Datadog (DDOG) Q3 2022 Earnings Review

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.

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At this point, I think investors have become comfortable with the notion that macro pressure is impacting growth rates for software infrastructure companies. These are coming down from the 50%-100% range achieved by some during the heyday of Covid, driven by a confluence of factors. As consumers were forced indoors, enterprises used the opportunity to aggressively spend on digital transformation initiatives. A bevy of new consumer services popped up that enabled activity during lockdowns, ranging from remote work (Zoom), fitness (Peloton) and food delivery (Doordash). On top of that, new crypto services (exchanges like Coinbase) experienced a frenzy of investment.

All of these factors drove a spike in demand for software infrastructure services. The hyperscalers recorded some of their best quarterly growth rates for several years, as customers scrambled for hosting and infrastructure offerings. Software companies providing supporting services in communications, content delivery, databases, search, developer tooling and of course observability, all thrived as well.

In 2022, we are seeing a pullback in this demand. This slowdown is being driven by the opposite effects. As consumers emerge from their homes, they aren’t relying on virtual services as heavily. Crypto growth has normalized and exchanges have consolidated, experiencing less activity than previously. Enterprises are feeling the pressure of high inflation and rising interest rates, moderating their investment in new digital transformation efforts.

Surprisingly, activity hasn’t fallen off a cliff and investment in software infrastructure is continuing, albeit at slower rates of expansion. While comparisons to 2021 make this year’s growth difficult, I think we can expect a better set-up over the next 1-2 years. The macro backdrop may not resolve itself in 2023, but the pullback in IT spend likely will.

It has been easy for many enterprises to pause cloud migration projects and optimize spend in 2022, as the rapid ramp-up in 2020-2021 arguably created excess capacity. Flush IT budgets provided cover for experimental initiatives and bets on narrow solutions that might drive additional growth. Allocating engineering cycles to optimize existing utilization was skipped in favor of new development.

The deteriorating economic conditions of 2022 have quickly reversed this exuberance. IT spend (and really all corporate spend) is being scrutinized for ROI. Experiments and moonshots are being put on hold or cancelled. However, I think overt IT spend reduction will be relatively short-lived. Enterprises saw value in cloud migrations and digital transformation pre-Covid for the right reasons. Software increases automation, lowers cost and improves customer service. Enterprises just pulled investment forward in 2021.

While the sudden slamming of the IT spending brakes has created turmoil for software companies in 2022, I think this will return to sustained growth in IT spend on digital transformation and cloud migration projects in 2023 and 2024. Projections from industry analysts for growth of cloud spending largely reflect this view. Gartner recently predicted that cloud spending will actually increase in 2023 to almost 21% year/year growth. This is higher than the 19% increase observed so far in 2022. PaaS and IaaS are projected to grow even more quickly in 2023, at 23% and nearly 30% respectively.

As a recent example, Fortune 150 energy leader Duke Energy just signed a large deal with AWS. They plan to invest $75B over the next decade in grid modernization and IT represents a fair sized slice of that. They will use AWS to run applications that predict and optimize electricity utilization across their grid. According to Gartner, these types of digital transformation investments are common among providers in the energy industry.

Datadog is experiencing the impact of the broader economic trends on its business now. While enterprises are pulling back, they are not stopping investment altogether. Sales cycles are elongated and customers are seeking opportunities to optimize spend. But, they are still adding product module subscriptions, largely at the same rate as in the past. And Datadog is still closing new deals, with total customer additions within the range of prior years (at the lower end, but still in range).

I think these factors bode well for Datadog going into 2023. The next few quarters will be lumpy and growth will decelerate with difficult comps going back to 2021. However, I expect this behavior to normalize in the second half of 2023, where enterprise cloud spend returns and may even re-accelerate slightly. I can even rationalize a scenario where IT spending cuts have been front-loaded in 2022. Enterprises could eliminate the excess now and then return to a normal investment cadence for all the reasons that they buy software. As we are sliding into the trough, though, it’s easy to assume the situation will just continue to get worse.

With that set-up, let’s see what Datadog delivered in Q3.

Growth Metrics

Let’s start by examining revenue and related growth metrics. Coming out of Q2, Datadog set Q3 targets and full year estimates just above expectations. Management was decidedly conservative, as customers were dragging out sales cycles and delaying some cloud migration projects.

