Dynatrace Aktie

All right. Good morning, everybody. Day 2 Communacopia. We have Dynatrace kicking it off for us this morning. Rick McConnell, CEO, Jim Benson, CFO. Thank you guys for being back at Communacopia conference.

Always happy to be here.

All right. Good. Rick, let's start with you. The observability market feels like it's entering a new phase. The AI is changing both the workloads customers need to manage and what they expect from the platform. How do you see the category evolving? And where can Dynatrace play a broader role over time?

Well, there is no doubt that the observability category is evolving in a substantial way in the age of in an AI-first world, we would submit at Dynatrace that observability is more critical each day. And I remember -- I remember 6 months ago, 9 months ago, when we're in the midst of SaaSpocalypse when it was deemed that the vast majority of software could be rewritten, it has evolved since then. And I think we've evolved to the point where there are clearly categories there are winners and maybe some other categories that will struggle more in an LLM driven world. And I would submit to you that observability is one of those categories that is going to win. So we are entering a new era.

And in observability, we have all the traditional software observability that we've been driving today. This is the couple of billion dollars of ARR, plus it's growing at 17% as our core business, and that continues to evolve to a world where customers want automation. They want autonomous operations. And the goal in this traditional business is that how you have software that essentially runs itself that corrects itself that auto remediates and that can reduce the human load required to manage it.

Similarly, though, you have this new expanded category, which we're thinking of as AI observability. And AI observability is the observability of AI workloads and agentic models. And this is a market that we believe is going to expand to in the range of a $10 billion market by the end of the decade, growing well more than 50% per year.

So we've got large traditional $80 billion-plus traditional observability market growing in the mid-teens, supplemented by a brand-new category in AI observability that is even more critical because you need more observability to oversee AI workloads not less. And what you need is context to be able to do that and manage what observability systems provide in real time by analyzing billions of interconnected data points. And that is a huge differentiator from LLM models and what they would be doing.

Okay. Great. That's a great level set. Let's dig in a little bit deeper there. Given Dynatrace's footprint across the Fortune 500 you have a pretty unique purview into how large enterprises are adopting AI. It seems like the opportunity is quite significant. You're talking about a $10 billion market, 50% CAGR, but production deployments are still developing, at least from what we can see. So what are the main bottlenecks today and what needs to happen for AI workloads to become a more material driver of consumption for Dynatrace.

It's a great question, Matt. The biggest delta that I see, and I'm -- I always have believed that I'm in a privileged position of being able to meet with CIOs, CXOs, all over the planet of Global 500, Global 2000, even Global 15,000 organizations and here I could tell you, the inhibitor to broader-based deployment of AI is confidence.

And what I mean by that is when you start relying upon LLMs to provide data to end users from your mobile app, from your website, from whatever it might be of the company that would be our customer. The challenge is you better make sure that, that information coming out of that LLM is right because if a bank, for example, has a prompt in a chat bot sitting in their mobile app and somebody says, "Well, where should I invest $10,000? And the answer comes back, "Well, the highest return over the last 90 days was to invest in Bitcoin. So why don't you put all of your money in Bitcoin?"

Probably not the right answer depending upon the characteristics of that investor. And so the calibration of the input coming from the LLM who deliver to end users is really critical. And it is that confidence level that I think is required to get it over the next time.

And this is precisely what the domain of AI observability is AI, observability is around LLM experimentation, LLM observability. It's around AI development life cycles and workloads. And the result of this or the intent of this is to ensure that you're answering an incremental question from the traditional question of observability. The traditional question that we're trying to answer in observability, this couple of billion dollar business, what I talked about of Dynatrace's, is it running is it running? Are your workloads running the way that you would expect? Are they optimized? Are they delivering? Are they up? What is the availability time? These are the elements of the domains of traditional observability.

Once you move into an AI world, you have to ask an additional question. Those AI workloads also have to be running. And so you start with the same question, but you then expand to another question. And the next question is really around, is it accurate? Meaning that are the LLM delivering accurate information that can be relied upon to be delivered to end users so that you, as an organization, have confidence that the end user taking action on that can actually make discernible progress.

And then the third question that is associated with those AI workloads is, are my models working right? Are my agents behaving the way that you would expect them to behave? So the point is, in a traditional observability sense, is it resilient? Is it working? Is a pretty good start when in, in AI workloads, you need to supplement with a couple of additional questions related to the accuracy of the data flow? And that's where, a, it's getting really exciting for observability, but b, observability is becoming completely mission-critical in delivering AI workloads with confidence and successfully.

So how would you frame sort of like the maturity of the product capabilities to meet that moment relative to sort of the enterprises in terms of their own progress and readiness?

Well, we at Dynatrace had been investing internally in this AI observability category over the last 18 months, and we're delivering capabilities in AI development life cycles and experimentation and so forth. But we see this as such an extraordinary category for which the time is right now, that a few weeks back, we decided to acquire a company by the name of Arize based here in San Francisco. Dead smack in the middle of AI central, so to speak. And as Arize is the category leader in AI experimentation and AI observability. And so the result of it is that we're betting with our pocket book, so to speak, that this category expansion is mission critical to our ability to deliver broad-based observability because I don't think it's going to end up bifurcating.

We're not seeing these 2 disparate markets. Rather, it is a converged market where our companies, our customers want to deliver traditional and AI observability. They want to do it on the same platform in a bimodal way, and they want to be able to leverage the assets that they've deployed with Dynatrace to be able to do so. So they really want an end-to-end platform to be able to deliver these capabilities, which is why we'll take Arize marry it together with the Dynatrace platform. And then you go all the way from developers to production. You go all the way from preproduction to more sophisticated workflows. And in doing so, you can also go from developers to IT ops. So you span the gamut of personas as well.

Okay. Great. I want to move into Arize a little bit later in the discussion. But first, Jim, I want to pull you in. You've given us some disclosure that customers monitoring AI workloads are already consuming the platform at a faster rate. What's different about how those customers use Dynatrace? And what does the early behavior suggest about the longer-term expansion opportunity?

Yes. And just to level set for everyone that the statistic was that we went from our fourth quarter to our most recent first quarter, from a little over 800 customers, where we were monitoring some LLM workload to 1,000 in 1 quarter. And then we went from 500 to 850 customers that are leveraging Dynatrace for some of our agentic capabilities. So kind of 2 unique cohorts.

And so to your point around enterprises, which certainly are the bread and butter of our business, enterprises are experimenting. There is work being done. You can kind of see that. I would say that what we're seeing is this is in maybe some of the more progressive enterprise companies. And what we found is the characteristics of these cohorts as they consume the platform at a much more significant rate pace.

And so their consumption growth is 50% higher than customers that are not leveraging us for an LLM workload or for agentic capability. And so we view it a little bit as this is a precursor of what we think is going to happen when others start to do it and it will build. And you say, why is that the case? Well, one, I think, Matt, what ended up happening is they start leveraging more of the Dynatrace capability. So those customers tend to use Dynatrace for more of our capabilities. And so think of it the breadth of the platform that they're consuming is not maybe just using us for application performance monitoring our infrastructure.

They're usually using us for application performance monitoring, infrastructure monitoring, digital experience, logs and in some cases, security. And so they use more capabilities of the platform. And I think it's also true that the LLM workloads tend to be chattier. And so the kind of the characteristics of those workloads is you consume at a higher rate. And so again, going back to our -- I'm sure we'll cover it later, our Dynatrace platform subscription model, which at its core is consumption-based.

If you consume at a greater rate and pace, which AI workloads, you will, you will burn through your commitment earlier. If you burn-through your commitment earlier, you'll do an earlier expansion. And so the economics work for Dynatrace. And I'd say where we are, I would admit we're still in early innings, but I'd say we are already seeing the benefit of consumption of the platform.

So it sounds like you actually are starting to see J-curve on the side.

Yes.

Okay. Let's double-click on the DPS opportunity. This is the first year the major DPS are moving through annual resets and contractual renewals together. What are you learning about how consumption maps to committed spend as customers move through these milestones? And has that relationship developed as you expected?

Yes. I think we've said in the past, and you're right that this is the first year that it all converges, where all of your cohort classes come up for their annual reset or renewal. And then that will be the new normal every year, Matt. So this is the first year, you'll see that. And I'd say the general characteristics that we've seen, most of our DPS contracts are 3 years, not all, but most. I think the general characteristics that we saw is customers that are maybe earlier in their life cycle, maybe year 1, they may have a predisposition if they're consuming at a pretty significant rate to go on to that.

They had just on a renewal maybe a year ago, and it isn't worth necessarily going through the bother of an expansion. And then in year 2, year 3 cohorts, more inclined to do an expansion. So I'd say that behavior remains. And what I would tell you is that we're going to -- most of the renewals are in the back half of the year. So 70% of what you outlined is renewals or annual resets that are going to happen in our third and fourth quarter. So only 30% are happening now.

So to the characteristics that we're seeing now are consistent with what we expected but you still have a very -- your volume of them is significantly less. And so for us, the big push is continue to drive consumption because, again, that's the core of the DPS contract, and we've publicly talked about that our consumption growth rates are well over 20%.

Our ARR growth rate is 16% to 17%, and -- and so if you continue to grow consumption in the 20s, you will see a convergence of ARR, where you will start to see ARR inflect upwards more towards the rate of consumption and I think the back half of the year, if that continues, we expect that we'll see an increase in the expansion rates and the NRR will then inflect.

I guess just to put a finer point on that, like the 30% of renewals that came due in the first half of the year and what you've already seen inside the business today, is that commensurate with the 20% plus consumption rate that you guys have been talking about for the last several quarters?

It is. It's it's just a small percentage of your customers that, as you know, we had a huge quarter in the first quarter with new logos, and I'm sure we'll cover that later. But reps are incented on maximizing their quota. And so in some quarters, you're going to be more new logo weighted. Some quarters, you're going to be more expansion weighted. I think just given the nature of our renewals, I would say we're going to be more expansion weighted in the back half of the year, Matt, probably a little bit more new logo weighted in the first half. But that just coincides with what the renewals are when renewals come up for their exploration.

Okay. Great. Rick, let's move to another growth pillar for Dynatrace tool consolidation, right? That's been a major growth driver but consolidation can mean very different things across customers. So what are enterprises actually replacing today? How broad are the initial deployments and what tends to prevent a customer from consolidating more of their environment on to Dynatrace?

Well, if the goal, which is where I started, is autonomous operations that ultimately you need to move out of this manual oversight of your software because there's just too much software to oversee and to keep running. And then even once you find out what's wrong, it takes you too long to fix it. And with agents now developing code, it's expanding at a rate that no organization can keep up with, you've got to find a way to automate that.

The challenge with today's environments that are fragmented at many customers that we see day in and day out, are that they are using one vendor for application performance monitoring. They're using another one for infrastructure monitoring, another one for log management, another one from [indiscernible] experience and synthetics and applications and the list goes on and on. And the problem is that uses different data stores, different collection methods, the data is fractured and you end up having to do manual tagging, manual oversight, manual collection and assimilation of that data to make any sense of it.

And that doesn't make the problem go away. That makes the problem worse because now you've got all of these data flows and you really can't have an automated system that's overseeing it. So the trend, and this is sort of irrespective matter of Dynatrace even the trend in the industry that we would say is toward end-to-end consolidation of observability tools. And the reason is because then you get data collected in one environment, you can oversee it with an overall analytics engine that for us is Dynatrace Intelligence that we can then provide answers and not just guesses and the answers can be acted upon by an agent system and trust it.

So that's what's driving it. And I would say that end-to-end observability is getting driven in 3 levels. Number one is the data level logs, traces, metrics, behavioral analytics, business events, all in one integrated data lake house, Dynatrace is unique in delivering that as part of our Grail solution. An integration at the domain layer, which are application performance, log management, infrastructure monitoring, all as I mentioned, and then an integration at the persona layer.

You want platform engineering, SRE development teams, IT ops and so forth, all looking at the same data. end-to-end observability is really about aligning and integrating it all three of those layers, and then in delivering that a unified solution that can result in much, much more automated response through this analytics of data that you just couldn't do manually.

Okay. Yes, that's very helpful. I guess maybe to drill into the consolidation point, I think it makes sense in context of what we're seeing in your new logo ACVs and new logo ARR...

Yes, exactly.

But at the same time, it does seem like the AI start-up ecosystem, you're seeing almost like a refragmentation in ability category, right? Like there's no shortage of companies trying to build the AI SRE layer from AI native start-ups to the existing observability platforms, ITSM vendors. So how do you think that market develops? And I guess for Dynatrace specifically, what's the right to win there?

Well, I think where you see most of the fragmentation in fairness is on the AI observing front. This is where startups are tending to go to say, look, I can oversee your AI workload, but it's a microcosm of what Goldman Sachs would need. For example, as part of this overall ecosystem environment and making sure that your systems are working. So they're picking components, but they are not able to deliver what Dynatrace is able to deliver for a Global 500 organization wanting to oversee a very, very large footprint of software.

So we really don't see the evolution of these startups into that space. Having said that, I think observability is going to be a very hot space, and there are going to be different attack vectors on it. But look at the Gartner Magic Quadrant, for example, an observability in 16 years running, Dynatrace has been a leader in the Magic Quadrant. Just as one example of the staying power of Dynatrace in a market that's evolved in a measurable way over that period of time.

I think this segue is nicely into Arize. You kind of gave sort of the strategic rationale for why you acquired them. But just tell us a little bit more about why this was the right fit for Dynatrace. Like what were customers telling you that reinforced the decision -- and then like how large could that opportunity become across the 2 installed bases?

Yes. I'll let Jim take the -- how large the opportunity is. But here's what I would say, the 3 real pillars driving the acquisition thesis were number one, market. The AI observability market as an extended category of observability, we believe, to be having substantial staying power is a directional heading that the vast majority of large organizations are going to be on. Everybody near as I can tell, is trying to figure out to use AI for productivity efficiency and so on, but they're also using it to figure out how to get more efficient with customers on an outbound basis. And that's where your LLM workloads need to be trusted.

They need to be credible. You need to have confidence in them to be able to really use that to drive productivity. Otherwise, you have human oversight every step along the way and that doesn't really give you the benefits of the productivity in the first place. So the market of overseeing AI workloads is expanding and expanding in a notable way. Secondly, we looked at over a dozen vendors in the space of AI observability.

We analyze tools and capabilities, sophistication of solutions and so on. and ultimately found Arize as the category leader. And in fact, what was really noteworthy to us is they were starting to show up in all of our customers. So we would go talk to the CIO of a large communications company, for example, and they would say, "Oh, yes, we're already using Arize on our workloads here. And we saw that in a large automotive manufacturer, a large e-commerce provider, a large bank."

Arize was already making substantial inroads there with their product. And yet what Arize was getting asked to do is, could you please move from preproduction to production, just as we are getting asked to move from production to preproduction. And so putting those two together was really very synergistic. So product and product synergy was another. And then third and finally was the developer motion. It was, we believe, more and more critical or it's becoming more and more critical to target the developer as part of the observability buy.

Today, we, at Dynatrace, have largely a top-down selling motion. We sell to the CXO, we sell, in some cases, millions or even greater of annual contract value to customers on a top-down enterprise-wide format, but a lot of observability these days is getting built by -- or purchased by developer on a credit card and done so on a product-led growth motion bottom up. Well, Arize through its Phoenix open source solution as more than 3 million downloads from app developers, AI developers specifically.

So the ability to influence the environment on a bottom-up basis and then bring that to Dynatrace has substantial value. So market product developer access and the developer access leading to a PLG bottom-up selling motion married together with our top-down selling motion, we found to be incredibly synergistic in a very, very fast-growing market that we wanted to take advantage of.

And what I'd say on the kind of the growth side that it's still a very new emerging area, right? Rick talked about a $10 billion market opportunity that when you think about Arize is the category leader, and we talked that we mentioned when we acquired them, we thought they would contribute 2 points of ARR growth for us. So you do -- call it, it's $40 million. So it's a small company, but there's a huge opportunity to be able to cross-sell Arize into our installed base. And the good news is Arize also sells, as Rick said, to enterprise customers. So they're relevant with enterprise customers, which is a sweet spot of Dynatrace.

They also sell to cloud native customers, which is certainly an area that we've aspired to get into. And so we think for us, it allows us to have access to a new product area that is emerging customers are going to continue to use it. So there's great cross-sell, upsell opportunity, gives us a cloud native push that they sell to AI developers. That's not a rich point about personas. That's not a persona that we've historically sold to. We sell to the IT operations, CIO community. So it allows us to sell to a community that we don't sell to that.

