Tag: DDOG (Page 1 of 2)

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.

After enjoying several years of hypergrowth, Datadog’s revenue outlook for this year reflects a substantial slowdown. The challenge for investors is to discern whether annual growth in the mid-20% range is the new norm for Datadog or reflective of headwinds from the broader pullback in IT spend. Similar to the discussion around hyperscaler growth trajectories, enterprise IT teams are using this period of macro pressure to optimize their software infrastructure spend.

This optimization is a natural outcome of the rush to push through cloud migrations and feature releases during Covid with little oversight to spending levels. With IT budgets contracting, the opposite motion is taking hold, magnified by a concentrated effort to address accumulated optimization debt. After ignoring bloat for two years, enterprise IT teams are scrambling to lower costs. For the hyperscalers, this is a straightforward exercise. A plethora of options and flexibility make it very easy to reduce server instance sizing, commit to longer usage terms and even turn off services that are underutilized. The same advantages of cloud elasticity that drove rapid growth during boom times can apply a similarly acute impact on spend if reduction is the goal.

Opportunities to optimize spend on Datadog exist as well – not the same mechanics as with the hyperscalers, but familiar patterns. Customers can remove Datadog licenses from some hosts (APM, Infrastructure), shorten log retention periods (Log Management) or even limit the number of test runs (RUM, Synthetics, Continuous Testing). Further, as customers introduce new cloud workloads, they might forgo monitoring or apply limited observability coverage to their tiers. If customers delay a digital transformation project, that would impact Datadog spending growth as well.

As with the hyperscalers, these optimization efforts are creating a headwind to Datadog’s normal spend expansion. While customers are adopting multiple Datadog products at the same rate, they are ramping up utilization more slowly, or even optimizing down, on existing workloads. By not expanding spend as infrastructure tiers go through this one-time capacity adjustment, Datadog’s sequential revenue growth is being compressed.

Datadog’s Q3 earnings report was well-received by the market, with the stock popping as much as 9% after the release. Considering the macro backdrop and their outperformance during 2021, I thought the results were favorable. Datadog is maintaining its rapid cadence of launching new products and cross-selling them into existing customers, supporting its elevated DBNRR. The growth rates of customers with multiple product subscriptions have been reliably intact, implying little competitive infringement either from incumbent providers or new start-ups.

Datadog continues to consolidate customer spend onto its multi-product platform. Their flywheel of outsized R&D investment generates more products to cross-sell into existing customers, creating a widening competitive moat of rapid innovation. The frictionless adoption model provides significant efficiencies for the sales team, allowing them to focus on new customer lands. With 80% of incremental revenue each quarter coming from existing customers expanding their spend, Datadog can maintain a higher allocation to R&D than competitors. This creates more products to sell, and the flywheel spins on.

In this post, I will review growth metrics, profitability and customer activity from the Q3 report. I’ll then tie that back to Datadog’s product strategy and their competitive position. Investors new to Datadog can catch up on the narrative through my prior coverage. Additionally, our partners at Cestrian Capital Research provided a review of Datadog’s quarter, with detailed financials and technical analysis. Interested readers can check out that coverage for another point of view, as they consider an investment in DDOG.

Datadog introduced a number of new products and enhancements at their annual user conference last week. Expectations were high coming into the event, as Datadog often holds back new releases for several months in order roll out a parade of goodies. Once again, they stepped up the pace, highlighting 18 separate product announcements versus 10 at Dash a year ago. The breadth and scope of their product reach continues to expand.

While the feature list is sprawling, Datadog’s product strategy is consistent. They remain focused on serving all needs of a modern DevSecOps function from a unified interface and shared data set. This creates advantages in efficiency and clarity over bundling together multiple open source and commercial point solutions, eliminating toggling between tools and reconciling inconsistent performance indicators. While Datadog reaches further into new areas like developer tooling and security, they are disciplined about delivering what is relevant for a DevOps context. This deliberate strategy should allow them to continue to grow their addressable market without infringing on core offerings of entrenched competitors in adjacent categories.

Datadog (DDOG) announced Q2 FY2022 earnings on August 4th. In contrast to their strong report from Q1, this quarter’s results were mixed. The impact of the macro environment on enterprise spend was pronounced, resulting in a slowdown in commitments from large customers. This weighed on next quarter and full year revenue estimates as well as profitability measures. With that said, we observed a similar pattern in 2020, which provided a nice set-up for outperformance in 2021. Datadog is rapidly expanding their product footprint and demand for software infrastructure overall remains high. I don’t see any other factors contributing to Datadog’s deceleration. This implies that as the macro environment normalizes, Datadog should be in a favorable growth position looking forward to 2023.

I will share a summary of my reactions to the report, structured around financial performance, product announcements and the competitive landscape. I won’t rehash all of the metrics, as those are readily available online in the earnings report and Investor Presentation. Additionally, investors new to Datadog can catch up on the narrative through my prior coverage.