During a recent talk at the Goldman Sachs Communacopia + Technology Conference 2024, David Obstler, the CFO of Datadog, shared some really interesting stuff about where the company is headed, how they're positioning themselves in the market, and what they're planning for the future. This analysis breaks down the main points from the conference and gives a good overview of where Datadog is at right now and where they're headed.
Long-Term Vision and Success Metrics
Datadog's long-term vision, as articulated by Obstler, centers on becoming the indispensable platform for DevOps professionals. The company aims to be the go-to solution that customers engage with throughout their workday for monitoring and remediating mission-critical modern applications.
"The vision is that Datadog is the platform that, that customer base turns on when it comes in, in the morning and spends their whole day in it and does their job in it, which is essentially the monitoring and remediation of mission-critical modern apps."
This vision underscores Datadog's commitment to expanding its platform capabilities to encompass a broader range of DevOps functionalities. By positioning itself as an essential tool in the daily workflows of its users, Datadog aims to deepen its market penetration and increase customer reliance on its suite of products.
In terms of success metrics, while Obstler didn't provide specific numerical targets, he drew parallels with other successful platform companies:
"We've always thought about other platform companies, ServiceNow, Atlassian, Salesforce in the Sales Cloud, and thought about how we can create a solution that is easy to use, very flexible, ubiquitous, meaning everybody is in it and keeps innovating and in order to have product-led growth that's maintained and essentially does anything the client wants as applications evolve, which includes AI we can talk about."
This comparison suggests that Datadog aspires to achieve similar levels of market dominance and user adoption in the observability space as these companies have in their respective domains.
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Product Development and R&D Focus
A key differentiator for Datadog is its substantial investment in research and development. Obstler emphasized this point, stating:
"Datadog spends more in R&D than all the other companies in observability combined."
This aggressive R&D strategy is central to Datadog's ability to innovate rapidly and maintain its competitive edge in the fast-evolving observability market. The company's focus on product-led growth means that continuous innovation and expansion of its platform capabilities are crucial for sustaining its market position and driving future growth.
Market Trends and Business Performance
Obstler provided insights into recent market trends affecting Datadog's business performance. Notably, he highlighted a shift in growth dynamics between enterprise and SMB segments:
"In the last quarter or 2, we saw the larger enterprises, particularly the more traditional industries, get back to what they had done before investing in digital projects and experience more rapid growth than the SMB side of it. We see stability in SMB. We see growth in SMB. But we saw a little more of the investment impetus go be correlated not with size and with enterprises."
This trend suggests that larger enterprises, especially those in traditional industries, are ramping up their digital transformation efforts, which bodes well for Datadog's growth prospects in the enterprise segment. The stability in the SMB sector, while not experiencing the same acceleration, still contributes to a balanced growth profile for the company.
Obstler also addressed the apparent discrepancy between Datadog's growth rate and that of hyperscalers:
"We tended to have higher growth rates than the hyperscalers. But the hyperscalers have timing differences and potentially business differences. A good example is in order to deploy AI applications, you first have to invest in the infrastructure. And there have been significant beneficiaries, including the hyperscalers, NVIDIA, et cetera. And we tend to benefit a little bit later in that cycle once those are put in applications."
This explanation highlights the cyclical nature of technology adoption and suggests that Datadog's growth may lag behind infrastructure providers but could potentially accelerate as AI applications move from development to production environments.
AI Impact and Opportunities
The impact of generative AI on Datadog's business was a significant topic of discussion. Obstler revealed that companies providing AI tools across the AI stack currently account for about 4% of Datadog's Annual Recurring Revenue (ARR). While this may seem modest, it represents a growing opportunity as these companies use Datadog for monitoring their AI-powered applications and APIs.
Regarding the broader AI landscape, Obstler noted:
"We haven't seen it disrupted to the extent that normal digital projects are not being executed. So that's what I have to say about that."