Sometimes companies do acquisitions because maybe their core business is slowing. That's not the case. So this is -- think of this as an AND. The core business, we believe, is on the cusp of acceleration for all the reasons we outlined, whether it be logs, some of the product areas, AI workloads and more usage for them. And obviously, DPS with customers being able to consume us at an easier rate and pace. So we think the core business is poised to accelerate.

And then on top of that, you have this very, very fast growing product category, and we expected that combined, is going to lead to an acceleration in Dynatrace growth rate well beyond fiscal '27.

And Matt, the Q1, as we reported it and provides a bunch of proof points to that, but that really show that acceleration or demonstrable little bit. We had record net new ARR or new ARR growth from new logos 160% year-over-year. we delivered 41% net new ARR organic growth during the quarter, up from typically our teens run rate. We showed a doubling of our logs consumption from $100 million 2 quarters ago to $200 million or approximately $200 million just a couple of quarters later.

So whether it is in the consumption side with regard to logs consumption, whether it is with ARR, net new ARR, whether it was new logos, we felt like these are signs of the strength of the observability space overall, Dynatrace in particular and a good opportunity for us to lean in to play offense.

Yes. Let's talk about logs, Rick and Jim. I thought it was really compelling because when you go from $0 to $100 million, it took several quarters, right? So what are you seeing in recent wins that sort of speaks to the breadth of that opportunity? And what would allow Dynatrace to compete for a larger share of the logging estate over time?

I mean I think what I would tell you, it's like anything else that when we introduced Grail and upgraded the platform we introduced logs as a capability, Matt. And it's like any new product area that you get into that, call it, several years ago, you maybe don't check the box on all the features that a customer is looking for, maybe that they get from their current provider.

And so there were probably some product gaps early on several years ago. And so you start working on. And we always believe we had a better value proposition that we could save them money and give them a better outcome because they're now able to look to Rick's earlier point, have one provider that is looking at all the data types in context, including logs.

So we always thought we had a better solution but there were gaps. And so you're working on addressing the gaps. I'd say, call it, 18 months ago, I think we got to the point where those gaps were addressed. And so call it your product was right. And then we work to make sure we got the pricing and packaging of the product, right?

And so I think we were at a point where you could then step on you put your foot on the gas, and one of the things that we did was having the product and the pricing of packaging, right, we introduced what we call strike teams. And so these are people that are dedicated to a particular product category, logs being one. that are measured and compensated on driving logs consumption. They also have a secondary measure, which is logs bookings.

And so I think we were able to bring it all together, Matt, and you got the product and you get the pricing and the packaging right, great value proposition. And then on top of that, you introduced, I'd say, a selling motion with while they're not a specialist team in the sense that some companies that sales they just sell a particular product category, these are people that drive consumption.

And so I'd say we've brought it all together. And I'd say it's hard to find a customer to have a discussion with them and say, are you happy with your current logs provider. The answer is no, there's a gap between the value and the cost. And so it's a pretty easy conversation to have, would you be interested in trialing us. And the benefit of DPS is you don't have to have a separate sales contract that they're able to do that. So these strike teams can help with that. And so I view $200 million as a milestone. This isn't the destination. This is a $1 billion-plus category for the company. So this is going to continue to be a very, very fast growing category.

I mean, it comes down to in logs and really two core elements of value proposition. One is if you have logs traces metrics, all these other data types, you actually don't need as many logs. You get better outcomes from viewer logs, if you combine them with other data types and the result of that [indiscernible] as you can actually reduce cost because you don't need to store as many logs on vendor A, while you're doing all the observability types on the collection of end-to-end observability together, inclusive of logs, you get better answers and that delivers better outcomes, which ultimately delivers this autonomous operations opportunity that I described previously, if logs are over at vendor A, but all the other observability types are over vendor B, the integration of that is going to impede your progress toward delivering automation, and that is going to get in your way over the course of time is why you need to combine them to deliver better outcome, which is why we're seeing some of the logs growth that we are.

Perfect. Jim, let's talk about just the strategic account model. It's producing larger lands and broader platform deals after several years investment. So just kind of give us a mark-to-market as to where we are on sort of that go-to-market productivity.

Yes, you're right. I mean it was 2 years ago that we had outlined for you that we were making pretty substantive go-to-market changes where we we're investing more in the top of the pyramid, our largest customers where Dynatrace really shines. And so we reduced the number of accounts per rep on the top of the pyramid, maybe call it the Global 500. That has been a huge success, our fastest-growing area in what we call strategic accounts that are up there.

We're not done. We've now extended that model down from the Global 500. I think we've added 200 to 250 more accounts with a similar model. So think of it as same density, same level of resource alignment. And that's resource alignment on the sales side, that's resource alignment on your solution engineers, that's resource alignment on some of these strike teams.

So you have dedicated teams of people that are focused on these large accounts. And so I expect that, that will continue to be the fastest growing product category for us. Having said that, we're not seeding what we call the enterprise, which is think about the accounts that are below the 750 global accounts to call it 5,000.

So we have a territory motion there, where I think we're continuing to get good traction. I'd say below that, so think of accounts in the to 5,001 to 15,000 as we target the Global 15,000. We have work to do on building a velocity motion. Right now, we have a great land motion with customers that have a high propensity to spend to our larger customers, we need a better land motion.

There's some things we're doing. We've introduced a Dynatrace starter pack to make it easier to introduce Dynatrace to teams of smaller teams, lower ASP, easier kind of onboarding process. And so across the board, we're trying to make sure that we can address kind of the build a volume motion, and I think we have a little bit more work to do in that regard, but I think we have the ingredients in place and continue to sustain the growth rates that we're seeing and improve them in both the strategic accounts and the enterprise accounts.

And so would you just say broadly speaking, we're in a better place in terms of sort of the baseline around sales productivity, pipeline generation, we talk about those...

100% that we had outlined very clearly when we put this in place that we thought sales productivity in our fiscal '25 would probably drop. You move a lot of accounts, you caused a lot of disruption within your customer base with accounts moving. And actually for us, it was -- the disruption was less than what we expected, but there was a sales productivity drop map.

If you look at it around how much bookings or ARR was generated per rep. And then we said, in fiscal '26, you'd start to see some productivity improvement and that we would see ARR growth rates stabilize. We saw that. And we also laid out, we said in fiscal '27, if we're successful with this journey, you will see an improvement and productivity, and you'll see an acceleration in the growth rate of the business.

And that's where we're at right now. And I think to Rick's point, we -- we've shown some proof points. I think the better measure of progress is what I call trailing 12-month net new ARR. Sometimes you've got a quarter, quarters can be lumpy just because of large enterprise accounts that we've had 4 quarters in a row of trailing 12-month net new productivity, to your point, which is the sales motion changes that we put in place are really starting to drive productivity improvements, and I expect that that's going to continue.

Okay. Great. Well, in the last 2 minutes that we have, Rick, I want to bring it back to you. The market is evolving very rapidly. Dynatrace has advanced platform significantly over the last year, you've improved things operationally. When you look out over the next 5 years, what would you -- what would define success for Dynatrace?

Well, the principal financial indicator that Jim and I look at is ARR growth. And the ingredient to that, that drives it as net new ARR. So those are the financial factors. And so success is how do we find a way to continue to accelerate ARR growth, to Jim's point, we saw ARR growth come down in FY '25. FY '26, we stabilized it. FY '27, we want to see that ramp. And as we look to the future, our expectation is for ongoing ramp. We went from 16% to 17% with the Bindplane acquisition.

Arize will help further. We have organic elements that we're driving, like things like pricing and packaging adjustments that we believe can add new ARR growth. So I think it's really that simple. As go deliver accelerated ARR growth as a core metric for success. And in so doing, continue to be a leader in observability broadly in the combination of traditional observability as well as emerging categories like AI observability to come.

All right. Fantastic. It's a great place to leave it. Thank you very much, Rick and Jim for joining us.

Thank you.

Thanks, everybody.

Greetings, and welcome to Dynatrace's planned acquisition of Arize Conference Call. [Operator Instructions]. As a reminder, this conference is being recorded. It is now my pleasure to introduce your host, Noelle Faris, Vice President, Investor Relations. Thank you. Please go ahead.

Good morning, and thank you for joining us to discuss Dynatrace's planned acquisition of Arize. 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 the expected benefits of the proposed transaction, expected financial impacts and deal timing. 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 Dynatrace's views on August 13, 2026. We assume no obligation to update these statements as a result of new information, future events or circumstances. We also mention a non-GAAP financial measure in today's comments and refer you to our August 5, 2026, earnings release for additional information and related definition.

Please note, we have included a supplemental presentation related to this morning's announcement on the Investor Relations website.

And with that, let me turn the call over to our Chief Executive Officer, Rick McConnell.

Thanks, Noelle, and thank you all for joining us on such short notice this morning. We are very excited to discuss with you our proposed acquisition of Arize, the category leader in AI observability. From a strategic standpoint, we believe this acquisition will increase our exposure to the high-growth AI observability market, extend our end-to-end observability leadership position, advance our AI observability road map, expand our reach with developers, accelerate our ARR growth and add a world-class AI native team to Dynatrace.

I'd like to cover a few of these areas in more depth to highlight our strategic rationale for this transaction, then turn it over to Jim to speak to some of the financial elements. We'll then open it up for your questions. First, the acquisition of Arize expands our market opportunity and strengthens our leadership position in AI observability and evaluations, a large and rapidly emerging category that we expect to exceed $10 billion by 2030.

AI observability spans the full life cycle of an AI-powered application from continuous evaluation before and after release to real-time visibility into how LLMs, agents and orchestration layers behave in production to the impact they have on applications, infrastructure and business outcomes.

During our earnings call last week, I indicated 3 core questions that AI observability must address, beginning with, is it working just as with traditional workloads. Second question is new. Is it accurate? Is the AI model delivering output that can be trusted and relied upon with confidence? And the third, are my agentic systems delivering the outcomes for which they were built.

Enterprises are deploying agents to build software at a pace that wasn't possible before, and code must be built well, shipped safely and run reliably. Gartner describes the difference in AI observability well in their innovation insight on AI observability from May of this year. They say AI systems fail differently from traditional software. A misconfigured application typically produces an error that is visible, traceable and reproducible.

An AI system can fail silently. It can generate confident, plausible sounding outputs that are biased or factually wrong with no corresponding alert in an infrastructure dashboard or application log. Back to our perspective, as AI evolves from simple prompts to autonomous multistep systems and increasingly operates without a human in the loop, visibility becomes increasingly critical.

AI observability provides AI engineers, SREs and platform teams with the insights they need to debug, optimize performance, control costs and improve accuracy, helping to ensure AI-powered services remain reliable, efficient and trustworthy.

A second area I'd like to cover is that acquiring Arize will accelerate our own AI observability portfolio and roadmap. Through this acquisition, Dynatrace will have an even stronger end-to-end observability solution from preproduction to production. Arize adds leading capabilities in AI and agent evaluation, experimentation and agentic workflow optimization across development and production.

These complement Dynatrace's existing strengths in application and AI infrastructure observability, model performance and business impact. Together, these capabilities position Dynatrace to provide observability across the full AI stack, connecting model and agent quality with application performance, infrastructure health, AI usage and cost and business outcomes. This is not a point solution. It is a portfolio expansion that positions Dynatrace to better capture a greater share of AI spending in this rapidly emerging category.

Third, Arize unlocks the developer audience for us. Through its widely adopted open source community, Arize has already earned the trust of AI developers who increasingly influence enterprise technology decisions. This acquisition will give Dynatrace direct access to this important audience while strengthening our position with AI-native companies.

Horizon's strong engagement with developers, coupled with Dynatrace's deep enterprise relationships and global reach will create new opportunities to land, expand and deliver greater value as customers operationalize AI. Through millions of monthly downloads of Horizon's open source Phoenix platform, we believe there is a significant opportunity to accelerate ARR growth over time by further extending Dynatrace's reach into developer-led buying motions and AI-native workloads.

And we have high confidence in our ability to sell our products together because numerous customers already use Dynatrace and ARS in unison, spanning industries from automotive to communications to e-commerce to financial services and more. We expect access to Dynatrace's customer base to further accelerate ARS's trajectory.

And finally, to round out my remarks, as I mentioned, we're acquiring an exceptional team with deep AI expertise, strong roots in the Bay Area's AI ecosystem and what we believe is a terrific cultural match for Dynatrace. We're excited to welcome Arize's co-founders, Jason and Aparna, along with the broader Arize team to Dynatrace. We are focused on a thoughtful post-closing integration plan, and we intend to give the Arize team the runway to build on what they've created while leveraging Dynatrace's scale, engineering expertise and global reach to accelerate product innovation and customer impact.

In summary, we are convinced that Arize is an excellent fit for Dynatrace. Adding Arize to the Dynatrace platform further reinforces our end-to-end leadership in the overall observability market and will create substantial customer and shareholder value.

With that, I will turn it over to Jim to cover the financial aspects of this transaction.

Thanks, Rick. We are very excited about this acquisition. Under the terms of the agreement, we will acquire ARISE for a total consideration of $915 million, subject to customary adjustments, consisting of approximately $815 million in cash as well as replacement equity awards for Arize employees joining Dynatrace. We have sufficient cash on hand, along with our existing credit facility to fund the purchase price. In addition, our plan for ongoing share repurchases remains unchanged.

We expect Arize to be immediately accretive to ARR growth and unlock significant cross-sell and upsell across our combined customer bases. While Arize is a small company, it is rapidly growing, and we expect it to add approximately 200 basis points to the Dynatrace ARR growth rate in fiscal '27. From a profitability perspective, we expect Arize to be approximately 175 basis points dilutive to Dynatrace non-GAAP operating margin in fiscal '27.

For the anticipated synergies, we expect to generate incremental operating margin expansion from fiscal '27 levels into fiscal '28 and beyond. The acquisition remains subject to customary closing conditions, including regulatory approvals and is expected to close by the end of September or in early October.

Given this timing, we do not expect any material impact to the Q2 guidance we provided last week. We will provide a more detailed update to our fiscal '27 guidance on our Q2 earnings call in early November. In summary, acquiring ARISE represents a significant opportunity to accelerate Dynatrace's leadership in AI observability, strengthen our long-term growth profile and create substantial value for both customers and shareholders.

And with that, I'd like to open it up for questions.

[Operator Instructions]. Today's first question is coming from Matt Hedberg of RBC Capital Markets.

Congrats on the deal. Really, really exciting, I think, prospect for Dynatrace. I wanted -- something you said was super interesting to me, getting closer to AI-native customers. Obviously, that's an area that Dynatrace has been working on given kind of your existing global kind of 5,000 base. But I'm guess I'm curious, could you guys expand on how you see success there? And I guess, does it mean adding new sales reps to maybe better target some of these AI-native customers?

Yes. So let me take that, Matt, and Jim can add in. The first thing we want to do is we want to just continue to leverage the capabilities that Arize has already built. They have really strong growth. We want to continue to leverage that. Their sales force is focused on enterprise customers as well as AI natives, and they're expanding to include the benefit of this developer-led motion. We see the developer as being a quintessential part of this acquisition that we can then access not just for AI observability, but observability more broadly.

We see developers being a core persona, if you will, in the observability decision. And so this brings that together as well. And then finally, back to the ICP or the overall customer profile, we absolutely do believe these capabilities enable us better access into the AI native cohort, and Arize is focused in this area as well. So I believe for a number of reasons, it really brings together this notion of selling into AI natives as an accelerant of our opportunity ahead.

The next question is coming from Will Power of Robert W. Baird.

Okay. Great. And I'll echo my congratulations as well here. Look, I mean, I guess, Rick, as you were evaluating opportunities to expand and bolster your AI growth opportunity, did you evaluate other acquisitions? And if so, what really made RISE kind of stand out among the options to go after this market more aggressively?

And then maybe number two, for Jim, any other color you can share on ARR growth, what it's looked like over the last year? And is this ARR growth something we should expect to double, triple over the next year? I recognize it's still pretty early, but any other color there would be great.

I'll take the first one, Will, and Jim can take the second. On the first, relative to AI growth, it is fair to say that we have been watching the evolution of the AI observability market for some time. We have been investing in this area for the last 18 months in terms of internal investment, and this is an incredible catalyst to accelerating our road map to bringing in complementary capabilities that we haven't had on our road map.