For Q3, analysts expected $414.2M in revenue, representing 53.1% annual growth. The company had guided slightly below this at the midpoint for a range of $410M – $414M or 52.5% annual and 1.5% sequential growth. Datadog beat this cleanly, delivering $436.5M in revenue, up 61.4% annually and 7.6% sequentially. The team surpassed their own estimate by about $22.5M, representing 9% of annual growth and 6.1% sequentially, underscoring the relative conservatism. The beat this quarter on a sequential basis was about the same as Q2 (7.8% sequentially higher than their prior estimate).

Looking forward to Q4, Datadog set their revenue target for a range of $445M – $449M, representing growth of 37.0% annually and 2.4% sequentially. This matched the analyst estimate for $447.5M in revenue, within the range, but slightly below at the midpoint. If we apply the same relative beat from Q3 to their guidance, it implies actual revenue growth of 46% annually and 8.5% sequentially.

For the full year, Datadog raised revenue guidance to a range of $1.650B – $1.654B, representing growth of 60.4% over 2021. The raise was $32M above the midpoint of Q2’s guidance of $1.62B. Given that Datadog increased their Q3 revenue target by $22.5M, management left about $10M to exceed the Q4 estimate. This follows Q2, in which they raised the full year outlook by just $10M, which was less than their beat of $28M for the quarter. It’s nice to see the annual raise back to being larger than the beat in Q3.

If we apply the implied $10M beat to Q4’s estimate, we get 40.1% annual growth. This is better than the 37% growth estimate, but far below the 84% annual growth delivered in Q4 2021. We saw a similar pattern in 2020, where annual growth dropped from 87% in Q1 2020 to 51% in Q1 2021. As investors will recall, once the difficult comps were passed, Datadog’s annual growth re-accelerated. I think we will see a similar pattern in the second half of 2023.

During the earnings call, management provided some color on the results. At a high level, they described the Q3 performance as being similar to Q2. The market appeared relieved that Datadog achieved some linearity. Management attributed strength in the quarter to new logo additions and product attach activity. Weakness was associated with usage growth in existing subscriptions from customers, similar to Q2. The net ARR added in Q3 was on par with Q2.

Spend expansion from existing customers varied by industry vertical. The effect is more pronounced in consumer discretionary, and in particular, those companies who are cloud native and fully scaled into public cloud. Consumer discretionary includes e-commerce and food delivery companies, and represents a percentage of revenue in the low teens. Examples of these types of Datadog customers are DeliveryHero, Zillow Group, Wayfair and Peloton.

Looking forward, while Q4 guidance was tempered, management is optimistic about the sales pipeline.

Second, our sales pipeline is strong heading into Q4 for both new logos and new products. And we’re seeing great opportunities across customer sizes, geographies and industries. Alongside our strength in new logo ARR, this gives us confidence that digital transformation and cloud migration remain a top priority. It is perhaps even more critical in difficult times when businesses need to be more agile and do more with less. Remember that given our usage-based operating model, new logo wins generally do not immediately translate into meaningful revenue, but they are very important to us as new customers expand their usage in succeeding quarters and succeeding years.

Datadog Q3 2022 Earnings call

Another indirect consideration is the impact of currency fluctuations. Datadog doesn’t report a constant currency adjustment, because they charge for their products internationally in dollars. Other software companies that adjust for currency, like ServiceNow and Microsoft Azure, attributed about 5-6% of revenue impact for Q3. Competitor Dynatrace applied a constant currency adjustment of about 6-7%. While Datadog does not make this adjustment, they acknowledge that “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.” (per 10-Q filing)

On the earnings call, in response to a question, the CEO acknowledged that “the strong dollar is a headwind for us.” While they don’t quantify the impact and feel the macro pressure on IT spend is a larger influence on demand, we can assume that any future moderation in the U.S. dollar’s value will remove this headwind or even shift into a tailwind. These factors are important for investors to acknowledge, as several percent of revenue growth headwind (particularly sequentially) has an outsized impact on future earnings projections and the valuation multiple.

