Datadog’s Q2 2022 Earnings Report

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.

Growth Metrics

Let’s start by examining revenue and related growth metrics. These tell a story of a quarter that started relatively well and then experienced longer sales cycles and spending reductions in areas that are easy for large enterprise customers to dial back. Like several other companies that have already reported, the pullback became most pronounced in June. July is showing some stabilization, but the environment is so volatile, that Datadog (and other software companies) have taken a conservative approach to guidance for the remainder of the year. The safest approach for Datadog was to set next quarter and full year results just above expectations.

In terms of numbers, Q2 revenue was $406M, representing growth of 73.9% year/year and 11.9% sequentially. This beat the company’s prior estimate for $376M-$380M (up 61.8% annually and 4.1% sequentially) by a healthy margin of 12.1% of annualized growth and 7.8% of sequential. For comparison, in Q2 2021, that spread was 51.4% estimated to 66.8% delivered (15.4% annualized beat) and 6.8% to 17.6% sequentially (9.8% increase of sequential growth). So, we saw about a 2-3% less relative beat in Q2 as compared to last year.

Looking to Q3, Datadog set their revenue target for $410M-$414M, representing annual growth of 52.5% and 1.5% sequentially. Analysts had expected $410.7M, so Datadog delivered a slight beat. If we apply the same relative actual Q2 beat to Q3 estimates, it implies that growth drops to 63.6% annually and 9.3% sequentially. The sequential growth starts to look problematic, as we are used to Datadog putting up sequential growth in the 12% to 20% range, which was the case in 2021. Sequential growth below 10% implies annual growth below 50%.

For the full year, Datadog updated their revenue target range to $1.61B-$1.63B, up 57.3%. This represents a raise of just $10M from their prior guidance of $1.60B-$1.62B. In Q2 2021, Datadog raised annual guidance by $56M. The $10M annual raise this quarter is $18M less than the $28M beat. Management effectively “lowered” the full year guidance by not increasing it at least by the beat in Q2.

This leaves investors in a quandary for Q4, as the implied annual growth rate based on current guidance is 34.6%. To be clear, this assumes no beat for Q3 or Q4. A final value higher than that is likely, but would still represent a noticeable deceleration on annual growth. Part of the challenge is a strong comparable to Q4 2021, which delivered 83.8% annual revenue growth and 20.6% sequentially. It’s also worth considering that Datadog beat their own guidance in Q4 2021 by almost 20% of annualized growth (63.9% estimated versus 83.8% delivered).

It’s worth noting that we saw a similar pattern in 2020. That year started with Q1 achieving 87% annual growth. Then, Q2 experienced a synchronized pullback due to Covid, resulting in annual growth of 68.2% and only 6.9% sequentially. From there, Q3 was 61.1% annually and 10.5% sequentially. And Q4 dropped to 56.2% annually with 14.7% sequential growth. Growth bottomed in Q1 2021 (due to the tough compare) with 51.3% annual and 11.8% sequential. From that point forward, though, growth accelerated again peaking at nearly 84% annually in Q4 2021.

During the earnings call, management reiterated their typical conservative approach to setting guidance. This is a familiar theme to investors, as Datadog usually beats estimates by a wide margin each quarter. However, this quarter management implied that they are being even more conservative, due to the unknown economic backdrop. If enterprise spending resets, this could provide an opportunity for upside to estimates. On the other hand, if spending doesn’t recover and Datadog delivers revenue as projected, the updated guidance represents pretty significant deceleration. I think management is rightfully guiding lower given the unknowns.

Another mitigating consideration is the influence of currency fluctuations. Other software infrastructure providers (like Azure, ServiceNow, Dynatrace) reported revenue growth in constant currency to help explain their deceleration on an as reported basis. For these providers, the adjustment in Q2 and for rest of year represented as much as 5-6% of annualized revenue growth. Datadog doesn’t report a constant currency adjustment, because they charge for their products internationally in dollars.