And as we look at a -- what we view as a 7-layer stack for AI-powered applications, the synergy with ARISE was simply better than anybody else in the market by far. Their offering is very sophisticated. We do see them as the category leader in AI observability from a pure-play standpoint. And that gives us capabilities, for example, in LLM experimentation, LLM observability -- so targeting both AI workloads as well as agentic workflows. It brings us the AI software delivery life cycle of components that we need to supplement the capabilities that we have at Dynatrace in model operations, cloud and infrastructure operations and elsewhere.

So we -- as Jim has often put it, we're aggressive shoppers but very, very disciplined buyers. So Will, you can absolutely rest assured that we have spent a lot of time analyzing this market and believe fervently that ARISE is the most synergistic opportunity that we could have gone after and the biggest catalyst to the business for Dynatrace to come in the AI observability area.

And maybe one last point is that we absolutely see observability entering a new era. And it is a new era in which it isn't just about business resilience. It is about AI reliability as well, and it is about ensuring that models and agents are working as expected. And these areas together are going to represent an explosion in observability opportunities as we look ahead. And for us, we wanted to play offense, and this is an opportunity to accelerate our ability to do so.

Jim, on the second?

Very well outlined, Rick. A little color, Will, on their business that we're going to share more details about Horizon's ARR and revenue growth profile after the close. They're a small company, growing very rapidly. As I said in my prepared remarks, we expect it to contribute 200 basis points to our ARR growth, so call it, $40 million for fiscal '27. But just to give you a little bit more color beyond kind of just the ARR revenue stream, that isn't really the only reason why we were buying them. We're acquiring a category leader. There is huge...

I'm sorry, it seems like we lost Jim. Would you like me to move on to the next question?

Yes.

The next question is coming from Sanjit Singh of Morgan Stanley.

Dynatrace has historically had a pretty robust organic innovation engine pumps R&D. So I'd love to get a little more color, Rick, on the track that you guys are on in terms of autonomous operations, AI and more specifically how this acquisition Arize, which is the category leader, how that's significantly accelerated the road map. Just a little bit color behind the buy versus build decision.

Thanks, Sanjit. The way that we see the observability space is, on the one hand, we have the evolution of this $82 billion market for what we would think of as core observability or traditional observability, which is now getting supplemented by the end of the decade, this $10 billion AI observability space.

Let me break those down into 2, and maybe it will help answer your question. In the traditional observability sense, the directional heading of that market is toward autonomous operations. And that involves agents leveraging deterministic AI and answers to be able to then to take action on those answers and insights to deliver a more automated outcome. And that is the holy grail, if you will. That's where we are headed.

And we are spending, as you know, a lot of time on areas such as Dynatrace Intelligence to deliver both the deterministic AI elements of analytics and insights, along with the agentic AI capabilities and actions needed to deliver not only the intelligence engine, but also the AI control plane to provide for this automated operations outcome. So we've been spending within Dynatrace a huge amount of time there.

But we've also been investing in the second area of AI observability in a number of different associated areas that I referenced earlier, cloud and infrastructure operations, for example, model operations, for example, business impact and application experience. But there are a number of areas where the Arize acquisition does fill in areas that we haven't been investing in at the same level of detail.

For example, AI software development life or delivery life cycle. especially with regard to LLM experimentation and evals, LLM observability to make sure that we're dealing with things such as model drift and hallucinations. These are areas in which Arize simply leads in the market from our perspective as a category leader.

And so we have been investing, as you suggest, we have been developing a really incredible innovation engine and an opportunity for acceleration in this area on our own, but it is such a critical moment in the era of evolution of observability that we wanted to take this opportunity to essentially become an AI observability category leader ourselves. And by putting together the innovation of Dynatrace with the innovation and product portfolio that exists with strong deployment at customers today by Arize, this is just an incredible opportunity to achieve both objectives.

The next question is coming from Ittai Kidron of Oppenheimer.

Congrats, guys. Looks super interesting. I guess, Rick, I wanted to ask more from just a pure technology standpoint. How do you see -- can you talk about the integration of the technology with your platform? How does this tie into? Help me understand how that process would work, the time line to completion? And is it -- what are the hurdles or challenges in doing this?

Sure. Well, the first element is that Arize is on a very strong growth trajectory all by itself, and we don't want to get in the way of that. So what we are not going to do in this transaction is go spend a year integrating immediately out the shoot all of the capabilities and lose that momentum. We have a solution with ARS or I should say, Arize has a solution with Phoenix with millions of deployments per month of LLM evals that is focused on open source. This is an incredible catalyst for the developer community and an opportunity for leverage growth into a PLG motion, product-led growth motion to come. We want to leverage that.

We have 200 or so -- or I should say, again, Arize has a couple of hundred customers there in the enterprise category. We have obviously substantially more customers than in the same category at Dynatrace that we believe offer tremendous cross-sell, upsell potential of the Arize capabilities. So the first elements of business are to catalyze the Arize business by integrating really on the go-to-market side with Dynatrace and enabling Arize to continue on their own road map journey.

Now you do correctly raise the issue of well, obviously, to get 1 plus 1 equals 3 over the course of time, you want to be able to integrate and leverage these technologies. And we will do so through a very thoughtful deployment and integration road map that we have put together as we look to the future, and that's going to evolve over the course of time at the appropriate time. You mentioned Grail, for example, that's a logical evolution of perhaps the ADB database that they use on the AX portfolio arise.

And so that's something that we will look at. But again, that is not a day 1 focus. Day 1 focus is acceleration of the go-to-market opportunity and enabling them to continue to operate with the innovation engine and incredible velocity and speed at which they're developing today.

The next question is coming from Samik Chatterjee of JPMorgan Chase.

On the transactions here as well from my side. Maybe just a quick one. You did mention that there is a bit of customer overlap in the sense that you have customers that are using both Dynatrace and Arize. Can you just sort of talk to how much of a customer overlap do you see with ARISE already and flesh out the revenue synergy part of this a bit more when you sort of think about taking ARISE to your customers, what would be the sort of initial customer cohort that you think the product fits well with and where will we initially see -- which customer cohort would initially drive that revenue synergy?

Jim, we have you back?

Can you hear me?

I can. Yes. Do you want to take that one?

I can go back and finish up on Will's question from earlier before.

Yes.

So as you can imagine, this is -- Arize is relatively small. So they have 200 customers. I think the overlap between their customers and ours, I think, is about 20% to 30%. But if you think about it, I think Rick outlined it earlier, there is significant cross-sell and upsell opportunities within our installed base specifically to sell ARISE. This gives them immediate access to an enterprise customer base that they've already started to get penetration in. They're already in many Fortune 500 accounts. And so it's very complementary relative to Dynatrace from a customer base perspective.

And then Rick also noted that they also have traction in the AI native builder community. So this gives us access to a community that we've been striving to get to. So we actually get a 2 for an ability to go after the AI native community, an ability to continue to penetrate Arize into the broader customer base. And the go-to-market motion is very synergistic with Dynatrace. And so we're quite optimistic that this is definitely a 1 plus 1 equals 3. And to finish off on Will's question around the ARR for the business, as I was saying, we are acquiring a category leader that the value is they -- it brings us continued AI observability leadership. It accelerates our road map. I mentioned the developer reach it gives us. I mentioned the customer expansion opportunity it gives us.

And so Arize positions us in a clear leadership position and in a really attractive area of observability. And so we're quite optimistic about what this can bring. We mentioned 200 basis points of ARR growth. I might will cover upfront. It is near-term dilutive. But I want to be clear, we believe with synergies that this is near-term dilutive and that we will be able to drive operating margin accretion in fiscal '28 and beyond. So this is just a win-win across the board, and we couldn't be more excited.

The next question is coming from Eric Heath of KeyBanc Capital Markets.

I wanted to come back to one other element of Ittai's question, just get a better understanding of how similar or dissimilar is the data you're capturing for evals is to what you're capturing for production systems, whether it's logs, metrics, traces. And one other one, just if I could, on the AI native customers. Is there any frontier labs in this customer base? Any outsized customers to be aware of?

I'll take the first one on data. The primary data type that is used in evals is traces actually. And this is highly synergistic to, of course, the traces metrics, logs and capabilities that we store in Grail. So it all starts from the same foundation of core metrics, Eric, that we would be capturing otherwise.

One of the differences is that, obviously, evals tends to happen in preproduction environment versus observability and what we do with Grail is primarily production-oriented. And that, of course, brings back one of the core motivations for the transaction is to really enable Dynatrace to radically extend left into the developer community because we do believe that observability starts with the first line of code.

And that first line of code, by the way, as we've said in our earnings calls and elsewhere, is no longer coming from humans. That first line of code increasingly is going to be coming from agents. So the ability to capture evals on a preproduction basis using traces and other core data types of observability and then extending that all the way into a production environment to handle operations and then to triage issues and then to create a full loop, a full developer loop all the way back to the eval cycle becomes the domain of observability to come.

It isn't just about throw a system into production and observe it and come up with and look at dashboards and try to create automated triage. It is so much more sophisticated than that in delivering not just resilient systems from the first line of code that's written by agents all the way through the ability to ensuring that AI is delivered reliably with answers that can be trusted to be provided to end users. And so this is really the cycle to come of what we're looking at delivering here on an integrated basis. And there's huge synergy in the underlying data types.

Yes. And Rick, I'll offer on the customer side. So they do not have any of the frontier model companies as customers. But what I will say is at their most recent customer conference, they had the frontier model companies actually speaking at their conference. So they are very well integrated into the AI community in a significant way.

And then your second question around customer concentration, they don't have any customer concentration. Again, they sell to the enterprise and they also sell to these AI native companies. And so as I mentioned earlier, it's very synergistic to what we do, and it goes after an audience and a profile of companies that we've been aspiring to get to.

The next question is coming from Mike Cikos of Needham & Company.

Congrats on the announcement. I was just hoping to get a little bit more color on the negotiations that played out here, particularly because both Datadog and PagerDuty were obviously investors in the Series C round. Can you talk about what the competitive bidding dynamic was like or how competitive this deal was overall?

Do you want me to take that?

Yes.

Go ahead, Jim.

Sure. As you can imagine, Mike, in any situation like this, that this is a very attractive asset in a really exciting space, and they are a category leader. I will tell you that there was huge interest on their part of the combination of Dynatrace plus ARIS. So one of the benefits we had was they saw the potential of what they were doing and their vision of where they were and combined with Dynatrace, what they were going to be able to extend to.

As Rick outlined in his earlier comments around kind of pre-praud to fraud, as they've been going within their eval models, they've been requested by customers, well, can you do this and can you do that? And so it was very synergistic with Dynatrace. And so in that regard, I think we were in a very good position that there was strong interest on their part to partner with Dynatrace.

Yes. I would -- it's a good question, Mike. I would just supplement Jim's comments to say this is a highly sought-after asset and company as being a category leader in AI observability, which is obviously a very hot space. Having said that, and I don't want to speak for Jason Aparna, the co-founders of Arize, but I would say that from the very beginning, we felt that this was just hugely synergistic on both sides.

What's interesting is that I remember back to some of the earlier conversations with them, their comments that, geez, we're focused mostly on pre-prod and we're getting pulled into production, but that would entail really riding and evolving a substantial set of capabilities where Dynatrace already existed and vice versa. And we're each trying to fill in the missing pieces on the respective sides of pre-prod and Prad.

Bringing these together was just always viewed by both of us as hugely synergistic with a team that is just an awesome, awesome AI native development team that is building at incredible velocity. And when we put all of that together, we just thought that this was, I would say, mutually just an exceptional combination. And I'm super excited to proceed for it to get it closed and get moving in the market.

The next question is coming from Matthew Martino of Goldman Sachs.

Rick, with BindPlane and now Arize, you've picked up 2 of the more credible open source franchises in telemetry. How does that fit with the broader platform strategy? And what does owning the standard get you commercially going forward?

That is a great question and a core thesis behind both transactions actually. The ability to leverage Bindplane, which, as you know, from our earnings call last week, is already running ahead of plan. We were projecting in the order of about $10 million of ARR coming at the end of last quarter. It was running ahead of plan, delivered around $13 million.

The open telemetry or hotel capabilities that it brought has just catalyzed our logs business in addition to generating ARR in and of itself. And so that's been an extraordinary win and I think really has opened up this notion of being agnostic as to the telemetry methods of ingest into the Dynatrace platform, which is a core objective of many of our customers. So that was a good start. We take a look at now ARISE -- they've got open inference as the standard. Obviously, Phoenix with open source capabilities in the eval space that enable you to get started immediately with evals using Phoenix with millions of downloads per month.

Aparna, the -- one of the co-founders of Arize put out a blog. It had 1 million reads. This gives you some concept of the representation of reach that Arize has in the market and an AI developer space. So this just reinforces the depth of brand leadership that we believe that Arize has. It reinforces their positioning in the market around AI leadership and an AI-first approach.

It reinforces their ability to target the AI developer and all of the open source elements that we bring together with the combination of Bindplane and Arize really are foundational to generating the developer mindshare and the PLG and developer motion opportunity to come.

The next question is coming from Keith Bachman of BMO Capital Markets.

I wanted to also revisit on integration and break that into a couple of different pieces. If, a, you could start off on the people side. How will the different R&D teams interact? I know, Rick, you said you didn't want to put any collars on these guys and enable them to grow -- continue to grow. But I'm just curious on both the R&D and go-to-market, how do the people interact? You mentioned this was a Silicon Valley-based company. Most of your R&D is, for instance, overseas. I'm just wondering how that integration might look.

The second part of the question is you answered the data side. I'm also curious, what do you think this acquisition does to perhaps foster greater growth within Dynatrace's portfolio? And where I'm really going with this is you talked about software development life cycle or software dev hasn't really been a place where Dynatrace has had success. Does this provide any lift to your existing portfolio, including even on the security side, where I think investors are sort of questioning what the potential there is when you're capturing the software dev side.

And then just finally, Jim, to finish off with you, I think a question was asked before you got cut off, and that is what's -- based on that $40 million, is there any characterization you could give as we think about what the growth rate of that over the next couple of years? That's it for me.

Okay. A lot in there, Keith. We'll try to parse them as quickly as we can. On the award side, the way that this is going to work is that Jason and Aparna and their leadership team are absolutely, to be clear, going to be part of Dynatrace. That is a lot of what we're investing in. I think I'd like to pass on their remarks to me to say that they're super excited to become part of Dynatrace. We're incredibly excited to have them. And so this is really key to bring that team in. This is in the midst of making sure that we can set them free and help them where we can but make sure that they've got the runway to run with it.

So Jason will come into my leadership team, maintain Arize as essentially a business unit within the overall Dynatrace umbrella and report directly to me with his existing team as it is today. we will do some dotted line of the sales force into Dan Zugelder, our CRO. And the reason we'll do that is to make sure that we can leverage the Dynatrace customer base to sell ARISE products to. And so we do want to take advantage of that on day 1.

And then as I mentioned, Keith earlier, over time, we will further and more deeply integrate the product portfolio, but we'll do so thoughtfully and methodically to make sure that we're really getting leverage out of that as we integrate the thinking associated with the Arize portfolio. So that's that.

On the growth side, we absolutely see the capabilities of ARISE accelerating the Dynatrace selling as well. And the way that we see that happening is that the developer is becoming more critical day by day in the overall observability decision because of this shift left phenomenon or extend left phenomenon in the observability decision-making. So it isn't just about top-down selling to the CXO and IT ops anymore. It is about top-down selling combined with bottom-up selling from the developer.

So gaining access to the developer does enable us, we believe, to sell more core observability or traditional Dynatrace observability into the market as well by having a better end-to-end or a more fulsome end-to-end portfolio. So that's how we see that playing out is that there is cross-sell that's bidirectional and that there is leverage in selling more of Dynatrace as a result of having Arize, not just selling Arize to Dynatrace customers. So we believe that's hugely, hugely synergistic as we talked about earlier. And then, Jim, maybe you could take the growth rate question, if you wish.

Yes. I dropped a bit. So if the growth rate question could be repeated, I'd appreciate it.

The growth rate question was how do we think about the growth rate of ARISE as it integrates into Dynatrace?

Well, I think that as you think about, it is certainly going to be significantly accretive to our growth rate. So our ARR growth rate that we guided was 15.5% to 16.5%. I mentioned that they're going to contribute 2 points. So we'll say more about their contribution as we go through the close and we have the Q2 call, but we expect it to be significantly accretive this year and significantly accretive going forward.

The next question is coming from Andrew Sherman of TD Cowen.