Shifting to forward-looking growth metrics, we see similar softness, but not a dramatic drop. Billings were $467M, up 51% y/y. This represents a nice sequential acceleration of 17.6% from Q2, which posted $397M for 47% annual growth.

Total RPO was $941M, up 31% year-over-year. Current RPO growth was in the mid-40% range y/y. Management did point out that they signed several large multi-year renewals in Q3 2021, driving total RPO up 100% in the quarter. This is making the total RPO compare difficult and explains the better performance in current RPO. In Q2, total RPO was $881M, up 51% y/y. Current RPO was in the mid-50% range. In Q1, total RPO was up 85% y/y and current RPO growth was in the mid-80% range.

As RPO is coming down, management advised investors to pay more attention to revenue growth. In this environment, they said that customers are being conservative with their forward commitments, while keeping their usage growth rate the same. I think this is expected behavior 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.

Management did comment on demand for some specific product offerings. Products launched after 2019 (basically everything except APM, infrastructure and logs) remain in “hypergrowth” mode. They highlighted the performance of two of their newer products, Database Monitoring and CI Visibility. Datadog started charging for these products just a few quarters ago, yet they already have more than 1,000 customers and have exceeded 8 figures in ARR.

One area I would like to see more updates is in the traction of security products. Given the demand environment for security solutions, I think Datadog should be leading business updates with growth in security product sell-through. In response to an analyst question on the Q3 earnings call about traction in security, the CEO discussed that they are happy with their progress, but acknowledged that they have more product development planned (some of which was introduced at the Dash conference in October). He reiterated the “thousands of customers” metric revealed a couple of quarters ago and that the security strategy is “working as planned”.

For me, Datadog’s Q3 growth performance was as expected, considering the macro backdrop. I am not surprised by the slowdown in growth, particularly given the rapid and heavy investment by enterprises in 2021. The Datadog team continues to bring new customers onto the platform and expand their product subscriptions. Even as revenue growth dips below 50% annually, I didn’t expect Datadog to remain in the 80% range indefinitely. I think the current subdued performance will provide a nice set-up for sustained high growth or even some acceleration as we get into late 2023 and 2024.

Profitability Measures

In spite of pressure on revenues, Datadog was able to maintain their operating leverage. This is nice to see, as some software companies are reducing both their revenue outlook and their profitability metrics in response to the spending environment. On a Non-GAAP basis, gross margins improved year/year. In Q3, Datadog delivered 80% gross margin, compared to 78% a year ago and 81% in Q2. As they scale, Datadog continues to find efficiencies in their cloud operations. They did reiterate their long term target of gross margin in the high-70% range.

This outperformance in gross margin, coupled with strong revenue growth, drove a gross profit increase of 65.7% year/year. This is higher than the 61.4% increase in revenue. As a result, Datadog grew their Non-GAAP operating expense by a similar amount, enabling continued growth in headcount for R&D and S&M.

Operating income in Q3 was $75M for an operating margin of 17%. As part of the Q2 earnings report, Datadog had projected operating income of $51M – $55M, representing a beat of $22M at the midpoint. A year ago, Datadog generated $44M of operating income for a 16% operating margin. Operating income increased by 70% year/year. This translated into a Non-GAAP EPS of $0.23, which beat the analyst estimate for $0.16. A year ago, EPS was $0.13.

Datadog ended the quarter with $1.8B in cash and equivalents. Cash flow from operations was $84M, representing about 20% of revenue. Capital expenditures and capitalized software totaled about $17M, resulting in free cash flow of $67M for a FCF margin of 15%. A year ago, cash flow from operations was $67M. FCF was $57M for a 21% FCF margin.

For Q4, management expects Non-GAAP operating income in the range of $56M – $60M and Non-GAAP net income per share of $0.18 – $0.20. For the full year, they raised the operating income target significantly from a range of $255M – $275M in Q2 to $300M – $304M as part of the Q3 report. For Q4, they called out about 300-400 bps of operating margin impact from two user events in Q4, Dash and the AWS re:Invent conference.