In Q1, revenue generated from sources with a billing address outside of North America represented 28% of total. Regarding the potential impact of currency fluctuations, Datadog includes this risk factor in their 10-Q filing:

Our sales contracts are denominated in U.S. dollars, and therefore, our revenue is not subject to foreign currency risk. However, a strengthening of the U.S. dollar could increase the real cost of our products and platform capabilities to our customers outside of the United States, which could adversely affect our results of operations. In addition, an increasing portion of our operating expenses are incurred outside the United States. These operating expenses are denominated in foreign currencies and are subject to fluctuations due to changes in foreign currency exchange rates. If we are not able to successfully hedge against the risks associated with currency fluctuations, our results of operations could be adversely affected.

Datadog 10-Q Filing, Q1 2022

While we can’t attribute a constant currency adjustment to explain part of the growth deceleration, the cost of Datadog’s services to companies outside of North America became significantly more expensive in their local currency over Q1-Q2. I imagine this cost inflation would encourage some companies to cut back more aggressively. Peer software infrastructure provider Cloudflare mentioned this effect on their earnings call, while not formally reporting a constant currency adjustment. Cloudflare generates 47% of their revenue outside the U.S.

Other forward-looking growth metrics decelerated as well in Q2. Billings were $397M, representing annual growth of 47%. This is down from 103% growth in Q1. However, the CFO clarified that a few large customers were billed in Q2 2021, but then billed for 2022 in Q1. This both inflated the Q1 2022 billings growth and diminished it for Q2 due to the higher comparison to the prior year. RPO was $881M, up 51% year/year. Current RPO growth (expected to close within 12 months) was in the mid-50% range. This compares to RPO growth of 85% y/y and Current RPO growth in the mid-80% range in Q1.

Management mentioned that some customers are being conservative with their forward commitments, while keeping their usage growth rate the same. This reflects some hedging by enterprises, due to the macro environment. It is natural for IT leaders to limit forward commitments like this, if they are uncertain about the longer term budget available. They might even pay a premium for the shorter duration allocations, while still continuing to increase their usage. Datadog doesn’t provide significant discounting for long term contracts, which may explain the performance difference in RPO relative to the hyperscalers and other software infrastructure providers.

In terms of the overall pullback in revenue, Datadog’s leadership team mentioned that some industries and customer segments showed a more pronounced impact. These included consumer discretionary customers, in categories like e-commerce and food delivery. They also saw a reduction in spend on products with volume based pricing, like log management and data retention for the APM suite. That pullback was isolated to large customers, versus lower spending customers, which continued growing at the same rate.

Datadog leadership also pointed out that ARR growth for infrastructure monitoring was relatively steady year-over-year. Gross retention remained unchanged in the mid- to high 90% range. Finally, they shared that the number of hosts and containers being monitored by their customers is growing steadily, which points to continued momentum of cloud migration and digital transformation projects.

For me, signals that point to a macro-driven pullback in spend are the focus on industry category, large customer impact and volume-based usage. If Datadog market share or cloud migration slowdown were the drivers of this relative underperformance, then infrastructure monitoring would tank as well and the number of hosts being monitored would slow down. For large customers, their Datadog spend represents a large dollar amount, which makes it an easy target for a cost optimization. In my experience, these exercises are usually episodic in nature, versus an ongoing expectation each quarter.

Profitability Measures

We saw a few factors impact profitability measures. In addition to the decrease in revenue targets, Datadog is ramping up spending on travel and in-person events. These are combining to compress operating and cash flow margins in the near term, but I don’t see anything overly concerning here. Management also mentioned their intent to continue hiring through this period, but will monitor spending closely.

For Q2, Non-GAAP gross margin ticked up to 81%, from 80% last quarter and 76% a year ago. The improvement was attributed to increased efficiencies in cloud costs as they scale. Long term, they reiterated the target for gross margin to be in the high 70% range.