On the deal. The Arize Phoenix open source version is interesting. You mentioned they're the only platform that has that. There's 4,000 enterprise AI teams using it, millions of monthly downloads. Talk about the opportunity to convert that to enterprise paid version over time? And how might you push that over the next couple of years?

Thanks, Andrew. That is one of the core elements of evolution, we believe, that exists as possible within the Arize base. They do have, as you pointed out, millions of downloads, a huge amount of exposure in the AI community. We want to be able to leverage that. And yet, we want to be driving this developer bottom-up motion. And so what they have today is they essentially have a PLG usage motion with very strong usage. They have many, many domains using Phoenix.

What we want to do is we want to create over the course of time for those users that would derive advantage from it, an ability to very simply move into the AX portfolio over Arize, which is our paid portfolio, and we want to not get in the way of the intention of open source access. But we do want to make sure that there is very logical reason for developers who want to expand into production to essentially take leverage of the AIX portfolio as they move up the stack.

And clearly, as you move into enterprise deployments, those are going to be paid deployments that evolve in many ways from Phoenix. And maybe just one last point on it. is that the sales cycle of Arize is radically accelerated when they start with a Phoenix user. And so what we have seen as we've gone through early discussions with them is that Phoenix users tend to convert to the paid portfolio on the enterprise side much more rapidly.

So this will be an area of focus to drive that PLG motion in an even more disciplined and sophisticated way, all driving toward enterprise deployments on a broader basis. And to the extent we can leverage Phoenix to achieve that, then that's a terrific catalyst.

The next question is coming from Fatima Boolani of Citi.

This is Joel on for Fatima. So it's clear this is solidifying your shift to left in R&D and go-to-market ambitions. Just -- how do you expect to commercialize this at scale? What does Arize look like with 1,000-plus customers? And then also, will it be DPS eligible out of the gates? So just those 2.

Yes. On the -- maybe, Jim, you could take the DPS piece. On the shift left piece, this is being commercialized by Arize today. And this is how they're selling essentially evals to enterprises today. You begin with Phoenix, ideally begin with Phoenix with a single user. That expands to a team of users. That team of users ultimately becomes a group, which results in an enterprise desire to deploy evals in order to develop and deliver ultimately trustworthy AI systems.

And that's really what Arize is all about. It is delivering trustworthy AI systems that deliver AI reliability as well as overall model management against model and agentic workloads. So they are in this flow today. What we believe that we can bring them given the synergy of our customer base with the target customer base of Arize is an installed base of customers that we've already seen in many of the industries that we talked about in our prepared remarks as well as many more.

What we increasingly were finding was that we would go into our buyer and they would say, well, we just deployed Arize. And then we would go to another industry, and we would hear them say, well, we just deployed Arize. And then yet another one where they had just deployed Arize. Increasingly, it was propping up that they were using Dynatrace for observability and Arize for AI observability and evals.

And the result of that was we wanted to put those together as we see just extraordinary synergy, and we're, in fact, getting asked by customers what we could do to bridge those solutions. So -- that's how we ended up here in terms of this leveraging what already at Arize is a shift left or as we would think extend left motion. And we believe that at Dynatrace, we can leverage that same motion with the Dynatrace portfolio as well.

Jim, do you want to talk about DPS?

Yes. And the quick thought on -- yes, that is our intent. It won't be day 1, but our intent is to add Arize as a capability within DPS. So we will do that. That is certainly on the road map. But as Rick mentioned, and just to make sure I clarify it again, so we intend to have them operate relatively stand-alone.

So we don't want to slow anything down. They are rapidly moving. They are rapidly growing, and we want to continue with that motion. We want to help them relative to access within our installed base. Obviously, over time, we can get them to DPS, but we can immediately get them access to our enterprise base where there's significant interest. And then obviously, on the offset, it's us getting into their customer base that we're not in today.

The next question is coming from Howard Ma of Guggenheim Securities.

Congrats on the acquisition. My question is, are there any technical barriers in connecting Arize's back end and dashboards for evals and agent quality with Dynatrace's existing APM and infrastructure health dashboards? Because as you just mentioned, Rick, it sounds like it's not on the near-term road map by choice. But do you think that is what's needed to combining the traditional observability and AI observability to truly unlock value for your customers?

I would say, Howard, over the course of time, yes, we obviously want to unlock even more synergistic deployment. But I will tell you that there are many overlapping customers, as we referenced already, that are using Arize today and Dynatrace, and they're using Arize for the preproduct and they're using Dynatrace for the production area. And the result of it is we will, over the course of time, as I've said a few times, fairly methodically integrate those solutions so that on the one hand, we don't cut off the Arize roadmap because it's incredibly compelling.

And we want to make sure that we're leveraging that road map and the velocity -- the sheer velocity of innovation that this company is delivering, which is just incredible. But of course, we also want to then provide that 1 plus 1 equals 3 connectivity on the back end where it's appropriate. So we're going to -- we'll be looking at that. We'll evaluate the timing of that integration. And we'll say more about that over the course of time as to how that actually does integrate to be able to leverage the capabilities together to achieve the end that you described.

The next question is coming from Joshua Tilton of Wolfe Research.

Congrats on the deal. Two kind of follow-up questions for me. First is, is there anything we have to think about in terms of rev rec before this becomes part of DPS? Like today, DPS is a usage consumption model that's sort of recognized ratably. Will this be more of like a pure consumption revenue consumption rev rec component to your business model until this becomes a part of DPS?

And then my follow-up question is just, I think you mentioned I'm going to give calls now, but I think Anthropic spoke at their conference but they're not a customer. Is that because they're using the open source version today? And what I'm trying to understand is like historically, the view has been it takes a lot of dollars, a lot of resources to run open source internally.

So the push there is once it scales to a big enough deployment, it makes sense to go for the enterprise license. But if the pitch here is we're going to go after sophisticated AI natives, like is it going to be harder to get them to move from open source to enterprise because maybe they do have the sophistication, the tools and the team to run these open source models themselves?

So I'll take the first one, and then I think Rick can take the second one. So the rev rec model is exactly the same as Dynatrace. It's actually something that has -- as the CFO, I like. So they sell annual contracts. rev rec is ratable. It's ARR when they book it. They bill in advance. And so the -- call it, the revenue model is exactly the same as Dynatrace, no change whatsoever. So migrating them over to DPS will be quite easy.

Rick, you can comment maybe on the Frontier model companies versus other AI natives. What I'd say to start is that you got to remember, this is a young company. And they're just -- they're early days in penetrating a lot of these opportunities that they are agnostic to the frontier models, all the frontier models are available within their platform. The fact that they are large within the broader AI community just tells you that there's an opportunity.

And so the fact that they haven't penetrated there is not really a function of anything other than I think they've gone early on into the enterprise customer base because enterprise customers are experimenting and doing a lot more eval-oriented activity with AI. And so there is money to be had there. Obviously, as they expand over time, we expect that to broaden into personas that we actually haven't gone after. And Rick, I don't know whether you have anything else to comment.

Yes. I mean we've talked a little bit about this open source to enterprise conversion. And I think that, that is the motion that we really want to embellish and lean into because it is just a huge opportunity to take advantage of millions of deployments of Phoenix, for example, to develop penetration into the market and awareness.

And then obviously, that needs to convert into a monetization model, which exists at the enterprise. So this is precisely the motion that we'll be leaning into and that there are a couple of sales plays to be able to drive that. One is directly to the enterprise from Phoenix user.

Another one is through AX Pro, which is essentially credit card capability within the Arize AX portfolio to get there, which would be a smaller group of users and then move that from there to the enterprise. So a couple of methods that they're running at sales plays today that we think that we can help enhance from the Dynatrace side.

The next question is coming from Koji Ikeda of Bank of America.

I wanted to ask -- go back to the offensive versus defensive aspects of the acquisition. And so what would Dynatrace have to do if you decided not to acquire Arize or a competitor acquired it? Question number one.

And then question number two, just wanted to make sure on the buyback. When I read the press release, it sounds more about any -- no near-term impact to the share buyback. So I just wanted to be sure there wasn't anything that we should be thinking about from a long-term standpoint on the buyback, especially with the Investor Day coming up.

Koji, I'll take the first one on what we would have done otherwise. Frankly, the -- what we would have done otherwise answer is that we were on a very, very solid plan of record. We had identified or I should say, have identified a 7-layer stack associated with AI observability. We are strong in 3 or 4 of those areas already.

We were beginning work or somewhere down the roadmap and working in some of the other ones, for example, in evals on our own. We have made a fairly significant progress with regard to evals with things like model drift and bias and hallucinations have made good progress in those areas.

So we've been working on this now for, as I say, on the order of 18 months or so as part of our third-generation platform that we've been talking about now for the last period of time. So we have not been standing still. We have been innovating at incredible pace inside Dynatrace and bringing these capabilities.

We've talked about BlueBox as a mechanism for assisting agents and actually writing code, operationalizing that code, moving it to production and evaluating it in production. This is all within essentially the AI observability umbrella of ensuring that AI systems are working and that they're working well. So that was plan B, if you will, or maybe I should say that was plan A, which is our own internal development with essentially even reallocated dollars within our own R&D investment envelope to make sure that we were investing appropriately in AI observability.

Having said that, Jim and I, along with our respective teams on the product side, the go-to-market side and elsewhere, obviously want to win in observability overall and AI observability, in particular, as an emerging category. And that's why we've been looking quite broadly at various different opportunities and companies in the space and ultimately ended up with the company that we believe is the category leader and will achieve the strongest results on a combined basis with Dynatrace for all the reasons I've articulated so far.

Yes. And then just quickly on the buyback, Koji, the reason we included it in the prepared remarks is we don't expect to have any change in what we're doing with the buyback. Buyback will always be a component of our kind of capital allocation priorities, both kind of near term and over the long term. So don't expect any change to our buyback philosophy.

The next question is coming from Miller Jump with Truist Securities.

Congrats. Just want to ask a quick follow-up on the enterprise. Clearly, there's a range of AI adoption right now. Is this something that your enterprise customers are asking you for that you think they're ready to adopt in FY '27? Or is the increase to the guide only a function of what you've added through this acquisition and that cross-sell opportunity is more about the out years?

So let me take that. So the increase in the guide is a function of what their core business is doing plus what we think it can do with Dynatrace over the next 6 months. So that specifically is what it is. And to Rick's earlier points, we have seen and heard of Arize in a lot of our installed base enterprise customers. And so we already know they've been there.

We've actually had customers even talk to us about ARISE. So there's a significant opportunity for us to continue to penetrate Arize more deeply into our enterprise base. And again, they get immediate access into our enterprise base. And then for us, even though their enterprise -- their base is smaller, it's our ability to now sell Dynatrace within their customer base. So it's very, very synergistic. So that's the way you should think about it.

I would just add to that, Miller, that when I do customer EBCs, customer meetings, CSO meetings around the globe, AI is in the discussion probably in the first 5 minutes of every meeting. They're trying to figure out how to move AI in production. They're trying to do it in a trustworthy way. They're trying to do it with credibility.

They're trying to deliver, of course, productivity, efficiency, cost effectiveness. They're trying to avoid massive token cost overruns. This is where they're looking for advice and counsel on how to do that. And we can assist them by making sure that there's a thoughtful approach to AI production deployment, AI DLC development and delivery and enable them to be successful with these AI initiatives.

And I think this is where going back to some of my earlier remarks, observability is escalating in terms of its strategic and mission-critical importance in the overall software ecosystem in a way that absolutely did not exist 3, 4, 5 years ago, and it's accelerating with each day that passes.

The next question is coming from Adam Tindle of Raymond James.

Congrats. I just have one question, and it's really around the go-to-market. If I'm understanding this correctly, obviously, Dynatrace is a traditional enterprise-led traditional enterprise sales motion to IT, whereas Arize is now bringing a PLG motion to developers. And as I say that out loud, it just reminds me a lot of Okta and Auth0 and not to get overly specific on a separate deal, but it was one that I think investors remember a lot of sort of this thesis of trying to bring those 2 different motions from go-to-market and different customer bases together.

And that was not necessarily a success story. It led to dropping sales productivity on both sides and they ultimately split them apart. So I just wonder, I guess, maybe the question might be some -- how you thought about that potential risk in bringing those 2 together? And maybe any kind of commentary from Dan on his plans for integration.

Thanks, Adam. Very good question. And what I would say is this is one of the reasons that we're going to leave the PLG motion largely on the Arize side, at least for the near-term future. What we want to do is we really want to ensure that the Arize, PLG motion, which is largely usage adoption penetration based today, really does begin converting into an enterprise sale.

I did mention earlier some of the stats that they see with respect to increased sales cycles that they've seen very tangibly with regard to Phoenix users. So that does exist today. They deploy Phoenix, and that becomes a catalyst for them to be able to more successfully sell into the enterprise, and we want to, of course, leverage that.

We will be -- we will learn from the past, and it's not just the Okta Auth0 example, there are many others as well of how you bring a PLG motion into other sales motions. And this is why we're going to take that one step at a time to make sure that we bring that to a broader Dynatrace, and we do so very thoughtfully. So that's how I would depict that. But we do believe that the developer leverage here for the overall Dynatrace model of being developer present with regard to end-to-end observability inclusive of pre-pro Prad is a core thesis of the acquisition.

Our next question is coming from Yun Kim of Loop Capital.

All right. Great. I'll make this real quick, Rick, does open source business change your monetization strategy a little bit? This question has been asked here and there, but the cross-sell into your installed base may possibly lag a bit given the open source nature and also the need to provide implementation support. And just real quickly, Jim, what's the average deal size today for Arize for those joint customers you have today? And how big do you expect that to be for a typical Dynatrace customer?

Yes. We see the open source model and element very differently. We don't see it as an inhibitor to achievement of actual monetization. We see it as a catalyst. And we see that as a catalyst that will grow over the course of time through the development of a PLG motion that moves from usage all the way through monetization, and we see it in the accelerated sales cycle from a Phoenix user, for example, to becoming a paid user relative to somebody who just begins a POC and goes straight to the enterprise model.

So we see open source as not only a huge mechanism for building an AI-first brand with a rise for maintaining market presence in the AI developer community, but also then facilitating this movement of the motion from open source to open source into paid. And I don't remember the second question.

It was the average ARR size.

And the average ARR size is comparable to what used to be an initial Dynatrace type deployment in the $100,000-plus enterprise type category. All right. I'm going to wrap up. We've spent an hour together. Thank you very much for participating today and joining us on this really, really important seminal day for Dynatrace and for Arize. We see this as a game-changing moment for Dynatrace. The acquisition of ARISE is an opportunity as we see it for us to accelerate market leadership in AI observability.

We add what we believe is the leading AI observability portfolio with ARISE, serves as an additional catalyst for growth. It provides Dynatrace with access to what we see as large and highly strategic AI developer community and what I have seen firsthand to be a world-class team, finally, provides this incredible opportunity in our view for us to build customer and shareholder value.

Please reach out to Noelle if you have any additional questions, and we look forward to connecting with you all at upcoming IR events in the coming weeks. Thank you very much again for joining, and have a great day.

Ladies and gentlemen, this concludes today's event. You may disconnect your lines or log off the webcast at this time, and enjoy the rest of your day.

All right. Top of the hour. So welcome, everyone. Kicking off day 1 of the KeyBanc 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.

Yes. All right. Well, Rick, thank you so much for being here. Noelle, you as well, enjoying Park City, Deer Valley here.

I wanted to talk about earnings, and it was a great earnings to kind of lead into this week. But 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. I mean, we used to have deterministic applications. And it was very kind of predictable in the way these applications change, but the application ecosystem is changing in terms of the UI. We have headless applications, non-deterministic applications and the way they behave.

So 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. So the observability market we see is going through a new era, an evolution that is occurring in a major way right now. And I think what seems to be clear, 6 months ago, everybody was worrying about SaaSpocalyspe, that it was going to impact every software company in a consistent way.

And I think over the last 6 months, it's sort of 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. And we would submit that observability is clearly an AI winner from a market space point of view. And 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.

And so 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's always optimized, that it's 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, 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. And so AI observability is extending the requirements of observability overall.

And 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. And as another use case, you [Audio Gap] elements than we've seen before.

Yes. Now I've had the view, and I'm going to kind of lead the witness here on this one. But look, the nature of these AI applications, the nondeterminism, 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 the infrastructure doesn't really give you that insight, and logs doesn't really give you the insights to understand the activity happening at the application layer, the reasoning, et cetera. So 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?

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 sort of the bread and butter of APM, if you will. I mean, this is why we became known as Dynatrace. It was because it was really all about APM, and it was about dynamic tracing. And so this is the -- this is -- the fundamental building block of Dynatrace was in this area.

