Datadog (DDOG) Q1 2023 Earnings Review

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.

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In this post, I review Datadog’s product strategy and competitive advantages, along with how they are positioned to benefit from broader application growth from AI. I will parse the Q1 results and discuss the relevant trends that are likely driving the surge in stock price over the past month. For interested readers, I have published many prior posts on Datadog, which provide more background on the investment thesis.

Datadog’s Product Strategy

At a high level, Datadog’s product strategy can be boiled down to bringing as many “observability” experiences into the same interface as possible. I put observability in quotes because the term generically refers to instrumenting any system or process to improve its visibility and performance. This has been applied to software infrastructure monitoring, but could as easily expand into any aspect of business operations. We have been conditioned to apply the context of infrastructure metrics, logs and traces because the software industry absconded the term.

Datadog’s primary purpose is to break down organizational silos by encouraging disparate teams to view their business through a common interface. Datadog’s founders came from two departments that had traditionally been at odds – software development and operations. By getting these two teams to work from a common dataset and single “pane of glass”, Datadog removed significant friction from the organization, resulting in faster troubleshooting, easier decision making and better performance.

This grounding in organizational cohesion is important to consider as investors evaluate Datadog and their product roadmap. At founding, their goal was not to combine the three pillars of software observability (logs, traces and metrics) into one tool. That was the means, not the end. Rather, it was to help the software development and operations teams collaborate to meet their business goals by instrumenting their digital operations within a single platform.

This approach has several benefits:

• Value of Single Experience. Starting with infrastructure, APM and log management, Datadog proved that DevOps teams value having all types of monitoring in one consolidated view. This was disruptive at the time, as most providers focused on just one (Splunk for logs, New Relic for APM, etc.). Since then, Datadog has rapidly expanded into many other important aspects of instrumenting and securing modern digital experiences. This expansion has pulled in other potentially silo’ed teams, like security, product and even finance. All these users benefit from making decisions based on data in a common tool.
• Consolidation Argument. With pressure on IT budgets, enterprises can reduce cost by replacing several point solutions and DIY projects with the Datadog platform. When each organizational silo requisitions its own toolset, redundancy quickly explodes. Inefficiency increases as multiple teams try to reconcile different interpretations of data stemming from having separate tools. Datadog’s goal of breaking down organizational silos gets everyone on the same page and has the side effect of reducing tool redundancy.
• Focus Leads to Depth of Capabilities in Each Segment. In addition to incorporating new modules into a unified experience, Datadog is continuing to improve each existing module. After introducing APM in 2017, Datadog progressed up the Gartner MQ over several years, moving from Challenger to Leader, passing legacy providers in the process. They are applying this to each segment they occupy. Discerning buyers appreciate the difference. When minutes of downtime can cost tens or hundreds of thousands of dollars, why would a DevOps team skimp on their monitoring tools?

Some investors and analysts worry that Datadog is too expensive. Yet, enterprises are willing to pay a lot of money for these benefits, as they save the organization time and reduce costs. Some Datadog customers spend more than $10M annually across multiple product modules. On the surface, paying $10M+ a year seems more expensive than spinning up a few open source tools. However, no open source project can address all of the solutions that Datadog offers, forcing teams back onto disparate and often conflicting datasets and interfaces. The total cost of ownership in staff to run all of those tools quickly reaches the expense of paying Datadog to do it.

This collapsing of observability into one interface continues as Datadog charts its product development plan going forward. After getting developers and operations onto the same page, the next logical step was to address security use cases. Datadog scopes security to the same applications and cloud workloads that its other tools monitor. Since the Datadog agent is already collecting relevant data, security use cases for cloud workloads are a natural extension. The key is that addressing these facets of application and workload security bring the security team members into the same conversation as the developers and operations personnel.

Datadog’s foundation in observability provides them with competitive advantages as they enter the security space. They are selectively applying these in the security segments that have a clear parallel. Fundamentally, the large volumes of data they collect in metrics (Infrastructure), traces (APM) and logs (Log Management) can be uniquely applied to improve the effectiveness of their Cloud Security, Application Security and SIEM products.