Datadog reported $84.7M in operating income, for 21% operating margin. This compares to their prior estimate for $49M-$53M. In Q1, they generated non-GAAP operating income of $83.7M for an operating margin of 23%. A year ago, operating income was $30.9M for 13.2% operating margin. This means that operating income increased by 174% year/year. That is more than twice the rate of revenue growth.

Related to operating expense, management pointed out the increase in R&D and GTM investments, which resulted in OpEx growth of 65% y/y versus 56% y/y in Q1. This included a return to in-person office travel and events, which contributed $11M to the sequential growth of OpEx.

Free cash flow was $60.2M in Q2 for a FCF margin of 14.8%. This is down from Q1’s banner $129.9M or 35.8% FCF margin, but cash flow can vary based on timing of billings. For the first half of 2022, free cash flow margin was about 25%. In Q2 2021, free cash flow was $42.3M for a FCF margin of 18.1%.

Looking forward, Datadog expects to generate $51M – $55M of operating income in Q3, or about 13% operating margin. For the full year, they expect Non-GAAP operating income of $255M to $275M, or 16% operating margin. While these represent a step down, management attributes this to a few factors related to the transition out of Covid lockdowns, including the resumption of travel, in-person events, their annual user conference and the AWS trade show. All in, those contributors represent 4-5% of operating margin impact. Additionally, Datadog historically beats their operating income guidance by a healthy amount.

Datadog leadership also mentioned their intent to continue to invest in R&D and S&M in order to capitalize on the opportunity. They will remain “judicious and disciplined” in the cost structure, though, given macro uncertainties. I am fine with these investments, in addition to the return to in-person events. I anticipate we might see a boost to deal close and expansion rates from the return to customer meetings.

Datadog continued their outsized investment in R&D in Q2. They are one of the few companies that I cover where R&D spend as a percentage of revenue is greater than S&M. I think this represents a huge competitive advantage. First, it reflects their efficient GTM market, in which the majority (80% in Q1) of their new revenue is generated from product expansions. Second, the large R&D spend keeps their product development funnel pumping out new offerings in adjacent categories to monetize and allows them to go deeper in existing segments. This is the impetus, for example, for their rapid move upmarket in APM over the last 3 years.

I like to look at spending allocations in each category relative to revenue on a GAAP basis to include the cost of SBC:

• R&D: 43.8% of revenue in Q2 2022 ($177.7M) , versus 40.6% a year ago
• S&M: 28.4% of revenue in Q2 2022 ($115.3M), versus 30.1% a year ago
• G&A: 8.5% of revenue in Q2 2022 ($34.4M), versus 9.0% a year ago
• GAAP Op Income: -0.8% of revenue in Q2 2022, versus -4.2% a year ago

As you can see, the allocation to R&D actually increased on a year/year basis, while S&M and G&A continue to tick downwards. Even on a GAAP basis, operating income has been hovering around break-even for the last few quarters and was positive $7.3M (or 1% margin) for the first 6 months of 2022.

For comparison, on a GAAP basis, competitor Dynatrace spent $48.4M on R&D in Q2 2022, or 18.1% of revenue. For S&M, they spent $105.0M on S&M or 39.3% of revenue. Dynatrace is allocating more than 2x spend to S&M versus R&D, while Datadog is nearly the opposite (spends 1.5x more on R&D versus S&M). With Datadog now spending over three times as much on R&D as Dynatrace, it’s likely they will continue to increase the gap in product offerings.

In fact, for kicks I conducted a simple survey of comparing S&M spend to R&D spend on a GAAP basis across a number of the software infrastructure companies that I cover. The results below represent the operating expense attributed to S&M divided by the operating expense for R&D, all on a GAAP basis:

Ticker	Ratio of S&M / R&D Spend
DDOG	0.65
ESTC	1.50
SPLK	1.55
MDB	1.56
NET	1.57
CRWD	1.57
SNOW	1.62
CFLT	1.75
GTLB	2.10
ZS	2.51

A few insights were gained by this exercise. First, Datadog is clearly an outlier with significantly less spend allocated to Sales and Marketing versus Research and Development. Second, most software infrastructure companies at scale seem to gravitate around a ratio of 1.5 to 1.6 of S&M spend over R&D. Younger companies with negative Non-GAAP operating margin haven’t reached this sweet spot. Finally, Zscaler is an outlier on the other extreme. They are allocating significantly more expense to S&M relative to R&D. This isn’t a new circumstance, but seems to be improving, as the ratio was 2.8 in the year ago quarter.

