Summary
- Datadog is the clear leader in Observability, and is expanding its reach into adjacent markets like security and analytics.
- Growth is moderating, but this is not particularly concerning given the long term potential of the Observability market and Datadog's dominant position.
- Datadog deserves a valuation premium relative to peers due to the company's potential ability to generate high margins.
Datadog ( DDOG ) has built a commanding lead in the Observability market and is now increasing its presence in adjacent markets, like security and analytics. While the company's growth at scale is impressive, it is Datadog's efficiency that stands out. Datadog has been able to achieve high growth rates with relatively modest levels of investment. This, coupled with high gross retention rates, points towards an extremely profitable future.
Market
Observability tools aim to provide visibility of all relevant data so that DevOps teams can more easily discover and troubleshoot issues.
There are 3 pillars to Observability:
- Infrastructure Monitoring - system metrics, data engines, network traffic, containers, serverless
- Log Management - service monitoring, custom triggers and events
- Application Performance Monitoring
Datadog believe that the transition of workloads to the cloud and the rise of digital applications is only just beginning, providing long term tailwinds to the observability market. Something like 20% of applications are currently being put in the cloud, and the percentage of applications that are created through modern DevOps is also still quite small.
Figure 1: Cloud Spend (source: Datadog)
Growth in the observability market is being driven by:
- Increasing number of applications
- DevOps
- Containers
- Microservices
Datadog has a consumption-based business model, with usage measured primarily by the number of hosts or by the volume of data indexed. Fees may also be based on serverless functions, custom metrics, log events indexed and data retention. Customers also have the option to purchase additional products, such as additional container or serverless monitoring, custom metrics packages, anomaly detection, synthetic monitoring and app analytics. As the unit of work shifts from virtual machines to containers, Datadog's potential market increases.
Organizations are also trying to identify problems earlier in the software development process and granular visibility into the performance of their pre-production testing and deployment pipelines helps with this.
Figure 2: IT Complexity is Increasing (source: Datadog)
At the time of their IPO, Datadog believed that their platform addressed a significant portion of the IT operations management market, which was estimated to be a 37 billion USD opportunity in 2023. This market estimate was based largely on legacy on-premises and private cloud spend, and did not fully include the opportunity of multi-cloud and hybrid cloud environments.
Based on Datadog’s ARR per customer (segmented by size) and an estimate of the number of potential customers (segmented by size), Datadog estimated that their addressable market was worth approximately 35 billion USD in 2019. Datadog’s addressable market has continued to grow since then, albeit somewhat slowly given the functionality they have added to the platform.
Table 1: Estimated Observability Opportunity (source: Created by author using data from Datadog)
Datadog
Datadog is a SaaS-based infrastructure monitoring company that was founded in 2010. Customers utilize Datadog’s platform to ingest and analyze metrics and events from their infrastructure and applications via an easy-to-install agent. The platform aims to address all observability use cases, and increasingly security and analytics, making it integral to the lives of developers, operations engineers and business leaders.
Figure 3: Datadog Platform (source: Datadog)
Datadog has an Observability platform that combines infrastructure, APM and logs monitoring, that is used by DevOps professionals to monitor customer-facing real-time digital applications. This is now being extended into areas like security, workflow and business intelligence.
- Infrastructure Monitoring - monitoring of IT infrastructure across public cloud, private cloud and hybrid environments ensuring performance and availability of applications.
- Application Performance Monitoring - visibility into the health and functioning of applications regardless of the deployment environment.
- Log Management - ingests data, creates indexes and enables querying of logs with visualizations and alerting.
- User Experience Monitoring - brings visibility up the stack to monitor the digital experience of the customer (simulation and real-user monitoring).
- Network Performance Monitoring - enables the analysis and visualization of the flow of network traffic in cloud-based or hybrid environments.
Consolidating on the one platform for multiple use cases (observability, security and analytics) may help customers to reduce costs. Datadog is not alone here though, as this is an approach that is increasingly being pursued by vendors in the observability and security markets.