So 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. And no longer is it the case really in our view that you can have customers of any size or substance running APM with vendor 1 and infrastructure management or monitoring with vendor 2 and really, user monitoring with vendor 3 and security or app security with vendor 4 and so on down the line, log management with vendor 5. And the reason is because you need to have the best data.

And you talked about deterministic, determinism, or we think of it as deterministic AI. What you really want is you really want to have confidence in your answers. And the way to have confidence in your answers is by having the collection of all these domains using all data types, traces, metrics, logs, really, user data, et cetera, all in one existing data lake house. 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. Those answers then become more actionable in an AI world, where you then want agents to be able to take action on those insights.

And so 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. And those agents can then ultimately lead to what we think of as autonomous operations.

And 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 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 from end-to-end. And that's really where the next generation of value is in observability solutions.

Right. So I want to come back to that topic, but I want to kind of recap some of the stuff from last week with earnings. So candidly -- and I think you guys acknowledged this, that when you gave your initial fiscal '27 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 just laid out.

And you guys had conviction, and you guys have been talking about the acceleration thesis for quite some time. So to see 1Q come to fruition and meaningful inflection in the net new ARR growth and acceleration. So talk about how the thesis is playing out to drive towards acceleration of overall top line, what -- where the strength came from in the quarter?

Yes. So I would -- first of all, 41% net new ARR organic 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. So it was a very strong performance, and we've been operating sort of in the mid-teens. On 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.

And so it did, in our view, derisked the beginning of the year guide, which we were providing indication of reacceleration essentially of ARR growth for the year. So FY '26 was about stabilizing ARR growth, FY '27, about reaccelerating that ARR growth. And I think coming out of our beginning of the year guide, some of the skepticism was, well, can you achieve that? And Q1 really did, I think, hit that quite hard on our ability to have conviction in that ARR reacceleration plan.

So that is that. I would say that maybe, Eric, three core themes to the earnings call and how we were able to achieve it. One core theme was AI. We've talked a lot about that, so I won't belabor it unless you want to come back to it. But AI workloads are growing, AI production of code is growing, AI, AI, AI. And it's a real thing for observability because it is requiring more observability. And so it really is a direct tie toward that.

The second was logs. We have -- logs are one of the data types, of course, that we bring in, log management, one of the domains. And we some time ago had set expectations for delivering during FY '26, $100 million in logs consumption. We did that only a couple of quarters ago, and we've just seen an explosion in our logs business to the point where we now have eclipsed $200 million in logs consumption within 2 quarters. So logs consumption basically doubled in 2 quarters. And this, I think, gives really the ammunition for us to feel very strong, positive about the evolution of that business.

And then third and finally was really a very, very strong new logo quarter for us with our new logo ARR up a record 160% plus year-over-year. Which is just, again, a phenomenal achievement in terms of the ability to really show conviction of new customers coming on to the platform and what they want to do with Dynatrace.

Right. So taken 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? Like what's causing this inflection? What's the durability of this new logo contribution?

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 2000, these kinds of customers realize that they can't have 5, 6, 10 -- I met with a customer just last week who said they had 16 different observability solutions.

And 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 into a network operations center, you have a sea of people staring at a sea of glass. And they're looking at alerts, sort of red, yellow, green, and they're trying to parse dashboards.

That environment is impossible, impossible in a world where you see the degree of acceleration in 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 and 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. And this is what organizations are realizing.

And I think we're seeing it 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, vastly reduced number of observability solutions. And that's what's, I think, really driving the new logo movement.

Yes. So people are doing more on the initial contract or 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 the broader platform. But you're seeing that activity play out in...

Yes. I mean, another -- not to get into too much detail about it is our pricing mechanism with our Dynatrace Platform Subscription or DPS is another part of that. We used to price some number of years ago based on SKUs. So you had to buy a certain amount of APM and a certain amount of infrastructure monitoring, a certain amount of log management, et cetera.

And that is just not how our customers want to consume the platform at this point. They want to consume the platform holistically. They want to -- maybe they need more log management or less month by month. Maybe they need more or less APM month by month or a full stack or whatever it might be.

So the contracts that we enter into now based on Dynatrace Platform Subscription are up. You just make a commit, $1 million or $10 million, whatever your commit might be. And 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. And so it enables to smooth out your usage of the platform and how you're using it, where you're using it.

DPS has now moved to 75% of our ARR, more than 2/3 of our customers. And so this does also provide a catalyst to new customers coming on board saying, "Hey, look, I'm just -- here is 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.

Yes. Now logs. Now 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've had a lot of confidence, and it sounds like you have a lot of visibility. So can you just share a little bit more, like why you have this confidence, this visibility?

Like -- it seems like when I was at the Dynatrace conference earlier this year, like talking to a lot of customers that they have logs, but they're early in their journey and they have a lot of pipeline of incremental workloads that they need to onboard and get more users on to the platform, looking at logs. So just -- what does the journey look like with the 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. And 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.

So this is mostly brownfield, not greenfield-type deployment. And so what they're looking at it as, geez, do I shift my logs to Dynatrace, not do I bring up new workloads on Dynatrace with logs. Although there's 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's on the market. I can't tell you the number of customers I've spoken to at CTO, CIO level that have come back and say, "My god, Rick, my log cost is meteoric." I'm going to -- I've actually heard that word before, a log cost is meteoric. And it is because of the volume of logs in addition to the overall cost.

And there are multiple ways where we can help solve that. 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 don't have to ingest them all into the platform, which, if you ingest and store those logs, has higher cost. So by enabling a better filtering system of those logs, then that can reduce that further.

And finally, it -- we would submit that you don't need all the logs if you have traces, metrics, really, user data and other data because the combination of those data sets 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. And so 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. So the outcome you're getting from observability is better if you have logs as a component of it. For -- since I've been at Dynatrace, gosh, now almost 5 years. And it never made any sense for me or to me to have logs over here with one vendor and traces, metrics, really 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.

So bringing all that back to the simple answer, simple answer is I can do it at lower cost, often substantially lower cost than what you're paying for it today. And I can deliver 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.

Yes. And then lastly on AI, to come back to that. Maybe -- we talked about technologically, like why this is important in how the world is changing. But tie it to one, just like what are you observing within your own customer base in terms of like AI maturity?

And then secondarily, kind of the financial contributions that you're starting to see. We talked about in the call, 1.5x customers that are using AI or monitoring AI are consuming 1.5x those that aren't. So what's the maturity of the customer base? And then secondarily, why are we seeing an inflection or much higher growth with those that are adopting Dynatrace for AI?

Well, we -- in terms of AI maturity and AI deployment in production, we still think we're in the early innings. It really, in many ways, has only just begun. Having said that, we did provide some metrics on the call. For example, we're now observing AI workloads for more than 1,000 customers. So that's pretty sizable. We are taking agentic actions, we've actually deployed Dynatrace agents to take action to do things like auto remediation and triage in over 800 customers.

And then lastly, you mentioned the 1.5x. We are seeing -- and we can take cohorts and look at cohorts of -- 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. And in part, this is simply because AI workloads are pretty chatty. They generate tons of telemetry. And managing that telemetry and then ingesting that and then taking that into a consumption model that's been doing a DPS drawdown, a Dynatrace Platform Subscription drawdown, is something that we're seeing.

And 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's great. Second of all, it drives AI observability, which are new categories or new capability of answering that question I mentioned earlier, which is, is it right or is it accurate. And thirdly, anytime you use a Dynatrace agent to take action, we'll pass through some of the charge of that because we have real cost there, too. So multiple different ways to monetize AI workloads incrementally.

Right. Now DPS. Now this has been something that's been compelling for this year, coming year as we have 3 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 fiscal '24, fiscal '25 cohort of DPS customers come up for renewal? How should we think about that opportunity for the balance of the year?

I think the primary way to stick away -- and just to make sure that you're all tracking that. Since we put DPS in place 3 years ago -- and most of the DPS contracts are 3-year contracts, even though they have annual resets. What happens is that you're now ending the end of that 3-year period for many customers, and those customers are having to renew. And so the question is, are they renewing based on their consumption of the platform? Flat, up, down, how would that play out?

And the good news is -- we talked about consumption. We mentioned on last week's call, the consumption continues to grow north of 20%. And so 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, and your consumption in the platform is north of 20%, over the course of time, you would expect ARR growth to migrate towards your consumption growth number. And so the question is how does that happen? And 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.

Now Q1, Q2, probably not too much impact there because those are really the first couple of quarters of DPS. And so 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, that the expansion business or expansion metrics would begin to tick up as well. Pretty sticky metric, 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, kind of. Right?

Yes, you could put a DPS contract in today, and you could overconsume your contract in the first 3 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 us based on consumption.

Yes. Maybe last minute. The other really compelling, interesting part about the observability stack is the ability to automate it to the AI kind of, I don't know, AI for observability.

Dynatrace Intelligence. What's been the traction at this point? How has the customer feedback been in terms of the value that they're getting from this? And ultimately, like how does this benefit the model from an uplift perspective and monetization strategy?

Yes. I mean, I mentioned a few different ways we can monetize AI workloads incrementally, so I won't go back through those. But 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 is -- has AI very much top of mind. They're driving AI workloads, they're driving agents to create code. They're working to accelerate application delivery and beyond.

And they are realizing rapidly that you simply have to have an observability solution that can oversee those workloads. And so that is -- 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 accelerate the consumption.

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. So we see those as -- we don't want to get ahead of ourselves in changing guides. But we simply don't know how rapidly that gets monetized.

And so we've taken a conservative approach to that, but we do view that the AI areas here become critical. And AI observability is -- absolutely is a $10 billion incremental sector or category to the $80 billion or so overall observability space is going to be phenomenal, and it's growing at 40%, 50%. So 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.

Yes. Super exciting. I love all the opportunities and tailwinds in this space. And observability is top of mind, for sure.

Awesome.

Thank you.

Greetings, and welcome to the Dynatrace First Quarter Fiscal 2027 Earnings Conference Call. [Operator Instructions] 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 discuss 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 presentation, 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 view of the noteworthy highlights from the quarter. Total ARR grew 17%. Net 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 strong reflected healthy enterprise demand for end-to-end observability, stronger execution and growing complexity across customer environments. We are seeing AI contribute in 3 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 in 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 enterprise is 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 by $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 3 of the questions that matter most today in an AI first world. The first is it working. Our applications, infrastructure and systems working as intended. This question is about business resilience, and it's 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 is intended before it ships is emerging as one of the most important aspects of observability.

The third, are agentic 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 life cycle and trust the results. Code fit well, ship safely and runs reliably. The last question is where our newest offering, Bluebox comes in, built for AI first teams Bluebox 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 changes live, it's agentic SRE capability finds root cause and returns an evidence-backed fix, with the developer and control across the entire AI delivery life cycle. 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 led both people and agents act with confidence. These core differentiators give customers on 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. Officially, we are purposefully building for an open, interoperable ecosystem. Our newly acquired Bindplane 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 environment 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 reflected in higher consumption, broader platform adoption and the following 3 new monetization opportunities.

First, AI workloads are similar for observability workloads and 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 2 quarters ago. Fine plan facilitates easier data ingestion and it is already performing ahead of plan.

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 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 goals or MCP integrations or when one of our agents like the SRE or this 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 2 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 1.5x higher than that of non-AI cohort customers. Our platform integrates natively with Quad 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 7-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 6-figure ACV expansion also nearly doubling ACV with a leading recreational vehicle retailer. This customer use Dynatrace is their operational system of record while building a custom CRM application through AI-assisted development, generating approximately 7 figures of savings and expanding usage of our platform.

A leading digital insurance provider use 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 8-figure ACV new logo win with one of Latin America's largest financial institutions. Now 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, Gartner named Dynatrace a leader in the Gartner Magic Quadrant for observability platforms for the 16th consecutive year. Gartner describes 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. We believe this recognition validates both the strength of our architecture and our ability to help customers confidently scale AI and agentic workloads.

Finally, as many of you have seen, Jim plans to retire from Dynatrace by the end of the fiscal year. We will conduct a thorough search for his successor over the coming months and I am confident we will have a smooth transition. Jim has been an exceptional partner playing a critical role in scaling the business, strengthening our financial profile and positioning Dynatrace for its next phase of growth.

I am deeply grateful for his leadership and many contributions and we will miss his valuable insights and guidance when he retires. To wrap up, Q1 was a tremendous start to FY '27 and a powerful reflection of the momentum we are seeing across the business. Organizations are increasingly looking to consolidate on platforms that can help them manage growing complexity, unlock greater productivity and realize the full potential of AI.

As enterprises accelerate their AI initiatives, we believe Dynatrace is uniquely positioned to help them innovate faster, operate more efficiently and maximize the return on their technology investments. In an AI-first world, observability autonomous operations become critical day by day. We are more enthusiastic than ever about the opportunity.

Jim, over to you.

Thank you for the kind words, Rick, and good morning, everyone. Q1 was an exceptional start to the fiscal year. Once again, we exceeded the high end of all our top line growth and profitability guidance metrics fueled by record new logo ARR growth, expanding traction in logs and continue robust consumption of the platform. These results reflect broad-based momentum across the business and reinforce our conviction that we are on the path to ARR acceleration this fiscal year.

Let me review our first quarter results in more detail. Unless otherwise noted, all growth rates are year-over-year and in constant currency. Starting with ARR. We ended the quarter at $2.14 billion, up 17% year-over-year. Q1 net new ARR was $85 million, adjusted for foreign exchange movements, growing 66% from a strong first quarter last year. Excluding the $13 million ARR contribution from our Bindplane acquisition, Q1 net new ARR was $73 million or 41% organic growth.

This strong performance was driven primarily by record new logo ARR growth and our continued success in winning large end-to-end platform consolidation opportunities, including an 8-figure ACV. The maturation of our go-to-market transformation, which began in fiscal '25, is clearly reflected in improving net new ARR productivity. To help illustrate the momentum in the business, we believe trailing 12-month net new ARR is a useful metric because it smooths the quarter-to-quarter impact of large enterprise transactions.

Due to that lens, we have now delivered 4 consecutive quarters of acceleration in trailing 12-month organic net new ARR growth. Growth reached 17% on an organic basis in Q1. up from 12% in Q4, demonstrating continued momentum. In Q1, we added 122 new logos to the Dynatrace platform. The average land size continues to build and was nearly $285,000, contributing to record new logo ARR growth of more than 160%. We remain focused on landing with high-quality customers with strong expansion potential.

Our value proposition continues to resonate with enterprise customers that are outgrowing DIY or commercial point solutions and AI-driven complexity is only increasing the need for a unified platform. Customers are seeking business value through tool consolidation and are turning to Dynatrace for the depth, breadth and automation of our unified AI-powered observability platform. Simply put, we believe the Dynatrace platform was built for the AI era.

Once customers experience the benefits of the Dynatrace platform, they often expand quickly. Average ARR per customer continues to increase and is now well over $500,000, reflecting broader adoption and the value we deliver. As we have shared in the past, given the significant cross-sell and upsell opportunities within our enterprise customer base, we believe the average ARR per customer can exceed $1 million or more over the medium to long term.

Gross retention rate in Q1 remained in the mid-90s, underscoring the strategic importance of Dynatrace as a mission-critical component of our customers' operations. Net retention rate, or NRR, was 110% on a 12-month basis. We continue to see broader usage and deeper adoption across the platform, particularly in log management. Logs remains our fastest-growing product category, growing -- continuing to grow well above 100% and reaching nearly $200 million in annualized consumption.

As a reminder, we crossed the $100 million milestone just 2 quarters ago. We expect logs to remain a powerful growth driver, accelerating consumption while providing a path to future ARR expansion as customers consume their commitments and move into larger contracts. Turning to revenue. Total revenue was $555 million and subscription revenue was $530 million, holds up 15% and 100 basis points above the high end of guidance driven by strong net new ARR performance.

Moving to profitability. Non-GAAP operating margin was 29%, exceeding the high end of guidance by 100 basis points driven by revenue upside flowing through to the bottom line and some expense timing between quarters. Non-GAAP net income was $140 million or $0.48 per diluted share, $0.03 above the high end of our guidance.

Now turning to free cash flow. We have updated our free cash flow definition to adjusted free cash flow, which excludes the impact of restructuring acquisition-related and other nonrecurring cash expenses. This better aligns the metric with our non-GAAP operating income definition and provides investors with a clear view of cash generation from ongoing operations of the business. We generated $309 million of adjusted free cash flow in the first quarter.