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Customer Activity

While Datadog is reducing revenue guidance due to a slowdown in large customer spend expansion, their new customer pipeline is robust. In Q2, they reported record customer additions of 1,400, which would have been 1,600 accounting for the shedding of 200 customers in Russia and Belarus at the beginning of the quarter. Prior to that, the highest count of customer additions was 1,300 in Q4 2021.

This surge in new customer sign-ups reflects continued demand for Datadog’s solutions, lack of competitive encroachment and sustained secular tailwinds from cloud migration and digital transformation. Given Datadog’s pattern of expansion in normal times, these customers should provide a tailwind for revenue growth when the macro environment moderates.

Growth in large customer additions slowed down in the quarter, though. Datadog ended Q2 with 2,420 customers with ARR of $100k or more, which make up 85% of ARR. This is up 54% from 1,570 a year ago. In Q1, they reported 2,250 large customers for annual growth of 60%, with an addition of 240 from Q4 (10.7% sequentially). The addition of only 170 large customers or 7.0% sequential growth in Q2 is the lowest rate in over a year. I attribute this to the overall slowdown in spending growth, as there were likely a number of customers on the cusp of this spend level that didn’t push over the threshold.

On the earnings call, management discussed the drivers of the moderation in spend expansion. They attributed it to customers with large spend, where optimization would have a meaningful impact. They didn’t observe this optimization effect in companies with smaller spend (analysts called this SMB, but really it was dependent on the amount of spend). Additionally, the pullback in spend expansion was most pronounced in particular industry segments, like e-commerce and delivery, which are experiencing growth moderation. These companies benefited from huge Covid tailwinds in 2021, likely resulting in overspending on many expense line items. A “clean up” phase makes sense, as they reset their budgets this year.

Finally, management explained that the optimization in spend for these companies was most noticeable in the product offerings where pricing is volume-based, like logging and data retention. It is easier to rationalize a cut or clean-up in log data collection and apply a shorter retention period to quickly reduce costs. Datadog management shared that they didn’t see this effect in infrastructure monitoring, which makes sense to me. That would involve pulling an agent off of a server or other infrastructure, which could leave an application without visibility.

The CEO added that “the number of hosts and containers being monitored by our customers is growing steadily, which points to continued momentum of cloud migration and digital transformation projects.” I agree that these data points do not indicate a pullback in enterprise investment in software infrastructure overall or a shift away from observability.

Following the pattern after the 2020 dip, large customer spend should re-accelerate at some point. As spending expansion returns, we should see a greater increase in the large customer count. In Q2 2020, large customer additions dropped to 5.7% in sequential growth. In Q3, that nudged up to 6.6% sequentially. However, as spending picked back up in Q4 2020 and Q1 2021, the sequential rate of large customer additions surged to 15.8% and 14.7% respectively.

To help investors appreciate their strategy of getting customers to continually add new product subscriptions, Datadog reports on the percent of customers with 2 or more, 4 or more and recently 6 or more products consumed. These percentages have continually grown at a consistent rate. This motion drives their high DBNRR.

• Customers using 2+ products were 79%, up from 75% a year ago.
• Customers using 4+ products were 37%, up from 28% a year ago.
• Customers using 6+ products were 14%, up from 6% a year ago.

I think these product growth metrics are particularly important for investors to digest. We don’t see any slowdown in land or expand according to this data. The dip in the count of 2+ products was addressed on the earnings call. The large increase in new customer additions in Q2 pulled down this percentage a little. Also, I would expect a ceiling on this metric and wouldn’t be surprised to see Datadog retire it shortly and add a tracker for 8+ products. Crowdstrike made a similar adjustment in their recent quarter.