Figure 4: Expansion of Datadog's Platform (source: Datadog)
Datadog now has 15 modules on their platform:
- Infrastructure
- Log Management
- APM
- Continuous Profiler
- Network Monitoring
- Real User Monitoring
- Synthetic Monitoring
- Incident Management
- Cloud SIEM
- CSPM
- Cloud Workload Security
- Application Security Monitoring
- Database Monitoring
- CI Visibility
- Observability Pipelines
Datadog recently announced cloud security management, which combines cloud workload security and cloud security posture management in a cloud native application protection platform. It will be interesting to see how much traction Datadog gets in this area given the importance of security. Most endpoint vendors already have a suite of cloud security tools, and presumably expertise and data that Datadog do not.
Datadog is cloud agnostic and comes with hundreds of out-of-the-box integrations across all the server-side systems and major cloud providers, plus developer support tools like Github, Jira and ServiceNow. In this area Datadog and their top SaaS competitors have an advantage over the hyperscaler's native solutions, as those are only capable of monitoring their own cloud platform.
Datadog has a preference for internal product development and invests a large amount in R&D in support of this. They have also shown a willingness to make smaller acquisitions as acquihires or for specific capabilities, such as:
- CoScreen - allows groups to go on a video call internal to the platform to remediate issues.
- Timber - company behind the Vector open-source project, which Datadog is leveraging in their Observability Pipelines. Observability Pipelines help customers to control the cost and volume of data, decouple data sources from their destination, standardize and improve data quality and ensure compliance.
- Seekret - API Observability Platform that gives engineering teams better control of their APIs. Datadog plan on using the acquisition to bring customers greater visibility into their APIs and unlock new security capabilities for their APM suite.
- Cloudcraft - a cloud infrastructure modelling solution which offers real-time visualization of cloud infrastructure, enabling users to build models that detail configurations, cost, and interactions.
Competitors
Observability is a strongly contested market, with competitors including:
- Dynatrace ( DT )
- New Relic ( NEWR )
- Splunk ( SPLK )
- Elastic ( ESTC )
- App Dynamics (acquired by Cisco ( CSCO ))
- Cloud providers (AWS, Azure, GCP)
A range of data points towards Datadog being the clear market leader, with Dynatrace also performing reasonably well. The reason for Datadog’s dominance is less clear though, outside of the fact that customers really like the product. It is certainly a premium product, and success is in spite of cost rather than because of it, although for this type of product direct cost may not be reflective of true TCO.
New Relic was a leader in APM, but appears to be losing ground to Datadog and Dynatrace. New Relic acquired SignifAI in 2019 to add to their ML/AI capabilities, which may have been a perceived weakness. Dynatrace is similar to Datadog and heavily promotes their ML capabilities. They offer "zero-touch configuration", which is an agent that auto-determines services used and automatically configures these for users.
Figure 5: Job Openings Mentioning Dynatrace in the Requirements (source: Revealera.com) Figure 6: Job Openings Mentioning New Relic in the Requirements (source: Revealera.com) Figure 7: "Datadog Pricing" Search Interest (source: Created by author using data from Google Trends) Figure 8: Observability Vendor Revenue (source: Created by author using data from company reports)
Financial Analysis
Datadog’s consumption-based pricing and relatively high cost have caused investor uncertainty about how the company would likely perform through a downturn. During COVID, Datadog did not really observe increased churn , but did see a rationalization of cost management, and the same is likely to occur during any downturn going forward.
Management have suggested that adoption of public clouds may be plateauing somewhat in the near term, as customers look to delay spending. Usage amongst some larger customers has been weaker , particularly in consumer discretionary. This has been most pronounced amongst e-commerce and food and delivery customers and products with a strong volume-based component such as log management and APM . Datadog has not observed any weakness within the SMB segment, which stands in contrast to many SaaS vendors who have observed the opposite. Datadog bills entirely in USD, and as a result, the strong USD was likely an indirect headwind to the international business in Q3. This pressure should ease going forward as the USD has weakened significantly in recent months.
