All right. Top of the hour. Welcome everyone. Kicking off day one of the KeyBank Technology Leadership Forum. Very pleased to have Rick. I do own those same socks as you, and I should have worn them, but I didn't. Shame on me again. We'll send you another pair. Yeah. All right. Well, Rick, thank you so much for being here. Noel, you as well. Enjoying Park City, Deer Valley here. I wanted to talk about earnings, and it was a great earnings to lead into this week. I wanted to talk on the technology side a little bit, and I want to understand better how the observability market is changing as a result of AI. We used to have deterministic applications, and it was very predictable in the way these applications change, but the application ecosystem's changing in terms of the UI. We have headless applications, non-deterministic applications, and the way they behave. Big picture question, how is this altering the observability market, the ecosystem? How is this changing the way Dynatrace operates and the strategy that you embark on? Great place to start, Eric, and thanks for having us. The observability market we see is going through a new era, an evolution- Yeah. -that is occurring in a major way right now. I think what seems to be clear, six months ago, everybody's worrying about SaaS-pocalypse, that it was going to impact every software company in a consistent way. I think over the last six months, it's proven itself out that you have to get more granular than that. You have to understand what areas and sectors of software are actually AI winners versus AI losers. We would submit that observability is clearly an AI winner from a market space point of view. The reason is because AI workloads require more observability, not less. You have to oversee these workloads in a fundamental way, and in so doing, observability expertise is more required. What is evolving was a market that has existed for a couple of decades in observability oriented around what I would think of as business resilience. You need to make sure that your software is always running, that it is always optimized, that it is effectively delivering against its expected requirements. In an AI observability world, it's evolving to answer a couple of incremental questions. If the first question around business resilience is it running or is it working? Then in an AI observability land, is it right? Or is it accurate? Is the information that an AI workload is going to an LLM to extract actually the right information that would be given to an end user of that particular customer? AI observability is extending the requirements of observability overall. Then in a world that extends even further, what's fascinating is that we foresee a world happening in the very near future where humans just aren't writing that much code. That agents are writing code. As another use case, you elements than we've seen before. Yeah. Now I've had the view, and I'm going to lead the witness here on this one, but look, the nature of these AI applications, the non-determinism, the reasoning that goes behind it, to me, I understand that to mean that the value happens more up the stack, which is where APM is. Because infrastructure doesn't really give you the insights, and logs doesn't really give you the insights to understand the activity happening at the application layer, the reasoning, et cetera. My way of saying, does this put more emphasis, more onus on the APM side of the stack to understand that logic that the AI is undertaking? I would clearly say you look at AI observability areas like LLM eval. LLM experimentation. This is an area where traces become particularly critical, and traces are the bread and butter of APM. Right. If you will. This is why we became known as Dynatrace. It was because it was really all about APM, and it was about dynamic tracing. This is the fundamental building block of Dynatrace was in this area. I would agree with that, but I would supplement it in the following way. We believe strongly that in order to do all of this new stuff, if you will, that is happening in AI observability, you must have a foundation that is increasingly oriented to end-to-end observability. No longer is it the case really in our view, that you can have customers of any size or substance running APM with vendor one and infrastructure management or monitoring with vendor two, and real user monitoring with vendor three, and security or app security with vendor four, and so on down the line. Log management with vendor five. The reason is because you need to have the best data, and you talked about determinism, or we think of it as deterministic AI. What you really want is you really want to have confidence in your answers. The way to have confidence in your answers is by having the collection of all these domains using all data types, traces, metrics, logs, real user data, et cetera, all in one existing data lakehouse. The ability of having all of those data types in one place, along with all domains managed by the same platform, enables you to deliver better deterministic answers. Yeah. Those answers then become more actionable in an AI world where you then want agents to be able to take action on those insights. The result of it is, think of it this way, you start with end-to-end observability. That's the foundation. You bring together all of these multi-vendor solutions into a common and integrated platform. That enables you to have the insights and answers you need to action those through a series of agents. Those agents can then ultimately lead to what we think of as autonomous operations. That's really, if you will, the holy grail. That's what customers want. They want the ability to manage their workflows in an autonomous way. So when an incident happens, Dynatrace or the Dynatrace platform can see what happened, immediately analyze it, know what the triage plan is, and then execute that triage plan through a series of agents. So it becomes holistic- Yeah. -from end to end. That's really where the next generation of value is in observability solutions. Right. I