Dynatrace

Dynatrace delivered a strong fiscal first quarter, with revenue up 16% and organic annual recurring revenue (ARR) growth accelerating to 41%. Record ARR from new customers, rising AI consumption, and rapid log-management growth suggest stronger observability demand is translating into results.

Why it matters: AI is increasing the volume and complexity of telemetry that enterprises must monitor, supporting observability demand that consumption and customer trends suggest Dynatrace is capturing. However, we continue to believe Datadog is structurally better positioned to capitalize on these tailwinds. AI customers grow consumption at 1.5 times non-AI peers. Log management nearly doubled to about $200 million annualized in two quarters, and more than 1,000 customers now observe AI/LLM workloads in production, up from about 850 last quarter, a healthy demand backdrop. New-customer ARR grew more than 160% year over year with average-size reaching about $285,000. Net retention remains at 110%. Heavier second-half renewals should support expansion, implying the full benefit of stronger demand has yet to reach reported growth.

The bottom line: We maintain our $42 fair value estimate and narrow moat rating for Dynatrace. Shares appear slightly overvalued. The quarter increases our confidence that Dynatrace can participate in accelerating observability demand, but we aren't ready to conclude its competitive position has structurally improved. Organic net new ARR grew 41%, but trailing 12-month growth was 17%, highlighting quarterly volatility from large enterprise deals. We need sustained acceleration before concluding Dynatrace is closing its historical growth gap with Datadog.

Our $42 fair value estimate implies an enterprise value of 4.7 times our fiscal-year 2027 sales estimate.

The main drivers of Dynatrace’s revenue are enterprise cloud migration, the adoption and development of applications, increasing complexity of technological infrastructures, growth in unstructured data, enterprise demand for efficiency-enhancing software, and Dynatrace’s ability to penetrate these markets while turning customers on to its highly automated observability solution.

To develop our revenue forecasts, we consider Dynatrace’s penetration of the observability and adjacent security market based on the attractiveness of its core offerings, and analyze the growth trajectory of data generation and the role that telemetry will play within an influx of artificial intelligence workflows. This yields an average annual revenue growth rate of approximately 15% and 13% over three and five years, respectively, then decreases to 7% by fiscal 2036.

Our sizing estimate for the Dynatrace-specific observability and security market, which focuses on businesses with annual revenues of $1 billion or more, is approximately $70 billion in 2026. We expect a compounded annual growth rate of 7% to reach $80 billion by 2028. We anticipate that as observability workflows mature, this growth rate will slow and eventually reach a $120 billion market by fiscal 2035.

In 2020, the IT operations market was about $35 billion, and Dynatrace had a 2% market share. However, due to a strong product offering that focuses on automation and the rising adoption of cloud workflows, Dynatrace has grown faster than the overall market. We currently estimate its penetration at roughly 3%, and we believe that switching costs will retain customers while its predictive, causal, and generative AI observability offering will capture more market share, ultimately reaching 5% penetration of the $120 billion market by fiscal 2035.

Salaries are the biggest component that makes up each of the cost of revenue, R&D, and sales and marketing, Dynatrace's largest expenses. The cost of revenue also includes third-party cloud hosting costs. Cost of revenue has decreased from 25% of revenue in 2019 to 18% in fiscal-year 2026, and we project it will decrease to 15% by 2036, thanks to better volume discounts from hyperscale cloud providers and internal efforts to optimize data compression, which should reduce computing needs. This amounts to gross margins of 85% by fiscal 2036. Research and development as a percentage of revenue has ticked up from 17% in 2022 to 23% in 2026, but remains well below Datadog’s mid-40% range since 2022. Although we expect R&D spending in dollar terms to increase, the fixed-salary component should scale well as the company adds more customers, leading us to estimate R&D at 21% of revenue by fiscal year 2036.

We assign Dynatrace a Morningstar economic moat rating of narrow, primarily due to switching costs.