This suggests that while AI is generating significant buzz, it hasn't yet materially displaced traditional digital transformation projects, which continue to drive demand for Datadog's core offerings.
Interestingly, Obstler pointed out that Datadog is in the early stages of benefiting from AI workloads:
"We have very little revenues from production so that we have some use but not a lot in terms of revenues. It's still too early. For instance, the LLM modeling, this is typically what we do. We actually go beta but don't charge for it. We kind of learn from it. And so it's being used, but we don't have revenues from it yet."
This indicates significant potential for future revenue growth as AI workloads mature and move into production environments.
Platform Consolidation and Market Share Gains
A key trend benefiting Datadog is the ongoing consolidation of observability tools within enterprises. Obstler emphasized the company's strong position in this consolidation trend:
"Our customers are telling us they want more and more functionality in the platform. We talked about it on the way in. A lot of customers, we didn't have some of these solutions. So if you have many point solutions, you have to run around when there's a problem, do your investigations. You don't have the correlation, et cetera."
This consolidation trend is driving Datadog's multi-product adoption strategy, which has been a significant growth driver for the company. Obstler elaborated:
"So as we've had these products and they've matured and they've become best of breed, over time, we've been able, and this has been going on, to take market share from either cloud-native tools, open source in some cases and other solutions because our clients want to see all of this in one platform in order to do their jobs better."
The success of this strategy is evident in the growth of Datadog's product lines, with Application Performance Monitoring (APM) and logs each surpassing $500 million in revenue, and newer products like Synthetics and Real User Monitoring (RUM) reaching $100 million milestones.
Growth Opportunities in Core Infrastructure
While much attention is given to Datadog's newer products, Obstler highlighted significant growth opportunities remaining in the company's core infrastructure monitoring business:
"Well, I mean, that's very correlated to workloads. And if you look at research, 20%, 30% of applications are in the cloud. Enterprises themselves are, in many cases, very early. So the opportunity is, one, workloads and digital migration. Two would be other types of infrastructure, whether it be GPUs, containers, serverless, et cetera. And then on top of that, you have the concept of service management of a platform, which allows the customer to do more things."
This perspective underscores the substantial runway for growth in Datadog's core business as enterprises continue their cloud migration journeys and adopt new infrastructure technologies.
APM and Logs: Strategic Focus Areas
Obstler provided insights into Datadog's strategy for its APM and logs products, which have grown into substantial businesses in their own right:
"We didn't have those products 6 years ago. We've been successful in attaching but we're not saturated. So I think we're getting better and better of selling the platform and getting understanding what other solutions are out there and working with the client over time."
He also highlighted the strategic importance of innovations in these areas:
"In addition, there are certain infrastructure things like in logs. We talked about Flex Logs. This is essentially making logs more flexible, of course, splitting out compute and storage. And that is an infrastructure element that will allow other products to benefit, for instance, the SIEM, the Cloud SIEM."
These innovations not only enhance Datadog's competitive position in logs and APM but also create synergies with other products in the company's portfolio.
Customer Growth and Enterprise Penetration
The growth in Datadog's large customer base, particularly those spending over $1 million annually, is a key indicator of the company's success in enterprise penetration and cross-selling. While specific targets weren't provided, Obstler emphasized the importance of this metric:
"So because we're land and expand, because most of those million-dollar customers were less than $1 million, they crossed to $100,000, $500,000. It's important. And when you look at our ability to cross-sell, get more of the platform sold, get more enterprises on, I think we do look at this as one of the metrics."
This focus on growing large customer accounts underscores Datadog's strategy of deepening relationships with existing customers and expanding its footprint within large enterprises.
Internal AI Adoption and Productivity Gains
Interestingly, Datadog is also leveraging AI internally to drive productivity gains. Obstler shared some surprising insights:
"What we're trying to do is we're trying to remove the barriers to adoption. So we're treating the large language models as a base part of the kit like you get Google suite and then look and see how it's being used. And a use case that surprised me to the upside is half of the use cases are in sales and marketing."