Datadog is also being thoughtful about where it chooses to compete in the security space. For example, they don’t intend to address endpoints (like employee devices) outside of cloud infrastructure. That makes sense, as it drifts beyond their competitive moat. Because of this, Datadog wouldn’t be relevant as the full-featured security platform for a Global 2000 company with a large employee base that has a smaller application footprint. On the other hand, a digital native that derives the majority of its revenue from online operations (Airbnb, Uber, Doordash, etc.) would find the combined security and observability platform appealing. New stand-alone AI companies fall into this buyer category as well.

I think it is this focus on the relevant aspects of security within the right organization types that has led to Datadog’s success so far. In the Q1 earnings report, the CEO announced that their security products have been adopted by over 5,000 customers. That represents pretty rapid progress as the bulk of their cloud security offering was introduced during 2021-2022. Management has signaled that there is much more to come in security and I expect this to grow into a major contributor to revenue in the future.

Competitive Advantages

Datadog’s competitive advantage lies in their rapid and highly efficient product development process. They build and release new product offerings faster than any of their competitors. Their speed of module development, roll-out and iterative improvement is only accelerating.

What is truly unique about Datadog is the efficiency of their sales motion. For the full year of 2022, approximately 75% of incremental revenue over the prior year was attributable to growth from existing customers. This is a consequence of Datadog’s robust land and expand model. Not only do customers increase their utilization of product module subscriptions as their businesses grow, they add new modules at a fairly linear rate. And, Datadog is continuously creating new relevant product modules for them to consider in the future. I like to call this flywheel for Datadog “land and expand… and expand.”

Investors sometimes get hung up on Datadog’s ability to enter adjacent product categories and gain customer traction. They worry about Datadog’s relevance in each and their ability to compete. They compare feature sets or point out how an open source tool could address some monitoring or security use case more cheaply. Yet, they forget the overarching goal – to reduce friction and speed decision making by getting all teams onto the same toolset. This efficiency of a consolidated view trumps any savings from implementing a point solution or open source project.

From experience, I can tell you that this cohesion is a big benefit. Organizations can waste excruciating cycles trying to reconcile the interpretation of operational or security data generated by two different toolsets. Additionally, even if the dataset is shared, having multiple user interfaces to track is highly inefficient, as users toggle from one screen to another. With Datadog, all data and visualizations are in one interface, allowing users to drill in, out and across any indicator seamlessly.

More directly, if there were a problem with Datadog’s product expansion strategy, we would see it in the numbers. Specifically, Datadog management regularly reports the percentage of customers who adopt two or more, four or more and six or more product modules. These percentages continue to progress in an almost linear fashion. If the Datadog product team were selecting irrelevant categories or getting beat out by open source or competitive offerings, these expansion rates would slow down.

Datadog currently lists 20 products for sale on its Pricing page. This is up from 10 at the beginning of 2021. Yet, over that period, the product adoption expansion rates for 2+, 4+ and 6+ subscriptions have not changed. This continued over 2022 and into Q1 2023. Anecdotally, management even mentions customers with more than 10 products. On the Q1 earnings call, the CEO highlighted a large FinTech company that now subscribes to 14 Datadog products and spends 8-figures in ARR (>$10M).

We signed a high seven-figure expansion to another eight-figure ARR deal with one of the world’s largest FinTech companies. This customer has expanded meaningfully over time and, today, sees Datadog platform used by thousands of users across dozens of business units. With this expansion, this customer now uses 14 Datadog products and is consolidating multiple open-source homegrown and commercial tools across observability and security into the Datadog platform.

Datadog Q1 2023 Earnings Call

Given their mission to break down silos and create organizational efficiencies by delivering a single platform for managing digital operations, Datadog has plenty of adjacent product offerings to address. The product team clearly has the insight to identify and execute on adjacencies, as evidenced by ever expanding product adoption rates. Yet, Datadog has another competitive advantage that allows them to continue to build and improve product offerings at a rapid clip. This has to do with their capital allocations to R&D and S&M.

As a consequence of their highly efficient sales motion, Datadog can afford to spend significantly more on Research and Development than Sales and Marketing. In Q1, R&D spend was 1.58x larger than S&M on a GAAP basis (1.24x on a Non-GAAP). Comparing to Datadog’s competitive set, they are the only company that can do this. Dynatrace spends less than half as much on R&D as S&M (0.49x on a GAAP basis) to achieve a lower revenue growth rate. Splunk is a little better, allocating $0.58 to R&D for every $1.00 spend on S&M. Elastic spends $0.66 on R&D for every dollar in S&M.

