Right. Still got it. All right, guys. So, Ryan Macs, small and big cap software analyst here at Wells Fargo, here for the 9th Annual Wells Fargo TMT Conference. The joke that I've been making—this is my, third fireside on this stage—is we should open the doors up so me and you just get a view of the ocean. A view much better. Yeah, let's, let's do that. That sounds good. Although it was pouring down rain earlier, but now it's nice out. That's what people keep saying. I mean, people are so used to perfection of how great it is here. This is my first year here, so I'm just, like, taking it all in. I'm blown away, but, Pretty, pretty nice. You need to do the, you need to do the bluff path along the water. Yeah. People have been telling me the hike and everything, so. Yeah. I'm coming here for the weekend before next time. Really? That seems like the pro move. I think that sounds great. Now everybody's gonna leave the room and go for a real pro hike. I mean, look. Can't leave until we're done. Or maybe one year we can just do a hike fireside. We can too. Yeah. A walk and talk. Look, guys, we won't be taking questions directly from the room, but if you have them, you can email me at ryan.macwilliams@wellsfargo.com. We get them in. Here from Dynatrace today is Rick McConnell. Rick, thanks for being here. Just to start, you know, we'd love to kinda hear about your year so far. You know, a lot of change with AI, a lot of change with, you know, some of the go-to-market changes, and some of, like, the, you know, ODC and DPS. Overall, we'd just love to hear kinda at a high level, like, maybe what surprised you, what are you pleasantly surprised with, for the year so far? First of all, very, very strong first half of the year. I think the core messages from our Q2 earnings report that we just completed a couple of weeks ago were, number one, really strong first half. Number two, raised guidance for the second half. Number three, largely de-risked that second half. You know, we felt good about those core messages. In terms of the core drivers of the business, we like what we see going in. Log management is a key emerging business for us, now very close to $100 million in overall consumption and growing at north of 100% per year. Yep. When Logs was growing north of 100% per year, had $20 million on a $2 billion business, it has limited impact. $100 million consumption business growing 100%, much, much greater impact as we expect going into the future and a huge beachhead for us as we look ahead. The overall strategic pipeline, 45% year over year, very strong. We feel good about the pipeline at the largest end of the segment, which is where we are really seeing the end-to-end observability and the total consolidation. Our pricing method of our Dynatrace platform subscription continues to grow. That is now over 50% of our customers, 70% of our overall ARR. We feel great about that. Consumption growth is super strong, more than 20%, and that we believe to be a leading indicator of future net new ARR. Core growth drivers are in place. The business is strong, very healthy as we look into the second half, and customers are expanding. I suppose the macro overlook comment of all is that observability is becoming more and more critical day by day, especially with expansion to AI workloads. You just, you need observability. It's no longer optional. It's become mandatory. Yeah. A lot to work with there. I think the last part is really interesting. Can you just, you know, tell me in your customer conversations, like, how do you feel like there's a higher priority placed on observability? What does AI have to do with that? How does that change, I guess, versus, like, a year ago or in prior years? You know, many, many different ways, I would say. AI is changing the calculus on observability. The most basic one is that more workloads mean more observability. Yep. You really, especially in our target segment in the global 15,000 and at the high end of the global 15,000, you simply can't manage software workloads manually anymore. The number of resources required to deal with alerts, to manage those alerts, to then triage those alerts is extensive. The result of it is you need to be driving toward a methodology of automated processes to be managing software workloads. This is where Dynatrace in particular comes in with our overall environment, which we can maybe get into, but some of the technologies underlying Dynatrace and the Dynatrace platform really lend themselves well to delivering an AI-powered observability platform that is increasingly driving autonomous operations. I've been privileged to have been in, I don't know, a dozen countries in the last three months meeting with many dozens of CXOs. I would say that not only is observability top of mind, but figuring out how they can leverage AI to automate outcomes to drive lower cost and better success in software are very top of mind. I think that'd be a great place to start on the AI theme is, you know, in terms of the Dynatrace architecture, you've been doing observability at the largest of scales for some of the biggest of customers. As people think about, like, where can they start on their