cloudflare represents a bet that their architecture choices and platform strategy will allow them to penetrate new markets through lower cost, better performance and a more robust feature set. They have applied these architectural advantages to pursue an enormous addressable market, representing some of the fastest growing segments of software and security infrastructure.

Cloudflare’s elevated valuation relative to peers hinges on the market’s assumption that Cloudflare will continue to innovate and increase enterprise adoption of their newer product categories like Zero Trust, network services and a distributed developer platform. Rapid expansion in these areas will offset the gradual decay of growth rates in their traditional product offerings like DDOS, application security and CDN.

Q1 2023 Hyperscaler Earnings Review

April 30, 2023 / poffringa

Coming out of 2022, the catchphrase of the Q4 earnings reports from the hyperscalers was “customer workload optimization”. This referred to the process by enterprise customers of scrubbing their cloud infrastructure bills for savings. This exercise introduced a headwind to revenue growth, offsetting the positive impact of new customer cloud migration projects and digital transformation efforts. This effect drove deceleration in Q4 revenue growth rates for AWS, Microsoft Azure and GCP.

To further dampen sentiment, all three hyperscalers reported continued weakness in Q1 and avoided direct predictions as to when optimization might end. Given that they only provide guidance one quarter forward, management could remain vague around the expected revenue trajectory for the full year 2023.

While discouraging, the hyperscalers largely delivered results that were “better than feared”. Microsoft stock even rallied 8% the day after earnings. AMZN initially surged on an overall Q1 beat, but pulled back on commentary reflecting ongoing revenue growth pressure in AWS expected for Q2. Hyperscaler results also dragged notable software infrastructure companies along with them, with stocks like DDOG, MDB and SNOW surging and then dropping as each hyperscaler reported.

Investors did receive some hints in the Q1 report commentary about the potential trajectory of optimization going forward. The Microsoft team asserted that optimization impact will end eventually, while Amazon reiterated their long-term view that AWS has an enormous market to pursue. When the impact of optimization efforts does taper off, hyperscaler revenue growth can revert to being driven by new customer workloads from cloud migration and digital transformation projects.

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