The same circumstance generally applies to other companies in software infrastructure. For example, Crowdstrike employs a similar expansion motion of selling additional product module subscriptions to existing customers. Yet, they spent $281.1M on S&M and $179.0M on R&D (0.64x R&D to S&M). Datadog’s favorable ratio also benefits them on an absolute basis, with $229.5M spent on R&D in Q1. This has almost caught up to Splunk’s $236.9M total allocation to R&D in their most recent quarter, even though Splunk’s total revenue was more than 1.5x larger.

This is all to say that Datadog appears to have developed a very efficient GTM model, that supports high revenue growth and elevated investment in R&D. The latter is critical to continue expanding Datadog’s addressable market, supporting a long runway of growth. It also provides the foundation for their value proposition in consolidation of tools onto a single platform, allowing all teams within an organization to share a common view of the business.

Going forward, a lot of product development effort will be invested in rounding out capabilities across DevSecOps, with security and developer tooling getting the majority of attention. On the Q1 earnings call, the CEO mentioned that Datadog now has over 5,000 customers using their cloud security products. I would expect a similar trajectory for developer experience. We will likely see another product grouping introduced over the next year, as the last one was launched in 2021.

AI Tailwinds

AI is the buzzword of the day currently. It seems that every SaaS provider is trying to work an AI story into their narrative to gain some of the momentum in this space. On their earnings call and subsequent analyst events, Datadog highlighted a couple of ways they would benefit from the rush to harness AI. Some of these themes were not isolated to just Datadog, however, as competitors like Dynatrace and peers like MongoDB made similar arguments.

More applications to monitor. Multiple software infrastructure companies point to the expectation that generative AI will make developers more productive. This will drive an increase in the number of applications created. All enterprises have a long backlog of software projects to work through. If these can be built faster, then there will be more applications to be hosted, secured and monitored. Additionally, if developer resources are more efficient, then the cost of development will decrease, allowing more IT budget to shift to cloud infrastructure.

From a market perspective, over the long term, we believe AI will significantly expand our opportunity in observability and beyond. We think massive improvements in developer productivity will allow individuals to write more applications and to do so faster than ever before. And as with past productivity increases, we think this will further shift value from writing code to observing, managing, fixing, and securing live applications.

Datadog Q1 2023 Earnings call

New AI companies as customers. Datadog and other software infrastructure providers experienced a surge in demand during Covid-19 from the many new companies created to address problems specific to the pandemic. These included services like food delivery, telehealth and remote fitness. All of these companies were flooded with VC investment and encouraged to grow quickly to pursue a large audience.

A similar flood of investment is flowing into new AI companies. While their core AI software stack may be different, the digital experiences delivered over the Internet to end users still need to be secured, accelerated and monitored. Datadog can provide observability for these AI services. On the earnings call, Datadog’s CEO kicked off his typical customer wins segment by highlighting an expansion deal with a leading AI company that brings ARR into the 8-figures (> $10M).

First, we signed an expansion into eight-figure ARR with a leading AI company. This customer saw an order of magnitude increase in user demand and a surge in new customers following enormous innovation and interest in generative AI. As a result, this customer now uses 6 Datadog products and relies on our platform to track and correlate key business metrics, ranging from uptime data to newer subscriptions and revenue.

Datadog Q1 2023 Earnings Call

There are probably more. As another example, MongoDB called out 200 new customers in Q1 that were “AI or ML companies.” This helped contribute to their record total customer additions for the quarter. Cloudflare has cited OpenAI as a large customer, recently surpassing $1M in annual spend. These additions are all incremental to the ongoing secular trend of enterprise digital transformation and cloud migration. Twelve months ago, these companies didn’t exist or had negligible spend.

AI Specific Products. In May, Datadog announced a new integration that monitors OpenAI API usage patterns, costs and performance for various OpenAI models, including GPT-4.  Datadog’s observability capabilities simplify the process of data collection through tracing libraries so that customers can easily and quickly start monitoring their OpenAI usage. Developers can now gain visibility into ChatGPT usage, latency and token consumption in order to optimize the performance, end user experience and cost of their AI applications. Products tailored to AI service monitoring and contextual use cases provide more opportunities to monetize the growth of AI.

