Dynatrace, Inc.

Greetings and welcome to the Dynatrace first quarter fiscal 2027 earnings conference call. At this time, all participants are in a listen-only mode. A question and answer session will follow the formal presentation. If anyone should require operator assistance during the conference, please press star zero on your telephone keypad. Please note this conference is being recorded. I will now turn the conference over to Noelle Faris, VP of Investor Relations. Thank you. You may begin.

Good morning, and thank you for joining Dynatrace's first quarter fiscal 2027 earnings conference call. Joining me today are Rick McConnell, Chief Executive Officer, and Jim Benson, Chief Financial Officer. Before we get started, please note that today's comments include forward-looking statements, such as statements regarding revenue, earnings guidance, and economic conditions. Actual results may differ materially from our expectations due to a number of risks and uncertainties discussed in Dynatrace's SEC filings, including our most recent annual report on Form 10-K and subsequent quarterly reports on Form 10-Q. The forward-looking statements contained in this call represent the company's views on August 5, 2026. We assume no obligation to update these statements as a result of new information, future events, or circumstances. Unless otherwise noted, the growth rates we discussed today are year-over-year and non-GAAP, reflecting constant currency growth, and per share amounts are on a diluted basis. We will also discuss other non-GAAP financial measures on today's call. To see reconciliations between non-GAAP and GAAP measures, please refer to today's earnings press release and supplemental presentations, which are both posted in the financial results section of our IR website. And with that, let me turn the call over to our Chief Executive Officer, Rick McConnell.