Datadog’s sales pipeline remains strong heading into Q4 for both new logos and new products. Revenue growth is expected to be 37% YoY at the midpoint, which would represent a dramatic slowdown from the previous quarter, although guidance is likely conservative. Outside of macro headwinds, a moderation in growth should have been expected for Datadog after the burst coming out of the pandemic. Sub-50% is likely a more sustainable level of growth for Datadog at this point in time.
Figure 9: Datadog Revenue Growth (source: Created by author using data from Datadog)
Customer acquisition continues to be fairly robust for Datadog and as a result they are still seeing strong growth in new logo ARR, including some large wins in traditional industries. Revenue per customer also continues to increase, driven by Datadog’s relatively high net retention rates.
Figure 10: Datadog Customers (source: Created by author using data from Datadog) Figure 11: Datadog Large Customer Growth (source: Datadog)
The number of job openings mentioning Datadog in the job requirements trended down in 2022 and has begun to fall more sharply in 2023. Similar trends have been observed across competitors, and hence this appears to be more related to softness in the observability market than anything else.
Figure 12: Job Openings Mentioning Datadog in the Requirements (source: Revealera.com)
From a growth and efficiency perspective, Datadog really stands in a class of its own, even relative to best-in-class peers like CrowdStrike ( CRWD ) and Atlassian ( TEAM ). While the SaaS business model is currently out of favor, it should be recognized that prior to its IPO Datadog had only raised 92 million USD capital , net of share repurchases, since inception and still had 64 million USD cash on the balance sheet.
Datadog has a premium product, and in turn high margins for a SaaS company. Datadog’s software is focused on ease of use and as such, professional services are generally not required to support their software. This is supportive of Datadog’s gross margins, which are relatively high for an infrastructure focused company.
They also have a highly efficient go-to-market model, which consists of a self-service tier, a high velocity inside sales team, and an enterprise sales force. The strength of the product has enabled Datadog to grow at extremely high rates without excessive investments in sales and marketing. The company has also never had the general and administrative bloat of many peers, a sign that the company is being run for the benefit of shareholders and not just employees.
Figure 13: Datadog Operating Expenses (source: Created by author using data from company reports)
As Datadog has grown, they have begun to invest large amounts into R&D, which has resulted in new products that are meaningfully adding to the company’s addressable market and growth.
Datadog could probably have high margins over night if they wanted, and this would likely not overly impact growth in the near term. Management appears to recognize the large opportunity ahead of them though, and continues to invest aggressively in capitalizing on this.
Figure 14: Datadog Operating Expenses (source: Created by author using data from Datadog)
Part of the strength of Datadog’s business is their high gross retention rates, which have trended up from the low to mid 90s at the time of IPO to the mid to high 90s now. This increase is likely largely driven by the introduction of new products and the adoption of new modules by customers. As customers begin to standardize on a platform for a range of use cases, the probability that they will churn tends to decrease.
Figure 15: Datadog Product Adoption (source: Datadog)
Datadog’s efficient customer acquisition and low churn rates should lead to high margins in time, assuming the company’s competitive position does not deteriorate. As Datadog continues to grow it would be reasonable to expect acquisition costs to rise and possibly even for churn to increase, which may increase the burden of sales and marketing. It is also difficult to predict how much R&D will be required to maintain a competitive product over the long run.
Figure 16: Datadog LTV/CAC Ratio (source: Created by author using data from company reports)
Job openings suggest that Datadog is cutting back on investments in growth significantly, which should help them to realize operating leverage going forward, but should also raise concerns about the strength of demand.
Figure 17: Datadog Job Openings (source: Revealera.com)
Valuation
Based on its current growth rate, Datadog appears broadly priced in line with peers. The company clearly deserves a premium valuation due to its superior ability to generate free cash flow though. Growth is likely to continue moderating, but this could be offset by increased investor appetite for risk assets. Longer term, Datadog’s success will be determined by their ability to expand outside of the observability space, which will increasingly bring them into contact with stronger competitors.
Figure 18: Datadog Relative Valuation (source: Created by author using data from Seeking Alpha)
For further details see:
Datadog: Best-In-Class Efficiency