own AI journey with the products that they're coming out with, you know, how are your existing customers, you know, one, looking to utilize you today? And then two, like, what about your technological background makes it a better choice to start with for your customers? Yeah. A partner asked me the other day, and this particular partner was of the mode of, "Geez, you know, we've sold AI into maybe 10% or 15% of our customers, and the actual deployment of AI is maybe 10% or 15% of those. Yes. His question to me was, "What percentage of Dynatrace customers use Dynatrace AI?" My answer is 100%. It is not 100% using agentic AI with autonomous operations where everything is auto-correcting. That is still to come. The foundation of AI, causal AI, predictive AI, generative AI, all analyzing an integrated data lakehouse of content, including logs, traces, metrics, behavioral analytics, business events, etc., that is then being utilized to deliver answers, not just dashboards, not just red, yellow, green alert, but this system broke here and you need to fix it in this way. That is becoming more and more crucial day by day. It is that AI engine of Dynatrace that has not existed for a year and a half or two years or since AI became a thing. Yeah. An exciting new thing. It, we, it's been around for more than a decade. If you then add to that the next phase of AI for observability and Dynatrace, it gets super exciting. We can go there if you wish. Please, please. I mean, you're welcome. The short form, and to keep it as simple as possible, is observability has gone through a number of phases. Yep. We're now approaching phase four. To cover the first three phases simply, I would do it in sort of single adjectives. Phase one was reactive. Something broke, you tried to fix it, or you tried to figure out what was wrong, and then you tried to fix it. Phase two, I would say, was proactive. It was about automated root cause analysis. You wanted to know the second something broke, you wanted to know what was wrong, but you had to manually fix it. Phase three was predictive. That's really where we are today. That is taking all of the AI elements that we had before and applying additional machine learning, anomaly detection to anticipate issues to be able to resolve them before they became user impacting. Phase four, though, is really where we all get super excited at Dynatrace. This is around autonomous operations, as I mentioned. What does that mean? That means integrating this predictive element into an agentic AI ecosystem that is essentially focused on delivering auto-prevention, auto-remediation, and auto-optimization, these three things. In order to do that, to auto-prevent, auto-remediate, auto-optimize, we recognize at Dynatrace that we are a core, a necessary input to that equation because we can tell you precisely what's wrong. We aren't always the solution to fix it. Yep. You may need to provision more storage on AWS. You may need to do a rollback of an application that was posted that had a bug in it, and that may be an operation for GitHub or Jira. You may need a workflow integration, and that would be with ServiceNow. As we move to an autonomous operations environment, what gets exciting is that we provide a core foundational element to what needs to happen, posted into an ecosystem of agents that then can take action through any number of these partners to then resolve the issue. Maybe a final point on it is you say, "You know, you're never gonna be able to resolve 100% of the issues in an autonomous way because you won't trust the answers." That is true, but I've had so many CIOs, so many CXOs tell me, "Rick, if you could resolve 20%, 30%, 40% of my incidents, you would save me, especially in our target segment of the largest companies on the planet, you would save me tens of millions of dollars. Who doesn't want that? I mean, as you go through the phases of observability historically, right, and as you kinda outlined, where you went from, you know, reactive to proactive in terms of, like, how to think about your observability footprint, like, what does that mean for how do you think about, like, which vendors you use or one platform versus best of breed for observability? 'Cause it seems like to me in the dynamic that you outlined on a go-forward basis, right, you almost wanna have, like, one end-to-end platform in order to, like, to do those use cases. Do you think that changes with more focus from AI? This is a layup question that's gonna sound self-serving in answer, but yes. I mean, the short form is we have seen material consolidation in observability. It used to be the case that you would have one vendor for application performance monitoring, APM. You would have another one for log management. You would have another one for real user monitoring. You would have another one for synthetic monitoring, another one for infrastructure, and so on. You know, there's a large airline comes to mind that we closed last year in the summer, and they had one of everything. When they started on the journey with Dynatrace, the principal there even told me, "Gosh, you know, I can't even imagine deploying Dynatrace because I've already