Given seasonality and quarterly variability in billings, we believe this metric is best evaluated on a trailing 12-month basis. Adjusted free cash flow over the trailing 12 months was $579 million or 28% of revenue. This includes 500 basis points of impact from cash taxes. On a pretax basis, adjusted free cash flow was 32% of revenue.

Turning to capital allocation. We increased the pace of our share repurchases in Q1, buying back 7.1 million shares for $275 million compared to $224 million in Q4. This increased level of repurchases reflects our confidence in the company's operational momentum, long-term growth and cash flow trajectory and our view that the shares remain undervalued. We will continue to take a disciplined approach to capital allocation, balancing investment in innovation and growth while also returning capital to shareholders.

Moving now to guidance. Our confidence in accelerating ARR growth in fiscal '27 has strengthened following our first quarter performance. Demand for observability remains robust. The growth drivers we outlined in May continue to trend positively and our teams are executing well. That said, we are still early in the fiscal year and consistent with our historical approach, we will remain prudent in our guidance.

Lastly, with nearly 40% of our business denominated in foreign currency, the strength of the U.S. dollar since our last call creates a sizable headwind. We now expect FX to be a headwind of $14 million to ARR and $4 million to revenue. This represents an incremental headwind of $23 million to ARR and $19 million to revenue. And with that as context, let me summarize our updated full year outlook that we detailed in this morning's press release.

ARR, we are maintaining our constant currency ARR growth guidance of 15.5% to 16.5%. As usual, we plan to revisit our full year ARR growth guidance at the midpoint of the fiscal year. For revenue, we are retaining our constant currency total revenue and subscription revenue outlook by 25 basis points at the midpoint, reflecting the operational outperformance in Q1. We now expect total revenue and subscription revenue growth of 14.5% to 15% year-over-year.

Turning to profitability. We are increasing the high end of our full year non-GAAP operating margin guidance by 25 basis points to 29.75%, while continuing to dose top line growth acceleration with margin expansion. We are also raising non-GAAP EPS guidance for a range of $1.97 to $1.99 per diluted share, an increase of $0.04 at the midpoint. This outlook assumes a diluted share count of 295 million to 297 million shares and an effective cash tax rate of 18.5%.

Finally, we are maintaining our adjusted free cash flow margin guidance of 26.5%. Our prior guidance was based on our current free cash flow definition, which didn't reflect the new adjustments. As a reminder, for your cash flow models, seasonality and billing patterns historically result in higher cash flow in the first and fourth quarters and significantly lower cash flow in the second and third quarter.

As such, we believe trailing 12-month performance remains the most meaningful way to evaluate this metric throughout the year. Looking at Q2, we expect total revenue and subscription revenue growth of 15% to 16%. Non-GAAP operating margin of 29.5% to 30% and non-GAAP EPS of $0.48 to $0.49 per diluted share.

In summary, we are pleased with our strong start to fiscal '27 and remain confident that we are on the right track to accelerate ARR growth. We are focused on executing against the significant opportunity ahead in a rapidly growing observability market. We have a proven track record of consistent execution and remain committed to delivering a balanced combination of strong growth and profitability while continuing to invest in opportunities that will drive long-term value.

With that, we will open the line for questions. Operator?

[Operator Instructions] Our first question comes from the line of Brent Thill with Jefferies.

Just on the net new adds. You mentioned the overwhelming strength. Maybe if you can just drive into what you're seeing in terms of the success with those new logos where you're finding them? Any more color would be helpful.

Brent, this is Jim. That again, I think what you've seen, this is not the first quarter that we've seen strength in new logos. I think this is the fifth consecutive quarter that the average land size has continued to build. This quarter alone was almost $285,000 on average land size. So it is a function of some of the go-to-market changes that we made 2 years ago, where we waited investments in strategic and enterprise accounts.

And what you're seeing is growing traction. And specifically, what I talked about roughly 2 years ago about this emerging trend of customers looking for platform consolidation, tool consolidation and going from fragmented tools to 1 vendor, we are benefiting significantly from that. That's been our #1 sales play, and we expect it to continue even in this environment where people are evaluating where budgets go.

Consolidation actually is an economic benefit to customers because when they consolidate fragmented tools and they go to one vendor, they can get better economics and their outcome from Dynatrace. So those are really the reasons why we're benefiting and continue to benefit from new logo lands at a very large size.

Our next question comes from the line of Gray Powell with BTIG.

Great. And congratulations on the really strong results. So yes, if I -- I'm just looking at the numbers and I just want to make sure that I understand everything correctly. But if I back out buying plan, it looks like you need to grow net new ARR by about 17% to hit your fiscal '27 guidance. I know it's only 1 quarter that you're going to wait until Q2 to update, but you just posted 40% growth in net new ARR. It sounds like there are a lot of tailwinds out there, particularly log monitoring. So can you maybe just help us rank the upside drivers that you saw in Q1? And then just how should we think about the sustainability of those drivers?

So Gray, it's a great question. And actually, your math is right, but the math would suggest for Q2 through Q4 at the high end of our guide that you're at the high teens growth rates. We're coming off a quarter, obviously, organically where we grew 41%. You're not going to see that every quarter. We've talked about that in the past that the nature of an enterprise sales motion, in particular, where we are going after large strategic accounts is your land sizes are going to be very big, both for new logos and for expansions.

And so it is a bit of the nature of the business. And so you're going to see timing. We had an exceptional start to the year, actually stronger than we expected. We knew the first half would be strong. But to your point about tailwinds, we expect the tailwinds to continue. I expect we will continue to be able to land large with new logos. I expect that in the back half of the year where we have a significant increase in our DPS contracts that are coming up for renewal that you're going to have an opportunity for expansions.

Consumption is continuing to grow at a robust rate. logs, as we mentioned, is nearly $200 million. So there's just a lot of momentum building in the business. We have a lot of confidence that we can continue that. I'd say one of the questions that I got from last quarter's earnings call was, "Hey, this guide looks a bit ambitious, Jim. Hopefully, what this demonstrates is that there is building momentum in the business. This is the fourth quarter in a row that we've had trailing 12-month improvement in net new ARR growth." So that smooths out variability quarter-to-quarter, 17% in the first quarter on a trailing 12-month basis organically. So this business is showing significant momentum, and we expect that, that will continue.

I think Jim definitely covered the highlights, great. It's about consumption. It's about logs. It's about those elements. But one element that I'd love to add in is just the notion that AI is driving even greater consumption of the platform. So we talked about some of the metrics in the prepared remarks. But those workloads are driving increased consumption. It's also also shifting the market mindset to an AI observability, which we also referenced. So that's an incremental driver to some of the other ones that Jim already mentioned.

Our next question comes from the line of Will Power with Baird.

Okay. Great. I guess, first, to you, Jim, just congratulations on your retirement, and thanks for all the help here over the years. I guess, Rick, you made the comment that you're seeing organizations increasingly run autonomous operations. I think you said just sequentially, the number of organizations moving to autonomous operations that have gone from 500 to 800.

Maybe just talk about kind of the key drivers of that. I mean, AI, obviously, feeds into that. But yes, any color you can share on kind of what's driving that? And then just anything you can drill down in terms of how you monetize that? I know DPS is the contracted vehicle, I guess. But the color on that part would be great.

Sure, great questions. Well. So to start, we absolutely see a Gentech evolution, both in our traditional workloads as well as AI workloads. So for example, perhaps a couple of quarters ago, I mentioned the evolution of traditional observability in moving from reactive to proactive to predictive on to autonomous. And it is in this autonomous environment where you get the benefit of using Dynatrace's deterministic AI to provide causal interpretation and insights as to what's going on in that environment. Bad deterministic AI enables agents to then take action in an autonomous way and those agents can actually provide then results to allow for elements such as auto remediation.

So that's in traditional workloads, and that's where agents can actually take action. On the AI observability front, you're adding incremental questions like, for example, is the information coming out of LLM is accurate. Again, agents are then helping you build and run code. In both areas, you have agents that are increasingly operationalizing your observability systems and in both cases, we expect to monetize those agents directly to enable us to benefit from that agenetic usage. And so it is, as you mentioned, these 800 worker -- 800 customers using our agentic capabilities up from roughly 500 last quarter that we'd like to see because that's increased aging usage.

One thing that I'd add to that, Will, and I think to your point is while enterprises are still early in their adoption of AI, I think to Rick's point, it's building -- and I think what we outlined there is consumption is growing at a very robust rate for the company in total and customers that are leveraging AI grow at 1.5x that rate. So there's significant volume increase in consumption that, again, with the nature of the DPS contract, you don't have to purchase something, you had access to full capabilities of the platform, you will burn down your commitments earlier and our expectation is that this continues, that should lead to a source of future expansion.

And perhaps just to put a pin in it, I did cover this in the prepared remarks, but just to highlight it because I really do think it is important, we're thinking very acutely as to how we monetize and evolving AI space. Number 1 is just around increased consumption of AI workloads. They consume way more telemetry. Number 2 is that you've got incremental workloads that require AI observability, namely, are they delivering accurate responses coming out of LLMs, and then finally, we're monetizing, as I mentioned earlier, the agents themselves. So 3 different very discrete methods of monetization of AI workloads and a genetic work models as we look at.

Our next question comes from the line of Keith Bachman with BMO Capital Markets.

I wanted to ask about the renewal installed base. And if you could just provide a little bit of context on how that installed base looks over the next 3 quarters versus what it was in the June quarter? Is it frankly, is it a greater target rich audience when you think about that installed base, any nuances that you want us to consider as we look out over the next 3 quarters. And additionally, any update on how that installed base when the renewals unfold the behavior.

In other words, what's the ratio that you're picking up as those customers, in fact, renew their DPS contracts any change in behavior, upsell rates associated with those DPS renewals, in particular, as we think about what that installed base may do. And I'm going to try to sneak in. Just Jim, any comments on what BuyingPlan will grow over -- you mentioned $13 million, which was ahead of our number, but any comments on how that will grow and contribute to the ARR? That's it for me.

Okay. A couple of questions in there. So -- the -- as we've shared before, Keith, that the weighting of our renewals, DPS and just other non-DPS renewals are very weighted to the back half of the year. And so Q1 and Q2 are light renewal quarters. And so I think the statistic is for DPS, I think 70% of our renewal activity or annual resets actually occur in the back half of the year. And so I've shared before that consumption continues to grow at the rates we're expecting, we believe that you should see NRR inflection in the back half of the year. I don't think you're going to see it in the first half of the year just because it's a light renewal period.

And as I shared before, your DPS contracts this year, you're having all 3 cohort classes come up for the first time. And so if consumption continues to grow at that rate, we do expect to see an improvement in expansion activity. And I'd say what we saw in Q1, very consistent customers that some customers grow on demand and some customers do expansions. And so again, because it's a light renewal period, you're not notably seeing an uplift in our ARR yet, but our expectation is you will in the back half.

And relative to Bindplane, you're right. So Bindplane, I think we used big numbers when we said $10 million. So what we inherited was $13 million. We are very pleased with buying plan. It's exceeded our expectations. And you should expect that, that will be a contributing source to aid our logs business to continue to accelerate at the rates that it's done because buying plane will be an accelerant to logs. And so we expect that to be an additive source. We haven't necessarily outlined a specific growth target for it, but you could expect that it's going to grow at a robust rate.

Our next question comes from the line of Andrew Sherman with TD Cowen.

Jim, congrats on your retirement. It's been great working with you. is it fair to think that the deals that slipped in EMEA last quarter closed, how did that region perform in the quarter? And then globally, how is the pipeline of big deals in Q2 and for the second half?

So first, thank you. I'll just remind you, I'm not going anywhere yet, so you guys are stuck with me for a while, but thank you for the kind words. Relative to EMEA, you're right. One of the -- we saw a little bit of softness in EMEA in the fourth quarter. EMEA actually had a very strong start to the year. So EMEA had a good Q1 and so -- I mean they weren't the primary source of the growth for the quarter, but they had a rebound from Q4 to Q1.

And then your second question was on pipeline -- the pipeline. Pipeline, again, pipeline continues to be weighted to large deals. That is not new. It's the nature of our sales motion. -- which is why we've mentioned that having a trailing 12-month metric to look at net new ARR as a good lens because it smooths out the timing that you'll see with large deals like that, but the pipeline continues to be robust.

Our next question comes from the line of Fatima Boolani with Citi.

Jim, I wanted to follow up with you on something that you were potentially hiding on last quarter and now because you started the new fiscal year that brings me to ask you, there was some consideration around potentially having more, let's just say, elastic or more premium healing pricing on demand consumption behavior.

Knowing what you know, seeing what you're seeing in the pipeline as it relates to the renewals cohort, their behaviors, their consumption patterns. I'm wondering if you maybe have an update or a finer point you could put on the decision around more premium pricing elasticity on ODC and how that factors or doesn't factor into your guidance for the remainder of the year?

So the short answer to that question, Fatima, is that we've not made any decisions to change our pricing mechanism for on-demand consumption. As you mentioned, we do not charge a premium. It is something that we've evaluated and continue to evaluate internally. So within my guidance does not assume any change in that. That is not to suggest that it wouldn't make a change. It's just not something that we've contemplated yet.

And obviously, when you make a change like that, you'd have to roll that out over a period. You could do that for new customers, you'd have to figure out the timing of doing it for existing customers. So even doing it would not have a material impact on the fiscal year.

Our next question comes from the line of Sanjit Singh with Morgan Stanley.

Congrats on the strong net new performance in Q1. It seemed like this quarter, the strength of post business, but really the story is the new logo performance, I think kind of more than doubling the size of the average lands. When I think about that NRR and I think about just the momentum that you guys are seeing, explosive momentum you're seeing on the log side of the equation. I guess the question is why is the logs driving that improvement in NRR. I know we have like the renewal cohort in the back half, but why was a bigger driver for an NRR improvement?

I thinks it's all timing, Sanjit, as we've said, that it everything is dependent upon who are the customers that are driving significant logs consumption. And so it's a bit of the nature of when do those customers fall into their renewal cycle. And again, as I mentioned, Q1 and Q2 are light renewal quarters. And so we're already seeing some of it, but just happened to be quarters where you're not going to see the same level of expansion activity.

But trust me, they are going to be -- and we expect to be a future source of expansion activity. As we mentioned, that just 2 quarters ago, we talked about the business being $100 million, and now it's nearly $200 million. So you should expect that in the back half of the year, this consumption growth continues that we should see an inflection in NRR.

Our next question comes from the line of Matt Hedberg with RBC Capital Markets.

Really a strong set of results here. And I'm still kind of curious when I look at the constant currency organic net new ARR growth. I'm curious, did you see any pull forward from Q2? And I guess where my question is going. You haven't changed the full year ARR guide, but following such strong Q1 results, does it change how you think about kind of that first half, second half split? I think our initial assumption was something like maybe 44% to 47% of net new ARR in the first half. But any commentary on sort of like the linearity of the results because it seems strong really across the board.

Thanks for the question, Matt. But you're right, we did say in our last call that we thought that the first half seasonally relative to what historically is would be a little bit more weighted and we certainly had a better start than we expected. Relative to pull forward, you're always going to have deals that push, you're going to have deals that you're able to close. And that is going to continue to be the nature of the business, especially with our pipeline weighted to very large deals. The timing of those are always going to be a bit variable.

I would say an exceptional start. I think we might be -- I expect a very strong half 1. I do expect that net new ARR, even though we don't guide quarterly, it's going to be in the double digits every quarter, timing of like how much more it will contribute, I'd say, is going to depend on the nature of when these deals land. But we are very confident that the momentum in the business is building and I think we're just trying to be prudent. We don't -- as you know, we don't historically change the guide after the Q1 results. But don't misunderstand that to mean that we don't believe that the business momentum is growing. One of the things I said in the prepared remarks and rick we are quite confident that we are on track to accelerate ARR growth for the year.

And this is a really critical point that I want to highlight as well. our focus is our acceleration for the year. That's what the drivers are suggesting, and that's where we're promoting with regard to strong consumption, log management and log growth the evolution of AI workloads, the increase in consumption overall of the platform relative to ARR, back half renewals coming up, there is a large set of drivers that we believe deliver confidence in the overall outlook for the year.

Our next question comes from the line of Koji Ikeda with Bank of America.

I wanted to ask about about logs. And it's very clear that your log strategy is working with log consumption over $200 million. Just real quick here. And so as I think further out, if log growth starts to slow, you did lay out 3 monetization levers for AI. That's higher telemetry, AI observability and then monetization of Dynatrace agents as a whole.