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Q1 Earnings Results

Datadog reported Q1 earnings on May 4th, before markets opened. While the results weren’t a blow out, they were better than expected. DDOG stock gained 14.5% that day. Since then, the stock has continued appreciating, recently hitting a new high for all of 2023 over $98. The stock is up about 34% for the year. To be fair, prior to earnings, DDOG stock was trading at a nearly 3 year low in the $60’s. We have to go back to early 2020 for DDOG to trade in this range.

While Datadog stock is up nicely from these lows, it is still far below its trading range for 2021. Like other software infrastructure stocks, DDOG peaked in November 2021 at $196. Of course, its P/S ratio was also extreme, pushing above 60 at that point. Currently, Datadog’s P/S ratio has dropped to a bit more reasonable value of 17, but is much higher than the 11 ratio it touched in late April. The big question for investors will be if Datadog can continue appreciating over the next couple of years, potentially getting back to that ATH price from 2021.

Revenue

Datadog’s Q1 revenue performance represented a reasonable beat and small raise. I suspect the market was expecting a worse result. After growing by 7.5% sequentially in Q4, Datadog’s Q1 revenue was up only 2.8% or by $12.3M over the prior quarter. There is a little seasonality at play here, as Q1 sequential growth has been lower than Q4 for the prior two years. Nonetheless, that level of sequential growth represents a new low for Datadog.

Q1 revenue was $481.7M, which was up 32.7% annually. This beat the company’s prior guidance for $466M – $470M, which would have delivered no sequential growth from Q4. Ironically, the actual Q1 result was about what analysts had estimated prior to the Q4 results, where Datadog initially set Q1 and FY2023 guidance below analyst expectations.

It is noteworthy that Datadog’s Q1 result includes a $5M impact from the service outage experienced in March. This was a fairly unusual event, where Datadog services were unavailable for nearly a day. If a day is 1/90th of the quarter, then a $5M impact makes sense. If it weren’t for this $5M hit, Datadog would have delivered $486.7M in revenue for Q1, up 34.1% annually and a slightly better 3.7% sequentially.

For Q2, Datadog leadership set preliminary revenue guidance in a range of $498M – $502M for growth of 23.2% annually and 3.8% sequentially at the midpoint. This hit the analyst estimate for $502M at the top end of the range. Some analysts called out the slight sequential acceleration implied. With a preliminary guide for 3.8% growth over Q1, and a similar beat as for Q1, Datadog’s sequential revenue growth would approach 7%. This is needed to reach (and hopefully beat) the full revenue target.

For 2023, management raised the revenue target by $10M to a range of $2.08B – 2.10B, representing annual growth of 24.8%. This beat the analyst estimate for $2.085B by $5M. While it was good to see a full year revenue raise, the $10M bump was less than the approximately $13M beat on the Q1 revenue guide. Management wants to be conservative in not raising the full year guide.

Summarizing the revenue performance, Datadog management highlighted the ongoing pressure from workload optimization and budget scrutiny. They did point out that Q1 usage growth was better than in Q4, but still below levels during the rest of 2022. In the Q&A session, leadership also said that April trends were roughly inline. During subsequent analyst events in May, they didn’t flag any further deterioration (or marked improvement).

Overall, we experienced business conditions that were similar to the previous several quarters. In Q1, usage growth from existing customers came in roughly as expected. We saw existing customer usage growth in Q1 improved from the levels we saw in Q4 but remain a bit lower than the levels we experienced in Q2 and Q3. And as in recent quarters, we continue to see customers optimize their cloud spend, particularly those further along in their cloud migration and hosting a larger portion of their infrastructure in the cloud.

Datadog Q1 2023 Earnings Call

Management is not anticipating a macro recovery or an end to workload optimization in setting their full year guidance. I think they are being sufficiently conservative and hope that optimization headwinds ease in second half of 2023, allowing growth to resume to more organic rates. These would be driven by the continued pace of cloud migration and digital transformation, versus having to counteract negative adjustments as customers find new ways to reduce spend.