got one of everything." You know, how's that gonna solve the problem? Yeah. In the end, they weren't getting the outcomes they wanted. They ended up with an end-to-end observability roadmap that basically was Dynatrace. It eliminated or supplanted all these other tools, deploying Dynatrace as a single integrated AI-powered platform instead. That gave them much better outcomes than they otherwise would. If you have the underlying data, data types of observability, things like logs and traces and metrics and real user data, and they're all in different data stores managed by different vendors, then you are out of necessity managing those on a manual basis to try to derive insights. The more complex the business, the harder it is to drive those insights, and the harder it is to piece things together. God forbid you have multiple incidents at the same time, and then what do you do? And, you know, how do you, how do you cross-correlate this data? It's impossible. With the Dynatrace platform, we have a single integrated data lakehouse, which we call Grail, which houses all of those data types. We oversee and analyze that with a single AI engine called Davis. We evaluate that in the context of a single overall IT ecosystem topology called Smartscape. The result of this is by providing a completely integrated platform, you have a comprehensive integrated picture that you can then act upon. In fact, this is the motion that we see our customers driving. Especially, the larger the customer, the more complex the environment, the more they need a single integrated platform. This is the direction we're heading. If you look at a Gartner Magic Quadrant or a Forrester Wave or others, over the course of years and you trended it, you would see that the point products are falling down into the left and platforms are very much resident in the leader quadrant in the far upper right. It is because, first, that's what customers are actually doing. They're betting with their wallets. Secondly, that's where you get the best outcomes. Yeah. I mean, you always hear in software, like, "Oh, we want one pane of glass." You know, it makes sense in theory and why it would be nice. Yeah. When you have, like, a more reactive use case, right, you're like an archaeologist, then it's okay to go platform to platform or vendor to vendor. If you're trying to make insights and forecasting, right, that seems a little more difficult. We'll come back to the AI piece in a second. Just on the consumption rates that you talked about, you know, around 20% consumption growth rates, that's higher than the current revenue rates. We'd love to hear, you know, how that's trending and what's kinda driving that higher level of consumption. Consumption rates are up, quarter over quarter. Our consumption rates are growing, in part fueled by the log business that we discussed earlier. Those consumption rates are, we believe, precursors of the opportunity for acceleration of growth in ARR and subscription revenue. That is what I wake up every day thinking about, is how do I help the organization as a whole to re-accelerate top line? That is really with a keen focus on expansion of net new ARR. The good news is, we saw that with an acceleration of net new ARR in the first half and in particular in the second quarter, 16% net new ARR growth was very, very favorable, result in the second quarter, 14% for the first half, very strong also. Those, that really is the fuel to re-acceleration of top line. Why consumption is important is it's important to remember that we report revenue on a ratable basis, not a consumption basis. Consumption growth doesn't translate to revenue growth. Sure. Precisely for us, it is with a lag. If the platform is being consumed more and more and more, then ultimately that should lead to a convergence of those rates and that ultimately upgrades and ARR and expansions would catch up because customers can't consume, you know, in, for example, the mid-20s of consumption while they're only growing revenue 15%. I mean, at some point, it, it. Somebody has to get, yeah. It converges. The expectation is that the faster we can drive consumption, the more demand there is for the platform, the greater the opportunity for ARR growth down the road. That's the leading indicator you're probably most focused on, right? Like, how much people are. Yeah. We've got 1,400 people in our services organization who do not think about net new ARR. Those people in our services organization, customer success, services, deployment, business insights, business metrics, these people are focused on consumption. Excellent. As you talk about a quarter over quarter improvement on consumption, what are some of the components driving that? Is it a better macro? Is it your customers doing more, maybe with agentic AI or, you know, what is or, like, maybe they're adding more logs? Like, what are you seeing to drive that higher consumption rate? A combination of things. Number one, just growth of existing workloads. You need, you need more servers, more logs, more whatever. Yep. Second is the inclusion or expansion of new workloads. Those could include core level new workloads, consolidated workloads