These internal use cases, particularly in sales and marketing, hint at the broader potential for AI to drive operational efficiencies across various business functions. Obstler elaborated on the potential impact:
"I think our hope is since we have more projects and more territories to cover than we can digest in people that the most profound effect would be on productivity and that, that could translate into top line."
This perspective suggests that AI-driven productivity gains could become a significant factor in Datadog's ability to scale its operations and drive top-line growth in the future.
Financial Efficiency and Margin Profile
One of Datadog's notable characteristics is its ability to maintain strong margins while investing heavily in growth. Obstler attributed this to the company's product-led growth model and efficient platform architecture:
"Well, I think the biggest birthright is the product and the platform. So the product in 2 ways is -- creates efficiency. One is that you can add additional functionality in a very efficient way. The architecture of the platform and the data infrastructure contributes to the velocity of product introduction. At the same time, it also makes it able to be used by clients without professional services and used by everybody, which helps with the sales velocity."
This efficiency allows Datadog to reinvest in product development while maintaining a healthy margin profile, creating a virtuous cycle of innovation and growth.
Acquisition Strategy
Regarding Datadog's approach to acquisitions, Obstler emphasized a disciplined strategy aligned with the company's product roadmap:
"So it all comes off of our -- in a product-led company off our product road map. We have a number of areas of functionality. We understand that we might be able to accelerate that to the extent we can identify good teams who want to continue on with Datadog and the technology and infrastructure that we can integrate in the platform."
This approach suggests that while Datadog is open to acquisitions, particularly acqui-hires that can accelerate its product development, the company maintains a high bar for larger acquisitions that would need to align closely with its platform strategy and cultural fit.
Conclusion and Forward Outlook
Datadog's participation in the Goldman Sachs Communacopia + Technology Conference 2024 provided valuable insights into the company's strategic direction, market positioning, and growth opportunities. Key takeaways include:
- A clear long-term vision focused on becoming the essential platform for DevOps professionals.
- Continued heavy investment in R&D to drive innovation and maintain competitive advantage.
- Strong growth in enterprise segments, particularly in traditional industries undergoing digital transformation.
- Emerging opportunities in AI-related observability, with potential for significant future growth.
- Successful execution of a platform consolidation strategy, driving multi-product adoption and market share gains.
- Substantial growth runway in core infrastructure monitoring as cloud adoption continues to expand.
- Strategic focus on APM and logs as key differentiators and growth drivers.
- Emphasis on growing large customer accounts and deepening enterprise relationships.
- Internal adoption of AI driving productivity gains, particularly in sales and marketing functions.
- Maintainance of strong margins through efficient product-led growth model.
- Disciplined acquisition strategy aligned with product roadmap and cultural fit.
As Datadog continues to execute on its strategy of platform expansion and market penetration, the company appears well-positioned to capitalize on the ongoing trends of cloud adoption, digital transformation, and the increasing importance of observability in modern IT environments. The emerging opportunities in AI-related observability present an additional avenue for growth, although the full impact of this trend may take time to materialize in Datadog's financial results.
The company's strong R&D focus and efficient operating model provide a solid foundation for continued innovation and market leadership. However, challenges remain, including intense competition in the observability space and the need to navigate the evolving landscape of cloud and AI technologies.
For those following Datadog's progress, key areas to watch in the coming quarters include:
- The rate of enterprise customer acquisition and expansion, particularly in traditional industries.
- Growth in multi-product adoption rates and the success of newer product offerings.
- The impact of AI-related workloads on Datadog's revenue as these move from experimentation to production environments.
- Continued margin performance as the company balances growth investments with profitability.
- Any strategic acquisitions that could accelerate Datadog's product roadmap or market expansion.
As the observability market continues to evolve, Datadog's ability to execute on its vision of becoming the essential platform for DevOps professionals will be crucial in determining its long-term success and market position.