want to come back to that topic, but I want to recap some of the stuff from last week with earnings. Candidly, I think you guys acknowledged this, that when you gave your initial FY 2027 guidance, I think there was a little bit of, I don't know, questioning of like, "Hey, that's a little bit of an optimistic guidance that you guys laid out. Yeah. Yep. You guys had conviction, and you guys have been talking about the acceleration thesis for quite some time. To see 1Q come to fruition and meaningful inflection in the net new ARR growth and acceleration. Talk about how the thesis is playing out to drive towards acceleration of overall top line, where the strength came from in the quarter. Yeah. First of all, 41% net new ARR organic- Yeah. -growth was a phenomenal achievement. It was an excellent quarter, really across the board. We were delighted with it. We were above the high end of the guide across all metrics. It was a very strong performance, and we've been operating sort of in the mid-teens of net new ARR growth to see net new ARR growth move up to 40% or north. We don't want to establish the expectation that we're going to be delivering that on a quarter-to-quarter basis, but it was a really strong start. Yeah. It did, in our view, de-risk the beginning of year guide, which we were providing indication of re-acceleration, essentially, of ARR growth for the year. FY 2026 was about stabilizing ARR growth, FY 2027 about re-accelerating that ARR growth. I think coming out of our beginning of year guide, some of the skepticism was, well, can you achieve that? Yeah. Q1 really did, I think, hit that quite hard on our ability to have conviction in that ARR re-acceleration plan. Right. That is that. I would say, maybe, Eric, three core themes to the earnings call and how we were able to achieve it. One core theme was AI. We have talked a lot about that, so I will not belabor it unless you want to come back to it. But AI workloads are growing, AI production of code is growing, AI, AI. It is a real thing for observability because it is requiring more observability, so it really is a direct tie toward that. The second was logs. Logs are one of the data types, of course, that we bring in. Log management, one of the domains. We, some time ago, had set expectations for delivering, during FY 2026, $100 million in logs consumption. We did that only a couple of quarters ago, and we have just seen an explosion in our logs business to the point where we now have eclipsed $200 million in logs consumption within two quarters. Logs consumption basically doubled in two quarters. This, I think, really gives the ammunition for us to feel very strong positive about the evolution of that business. Then third and finally was really a very strong new logo quarter for us with our new logo ARR up a record 160% plus year-over-year. Yeah. Which is just, again, a phenomenal achievement in terms of the ability to really show conviction of new customers coming onto the platform and what they want to do with Dynatrace. Right. So taking in reverse order, new logos, what's causing that inflection? Is this something that's been building for some time and now it's just starting to come to fruition? What's causing this inflection? What's the durability of this new logo contribution we're seeing? We certainly wouldn't set expectations that we're going to be able to deliver 160% year-over-year ARR growth in new logos. But it really is tantamount to all of the things that we've been discussing which is that, I think organizations in our target set, which tends to be global 15,000 and really even more targeted to, say, global 2,000. Yeah. These kinds of customers realize that they can't have five, six, 10. I met with a customer just last week who said they had 16 different observability solutions. I think that there is a very strong realization that that outcome is not going to work in a modern AI-oriented world. Because remember, and just to put the depiction in your minds of what it used to look like. You used to go in a network operations center, you have a sea of people staring at a sea of glass, and they're looking at alerts that are red, yellow, green, and they're trying to parse dashboards. That environment is impossible in a world where you see the degree of acceleration and the rate and pace of change in our environment, with more applications, more infrastructure, more capability requirements than ever before. You can't do it the old-fashioned way. You've got to find ways to automate that process and an end-to-end observability platform overseeing that, providing recommendations. Whether you are comfortable allowing agents to take action on that on their own, or you want to put a human intervention or validation step in the middle before those agents take action. Either way, you need to streamline that process. This is what organizations are realizing, and I think that we're seeing just a tidal wave of a shift of multi-vendor observability solutions to maybe not just one, maybe not just Dynatrace. Maybe you need to do some specialty things in certain areas. But a vastly reduced number of observability solutions, and that's what- Yeah. -I think is really driving the new logo movement. Yeah. People are doing more on the initial contract. They're taking more of the platform, they're consolidating more solutions upfront, which is something to be said, right? Because it's risk and there's challenges as you embark with a new vendor to go all in to adopt a broader platform. But you're seeing that activity play out and-- Yeah. Another, not to get into too much detail about it, is our pricing mechanism with our Dynatrace Platform Subscription. Yeah. DPS is another part of that. We used to price some number of years ago based on SKUs. You had to buy a certain amount of APM and a certain amount of infrastructure monitoring, a certain amount of log management, et cetera. That is just not how our customers want to consume the platform at this point. Yeah. They want to consume the platform holistically. Maybe they need more log management