On the earnings call, management made a few other points relative to growth:

• New logo acquisition and bookings in Q1 were solid, keeping in mind that Q1 is a seasonally slower quarter.
• Total ARR exceeded $2B for the first time. The APM suite and log management products together exceeded $1B in ARR. This demonstrates the expansion of Datadog’s business well beyond their first infrastructure monitoring product and successful execution on the broader observability platform.
• Continued to make steady progress in cloud security with growth in ARR and in customers. Announced more than 5,000 customers using cloud security products. 
• Observed the slowest growth in the consumer discretionary vertical, particularly in e-commerce and food delivery.
• Faster year-over-year growth in international than in North America.

Billings and RPO provide some supporting signals, but management cautions against relying on them too heavily. Billings were $511M in Q1, up 15% annually. However, adjusting for a large upfront bill in Q1 2022, billings growth would have been in the low 30% range year/year.

Total RPO reached $1.14B in Q1, up 33% annually. This increased by $80M or 7.5% over the total RPO value of $1.06B at the end of Q4 (which was up 30% y/y). Current RPO growth was in the “high 20%” range. So, total RPO grew faster in Q1 than Q4, while current RPO growth slowed a little. The large sequential growth in total RPO likely reflected some of the record bookings mentioned earlier on the call.

The CEO made an interesting reference at the end of his prepared remarks, underscoring the long term growth view and how Datadog plans to keep expanding into it.

Now, switching gears. Let me speak to our longer-term outlook. Overall, we continue to see no change to the multiyear trend toward digital transformation and cloud migration.

We do continue to see customers optimizing their cloud usage, and visibility remains limited as to when this optimization cycle will end, but we firmly believe it will. As before, we remain confident that we will continue to deliver value to more customers in their digital transformation and cloud migration journeys. And it is increasingly clear with each wave of technical innovation that every company in every industry and every geographic region has to take advantage of the cloud, microservices, container, generative AI, and more. By relentlessly broadening the Datadog platform, we will continue to help our customers save on costs, execute with better engineering efficiency, drive competitive differentiation and deliver value to their own customers.

Datadog Q1 2023 Earnings Call

This highlights the investment thesis for Datadog. While their current financial performance is lagging, the market is likely anticipating improvement later in 2023 and early 2024. This anticipation hinges on a few assumptions:

• At some point, the optimization of existing cloud workloads by large enterprises will taper off. It is not possible to optimize forever, and optimization generally involves one time adjustments to resource consumption to utilize services more efficiently. Once the wave of optimization ends, then the headwind of negative revenue growth will dissipate.
• As Datadog continues to add new products and customers increase the number of product module subscriptions, Datadog will capture a larger share of customer spend. This incremental capture will compound the growth re-acceleration when enterprises end their optimization and return to a normal spending posture.
• As I discussed in the product strategy section, AI should further drive more spend on cloud infrastructure. New AI-infused applications will still require monitoring. Additionally, increased developer productivity should result in many more applications being created. With fewer developers producing more applications, IT budget can shift from developer salaries to infrastructure services to manage all the applications being produced.

These factors could generate a favorable set up for Datadog in the second half of 2023 and going into 2024. On an annual basis, revenue comparisons will get easier. Additionally, sequential revenue growth rates should start to accelerate. We may not get back to the spending heyday of 2021 (unless AI really blows things up), but should see sustained revenue growth move back into a healthy 30% range for the near future.

Profitability

Coming out of Q4, Datadog management guided for Q1 Non-GAAP operating income of $68M – $72M for about 15% operating margin at the midpoint of revenue guidance. Datadog actually delivered $86.4M for an operating margin of 18%. This drove $0.28 of EPS, which beat the analyst estimate for $0.24. Operating income was up sequentially from Q4’s value of $83.1M, which was also an operating margin of 18%.

This outperformance on operating income was aided by continued moderation in spending. Q1 non-GAAP operating expense grew by 45% y/y, which was down 900 bps from the prior quarter. Datadog management said they are continuing to increase headcount, particularly in R&D and GTM functions, but at a slower pace than previously. They expect OpEx to grow by 30% for the full year, which implies that Q4 will reach the low 20% range.