Is there enough there to -- with the AI to realistically become large enough to replace any sort of slowdown in logs? Or does the medium-term growth algorithm still primarily depend on logs growing nice and durably over the next several years?

Let me start with that. I do not believe -- while we do think logs will continue to be a huge source of consumption growth. Growth outside of logs is growing at a very rapid rate as well. Infrastructure monitoring is growing at a very healthy rate. Full stack is growing at a very healthy rate. So consumption of the platform beyond logs is growing at a very robust rate. So I don't expect that that's going to change.

So your point about if log slows. First, we don't think logs is going to slow. We think that at $200 million, when you look at the size of this opportunity, and the differentiation that you have with Dynatrace, we think we're going to be a huge gainer of share in that space. So I don't think log is going to slow. And I think outside of logs, the rest of the business is growing at a very rapid rate. So I think between logs, customers continuing to move more workloads and the AI monetization attributes that Rick said, I think all 3 of them are going to be sources of growth for acceleration in ARR.

Koji, I would just say that whether you're looking at the evolution of traditional workloads to include autonomous operations where you're looking at the evolution of workloads altogether to include AI, AI and servability it is critical to have an underlying foundation that begins with end-to-end observability. Essentially, every customer event that I do, every customer meeting that I manage, every customer with whom I engage it's got to start with an end observability because that's what gives you the underlying insights, the underlying analytics to be able to operate in an environment in a more autonomous way.

You would have to have logs, traces metrics. You have to have application monitoring, infrastructure monitoring, about log management, all in a consistent, integrated platform in order to avoid manual oversight. And if you're looking for AI oversight of that platform, which you can get through Dynatrace vis-a-vis these causal insights coming out of terminate KI, that begins with end-to-end observability as a foundation for everything else.

And the reason I wanted to insert that into the answer to the question is because that is, in many ways, the core driver of the Dynatrace business before you get to any of these other elements. And then you look at log management growth as a component of that, you look at AI observability as an evolution of that, all of these elements are based on the foundation of end-to-end observability growth, that is really the core business.

Our next question comes from the line of Samik Chatterjee with JPMorgan.

If I could just follow up on Koji's question. On the last monetization driver that you mentioned Dynatrace agents and -- just curious to hear your thoughts about how you think about the sizing of that opportunity longer term relative to AI obsolete. I think you mentioned like a $10 billion incremental TAM. But wanted to hear how you're thinking about size -- or sizing of the agent sort of opportunity overall, maybe a bit more longer term? And any thoughts on sort of timing of seeing that sort of evolution.

It's going to evolve in a measured way over the course of time. So we're certainly not expecting any radical shift in mix from our core and end observability capabilities relative to to AI observability in the near term, but we do view that as a catalyst because the more AI observability you're doing, the more you're going to use us for end-to-end observability as well for traditional workloads.

So I really see them growing in concert. But we do believe that, that $10 billion TAM as we look out over the balance of the decade represents significant increase in the addressable market that we can go after with Dynatrace systems. So we wouldn't provide a specific percentage, but we would say that, that is going to evolve over the course of time in a meaningful way.

Our next question comes from the line of Ryan MacWilliams with Wells Fargo.

For a question here. Just love to hear how consumption is trending overall in the business compared to the quarter before. And then on the AI workloads requiring more telemetry, we'd love to dig into that. I know it's early. But how does the observability required for AI agent activity compared for the observability required for attritional applications?

Well, let me take the second one on the AI agents and AIA usage. I think this is where it really comes back to the 3 questions that we ask. We look at traditional durability, traditional workloads as really largely addressing the first question that I indicated, which is, is it working? And is it working? It's really all about business resilience, business resilience is indicating whether a mobile app is working, whether infrastructure is operating is expected, et cetera.

The AI workloads and the agentic flows are adding these couple of additional questions for example, is it right? Or is it accurate? And that really is assessing whether the LLMs are producing the right data and that data be relied upon to provide to our customers and users as a mechanism for action. And that is really the AI observability piece.

And then lastly, increasingly, what we are seeing is we are seeing that individuals or individual developers are really over the course of time, no longer going to be writing code themselves. It is agents that are going to be writing the code and individuals or humans are going to be overseeing that environment. So, this is why we have a lot of confidence in the evolution of this business in the AI observability space because we have all 3 of these questions at action.

Number one, is it working? Number two, is it accurate? Number three, are my models delivering as expected? And if you put all 3 of those together, this is what is creating the increased elementary coming from AI systems.

Our next question comes from the line of Ato Kajal with Oppenheimer.

Rick, I wanted to go back to the comments around the 1,000 customers that are using you to monitor AI. Can you talk about the visibility that you have into your customers and kind of how do you know what you're being used for, what kind of application use cases, number one. And number two, perhaps more importantly, how are you reorienting the sales force to make sure that they try and capture those types of use cases and workloads at customers?

Yes. Thanks for the question. Absolutely, we can -- we can understand how they're using the AI workloads based on the telemetry that we can capture through the Dynatrace platform. So that's how we can accrue the numbers that you see here that we have 1,000 customers now, now observing the AI and LLM workloads, we have that capability in the system. With regard to the sales force, the expectation is that we are driving a sales play regarding AI utilization and AI workloads to go after those workloads because of all the reasons we've been describing.

This drives increased telemetry, it drives the increased usage of agents, it enables greater monetization and so we are absolutely very visible in pushing our sales force to be not just looking for traditional observability workloads, but rather AI workloads that are evolving. So it's a key sales play.

Our next question comes from the line of Matthew Martino with Goldman Sachs.

Rick or Jim, on go-to-market. I know you're extending the strategic account coverage model beyond the top 500 customers. What are you seeing in those newly covered accounts in the early going? And what's the realistic time frame for that cohort to become a more visible contributor to the net new ARR.

Great question. So you're right, we were wildly successful with focusing on the Global huge source of ARR growth over the last year, continues to build as we extend down, we're already beginning to see traction in that. And so, the good news is we're already seeing building go-to-market productivity. Go-to-market productivity continues to improve every quarter. I kind of go back to my point about this building momentum, looking at trailing 12-month net new ARR to function of the go-to-market changes that we've made.

And admittedly those -- that productivity improvement is coming from both what we call strategic accounts and the enterprise accounts. So our -- this is not -- we're going to have to wait 12 months for that. We're already starting to see that. We're inserting that into the go-to-market equation. So the changes that we made 2 years ago, our expectation is that productivity will continue to build.

One key element also, Matt, I just described is that you have to think about it not just in terms of the ICP or the intended customer being in the strategic segment, for example, it's all around who is the buyer. It's also about who is the buyer within those accounts. And this is where we have traditionally sold the CXOs, we've sold to IT ops, but we absolutely do expect an expansion in an AI world toward SRE, toward platform engineering, toward developers themselves.

We are shifting left in the platform and our third-gen capabilities and our in our new platform, have the capabilities needed for developers to access the platform and to be able to use it in free trial mode and other elements to incorporate it much, much earlier in the development process. And this is going to be a key aspect of us winning in the AI space.

Our next question comes from the line of Roddy Salto with UBS.

Just wanted to double quick on the DPS renewal cohorts. I know the renewals are more back half weighted. But I guess, is there any trend you're noticing in those renewal conversations for the 3-year DPS renewal customers compared to the 1- and 2-year annual recycle works. I'm just curious how different the upsell opportunity is shaping up to be for the 3-year DPS renewals versus the 1- and 2-year annual rec cohorts?

Yes. I'd say there's no real difference in the trend. Again, what we -- I'd say the general nature of what you see is that when a customer is in, call it, the first year of the 3-year DPS contract, they're more inclined to maybe go on demand if they are exceeding their commitment. As they are in year 2 and year 3 more in line, more inclined to do an expansion. And so that behavior has not fundamentally changed.

Again, Q1 because it's a light renewal quarter is I'd say the body of kind of renewals is not substantive enough that there is any change in that trend other than it continues. And so our expectation is if that continues and consumption continues to build that we have a source of upsell opportunity in the back half of the year. As I mentioned, we have a significant amount of ARR coming up for renewal or going through an annual reset in the back half of the year and our expectation is it for the source expansion opportunity.

Our final question will come from the line of Eric Heath with KeyBanc Capital Markets.

Awesome. Rick, just coming back to logs, I guess, one more time. I'm curious to drilling further on where that strength is coming from, whether it's new customers or ramping of existing customers? And also curious to hear maybe how monitoring AI apps might be contributing to the logs consumption figure. And Jim, if I could just housekeeping question here, but Bindplane, is that part of the $200 million consumption figure you guys are exciting?

So what I would say is simply on logs is it's effectively all of the above. We're seeing traction in lands with logs because I'd say we have a very unique differentiated set of capabilities. Obviously, the consumption growth that you're seeing is both from new customers and existing customers, we're seeing that co-work class continuing to build from starting maybe smaller with us and continuing to expand their large journey. So we continue to have a building number of customers that started smaller, getting bigger, quite a few million dollar plus logs customers. And your hostkeeping question on Bindplane. Yes, Bindplane is included in our logs consumption.

All right. Well, thank you all for your engagement and ongoing support as usual. Thanks for your questions during the call. We had a very, very strong first quarter. We believe it reinforces our conviction in the path to ARR acceleration for this fiscal year. We are, as you can tell, very enthusiastic about the opportunities ahead and we look forward to connecting with you at upcoming IR events. Thank you, and have a great day.

Ladies and gentlemen, thank you so much. This does now conclude today's teleconference. We appreciate your participation. You may disconnect your lines at this time. Enjoy the rest of your day.

Welcome, everybody, and thank you for joining us. I'm [ Marty Vouin ]. This is [ Jane Patel ]. We are the JPMorgan equity research team covering enterprise software. And it's a great pleasure to be here with Rick McConnell, CEO; Jim Benson, CFO; and Dan Zugelder, CRO of Dynatrace. Rick, Jim Dan, really appreciate you guys coming back to TMC.

We'll start off on a good note. You guys finished fiscal 2026 surpassing $2 billion in ARR, fourth consecutive quarter of 16% ARR growth and a fiscal '27 guide that bakes in net new ARR acceleration. So it's been a banner year for you guys. But then just to start off, why don't you guys give a quick introduction and just let us know what Dynatrace is solving for the world's enterprises today and how that's evolving in the AI world?

Sure. DynaTrace is in the $75 billion plus observability space. Observability is targeted at enabling enterprises to run their software better. So think about us as helping to prevent incidents to remediate incidents and to optimize your environment. This is what observability software generally does. We often think of Dynatrace's vision in delivering the world software that works perfectly. So that would be our objective.

And obviously, as we evolve so rapidly into an agentic world, this brings a huge array of new opportunities to the observability space. Because agents enable us to actually take action and to execute those actions in a more autonomous way. So you can imagine systems that are self-healing effectively to enable software to always work better than it's worked before.

So I'm sure we'll talk more about this, but it is a very, very exciting space that we believe is a is a tailwind or receives a tailwind from all that is happening in AI based on the expansion of workloads to drive towards autonomous operations and in general, the necessity of delivering software that works perfectly.

That's great. And I think the self-healing part is a good way to describe it. I'm sure we'll touch on it, but the more that AI accelerated CoGen, I think it's made that observability even more important and critical and something that you need a third party can do it excellently to do it.

But let's just start with the fiscal 2027 guide. Jim, if we think about the nuances from the earnings call last week, the guide implies net new ARR growth of 16% to 23%, accelerating from 12% in FY '26, reported ARR of 15.5% to 16.5%. That range from 16% to 23% of the ARR, it's wide. Can you give us a sense of what drives the high end versus the low end of that and what you can control in that equation?

Yes. I'll start with fiscal '26 was a year first. You mentioned in your opening remarks that we had consistent ARR growth of 16%. So we grew net new ARR at 12% in fiscal '26. We haven't done that in 3 years. So one of the things I talked to investors about is the phases that we've been going through. Fiscal '25 was a fixed year where we did some things on the go-to-market side. Fiscal '26 was stabilized and fiscal '27 was accelerated. So I've talked about that over time.

We mentioned logs. We -- Dan will probably talk about it in a bit around platform consolidation, end-to-end observability is certainly something that more and more enterprises are looking to do. So that is certainly a tailwind. So relative to the range, I think what you see is that at the low end of the range, it still implies an acceleration of net new ARR growth of 16% and you mentioned 23% on the high end.

Some people have asked me, they said, geez, Q4 landed a little bit lighter than maybe people would have thought. And I would say it did land a little bit lighter than our internal expectations. And I'd say largely that is we had softness in our EMEA market, notably in the Middle East. The difference between growing double digits and growing 9% is a few million dollars. So when I look to fiscal '26, there's a lot of momentum building in the business. And we expect it to continue. All the things that I just mentioned will continue.

Relative to a range, I mean, our range is usually 1 point, plus or minus. So I think what you get at the high end of the range is a continuation of what we've been doing and building. Even at the lower end of the range, you're getting that. I think you know that historically, I've used the word prudent. that was called out on the earnings call. This guide is no different than any other guide I provided every year. So there is an internal plan and then there is a haircut for what we do that we're going to tell the investor community. And it's no different than what it always is. So we certainly have line of sight to doing better than the high end of the guide.

Obviously, we got to see how the year progresses. I mentioned in the call that we would be a little bit more half 1 weighted. And just to clarify that, our historical linearity is like 44% of our net new ARR in the first half, 56% in the second half. That doesn't mean we're going to do more net new ARR in the first half. It just means that we'll do a couple of points more, maybe 46%, 54%.

And it's because we have strong forecasted pipeline entering the year. And our pipeline visibility is always tighter in a 3- to 6-month window than it is 12-month window. So we actually just believe there's just a lot of tailwinds. And as the year progresses, we'll update our outlook accordingly.

Yes. And just to be 100% clear, your guidance philosophy essentially is unchanged from what you guys have done historically?

That's right.

Yes. Yes. If we just double-click on that 9% net new ARR growth in Q4 and then going forward, I know you mentioned there was some softness in the Middle East. I assume the Iran conflict doesn't help that situation. But if you can just talk about okay, if you have those slight headwinds, out of those tailwinds that you're seeing, which ones do you think are going to be able to drive a lot of that? What should give investors confidence in what we're seeing through the rest of the year?

I think the biggest one is I think we will continue to do what we did with logs. Our expectation is that logs will continue to double. So we ended the year well over $100 million in our -- we have a lot of confidence that we will continue that trajectory that we have the right product with the right pricing and packaging at the right time.

And I think we've shown it in and ability to go into our installed base and sell it and actually even introduce new logos. So I think logs will be a big source of it. I do think we'll continue to see workloads grow with end-to-end observability. More existing customers and new customers looking to consolidate. One, they can save them money. You consolidate multiple vendors and you could save money just through software costs. Beyond that, we allow their environment to run more efficiently because once you're on Dynatrace, you can -- you have a platform where you have deterministic answers to figure out issues as opposed to people chasing alerts and dashboards.

And so it really is just a -- this is not like new plays. It really is and it's a continuation. Dan can talk about the go-to-market model is largely unchanged. We're going to go down with our strategic accounts. We've had Global 500. We'll probably go down another 100, 150. We'll have 4 to 5 reps in that part of the pyramid. So you'll have fewer accounts per rep. We'll get better penetration. The penetration that we've had in those accounts since Dan made those changes, the fastest-growing segment that we've had in the company. And it's about running that play again. And we think that, that will continue to be another source of productivity improvement.

Yes, and having the continuation of the existing tailwinds feels a little bit more comfortable than anything new having to kind of layer in.

One other thing that I would mention is that you know that we've been at the Dynatrace platform subscription for a while. And so we now have over 75% of our ARR on that contracting vehicle, a little over 60% of our customers. Fiscal '27 is the first year where you're actually going to go through three annual reset cycles. We haven't had that in the past. And the first year of EPS was fiscal '24.

Most of our DPS customers have 3-year cohort classes. They are coming up for their actual renewal in fiscal '27. So there's a significant percentage of our installed base with DPS that is going to go through either a renewal or an annual reset. It's a huge opportunity for an expansion for us for all the reasons we just said. Consumption is growing at a very rapid clip. It's been growing north of 20% for well over 4 quarters. And if we can continue with that. Again, it will be a source of expansion opportunity.

Yes. Yes. It sounds like a lot of upward pressures in the right direction. Touching on some of the other things you were talking about, Dan, I think this is your wheelhouse. Dynatrace, you guys started this go-to-market transformation a couple of years ago, shifting reps to focus on 4 or 5 accounts in the Fortune 500, building the partner motion, comp plan changes.