from other vendors, or AI workloads, net new AI workloads. A final category might be new products. Logs would fit in that category, for example, where we see logs growth at 100% plus growth year over year in terms of consumption, all of a sudden becoming material. That really is driving new opportunity for consumption growth as well. Excellent. I mean, consumption definitely seems like the metric on a leading indicator you should focus on. Investors have been looking at, you know, net new ARR and ODC revenue, and subscription revenue. But, you know, as we think about the most recent quarter, there were some dynamics where, like, you had customers do early renewals, and that's great, and net new ARR improved. but then maybe ODC was a little lighter than. Yeah. Investors were looking at. That's okay. I mean, you're getting, you're encouraging the right incentives for your, you know, larger customers. Can you just talk about some of those early renewal dynamics and what investors should think about there? Yes. It's funny, since the earnings call, we certainly have gotten investors that have come back and that have said, "Wow, you know, ODC was light," which is your on-demand consumption revenue, which is essentially overage billing. Yep. I would say our response to that is, it's sort of twofold. Number one, like meant $1 million to $2 million. Yep. On almost half a billion of quarterly revenue. You know, this is. We're nitpickers here. You know what I mean? That's all we do. We're just rounding here. You always wanna exceed expectations. I, you know, I've got it. I understand that. Yeah. We blew out ARR, net new ARR, relative to even internal projections, let alone guidance. It was simply a quarter in which the cohort of customers that were renewing in that quarter decided that instead of paying overages, they would expand. Every cohort in every quarter is gonna be a little bit different. We cannot predict who's gonna decide to pay overages and who's for a couple of months before going into the next year cycle and who's gonna renew early. Overall, I would say if I had to pick, I'd rather have the ARR than the ODC because ODC is by definition a one-time event and ARR is recurring. An expansion, an early expansion by a customer, let's say one or two years into a three-year contract, is a very, very healthy signal that they are intending to stay with Dynatrace, intending to expand the deployment with Dynatrace, and probably going to be increasing consumption down the road rather than as a one-off with just overage revenue for a month or two. If we had to pick, we'd like to see the ARR conversion versus the ODC. Ultimately, it's the customer's choice as to whether they want to pay a month or two of overage versus expand. We give them that option. What do you think is driving that decision to renew early? Is it just, you know, they've realized, "Okay, we're gonna have a lot more utilization, so let's do it now"? If customers are expecting, and this is why the dynamic toward net new ARR is preferable, let's say, if a customer is expecting and willing to commit to a higher overall contractual commit through a DPS agreement, then they should expand early. The reason is because they're gonna get lower unit price. It certainly is the case the more volume you do, the lower the unit price. Now, the commit will go up. Maybe your original DPS contract was for $1 million a year, and, you know, now you're consuming at a higher rate, you go to $2 million. You're gonna spend more, but you're gonna get more than double the capabilities off of that 2X improvement. You might as well take advantage of the lower price point, increase the commit, make that higher commit, and the result is that you're gonna move to an ARR expansion as opposed to an ODC single payout. As you look at ODC versus net new ARR, that's just like a timing thing in regards to. Just timing, yeah. I mean, ultimately, ODC, as long as, you know, as long as ODC obviously ends up in subscription revenue. To the extent that you continue to expand, that is a sign in and of itself that you have exceeded your commit for that one year. By definition, if you're paying overage in that year, you exceeded the commit for the year. That is a healthy indication any way you look at it. That's a pretty healthy signal. Yeah. And since your DPS customers, 50% of your customers are on DPS, but 70% of your ARR. Yep. Right? As you think about who's doing these renewals, those could be bigger customers as well. Like, that would exacerbate the timing dynamic there if they had an early renewal. Absolutely. The larger at this point on DPS contracts is because that contract vehicle gets them access to the full platform. Excellent. It also makes a lot of sense to be on a DPS contract for seasonal fluctuations. You take a look at commerce. I mean, it's timely because we're coming into a period now of the holiday selling cycle with Black Friday and Cyber Monday. You know, you look at our large commerce customers, and their requirements for observability explode in the calendar fourth quarter. If you have exclusively a server-based pricing model. Yep. How do you manage that? I mean, do you quadruple