or less month by month. Maybe they need more or less APM month by month or full stack or whatever it might be. The contracts that we enter into now based on Dynatrace Platform Subscription are, nope, you just make a commit, $1 million or $10 million. Right. Whatever your commit might be, then you can draw it down based on the consumption of the platform as you see fit. For example, if you're an e-commerce customer, you're presumably going to be consuming way more of the Dynatrace Platform for observability during November and December than you probably are during February and March. Yeah. It enables to smooth out your usage of the platform and how you're using it, where you're using it. DPS is now moved to 75% of our ARR. More than two-thirds of our customers. Yeah. This does also provide a catalyst to new customers coming on board saying, "Hey, look, here's my overall commit. I'm going to draw it down over the course of time as I see fit and as I most need it, and that's going to enable me to really deploy and use end-to-end observability in a notable way. Yeah. Now, logs. You've expressed a lot of confidence in the logs opportunity. You guys talked about doubling this year before this quarter. So you've expressed that you have a lot of confidence, and it sounds like you have a lot of visibility. So can you just share a little bit more of why you have this confidence, this visibility? It seems like when I was at the Dynatrace conference earlier this year, talking to a lot of customers, that they have logs, but they were early in their journey, and they have a lot of pipeline of incremental workloads that they need to onboard and get more users onto the platform looking at logs. So just what does the journey look like with a customer? Is there a long runway when customers adopt logs and migrate more workloads and whatever else? Just what's driving the confidence there? There are two enormous value propositions to using Dynatrace for logs. Yeah. Logs are a very well-established market. Everybody in our target customer set has been doing logs and doing logs for years with incumbent vendors that are sizable. This is mostly brownfield, not greenfield- Yeah. -type deployment. What they are looking at is, geez, do I shift my logs to Dynatrace? Not do I bring up new workloads on Dynatrace with logs. Although there is some of that as well. In the AI workload space, then those might be net new workloads, but there are a lot of workloads that have existing log usage. There are two primary drivers of using Dynatrace for logs relative to that environment. One is cost. We can radically reduce your cost relative to what is on the market. I cannot tell you the number of customers I have spoken to at CTO, CIO level that have come back and say, "My God, Rick, my log cost is meteoric." I have actually heard that word before. "Log cost is meteoric." It is because of the volume of logs in addition to the overall cost. There are multiple ways where we can help solve it. Number one, we can give you a lower price on logs because of the efficiency of our platform. But the second piece is that it turns out that with our Bindplane acquisition, we can now filter those logs on an inbound basis so that you do not have to ingest them all into the platform. Which if you ingest and store those logs, has higher cost. By enabling a better filtering system of those logs, then that can reduce that further. Finally, we would submit that you do not need all the logs if you have traces, metrics, real user data, and other data because the combination of those datasets actually gives you a much better depiction of what is happening in your environment than if you just basically drive logs like crazy, and that's the only mechanism you're using to do observability. For those reasons, we can actually reduce cost. The second piece sort of segues from my last point, which is that incorporating logs into end-to-end observability gives you better analysis, better insights, and better analytics. The outcome you're getting from observability is better if you have logs as a component of it. Since I've been at Dynatrace, gosh, now almost five years, and it never made any sense to me to have logs over here with one vendor and traces, metrics, real user data, all of the other observability data types over here. That made no sense. Because you get better insights and better outcomes if all of those are embedded in an end-to-end observability solution. Bringing all that back to the simple answer, the simple answer is I can do it at lower cost, often substantially lower cost than what you are paying for today. I can deliver you better outcomes in a holistic end-to-end observability environment that then, as I said earlier, establishes the foundation for agentic AI and agentic solutions driving autonomous operations to come. Yeah. And then lastly, on AI, to come back to that, maybe we talked about technologically why this is important, how the world is changing, but tie it to, well, one, just what are you observing within your own customer base in terms of AI maturity? Then secondarily, kind of the financial contributions that you are starting to see. We talked about on the call 1.5 times. Customers that are using AI or monitoring AI are consuming 1.5 times those that are not. So what is the maturity of the customer base? Then secondarily, why are we seeing an inflection or much higher growth with those that are adopting Dynatrace for AI? Well, in terms of AI maturity and AI deployment in production, we still think we are in the early innings. Yeah. It really, in many ways, has only just begun. Having said that, we did provide some metrics on the call. For example, we are now observing AI workloads for more than 1,000 customers. That is pretty sizable. We are taking agentic action, so we have actually deployed Dynatrace agents to take action to do things like auto remediation and triage in over 800 customers. Yeah. Then lastly, you mentioned the 1.5 times. We are seeing, and we can take cohorts and look at AI cohorts, non-AI workload cohorts, and