Looking at spending allocations by department, we see the following for Q3 and the prior year on a Non-GAAP basis:

• R&D: 31.7% of revenue in Q3 2022 ($138.3M), versus 31.0% a year ago
• S&M: 24.6% of revenue in Q3 2022 ($107.5M), versus 23.9% a year ago
• G&A: 6.3% of revenue in Q3 2022 ($27.4M), versus 6.4% a year ago

Datadog continues to allocate more spend to R&D than S&M. As I have discussed previously, I think this trait makes Datadog unique, as compared to most other software infrastructure companies and all competitors. By spending more on R&D, Datadog has been able to maintain an extremely high product delivery cadence. This provides more products to sell and improves the quality of existing products.

Due to Datadog’s category focus, it is very easy to cross-sell new solutions into their DevSecOps audience within enterprise organizations. Their frictionless expansion motion takes pressure off of the sales team, allowing them to focus on hunting for new customers. According to Datadog’s 10-Q filing, 80% of their incremental revenue in the quarter was generated from existing customers, with just 20% coming from new customers.

For comparison, competitor Dynatrace (DT) allocated 14.5% of revenue on a Non-GAAP basis to R&D in Q3 and 32.4% to S&M. Dynatrace spends 2.2x more on S&M than R&D. Datadog is the opposite, with R&D spend representing just 0.78 of S&M spend. Yet, Datadog’s total revenue growth was 61% y/y in Q3, which was more than twice Dynatrace’s 30% annual growth in constant currency. And, to top it off, Datadog generates more than 50% greater revenue than Dynatrace. Given those metrics, I don’t see Dynatrace ever catching up to Datadog at this point. The same exercise can be performed for Datadog’s other competitors.

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Customer Activity

Datadog continues to add customers at a robust clip. They ended Q3 with 22,200 customers, adding 1,000 new customers during the quarter for 4.7% sequential growth and 27% annually. The 1,000 new customer additions is down from the record 1,400 additions in Q2, but still in the range of 1,000 to 1,400 per quarter over the last year.

Large customers are defined as those which generate more than $100k in ARR. These contribute 85% of total ARR at this point, making them the most important customer segment. In Q3, Datadog reported 2,600 of these large customers. This is up 44% from 1,800 large customers in Q3 of 2021, which contributed 82% of total ARR. Sequentially, Datadog added 180 of these customers, up 7.4% from Q2. While this is greater than the 170 added in Q2, it is down from 240 in Q1 and 210 in Q4. Given the aforementioned pressure on customer spend expansion, this is still a healthy number of additions.

There’s the customers that are largely cloud-native and normally pretty scale on the cloud environment, so then have cloud end-to-end on public cloud. They are definitely trying to save money. And these are companies that, in general, also tend to have their own growth rates affected or probably affected in the future by the macro trends. So that’s why they’re doing that.

But when you look at the other customers, the ones that are earlier in their cloud migration, they are actually not slowing down, and we see the same urgency and eagerness for them to keep scaling and keep moving into the cloud. And that’s also where the bulk of our opportunity is.

Datadog Q3 2022 Earnings call

On the earnings call, management discussed the drivers of the moderation in spend expansion. The pullback in spend expansion was most pronounced in particular industry segments, like e-commerce and delivery, which are experiencing growth moderation in 2022. 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.

Outside of the digital natives, Datadog management pointed out that more traditional enterprises that are early in their cloud migration are not pulling back on spend. This makes sense, on a few levels. First, companies with a physical presence are experiencing a resurgence in growth following Covid lockdowns. Many are trying to match the services and convenience offered by the digital natives, kicking off their own “catch up” phase. Second, while the macro environment is impacting some industries, there are others that are less affected and even thriving, like energy, traditional finance, physical grocery and manufacturing.

One important metric for Datadog that appears solidly on track is expansion of product module subscriptions. As part of their sales motion, Datadog usually lands a customer with 1-2 product modules. After that, customers gradually adopt more modules over several years, increasing their spend. Even if usage of an older module moderates, the addition of new modules can increase overall Datadog spend.

To measure this, Datadog management reports on the percent of customers with 2 or more, 4 or more and recently 6 or more product subscriptions. These percentages have continually grown at a consistent rate. This expansion motion drives their high DBNRR, repeatably over 130%. Here are the results from Q3:

• Customers using 2+ products were 80%, up from 79% last quarter and 77% a year ago.
• Customers using 4+ products were 40%, up from 37% last quarter and 31% a year ago.
• Customers using 6+ products were 16%, up from 14% last quarter and 8% a year ago.