the number of servers you need for a quarter and then redu—I mean, you cannot do that with us. DPS enables you to do that because you basically just sign up for the platform for the annual period. And you have factored in the fact that Q4, maybe calendar Q4, maybe a large multiple of the other quarters. That enables you to grow, to expand, and then contract. I'm gonna. It gives you much more flexibility. That drives greater consumption as well. That flexibility enables our customers to expand way beyond what they would have done otherwise. I'm gonna move on from this dynamic, but just so I'm clear here. So, like, if you're that type of, like, retail customer running into the holiday season and your consumption's already running pretty hot, it might make more sense to, like, "Okay, let's, you know, do the contract renewal now because we already know we're. I mean, if you're coming up on the holiday selling cycle and you're running short on your contract, you might as well expand. Yeah. That makes sense to me. So just on the one comment you made earlier about the log business, I would agree with you. At $20 million, growing 100%, it's like, "That's great. I'll see you in a couple of years. Y-yeah. Now we're here, but still. It was our fastest business, $200 million. I mean, we really just began selling logs in earnest in October of 2024. It has really been just about a year that that business has grown to that size. Can you talk me through, like, what you think the next, you know, 6 to 12 months looks like for that logs business? Like, it's a higher ticket item, right? So, you know, are you winning from a competitor here or your customers looking to do, like, get their logs under one roof, with AI? Like, you know, what's kinda driving, like, the next leg of growth for logs? There are two primary drivers to our logs business. One is cost, which is, I had a CTO of a large Australian bank tell me that their existing logs overall cost was meteoric. I mean, the word that he used was meteoric. And yet they did not feel like they were driving any additional value from that. They are storing more logs, they are ingesting more logs, they are querying more logs, but not a lot of incremental value. They very much wanted to get a handle on that. They need to find a path where they can manage the cost trajectory. The bigger element, though, I must say, is what I talked about earlier, which is your question, Ryan, on platforms. Yep. Which is, I'm not sure why. And I, you know, I've been in this industry with Dynatrace for four years, not ten. Yep. I have no idea why logs grew up on the one hand and observability data types and others on the other. You know, it makes no sense. The reason it makes no sense is what I described earlier, which is it is when you have all data types, inclusive of logs, in one integrated data lakehouse that you can deliver the best outcomes, the more precise insights that are most actionable. If ultimately your objective is really autonomous operations, that's when you need precise insights. You need precise answers. To do that, you really want logs because logs add value to that. Moreover, it contributes on the cost front, but you actually do not need to store as many logs if you have traces and metrics in many of the other data types because the incremental insights that you get out of all of the other data types combined actually helps compensate for not needing to have a log for everything. It actually consolidates the number of logs that you have to track. It makes it much more intelligent. It gives you a better mapping and it drives better outcomes. The result of all that is that our logs business is growing because of those dynamics. If we can save you a fair bit of money and we can do so by driving a more integrated, developed outcome out of an overall observability plane, then you're gonna be inclined to move in that direction. That is what we're seeing customers do. They probably have a logs provider today. There are some share gains as a part of this, but this seems like a more durable trend towards, like, shifting towards one platform than, like, you know, a one-year as people shifting away from different vendors. Yes. And it is because as you move to an integrated platform, it becomes much stickier. Yep. If you're simply replacing like for like, logs to logs, with the sole objective being cost improvement, you're gonna potentially run the risk of down the road another vendor coming in and doing it for yet cheaper. That then doesn't deliver the stickiness. In our case, I'm not sure I can even think of a case where we've done exclusively a logs deployment because that isn't why customers come to Dynatrace. The result of that is you are doing logs, traces, metrics. You are integrating all those elements. You are applying it to applications, infrastructure, real user management, log management, traditional use cases, and beyond. You are even applying it to diverse personas. You're deploying, you're applying it to AIOps, but also developers, SRE teams, platform engineering. They're all using the same underlying data stores, the same underlying AI logic, and that is resulting across the board in better