Thanks, Noelle, and good morning, everyone. Thank you for joining us today. On our last earnings call in May, we expressed confidence that the growth drivers we put in place would drive a year of ARR acceleration in fiscal 2027. The strength we saw across the business in Q1 reinforces our conviction and ability to deliver this outcome. Here are a few of the noteworthy highlights from the quarter. Total ARR grew 17%. That new ARR was $85 million, growing 66% and 41% organically. We achieved record new logo growth of more than 160%. Both total and subscription revenue exceeded the high end of our guidance. and we delivered a non-GAAP operating margin of 29%, reflecting the disciplined investment approach you've come to expect from us. Q1 strength reflected healthy enterprise demand for end-to-end observability, stronger execution, and growing complexity across customer environments. We are seeing AI contribute in three ways, which I will expand upon shortly. Increasing consumption across our platform, creating demand for new AI observability capabilities and directly monetizing agent usage. This Q1 performance reflects both the significant market opportunity and our strong execution to begin the fiscal year. This morning, I'd like to discuss the observability market, why we believe Dynatrace is built for an AI-first world, and how we expect to drive incremental AI monetization. The observability market has entered a new era. Software that once took months to build now ships in days. AI agents are taking autonomous action across infrastructure. And enterprise customers are now deploying AI rapidly, not because every risk has been resolved, but because standing still means falling behind. In this environment, unified observability matters more than ever. Systems are more interconnected, more autonomous, and more difficult to manage manually than ever before. The enterprises winning in this environment are the ones that can keep complex, fast-moving systems working reliably and quickly understand when they are not. Additionally, AI workloads do not simply add volume. They behave differently. They can operate perfectly and still produce incorrect results. That's a problem observability has never had to solve before, and addressing it represents a significant emerging opportunity. We estimate the AI observability total addressable market will exceed $10 billion by 2030, growing at more than 50% annually. We see AI observability as the next logical evolution of the broader observability market, and that evolution is already underway. What this means in practice is that observability in the age of AI has to answer far more questions than ever before. and while the majority of enterprises are still in early phases of their AI journey, the requirements are evolving quickly. Let me walk through three of the questions that matter most today in an AI-first world. The first, is it working? Are applications, infrastructure and systems working as intended? This question is about business resilience and is the same question we ask of traditional workloads. Second is new, is it accurate? More specifically, is the AI model delivering output that can be trusted and relied upon with confidence? Answering this means evaluating AI systems for accuracy and intended behavior. Determining whether an AI system behaves as intended before it shifts is emerging as one of the most important aspects of observability. The third, are myogenic systems delivering the outcomes they were built for? Enterprises are deploying agents to build software at a pace that wasn't possible before. The advantage goes to those who can accelerate the full lifecycle and trust the results. Code that's built well, ships safely, and runs reliably. The last question is where our newest offering, Blue Box, comes in. Built for AI-first teams, Blue Box helps development teams and their coding agents bring software into production in a way that customers can trust. It closes the loop between building and running. It gives coding agents live context from running systems before a change is released. And once that change is live, its agentic SRE capability finds root cause and returns an evidence-backed fix, with the developer in control across the entire AI delivery lifecycle. This is the moment for which Dynatrace was built. With AI agents increasingly acting alongside humans across development and operations, both need a common source of trusted context. Dynatrace provides that through GRAIL and SmartScape, giving agents and teams a unified understanding of system relationships and behavior. Dynatrace Intelligence turns that understanding into action, combining deterministic and agentic AI to deliver the precise causal insight That lets both people and agents act with confidence. These core differentiators give customers one operating foundation across both human and autonomous workflows. Our platform has a distinct advantage with this depth of insight. And as agents become a larger part of enterprise operations, that distinction becomes even more important. Additionally, we are purposefully building for an open, interoperable ecosystem. Our newly acquired bind plane supports the open standard for open telemetry data collection. Devcycle, acquired earlier this year, supports the open standard for feature flags. These acquisitions aren't coincidental. They reflect a deliberate commitment to open standards and interoperability. Customers are not locked into proprietary pipelines. Our platform is built to work alongside the tools enterprises already use. including partners such as ServiceNow and to operate natively in MCP environments as the AI ecosystem evolves. We believe openness is a competitive advantage. It is one of the reasons enterprises trust Dynatrace as the intelligent foundation for AI-powered businesses, both powered by AI and built for AI. Our unified architecture becomes more valuable as AI increases complexity. And that growing value is reflected in higher consumption, broader platform adoption, and the following three new monetization opportunities. First, AI workloads are similar to core observability workloads in that they leverage the same types of data such as logs, traces, and metrics. But AI workloads generate dramatically more telemetry than the systems that came before them. This is one of the reasons why log management remains our fastest-growing product category, with consumption nearly doubling since surpassing the $100 million milestone just two quarters ago. Sideplane facilitates easier data ingestion, and it is already performing ahead of plans. Second, as I mentioned earlier, AI observability is an incremental monetization driver. It increases consumption of the platform as it validates whether the AI workloads are producing accurate results, behaving as intended, and operating safely and efficiently. This is the newest capability of the platform, and adoption is expanding quickly. And third, beyond AI workloads and the data they generate, we monetize our own AI and agents. Every time a customer uses Dynatrace Intelligence to get answers through AI function calls or MCP integrations, or when one of our agents, like the SRE or assist agent, takes autonomous action to resolve an issue, it drives DPS usage. As agents increasingly become consumers of observability, this represents a growing opportunity that didn't exist two years ago. Today, more than 1,000 customers use Dynatrace to observe AI and LLM workloads in production, up from roughly 850 last quarter. And more than 800 are running operations autonomously with Dynatrace's agentic capabilities, up from roughly 500 last quarter. Additionally, consumption growth for customers in these AI cohorts is 1.5 times higher than that of non-AI cohort customers. Our platform integrates natively with Cloud Code, ServiceNow, GitHub Copilot, Atlassian, and the major hyperscalers, AWS, Azure, and GCP, enabling autonomous action across development and operations at scale. Here are several examples of how customers are leveraging Dynatrace to advance their AI strategies and observability initiatives. In Q1, we signed a seven-figure ACV expansion deal, more than doubling ACV with a top global financial institution. This customer is using Dynatrace to validate model consumption, control cost, and maintain full data lineage from prompt to response, helping it deploy AI with greater confidence while reducing compliance and audit risk. We secured a six-figure ACV expansion, also nearly doubling ACV, with a leading recreational vehicle retailer. This customer used Dynatrace as their operational system of record while building a custom CRM application through AI-assisted development, generating approximately seven figures of savings and expanding usage of our platform. A leading digital insurance provider used Dynatrace AI observability to reduce onboarding time from days to minutes and identified an outdated model version that was driving unnecessary token consumption and costs. And finally, we secured an eight-figure ACB new logo win with one of Latin America's largest financial institutions. In a highly competitive sales process, the customer selected Dynatrace to consolidate a fragmented multi-vendor observability stack across a complex environment, supporting mission-critical, citizen-facing services. Our differentiation continues to be recognized by independent analysts. Gardner named Dynatrace a leader in the Gardner Magic Quadrant for observability platforms for the 16th consecutive year. Gartner described SmartScape and Dynatrace Intelligence as the gold standard for real-time, high-fidelity dependence mapping to automate root cause with Dynatrace and third-party agents.