This expense management helped free cash flow surge in Q1. Free cash flow was up substantially, hitting $116.3M in Q1 for a FCF margin of 24%. In Q4, FCF was $96.4M for a margin of 20.5%. On a Rule of 40 basis using FCF, Datadog logged a 57 in Q1. This is down from Q4’s 64, with the revenue deceleration contributing to most of the gap.

Looking forward, Datadog management expects to deliver Non-GAAP operating income of $82M – $86M in Q2, representing an operating margin of 16% at the midpoint. Given the outperformance from Q1, the actual value could hit $100M for an operating margin reaching 20% of the midpoint of revenue guidance. For the full year of 2023, management raised the expected range of operating income by $40M from $300M-$320M to $340M-$360M. At the midpoint, this would represent an operating margin of 16.7%.

For Q2, this translated into a Non-GAAP EPS guidance range of $0.27 – $0.29. That beat the analyst estimate for $0.26. For the full year of 2023, management raised the EPS target from $1.02 – $1.09 set in Q4 to a new range of $1.13 – $1.20. This represents a raise of $0.10 or about 10%. While we tend to focus on the beat/raise cadence for revenue growth, it’s nice to see increases to earnings forecasts as well. This outperformance on the profitability side probably helped bolster the post-earnings market response.

Customer Activity

Customer activity continued to reflect pressure on IT spending, but also showed a nice uptick in product module adoption. Datadog total customer growth surged in Q1 to 25,500, which was up an amazing 2,300 q/q. However, 1,400 of those new customers came from the Cloudcraft acquisition. This means that Datadog added just 900 customers organically for growth of 3.9% sequentially and 21.7% annually.

This falls below the 1,000 total customer additions that Datadog logged for the past two quarters. We have to go all the way back to Q2 2020 for total customer growth to drop below the 1,000 mark. With that said, an incremental 1,400 new customers from Cloudcraft to target for cross-sell is a good thing and perhaps some small percentage of those might have been net new additions as well outside of the acquisition.

Shifting to large customer additions, Datadog ended the quarter with 2,910 customers with ARR over $100k. This represented an increase of just 130 over Q4 for 4.5% sequential growth. Like with total customers, we have to go back to 2020 to see a quarterly increase this low.

The calculation of this metric could explain the magnitude of the drop. The threshold of $100k ARR is determined by annualizing the monthly recurring revenue for each customer in the final month of the quarter (March). MRR is determined by aggregating “revenue from committed contractual amounts, additional usage, usage from subscriptions for a committed contractual amount of usage that is delivered as used, and monthly subscriptions.” This means that any reduction in usage (or lack of additional usage) for just that month would impact the total large customer count. Given trends with ongoing customer optimization, this sudden drop doesn’t surprise me. I suspect that a number of customers are hovering around the $100k threshold and either dropped out this quarter, or didn’t make the final step up.

Datadog’s 12-month dollar-based net retention rate (NRR) remained above 130% for the quarter, as customers increased usage and subscribed to more Datadog products. At the same time, we don’t know the exact value, but can assume NRR has been decreasing over time. We got a hint that this is the case, as management said that they expect NRR to drop below 130% in Q2. This would be the first quarter ever in which that happens.

Fortunately, Datadog’s dollar-based gross retention rate remained stable in the mid to high 90% range. This implies that customers are not churning off the platform or switching to competitive solutions at an accelerating rate. If gross retention is fixed, then a drop in NRR would be caused by a slowdown in customer spend expansion.

As I discussed in the product strategy section, a big part of Datadog’s growth thesis revolves around their ability to continue adding adjacent product offerings to their platform of services. These products appeal to the same audience with the underlying theme of bringing disparate teams together onto a single dataset and view of their digital operations. These offerings started with traditional observability functions like infrastructure monitoring, APM and log management.

From there, Datadog branched out into other aspects of software infrastructure, then digital experience monitoring, security and most recently developer tooling. Each of these product offerings is sold separately, allowing customers to construct a product subscription that works best for them. Datadog has long eschewed a packaged subscription for all products or other monetization model under the belief that their customers prefer to purchase Datadog offerings on an a la carte basis. All products with pricing are listed on the Datadog web site.