I think these product growth metrics are particularly important for investors to digest. We don’t see any slowdown in the expand motion, according to this data. The most substantial improvement was in the customers with 6 or more products. If we calculate the total customer counts with 6+ products, it increased by more than 2.5x over the prior year. As the 6+ product subscription level expands, Datadog will need to introduce an 8+ product tier soon. Crowdstrike made a similar adjustment in Q1. One customer upsell even referenced a subscription for 14 products, underscoring the fact that the upper limit on Datadog product subscriptions can be very high.

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 21st 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 incremental revenue from existing customers. Datadog’s 10-Q 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.

Datadog’s CEO referenced a handful of customer wins on the earnings call. He called out 5 different deals, each of which was either a land or upsell for 7 to 8 figures of revenue (likely this means >$1M and < $20M). These spanned multiple industries, including a grocery chain, a restaurant chain, a social networking app, an e-commerce site and and electronics company based in Asia.

A few of the wins involved consolidation onto the Datadog platform, replacing either an open source product or a legacy point solution. An example with APM reflected Datadog’s continuous product improvement, where Datadog couldn’t meet the customer’s requirements for language compatibility previously, but can now. The electronics company from Asia is adopting 14 product modules from the Datadog platform, reinforcing the platform consolidation narrative.

The types of products adopted varied as well. Beyond the basic three pillars of observability (APM, infrastructure, logs), customers were interested in Synthetics and RUM for tracking user experience. The Sensitive Data Scanner cemented the deal with the grocery chain, as they handle PII as part of their pharmacy operation. Service Catalog, which is a relatively new product offering, was a key part of the deal with the social networking app.

The consolidation motion onto Datadog’s platform continues to be a theme, as this generates cost savings for budget-strapped enterprises. This value proposition even applies where existing tools are open source. There is a general misunderstanding by analysts that open source tools are lower cost, representing a downgrade option for enterprise customers when budgets are tight. It is correct that there is no license fee, but enterprises still 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.

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”. In Datadog’s case, that would provide a convenient explanation for Datadog bears of the downward pressure on revenue growth.

The motivation for an enterprise to revert to home grown or open source observability solutions makes no sense to me, particularly as engineering talent remains relatively expensive. Developers should be building differentiating product, not reproducing software infrastructure that is available off the shelf.

Additionally, some investors might surmise that competitive displacement is the cause of Datadog’s slowdown in revenue growth. I see no evidence of this. First, Datadog leadership still cites very low churn rates. Second, their product module expansion metrics would slow down if customers were considering alternative solutions. Third, most start-ups necessarily focus on one part of the observability product spectrum. Cost savings realized through platform consolidation onto fewer vendor solutions in tight IT budget environments creates a headwind for point solution providers.

The metric we need to watch relative to possible infringement is the total customer additions, which did fall into the lower end of the normal range in Q3. If Datadog were losing some deals to competitors, it might show up there. But, it would also appear in product module expansion rates, which it is not. Therefore, I think the slower pace of customer additions is a consequence of the macro environment.

Big Picture

While Datadog is experiencing headwinds from the macro environment and currency fluctuations, they are maintaining growth and profitability relatively well. At the Needham Big Data and Infrastructure Conference held after earnings, Datadog’s CFO pointed out that they are better positioned now than in the past to weather cloud spend optimization as a result of their diversity of products. Even if customers are slowing down expansion in the number of hosts by delaying cloud migration projects, they are investing in user experience modules (like RUM and Synthetics), developer efficiency (CI/CD, testing workflows) and application security.

On the earnings call, leadership shared that newer products like Database Monitoring and CI Visibility, which are outside the scope of a direct cloud migration, are experiencing rapid uptake, reaching 8 figures of ARR and over 1,000 customers. As Datadog continues to add new product categories and build out product offerings in each, they will have more add-ons to cross-sell to customers. Even if an enterprise has paused their cloud migration projects, they may be looking to consolidate their DevSecOps category spend onto fewer providers. With its broad platform offering, Datadog is well-positioned.