Given subscription customers with four or more modules surpassed the 70% milestone and is now commonplace, we are retiring this disclosure and raising the bar by introducing a new metric customers with seven or more modules

Crowdstrike Q1 FY2023 Earnings Call

To measure the expansion of spend with existing customers, like other SaaS companies, Datadog reports their Dollar-based Net Retention Rate (DBNRR). This was above 130% for the 20th consecutive quarter. This means that existing customers a year ago spent 30% or more in the current period. Generally, Datadog generates the majority of its new revenue from existing customers. Datadog’s 10-Q from Q1 reported that 80% of new revenue in the the quarter was attributable to existing customers, with the remaining 20% generated by new customers. They also reiterated that customer churn remains low, with gross revenue retention steady in the mid-to-high 90% range.

These factors underscore the power of Datadog’s land and expand motion. It also provides further insight into why a slowdown in large customer spend would have such a pronounced effect on forward revenue growth. That cuts both ways, though. While the slowdown in forward revenue growth is alarming now, due to this magnified effect, the opposite will happen as enterprises exit this episodic optimization period and spend returns to normal growth levels. They may also resume longer term commitments, boosting RPO.

Datadog’s CEO referenced a handful of customer wins on the earnings call. These highlighted a few points. First, new large customers are continuing their migration from on-premise data centers to multiple cloud vendors. In these cases, they are often consolidating a handful of disparate legacy and open source monitoring tools onto Datadog’s single platform. He provided one example of a 7-figure upsell with a global audit services company that replaced 9 monitoring tools with Datadog.

Second, this consolidation onto Datadog generates cost savings, even where existing tools are open source. There is a general misunderstanding by analysts that open source tools are “free” (even highlighted by one analyst question on the call). It is correct that there is no license fee, but enterprises have to pay staff to configure and maintain open source tools. These DIY “developer platform” teams can quickly balloon into a major expense in salaries, benefits, SBC, etc. By re-purposing these development resources to application creation, enterprises can actually save money and increase output. The Datadog CEO referenced one 6-figure land with a logistics company that calculated they would save $3M in internal developer expenses by consolidating onto the Datadog platform.

I make this point because investors often assume that enterprises will shift back to open source solutions and away from managed software infrastructure in periods of tighter economic conditions to “save money”. That couldn’t be further from reality. Most software teams at scale now recognize the value of managed software services and the money pit that DIY infrastructure can quickly become.

To press this point, the CEO closed the customer wins section by pointing out that they had a number of sizable 6 and 7-figure new logo and expansion wins with companies that recently announced staff reductions. These customers were still looking for ways to streamline their operations, save on engineering costs and consolidate multiple vendors on the strategic platform. In these cases, they didn’t decide to lay off staff and spin up some open source.

Demand Drivers Still Intact

As we consider the path forward for Datadog, industry tailwinds appear to be persisting. The hyperscalers maintained their growth, if we adjust for currency fluctuations and account for the macro pressures on IT budgets overall. For all three hyperscalers, their cloud operations were the highlight of the earnings report. For IBM, software services and consulting showed continued strength.

These companies reported that enterprises are still pursuing a cloud migration and digital transformation strategy. In some cases, enterprises are delaying projects or requiring higher level spending approvals. These are impacting deal close timeframes, but these customers are not scuttling projects or questioning the ROI. They are also exhibiting heavy activity in new application development, analytics, big data, AI / ML and security services.

All of these trends would favor continued demand for observability and security services offered by Datadog. Additionally, they align well with future product directions, as Datadog doubles down on application security and potentially moves into product analytics and business insights.