To track the success of this land and expand motion, Datadog provides metrics on the percentage of customers who subscribe to multiple product modules. These continue to grow at a nearly linear clip. For Q1, management again reported the percentage of customers subscribing to 2 or more, 4 or more and 6 or more products.

Interestingly, the sequential growth in the percentages of customers for each segment (2+, 4+, 6+) actually slowed down for the first time this quarter. Previously, these percentages had been increasing fairly steadily, with 1% gains for the 2+ segment and 2-3% increases for the 4+ and 6+ product module segments. In Q1 2023, the 2+ segment percentage remained the same at 81%. The 4+ and 6+ segments only increased by 1% sequentially.

At first, I thought this represented cause for concern, as a reflection that expansion of product module adoption was slowing down quickly. Datadog’s revenue growth over the past year has been constrained by large customers trying to optimize their spend on existing module subscriptions, like reducing consumption of variable services such the duration of log retention. In spite of this, they would continue adding new module subscriptions at a linear rate. Until Q1, or so I thought.

In reality, the large jump in total customers in Q1, due to the 1,400 inherited from Cloudcraft, skewed the percentages. If we calculate the total customers in each segment, we find that the sequential additions of customers with 2+, 4+ and 6+ product module subscriptions actually accelerated nicely over Q4.

This demonstrates that Datadog’s product development roadmap is right on track. With 20 product modules with individual pricing listed on their Pricing page (up from 10 in January 2021), customers have a lot of options to choose from. In addition to landing new customers and growing usage of products already under subscription, Datadog’s NRR is driven by customers adding new module subscriptions.

As long as Datadog keeps building new product modules for customers that are relevant and replaces some existing tool (commercial or open source), then Datadog will have a continued path for revenue growth. Seeing the count of customers subscribing to 2+, 4+, 6+ and maybe one day 8+ or more product modules increase in this linear fashion gives investors confidence about the durability of Datadog’s revenue growth.

Investment Plan

Overall, Datadog delivered Q1 results that were better than expected. While pressure from enterprise budget scrutiny and cloud workload optimization continues, we may be approaching the peak of those headwinds. As the year progresses, moderation of workload optimization would allow software infrastructure providers like Datadog to resume growth rates driven by ongoing digital transformation and cloud migration initiatives.

Additionally, Datadog continues their strong land and expand… and expand motion. They add new customers, increase usage of existing module subscriptions and maintain new module adoption at healthy rates given the macro environment. After doubling the number of products listed for sale from 10 in January 2021 to 20 now, Datadog’s customers have not slowed down their adoption of new module subscriptions. The growth in total customers with 2+, 4+ and 6+ product subscriptions ticked up in Q1. Management even reported one $10M+ ARR customer with 14 product subscriptions.

While growth has moderated, profitability measures are tracking well. Datadog raised operating income targets for the full year and are delivering healthy margins. This is being driven by appropriate cost management, including a tempering of headcount growth. In Q1, Datadog still logged a 57 on the Rule of 40 measure using FCF margin.

Looking forward, I think Datadog can maintain strong revenue growth with favorable margins. While they started in software infrastructure observability, they are rapidly pursuing adjacent product categories, including solid traction in security and developer experience. Given that observability broadly applies to the instrumentation of any business process, I suspect that Datadog will announce some interesting new product categories over the next few years.

These factors all provide a favorable set-up going into 2024. While the stock has appreciated nicely in May, I don’t consider it grossly overvalued at this point. I plan to maintain my position as a “hold”. If the price were to drop significantly, I would add to my position. For new investors, you could look for opportunities to buy DDOG on temporary pullbacks.

NOTE: This article does not represent investment advice and is solely the author’s opinion for managing his own investment portfolio. Readers are expected to perform their own due diligence before making investment decisions. Please see the Disclaimer for more detail.

Additional Reading:

• Muji over at Hhhypergrowth included an update on Datadog’s Q1 results, as part of a broader write-up on the hyperscalers. Worth the price of a subscription to get additional perspective.
• Our partners at Cestrian Capital Research published an insightful review of Datadog’s Q4 results with detailed financials and technical analysis. They have continued coverage of a variety of software infrastructure companies and general market trends. This is helpful for investors looking at companies within the broader context of the technology sector.