And while many enterprises are pulling back on cloud spend this year, Gartner still predicts a fairly linear growth rate over the next four years. They estimate that cloud only makes up 10% of total global IT spend currently. By 2026, they predict that this will increase to 20%, effectively doubling cloud spending. In my experience, observability makes up about 5-10% of a cloud infrastructure budget, implying a total market for Datadog of $50B – $100B in four years.

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

Expanding Product Footprint Will Drive Future Growth

As I pointed out earlier, Datadog directs an outsized portion of their high gross profit towards R&D, weaponizing it against competitors. Their frictionless adoption motion for new product subscriptions enables 80% of their new revenue to be generated from existing customers. This, in turn, reduces pressure on the sales team, allowing them to focus their efforts on landing new customers to keep the expansion funnel primed.

Because S&M can be maintained at a lower level than R&D, Datadog has been able to increase their R&D spend at an accelerating rate over the last 5 years. This increased investment drives a rapid expansion in the number of product launches each year. This, in turn, provides more product modules for customers to adopt. Since the target audience for all Datadog products is generally the same (within the scope of DevSecOps), the upsell of new modules involves the same buyers. Additionally, activation of product modules is an exercise in toggling a setting in the Admin screen, versus some heavy development or infrastructure effort.

While investors could worry that Datadog is just throwing spaghetti on the wall and hoping some product offerings stick, the product leadership team is actually very deliberate in their choice of adjacent product categories and releases. Datadog only builds new products in response to customer feedback. And since the DevSecOps audience is focused, it is fairly straightforward to discern adjacent product opportunities. The metrics shared earlier of growth in 4+ and 6+ product subscriptions provide evidence that the expansion strategy is resonating. As does the high DBNRR over 130%.

This accelerating rate of new product introduction is in addition to their ongoing efforts 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.

At their Dash user conference in October, this product development cadence was on full display. Datadog’s scope of announcements at Dash 2022 trumped anything from prior years. Based on a chart shared during the Investor Meeting at Dash, there were 18 individual product releases highlighted. This compares to 10 product releases listed in a similar chart during the Investor Meeting during Dash 2021, representing an 80% increase.

Looking across all the announcements from Dash, a few new themes emerged, which the CEO emphasized during the earnings call. First, they are doubling down on observability by adding new products in response to customer demand. These included Data Stream Monitoring and Cloud Cost Management. They also extended APM (with mobile app testing, usage heat map and dynamic instrumentation), network monitoring (SNMP traps) and logging (log forwarding).

Second, they are expanding the security platform, adding Cloud Security Management. This combines Cloud Security Posture Management (CSPM), Cloud Workload Security and Resource Catalog to provide a cloud-native application protection platform (or CNAPP). For their Application Security Management product, they enabled blocking of attacks in real-time versus reporting attack attempts and relying on the operator to take action. This was a big step forward in itself, moving from reporting to protecting.

Third, Datadog is rapidly expanding their developer offerings. They announced Continuous Testing, which helps teams create effective tests quickly, improves test suite resiliency with a “self healing” mode and integrates with popular CI/CD platforms. They also introduced Intelligent Test Runner, which figures out which tests were impacted by a code change, reducing automated test runs significantly.

Finally, at the same time as the earnings release, Datadog announced the acquisition of Cloudcraft. Cloudcraft provides a planning and design tool for cloud migrations. It has been used by tens of thousands of cloud architects to create diagrams of their planned cloud infrastructure. Cloudcraft analyzes service relationships within customers’ AWS environments, reverse engineers a system architecture diagram and automatically updates that diagram in line with infrastructure changes.

The acquisition will move Datadog further left and shift into the planning process that occurs before infrastructure needs to be monitored. Datadog intends to offer the Cloudcraft service for free, but expects it will generate leads for other Datadog products. From the cloud migration design created by an enterprise DevOps team, Datadog can make product recommendations within the tool and alert the sales team to follow-up with the potential customer.

Competitive Landscape

As we consider the path forward with Datadog and its growth profile, it is necessary to check in on the competition. I still don’t see evidence of competitive infringement on Datadog’s performance. Publicly traded competitors are growing more slowly on the whole, both in terms of revenue and product development. No new private companies have come to the public market this year. While some start-ups are making noise, as they always do, none has risen to the point where they represent a disruptive threat. Additionally, private companies will be more pressured by the macro environment, as they have to preserve cash to avoid another fund raise in a down market.