Another driver for Datadog’s demand is the increasing complexity of the modern software environment. Development teams are expanding their scope and activity along several vectors:

• More technologies. Application development is increasingly relying on internal micro-services, third-party data sources and a myriad of individual infrastructure components.
• More surface area. The move away from single, large monolithic applications hosted on physical hardware in an on-premise data center is shifting towards many different instances of compute and storage, including serverless, micro-services and containers spread across multiple clouds.
• Rate of change. Release cycles are compressing rapidly, where teams may push new code many times a day versus the prior episodic release schedule on a monthly or quarterly basis.
• Number of teams. Development efforts are being monitored and tracked by many more constituents, including multiple software engineering teams, operations, security, analytics, product, leadership, sales, etc. Application status and changes have to be communicated to all these end users.

This increasing complexity and oversight by itself would drive more demand Datadog’s solutions, even if cloud migration and digital transformation stopped. This likely explains why Datadog’s growth has been faster than that of the hyperscalers. The increase in technologies, runtimes, people and release frequency all compound the need for Datadog’s solutions beyond the growth in cloud-based compute instances.

Expanding Product Footprint Will Drive Future Growth

As I have followed Datadog over the last couple of years, I have found that a useful signal for investors to track is the number of listings on the product grid displayed on their web site Pricing page. This has a honeycomb pattern of hexagons, which neatly snap together into rows. Each cell represents a product offering that has individual pricing associated with it. Customers can subscribe to just one of these offerings, or all of them.

At the end of 2020, this product grid had 9 listings. A year later, at the end of 2021, they expanded to 13, for a 44% increase. With their latest product release of Observability Pipelines to GA in June, there are 17 of these, increasing by 4 more so far this year (up 31%). And, we still have Datadog’s annual user conference, Dash, coming up in mid-October. I expect that event to add a handful of new products to the mix, a couple of which may be ready for GA this year.

The reason watching the product grid is important for investors is that it aligns with Datadog’s “land and expand” strategy. A new Datadog customer will typically start with 1-2 product module subscriptions. As I highlighted above, they add more incrementally with the subscription rates for 4 or more and 6 or more products steadily increasing. The process for a customer to add a new product module is seamless, which is part of the beauty of Datadog’s model. New subscriptions can be initiated within the customer’s Admin module, with no new contract required. In most cases, they don’t even need to make software changes.

We got a little teaser for the magnitude of new releases in 2022 on the Investor Presentation that coincided with the Q2 earnings release. It appears that Datadog has earmarked 5 more releases for the remainder of 2022. As we investors wring our hands about future revenue growth, this chart provided me with some consolation.

The acceleration in the number of product releases each year is the key take-away. Datadog is not just adding new products annually at a steady clip, they are accelerating the release rate. This is enabled by their over-allocation to R&D spend that I highlighted previously.

This accelerating release rate is in addition to their ongoing work to continue improving existing products. Datadog works with customers and industry analysts to ensure that their feature coverage for products already in market evolves with expectations. A great example of this continuous improvement is their APM suite. As I highlighted in a prior post, Datadog’s APM solution ascended the Gartner Magic Quadrant rapidly over a 3 year period, moving from Visionary to low-end of Leaders quadrant to top of Leaders quadrant successively.

The benefit of this continuous product expansion and improvement was highlighted on the Q2 earnings call. Management discussed how the original “three pillars of Observability” product set all grew strongly in Q2. The APM suite (APM, synthetics, RUM and Continuous Profiler) and log management now contribute more than $750M in ARR. Their first commercial product, infrastructure monitoring, continued to grow strongly as well, at a similar rate to prior quarters.

Outside of the core products of APM suite, infrastructure and logs, the newer products are growing ARR faster than 100% y/y. CI Visibility specifically was called out as having more than 1,000 paying customers. That product was released to GA in October 2021, representing less than a year in market. In his opening remarks, Datadog’s CEO also highlighted traction with other products released in Q2, include Observability Pipelines, Audit Trail and their newest release Service Catalog.