Both Dynatrace and its main observability competitor, Datadog, are typical asset-light software firms. As a result, both companies have generated high returns on invested capital.

Strong switching costs tied to their core software products are behind the excess returns. With the rise of cloud-based applications and the vast amounts of data these applications generate, switching observability platforms is a formidable challenge. Whether in-house or third-party, businesses run on applications. If anything goes wrong and companies lack visibility into what’s happening, businesses can lose millions of dollars in seconds or minutes. Switching costs stem from significant time investments to deploy these solutions. Switching to a new platform can be disruptive and expensive because customers need to reconfigure, relearn, and redeploy various observability solutions. Switching costs also stem from high customer risk aversion, the high cost of failure, and the mission-critical nature of the systems supported by observability platforms.

Large e-commerce firms provide a good example of the switching costs. Managing hundreds of thousands of sales on their websites, these firms must track how a customer moves from browsing to checkout, then to payment processing. Each stage generates unique telemetry (logs, metrics, traces) that must be analyzed, correlated across various services, and displayed clearly so users can see four signals: latency (how long requests take), traffic (system load), errors (failure rate), and saturation (resource limits). If any of these signals fail and go unaddressed, errors in customer requests can accumulate, risking unhappy customers and lost sales. This mission-critical aspect of monitoring, and its link to revenue, is what observability platforms address, which in turn increases the cost of switching from a proven vendor.

Switching to a new platform can be disruptive and expensive because customers need to reconfigure, relearn, and redeploy various observability solutions. Lightweight "agents"—software processes that run throughout the company's tech infrastructure—must be reintegrated with individual components of the tech stack to handle telemetry collection. Dynatrace’s OneAgent reduces some customer onboarding friction by automatically detecting applications in a customer’s tech stack. However, engineers still spend months setting up services with Dynatrace agents, customizing dashboards, and adjusting alert rules. Even if the initial setup is quick, customizing and fine-tuning the observability platform still locks in resources after significant engineering hours are invested. In a hypothetical switch, a customer would likely need to run parallel observability systems for months to ensure full-stack visibility isn’t degraded. Effectively, there is a time cost, risk, and financial burden with switching.

AWS, Microsoft Azure, and Google Cloud are significant threats in the software world. They could cut out the need for specialized software and take over the market. However, we believe observability buyers naturally gravitate toward neutral, unbiased platforms that operate seamlessly across clouds and markets. The Big Tech firms tend to act as walled gardens that optimize for their own ecosystems and aim to lock customers in. AWS, Microsoft, and Google all offer some version of an observability tool. Still, these are designed around the parent company’s taxonomy (its naming and language conventions) and topology, or dependency map. Using AWS’ observability tool to ingest, query, or display telemetry from an application running on Microsoft Azure would run into a complex mapping of filters, alert adjustments, and data-indexing rules to make sense of the telemetry in one cloud and display it in another. The large firms are unlikely to dedicate resources to fixing this cross-cloud problem because they make more money by keeping customers locked in to their environments. Firms like Datadog and Dynatrace build unified, cloud-agnostic observability platforms that effectively handle complex technology infrastructures.

Increasingly complex technological infrastructures, cloud migration, and more applications increase demand for observability platforms like Dynatrace’s that make sense of telemetry data.

Dynatrace’s causal, predictive, and generative solutions to observability reduce onboarding friction, are hands-off compared with competitors' solutions, and increase applicability to nontechnical stakeholders.

Dynatrace’s Grail database uses unique techniques to keep query, storage, and computing costs down, allowing the company to maintain margins while delivering a differentiated product.

Dynatrace has historically focused on customers with more than $1 billion in annual revenue, which puts it at a disadvantage in terms of market penetration and product breadth compared with Datadog, due to a less effective learning feedback loop.

Datadog is moving upstream and encroaching on Dynatrace’s core end market. This could lead to increased investment in sales and marketing just to defend share.

Dynatrace is priced as a premium product. If the economy stumbles, information technology officers could switch to cheaper, bundled alternatives from hyperscalers.