The challenge for competitors continues to be coverage. Datadog has more product module scope in a fully integrated platform across DevSecOps than any other observability provider. Even where an entrenched player like Splunk has many offerings cobbled together through acquisitions, Datadog’s platform is fully integrated down to the data layer. Plus, Datadog is evolving their platform more quickly.

Start-ups that may have a better offering in one segment of observability (like APM or logs) have the current headwind of point solution avoidance. The platform argument will stunt their opportunity, at least in this environment, where enterprises are favoring consolidation over specialization. Even within observability, Datadog offers broad reach in categories like user experience monitoring that go far beyond start-up capabilities centered on just the three pillars (infrastructure, APM, logs).

When we get back to growth mode, this may change. But, the start-ups have to last long enough to eventually pose a threat. In periods of economic downturn, we often see the competitive landscape wane, as private companies have to pause hiring and reduce their cash burn. This allows publicly-traded, established players with more capital to consolidate market share.

Datadog also has the benefit of little 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 of Datadog’s observability platform.

Take-aways and Investment Plan

I think the biggest concern for Datadog revolves around the path of revenue growth for the next 12 months. As discussed earlier, the revenue growth rate for Q4 will drop to 37% annually based on management’s current estimate. Even with a typical beat, growth will land in the 40% range. Obviously, this is much lower than the annual growth rates for prior quarters in the 60% to 80% range.

As Datadog scales, I don’t expect the company to return to those hypergrowth levels again, but I could see a range of 40% to 50% for some time. This projection is based on the assertion that they can continue to maintain new customer additions at 20% annually or better and their DBNRR above 130%. The latter will be the significant contributor to durable growth, as it reflects the fact that 80% of Datadog’s incremental revenue each quarter is generated from existing customers.

Datadog’s rapid flywheel of product development and cross-sell provides the foundation for an outsized DBNRR over time. Because of their heavy spend on R&D, Datadog will always have additional products to sell their customers. Their frictionless product module adoption makes cross-selling easy.

The addressable market for the areas of DevSecOps that Datadog is pursuing is still very large, estimated to reach $62B by 2026. While this year has provided enterprises an opportunity to optimize their cloud infrastructure spend, investment will continue at largely the same rate as before. Additionally, even if cloud repatriation takes hold, Datadog can still generate substantial revenue. Datadog products can be and often are consumed by enterprises that manage their own data centers. Running their own servers doesn’t require building their own observability tools.

If Datadog finishes 2022 with $1.65B in revenue (their current estimate), they would make up about 4% of the projected $41B market for observability (per Gartner IT Operations Market, June 2022). Applying a 50% CAGR for the next 4 years to 2026 brings their revenue to $8.3B. This is about 13% of the addressable market – not outlandish for the category leader.

If we back the growth rate to 40% annually, Datadog would reach $6.3B in revenue by 2026, making up just over 10% of the target market. If we assume a 25% FCF margin (within the historical range of 15% – 30%), this implies $1.6B of FCF in 2026. DDOG’s current market cap is $23B, yielding a price to FCF multiple of 14 at that point. Investors can plug in their own assumptions for a reasonable FCF multiple and how they want to model dilution. For comparison, ServiceNow (NOW) has a price to FCF multiple of 44 currently. This implies a greater than 3x appreciation for DDOG over the next 4 years, based on those growth and FCF margin projections.

Beyond the macro impact on enterprise spend, I don’t see any other changes in the demand profile for Datadog. Cloud migrations and digital transformation will continue at the same rate for several more years. Datadog’s competitive position is still favorable. They have a healthy operating model, with a low friction expansion motion, supporting an outsized allocation to R&D. This drives an ever-increasing rate of product development, feeding back into the expansion flywheel. For investors seeking a reliable grower with strong FCF generation, DDOG still represents a great choice. To track how I am allocating funds to DDOG, you can check out my portfolio on Commonstock.

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 published a review of Datadog’s results, along with a few other Q3 reports for software infrastructure companies. This provides interested readers with more perspective on Datadog’s performance and the path forward.