Finally, coinciding with the earnings release, Datadog announced the acquisition of Seekret, which provides an observability platform for APIs. As the dependency on internal and third-party APIs becomes an increasingly complex aspect of application development, software engineering teams are challenged to track the state of APIs. These can be difficult to inventory across all applications, monitor for changes, test through automation, version and package into releases. Seekret provides an integrated platform that handles all of these tasks, increasing development velocity and reducing risk.

Seekret appears to be an Israeli company with 3 employee founders. They were launched with a seed round of funding in 2020. They are currently offering their product as a private beta. The company doesn’t disclose any customers and doesn’t appear to generate meaningful revenue. Datadog is acquiring Seekret for the technology and talent.

Per Datadog’s standard practice, I expect Seekret’s solution will be incorporated into Datadog’s platform and released as a stand-alone product at some point in the future. I think it represents a great tuck-in that addresses a real and growing need for modern software engineering organizations. The technology also pushes Datadog further into developer operations (shift left), as aspects of the Seekret platform are tied to code verification, package management and automated testing.

Competitive Landscape

As we consider the path forward with Datadog and its growth profile, it is worth keeping in mind that Datadog effectively has no competition. Technically, there are other companies offering monitoring solutions, like Dynatrace, New Relic, Elastic and Splunk. However, those companies all have lower revenue growth rates. In the most recent quarter, they reported the following:

Ticker	Revenue Growth Rate
DDOG	74%
DT	27% / 32% in cc
NEWR	20%
ESTC	35% / 37% in cc (Cloud: 71% / 72% in cc)
SPLK	34% (Cloud: 66%)

For Elastic and Splunk, they haven’t reported the most recent quarter, which presumably will reflect some slowdown in growth. Further, both companies do have cloud offerings that are growing at a faster rate than overall revenue, but they make up less than half of total revenue (48% for Splunk and 37% for Elastic). These companies still have negative operating margin on a Non-GAAP basis, while Datadog has been positive for some time. Finally, as I discussed earlier, Datadog has the most efficient GTM motion, as evidenced by the relative spend on S&M versus R&D.

In their quarterly reports in the last week, both Dynatrace and New Relic exhibited similar trends as Datadog in demand and forward revenue guidance. They reported elongated deal cycles and higher levels of approvals required. Dynatrace lowered its full year guidance by about 1-2% growth (versus Datadog with a slight raise). Similar to Datadog, they reported the greatest impact in slowdown in June, but also that some deals closed in July and they saw a little stabilization.

Datadog also has no competition from the hyperscalers. This is an important consideration, as most other independent software infrastructure providers have to contend with at least one hyperscaler trying to focus on their market segment. Examples include Snowflake and Google Big Query, Crowdstrike and Microsoft Defender, MongoDB and Azure CosmoDB. Hyperscalers do have a basic infrastructure monitoring service, but nothing that includes the full feature set for an observability platform.

Take-aways and Investment Plan

I think the biggest concern for Datadog revolves around the path of revenue growth for the next 6-12 months. The optics for Q4 will be particularly challenging, given the tough compare to Q4 2021. As I mentioned, as full year guidance stands, Datadog’s revenue growth rate in Q4 could drop as low as 35%. On the earnings call, management emphasized that they were being more conservative than normal, given the uncertain economic environment.

If we look further forward to 2023, though, I see some opportunity assuming economic conditions don’t deteriorate further. As we progress through 2023, the quarterly growth comparables will become easier again. This mirrors what we witnessed starting in Q2 2021, after lapping the Q2 2020 Covid reset. The red box below should eventually lead to the green box.

Beyond the macro impacting enterprise spend, I don’t see any other changes in the demand profile for Datadog. They have virtually no competition. Cloud migrations are increasing, compounded by growing complexity. They have a healthy operating model, where the expand motion is strong, reducing the need to spend a lot on sales and marketing. They are able to allocate an outsized portion of revenue to R&D, which drives an accelerating product development pipeline.