we see 50% higher consumption of the platform of the AI cohorts relative to the non-AI cohorts. In part, this is simply because AI workloads are pretty chatty. They generate tons of telemetry. Yeah. Managing that telemetry and then ingesting that and then taking that into a consumption model that is then doing a DPS drawdown, a Dynatrace Platform Subscription drawdown is something that we are seeing. Right. It turns out all of those lead to various monetization levers for us in the AI space that are incremental to the normal monetization levers we see. First of all, it drives more consumption. It is great. Second of all, it drives AI observability, which are new categories or new capability of answering that question I mentioned earlier, which is it right? Or is it accurate? Thirdly, anytime you use a Dynatrace agent to take action, we will pass through some of the charge of that, because we have real costs there, too. Right. Multiple different ways to monetize AI workloads incrementally. DPS, this has been something that's been compelling for this year, coming year, as we have three cohorts of customers up for renewal. Can you just talk about maybe, look, it's the smallest quarter of the year, but it's the first quarter of the year, and what activity are you seeing in terms of the FY 2024, FY 2025 cohort of DPS customers coming for renewal? How should we think about that opportunity for the balance of the year? I think the primary way to take away, and just to make sure that you're all tracking, that since we put DPS in place three years ago, and most of the DPS contracts are three-year contracts, even though they have annual resets, what happens is that you're now ending the end of that three-year period for many customers, and those customers are having to renew. The question is, are they renewing based on their consumption of the platform, flat, up, down? Yep. How would that play out? The good news is, we talk about consumption, we mentioned on last week's call that consumption continues to grow north of 20%. The result of that, when you have 17% ARR growth, as we positioned, or as we indicated last week based on the Q1 results inclusive of our Bindplane acquisition, your consumption of the platform is north of 20%, over the course of time, you would expect ARR growth to migrate toward your consumption growth number. Yep. The question is, how does that happen? Is this DPS renewal mechanism that begins to kick in is one of the ways in which that could happen, because you need to increase your contract commit in order to manage the consumption growth that you're seeing. Right. Now, Q1, Q2, probably not too much impact there because those are really the first couple of quarters of DPS. Yeah. It would be a modest number. But as we get into the second half, the expectation is that those numbers increase, and the result of that should be that we would begin to see a positive move in NRR. Yep. That the expansion business or expansion metrics would begin to tick up as well. Pretty sticky metrics, so it's not like 110 just goes to 115 or something like that. But we would expect to begin to see some accretion of the NRR metric as we go through the year based on these DPS cohorts becoming larger. Right. So it's just a lag effect between the consumption activity versus the renewals and the NRR- Yep. -kind of, right. Yeah. You could put a DPS contract in today, and you could overconsume your contract in the first three months, and you wouldn't have to do an upgrade of that at that point if you didn't want to. You could just pay- Yeah. -us based on consumption. Yeah. Maybe last minute, the other really compelling, interesting part about the observability stack is the ability to automate it through the AI for observability. Yeah. Dynatrace Intelligence. What has been the traction at this point? How has the customer feedback been in terms of the value that they are getting from this, and ultimately, how does this benefit the model from an uplift perspective and monetization strategy? Yeah. I mentioned a few different ways we can monetize AI workloads incrementally, so I will not go back through those. I would say that it would come as no surprise to anybody, certainly in this room or this virtual room, that every organization we would sell to has AI very much top of mind. They are driving AI workloads. They are driving agents to create code. They are working to accelerate application delivery and beyond. They are realizing rapidly that you simply have to have an observability solution that can oversee those workloads. Yeah. We certainly expect that that does drive material momentum in our business and the business opportunity that we have with these customers, and we see it through the early innings of the monetization layers. And some of those metrics we talked about, the number of customers we have observing AI workloads, the number of agents that are being used across the customer base, accelerated consumption. Yeah. So those are sort of the key leading indicators. DPS is the vehicle to achieve it, and then these incremental monetization models that are unique to AI workloads become additive in terms of the capabilities. And candidly, Eric, we haven't really built those incremental monetization layers into even the guide. Yeah. So we see those as, we don't want to get ahead of ourselves in- Yeah. -changing guides, but we simply don't know how rapidly that gets monetized. Yeah. We've taken a conservative approach to that, but we do view that the AI areas here become critical, and AI observability is absolutely a $10 billion incremental sector. Category to the $80 billion or so overall observability space is going to be phenomenal, and it's growing at 40%-50%. Yeah. This is going to be a new area for us to monetize that is mission-critical to the evolution of the observability market, which is where you started. Yeah. Super exciting. I love all the opportunities and tailwinds in this space, and observability is top of mind for sure. Awesome. Thank you, Rick. Yeah. Thanks, Eric. Appreciate it. Thank you, all.