outcomes. You become much more embedded and therefore much stickier as you look forward. I think the likelihood that a log workflow comes to Dynatrace and then leaves later on is much lower as a result of that platform-based approach. Excellent. Maybe due to logs or maybe due to more of a broad-based strength or something else. I thought it was interesting that your new logo land size increased 30% in the quarter. I know you've had a big shift towards upmarket in your go-to-market. Yeah, anything that can call out for why those new deals are landing bigger at this point? It follows, I think, Ryan, very logically from all the rest of the discussion that we've had so far, which is that end-to-end observability is becoming more pervasive. Whether it is a large bank, a large insurance company, a large package delivery company, you know, many others I can think of, these are landing bigger. The reason they are doing so is because they are resulting from a pretty substantial tool consolidation. The tool consolidation is driving the bigger land. Instead of just an application performance monitoring deployment that is land and expand on a single application, it is, "No, I've got all these observability tools. I'm trying to make sense of it. I need to combine them. I need to converge them. I need to attack it in that way. That's what we're seeing, and that's driving the larger land size. Excellent. You talked about some pipeline strength with larger customers as well. You know, it's difficult to wrangle the timing on these things. Like, is that just, like, enterprise buying? It's tough to pinpoint. Yeah. Larger deals? Yeah. This provides some of the challenge in our guidance, to be honest, which is we delivered very strong Q1, very strong Q2, very strong overall first half. We raised, we raised guidance, but, you know, some investors have asked us, "Why not more strength in the second half guide?" The answer is, trust, number one, that our internal plan is higher than the guide, obviously, without question. Number two, it is just the variability in these large lands. Whether it is a large expansion or a large new customer land, it just adds more uncertainty. Now, I think we have done an excellent job of managing that uncertainty heretofore, with very good results. As I mentioned at the outset, we believe that we have radically de-risked the second half as a result of this. With the strategic pipeline growth at 45%, we believe that we have the pipeline to cover some of these machinations of these large deals. It is a reality of our business. Some conservatism around close rates in the second half. Yep. Of all these deals, that makes. That's exactly it. That makes sense. Two more AI questions since we're here. Okay. Then we'll wrap things up. Is there anything about APM that makes it a natural starting point for customers who are looking to observe AI use cases that they're about to roll out? You know, I would say in the AI natives, it's, it's a more logical use case to start with metrics and logs and, and probably infrastructure. Okay. That's probably where they land. You start first. Then, large language model itself, observability seems like a very early. Yep. Early innings for that. Are you starting to get more questions from customers around what Dynatrace can do in that use case? Definitely. It has not been our starting point as a company. We have sold to the CIO, or the CXO for AIOps for enterprise-wide deployments. We have, unlike some others in our market, not typically sold to developers. Yep. The evolution in the platform that we're delivering that we've been working on now for the last year that is slated for release very soon is very much oriented to expansion in the developer space. The result of that is that really provides us with much more fuel around AI native as a use case and a target customer base for the next evolution of Dynatrace beyond just the CIO AIOps deployment. We're very excited about it. We bring, we believe, a lot of value in this agentic world in a number of ways. Number one, for AI observability use cases, for things like hallucinations and guardrails and all the integrations you need to have into AWS and Azure, GCP, ServiceNow, you name it, all of these vendors to provide a comprehensive deployment. Now, having those capabilities really gives us the starting point. We have already deployed capabilities for AI observability into hundreds of customers at this point. They are already using it today. We are super excited about that. We think that establishes the foundation for an agentic AI evolution into the future, which is this ecosystem of agents that can really take action where we believe Dynatrace is, in many ways, uniquely situated to take that on because of the precision with which we deliver answers, not just dashboards and not just data, which is really our superpower. With developers more focused on, you know, those as a customer, your shift to DPS and consumption makes a lot more sense in terms of they can, you know, scale more quickly on Dynatrace at this point. Exactly so. We're at time. Thank you so much. Thanks, Ryan. Thanks, Ryan. Appreciate it. Thanks. Thanks, guys. Thank you all.