Now, let me speak to our longer term outlook. We recognize the macro environment is uncertain as we look into the back half of 2022. But we also see no change to long-term trends towards cloud-based services and modern DevOps environments, and observability remains critical to that journey. We continue to drive market leadership and offer our customers value, efficiency and cost savings to solve their complex monitoring problems.

As a result, we continue to feel very confident in our opportunities. We believe cloud migration and digital transformation are drivers of our long-term growth, and our multi-year trends that are still early in their life cycle. And we believe it is increasingly critical for companies to embark on these journeys in order to move faster, serve their customers better, and in times like these become more efficient with their infrastructure and engineering investments.

Datadog CeO, Q2 2022 Earnings Call

To summarize, putting aside the impact of the macro environment on enterprise spend, Datadog retains several advantages. I think these position Datadog for durable growth and even re-acceleration once the macro headwinds subside.

• Leading and expanding competitive position. While there are other observability providers (Splunk, Dynatrace, New Relic, some start-ups), I don’t consider any to pose a meaningful threat. Datadog is growing revenue faster and expanding their product footprint more quickly. There is no Databricks (to Snowflake) or Sentinel One (to Crowdstrike) to worry about.
• Along those lines, none of the hyperscalers offer a competing solution in observability. This is an important consideration, as we see various hyperscalers getting more aggressive in their product positioning around databases, AI/ML, security and developer tooling.
• Datadog has one of the most efficient GTM models, allowing them to spend less on S&M than R&D. As I demonstrated earlier, their ratio of S&M to R&D spend is significantly lower than any other software infrastructure provider that I follow.
• This outsized R&D spend drives a prolific product development pipeline. Growth in monetized product offerings has been exceeding 40% a year. This is like a retailer adding 40% more inventory to their shelves annually. That growth is in addition to attracting more customers and then selling them 30%+ more product each year.
• Those additional products are in demand. Datadog’s continual growth in adoption of 4+ and 6+ product subscriptions demonstrates this, in addition to anecdotal evidence of large customer lands with even more products than that.

There is risk, however, in the near term. The macro environment will decelerate revenue growth through this year. If macro deteriorates further, that may impact growth as well, as new projects are put on hold.

I think revenue growth will decelerate for the remainder of this year and could dip to the 40% range in Q4. Assuming they beat their current 2022 guidance by $20M (reasonable over 2 quarters), Datadog would end 2022 with $1.64B in revenue or about 60% growth. For 2023, I think quarterly growth could pick back up into the 50% range and Datadog could end the year with a revenue increase of around 50%. This would translate into almost $2.5B in revenue for 2023. At a current market cap of about $35B, that would represent a forward P/S of 14 in January.

Beyond 2023, I think Datadog could continue growing revenue at 40% or more a year. This is based on the trajectory of the hyperscalers at even higher revenue and an outcome of Datadog’s large market, expanding footprint and efficient sales model. Keeping in mind their strong cash flow generation, they should continue to fetch a high valuation relative to other software infrastructure providers.

Of course, the macro environment could jolt this whole thesis, but that is pertinent to all software stocks. Investors can consider their timeline for an investment in DDOG. Shifting some DDOG allocation into other companies might yield better returns for the remainder of 2022, before the 2023 tailwinds kick back in. At the same time, the market tends to be forward looking. The price action of DDOG since their Q2 earnings release (slightly above its closing price before earnings) implies that the market is doing that now.

Coming into earnings, DDOG was the largest position in my portfolio at about 33%. Following the results, I shifted a little allocation to NET, which is now my largest position, as a result of their banner results (post coming soon). For the time being, I plan to maintain an outsized allocation to DDOG, but may do some more re-balancing depending on the performance of other holdings. The next two quarters at least will be bumpy, with the Q4 guide likely shocking some investors. But, I think 2023 will look appealing, as year/year comparisons ease. This may lead to a repeat of the 2021 stock performance.

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 at Hhhypergrowth cranked out a great review of Datadog’s results on Friday. We agree on many of the same points. This provides interested readers with more perspective on the results and the path forward.