And as Jim said, you just finished kind of the stabilization phase. So when you're looking at that pipeline coverage entering FY '27 is healthy enough to guide net new ARR modestly to accelerate. One of the themes in our partner work is that the pipeline quality matters more than the quantity. I assume you guys kind of have the same perspective. Can you talk about what you're seeing in terms of that quality and how you measure it? Is it the stage progression, deal velocity, close rates? What is giving you confidence in that pipeline?

Yes. I think it probably starts with the methodology and the inspection, the quality of in, what goes in, the approach we take with that. But then the analytics play a role as well. The motion for Dynatrace, if you go back a few years ago, was a very different selling motion regarding pipeline. It was a very mid-level enterprise engagement, typically very feature-based.

And over the last 3 years, it's transformed into a much more enterprise value selling motion. So we have a different approach, a pretty dramatic different approach. And that's matured at this point. So we understand what goes into our pipeline. We understand the risk of what matures, what doesn't, at what pace. So I think it's just the maturity of the motion that gives us the confidence in the pipeline.

That's great. I want to turn it over to [ Jayden ] here. But Rick, complementing on that enterprise motion on the shift to the left, developer side, you got DevCycle, MCP server, cloud code, cursor. Is this an evolution of the enterprise motion or a different velocity engine kind of targeted AI natives and the AI native buyers?

Great question. Definitely the power of the and. I learned this from my earlier career in Cisco, and we were always talking about the power of the and. This is definitely it. We don't foresee a shift from the enterprise to AI natives, for example. We believe that it is the enterprise plus AI native. We have been working very diligently over the last 18 months to build developer capabilities into the platform.

And you've seen those over the last couple of quarters through a myriad of announcements related to integrations, for example, into AWS, Bedrock Agent Core into GitHub Copilot into Cloud Code into a myriad of different other elements. All of these enable our platform to be more developer ready. And we didn't grow up on the developer side like some others in our industry.

We grew up in the enterprise side, focused on IT ops selling to the CXO. And this is why out of our customer base, we have an average selling price of now about $500,000. It's because they are large end-to-end enterprise deployments. And they -- these customers use us to make sure that their software always works. And they want to deliver end-to-end observability at multiple levels. completely integrated data lake house, integrated fully across logs, applications, infrastructure, real user monitoring, et cetera. and then integrated for all personas.

And this is what we do better than anybody. This has done a traces superpower. We want to take that superpower that we've delivered to the enterprise and bring that to the developer community, bring that to AI natives. And that really is the next step in our evolution as an organization.

Great. Dan, I think this one is for you. Let's talk a little bit about the disconnect between what we hear from investors and what customers or what you're seeing with customers? One consistent theme we hear from investors that customers are more reluctant to spend because of all the uncertainties due to AI. A lot of this discussion centers on the application side, but I think it's worth touching on here.

Dan, I assume you're the one across the table from CIOs and CROs every week. What do customer conversations actually sound like right now versus what we're hearing from the investor community?

Well, I think there has been -- I don't know if it's new, there is cost concerns. People are looking to -- and that's why consolidation has been such a play is that -- no question it gets people's attention when you're saying I'm taking two or three tools and making them into one, and there's going to be a 10% to 15%, 20% cost savings. So there's an attractiveness of that conversation.

When you can couple that with saying I'm going to give you a better outcome, that's usually the combination that people are looking for. So we're not looking just to do things to cut costs, but if we can deliver a better observability outcome. So I think as we enter AI, and we just came out of our sales kickoff, I reiterated that our motion of tool consolidation cost out and logs is a great example of that, where we sometimes take 40%, 50% cost out because of combining logs with metrics and traces, you just need less of them in a more efficient way.

So that -- it does resonate with our customers. There's no question. It has been, and I think it continues to be -- get people's attention. What you do in this world that's been created with AI is a fantastic tailwind because you're creating then budgeted new projects their AI projects that need observability. So you continue to run your entire play around tool consolidation, the enterprise end-to-end observability. And now we have a motion that we've added that is more about saying, going after these specific AI projects that need observability.

So you have a little bit of a bespoke process in go-to-market, and then you have our very mature enterprise market. So that's created additional opportunity for us. And then Rick talked about the AI natives to have their own specific market as well. So AI has created additional markets and for observability for sure.

The one thing I would add, just to append to Dan's remarks is, we absolutely believe that observability is a beneficiary of AI evolution of AI-first organizations. It is critical in our view that in an AI-first world, you need more observability, not less. We do not see observability as a market and infrastructure environment, which is going to get disintermediated by AI.

Quite the contrary, we see that in our existing customer base, maybe 30% or so of workloads are actually observed in a sophisticated way like using Dynatrace in a probabilistic world where you're using lots of LOMs, agents to build software, we believe that you actually need more observability to oversee those workloads than not, number one.

Number two, we bring incredible contacts, billions of interconnected data points in real time, very specific to a particular organizations environment. And number three, we bring domain expertise of understanding that environment specifically and how that environment is operating. So observability is going to benefit from a tailwind in AI, not just in deployment of AI natives, but also in the enterprise itself, where obviously, enterprises are deploying more and more AI workloads that are inclusive of AI, largely built by AI, coded by AI and over time, will be operated by AI in a very agentic world headed to an autonomous environment.

All of these factors are driving an environment for observability that has become more and more mission-critical to organizations day by day. And of course, it's our commitment to do everything we can to be a beneficiary of this trend for observability overall at Dynatrace.

Yes. It seems like there's a lot of AI tailwinds that are benefiting the business on top of the execution that you guys are providing. Jim, on that point, when do you expect these AI tailwinds to meaningfully contribute to accelerated growth?

Well, I think we're already starting to see it. But you got to remember that the way our model works is you're signing up customers on a contract with DPS and then they have 3, 1-year commitments and then they push consumption. So the underlying component of DPS is consumption. It's not a seat-based model, it's a consumption model.

Now we recognize revenue ratably but we consume based on what customers are -- what workloads customers are adding and the growth in those workloads. So we're already seeing some of the benefits that -- again, I mentioned that it's been over 4 quarters that consumption is growing north of 20%. And the way to think about that is that -- and I think this is where people sometimes struggle is why is ARR not growing 20%. And there's a lag. There is a lag with just the nature of the way the DPS contracting model works. But if you can continue to grow consumption north of 20%, you will see a convergence of ARR and consumption over time.

And if I our selling motion is just as much about bringing workloads onto the platform as it is expansion of ARR because it's it's the preemptive piece of expansion on ARR. So our salespeople are out there just trying to get workloads on to Dynatrace, we they're AI workloads, whether they're logs, whatever expanding to more applications in general. Their motion is like, I know if I get more consumption -- when that DPS comes for renewal, that will give the fuel to that fire to be able to expand more. So you can understand how we are very consumption-based even though we're an or an ACV and ARR as far as the way we measure the business, the focus is now -- is all on consumption.

The other thing that I would say is that we have integrated account teams that Dan's account reps are the quarterback for us. So we have customer success teams, they are measured on consumption. They are compensated on consumption. Dan has bespoke strike teams or bespoke product areas. They are measured on consumption. They are compensated on consumption. So this consumption mindset is -- exists throughout the company.

And I'd say we continue to run plays now. There's going to be plays on consumption. So again, at its core, get them on the DPS platform, have teams of people work with them to get more value to Dan's point, reps ensuring they find new workloads. We've got customer success teams that are helping ensure that they're getting value out of the different product areas.

And then specifically on the strike teams, they're both -- they're on the front end with sales on deals, and they're also on the consumption side around driving more consumption. So it's -- I'd say we have a lot of the ingredients in place. They've been in place now for a year. I think it's led to what has been stabilization, and we're quite confident that it will now lead to acceleration.

Great. Let's tackle sort of the bear case on observability head on. Rick, this question is for you.

I must give the bear case to Jim. I'm just kidding.

The question that comes up most often on our end is this outstanding idea that as the cost of code goes to 0, customers can use open telemetry plus Vibe coding to roll their own observability layer and an LLM can reason over that telemetry. We've written tens of times that we think this thesis is off base, but better to hear from you than anyone else. What is your thoughts on that? And why is that true or not true?

Yes. I mean I sort of gave the precursor to this answer already. So I can -- I think I can do it briefly. But -- the starting point is I sort of oversimplify to bifurcate the world into application seat-based software with standardized workflows and highly dynamic software -- infrastructure software that needs to take real-time data into account. These are very, very different models. And in the former case, it is much easier to [ VIB ] code a standardized workflow than a highly dynamic workflow based on context.

And that is sort of the underlying thesis and our view is the combination of significant domain expertise, highly specialized for organizations like JPMorgan, for example, or the largest organizations on the planet to be able to integrate and observe their environments is absolutely nation critical to those organizations and are you really going to rely on a vibe-coded standardized piece of software. It doesn't have the same degree of domain expertise. Doesn't have the same degree of real-time context based on billions of interconnected data points. Doesn't have the ability to oversee or observe probabilistic models and by the way, there's an even added element here.

For the history of Dynatrace, we have been focused on a particular question or answering a particular question for our customers. And that question is, is it working? If you think about what does observability do, is it working or maybe is it working well? Is it optimized? That's what we've been focused on in an LLM world of models that are providing input to allow trustworthy actions to take place through agents, you have to answer a separate question.

And that is, is it correct -- in other words, is the information coming from models actually accurate so that it can be trusted and implemented. We have customers that are using us in payment terminals that have to work every time. They need agents to be able to take immediate action to provide corrective action to make them work. We have banks who are using them in mobile using Dynatrace as part of mobile apps to assist customers to transfer money from one account to another. That's got to work every single time, whether it's health care, manufacturing, their use cases in each of these environments that are critical to be driving trust worthy outcomes based on AI and LLM inputs.

And you simply can't rely upon an LLM to provide that degree of context, that degree of domain expertise to support an environment that then was created with a standard LiveCode. It just isn't going to work. And so -- and if there's one answer to all that, I would say, context is it.

Great. On that point that these tools need to just work, the place where that makes the most sense is the enterprise, right? So Dan, this one is for you. The most striking thing in your recent quarters is the consistency of 7-figure deals, $200,000-plus new logo lands and as you said, roughly $500,000 ARR per customer, which Jim has framed as having a path to $1 million plus over the long term. A partner told us that consolidation is the only conversation that matters right now. CIOs are tired of paying for 5 tools that overlap. Is that sort of the right read on buyer mindset right now?

Consolidation is definitely on their top of mind. But I mentioned this before, it is obviously cost. It obviously has to do with if you are a C-suite, up to the CEO of organizations, and there is a major outage. CEO will be -- the first question is, what's the problem? Obviously, that I/O, if they cannot answer that question very quickly, their job is on the line. So that is -- when you have a fragmented observability stack, it means that, that answer becomes that much more difficult. because everybody is looking at their individual tools and saying, I'm good. And they're asking the question, they're going around the room and people are answering, I'm good, I'm good. And somebody have stopped saying, but we have a major outage going on right now.

So we deliver that so you have visibility across your entire application and infrastructure stack. So the consolidation plays on a cost basis. There's no question it, but it plays on delivering a better outcome. I think that trend is not going away. It's a bit -- some of the C-suite I speak to all the time is that they're just trying to continue to pull things together, pull optimize costs but deliver a better outcome.

That is not -- that's been there. I think it's catching when. I think there's a lot more. Logs have played a big role in that, that if you look at it, we were 18 months ago, we really weren't playing in the logs business. So that's accelerated this view of a better outcome. Most people are using another -- some of the traditional log providers, and that was redundant to observability. So that was actually -- it's fairly low-hanging fruit for them.

I would just add that -- it seems to me that the holy rail that Dan and I hear from CIOs, CTOs, CXOs basically every day as they want to get to autonomous operations. They want to get to autonomous operations because the cost savings are extraordinary. And by the way, they're having more difficulty finding head count to actually manage environments rather than less. So they need to be able to do more on an automated basis. By the way, to the extent that, that can be predictive, it's even better. so that you eliminate issues before they even occur.

In order to get to autonomous operations, you have to have to Dan's point, end-to-end observability because it is the confluence of elements that enables you to have confidence and trust in the outcome that enables an agent to take action. And if you don't have that degree of confidence in trust, you cannot rely on the agent to take the correct action. So the mechanism to get to autonomous operations from accounts is to begin with end-to-end observability.

And this is why, last quarter, for example, 22 deals of greater than $1 million, which is pretty substantial. For run rate, all driven by really this end-to-end observability motion, all setting up not as the end but as the mechanism to drive toward autonomous operations as we look at.

You mentioned logs earlier and a bit on this last question as well. Logs has definitely been one of the biggest surprises, positive surprises in the Dynatrace story over the last 12 months with over $100 million of annualized consumption, which growing north of 100% year-over-year. Rick, you were out there quoted in the ether around the $250 million number, which we interpreted as aspirational. The underlying question here is, let's say we pass that $250 million, where can this business grow? What is the overall market here that you can drive this to be?

I'll start and look to Dan and Jim to comment further. But I mean, the logs business alone is a multibillion-dollar market. I mean you look at Splunk, you look at others in this market, and it's already many, many billions of dollars of log observability -- and we believe that -- we absolutely believe that our business can continue to grow toward that $1 billion mark and over the course of time, even beyond that.

And the reason is because we believe that we have a solution that is better than a stand-alone log solution. Stand-alone log solution is focused only on logs. What we can do, which is consistent with what we've been discussing here before, are two things related to logs. Number one, by integrating, as Dan was talking about, logs with traces, metrics, really user data, behavioral analytics, et cetera, you get a better outcome. you can see the entire environment by seeing the entire environment inclusive of logs, you deliver a better outcome. Those answers that come from that better outcome lead to the autonomous operations of the environment that I'm describing.

The second thing is you actually -- because you have traces, metrics, logs, real user data, it actually is more beneficial to have multiple different data types than just more logs. So you can actually accumulate fewer logs, but when combined with these other data types, you actually end up in an environment where you get a much richer environment to be able to provide analytics that allow for things like auto prevention, auto remediation, auto optimization.

And so better outcomes, lower price point and you have lots of our existing customers that are using a log vendor for logs us for observability, and they're increasingly moving that log workload to us in order to get the benefit of end-to-end observability that we've been talking about for all the reasons we've been discussing.

Great. I have one last question, and I can hand it off to [ Ari ]. Jim, this one is for you. We would be remiss not to ask about this. The Starboard team published a fairly detailed letter on April 28, and both of you and Starboard have described the engagement as constructive. Rather than dwell on the letter itself, help the audience understand how you're thinking about the value creation framework for Dynatrace over the next several years.

Yes. Well, I mean, we think of value creation beyond all investors. So that's kind of how we're viewing Dynatrace that we want to provide shareholder value for all. And I think at the end of the day, when you think about what we're trying to do, everything we tried to build and outline was an acceleration in the growth of the business.

This business can and should be north of what it has been delivering. And so for us, there's been a big focus, again, going back to my fixed stabilized accelerate, putting things in place to go on the offensive to go after the opportunity. I think Dan talked about some of -- the model we had before was good for when the company got to $1 billion and we needed a different model to be able to scale. And so when I think about acceleration, I think it's in our sights. And you've seen that for sure, at the high end of our guide, we expect that, that will happen this year.

On the margin front, we are a rare company that operates at 29% or 29%, almost 30% operating margins. We've driven 400 basis points of leverage in the model. In each year, we'll tweak it a little bit because sometimes you'll make investments in 1 year, you'll get a return the following year. So driving efficiency and driving leverage is always part of the story that Dynatrace is a balanced growth and profitability story and it's a sequencing of when you would do that. So I think we've demonstrated that we can do that, and we will do that.

I mentioned in the call that we're driving 150 basis points of operating expense leverage in fiscal '27. Offsetting what is the near-term gross margin headwind. So again, margin and leverage very important. And then we believe the shares are undervalued. We have significantly bought back stock we increased the pace of our buyback 40% from Q3 to Q4. You know that we doubled the authorization to $1 billion in February.

And so we believe that at these prices, it's very attractive, and we will continue to put capital to use in the form of buyback. We have -- we generate a lot of cash flow. We have a fair amount of cash on the balance sheet. So accelerate growth continue to focus on efficiency and then put your buyback to use at very attractive values. And I think that's what we've been doing.

Yes. Well, Rick, Jim, Dan, it's been fantastic to have you guys here and sharing your insights with us. A lot of stuff going right in your business. We're really looking forward to where that goes. So thank you.

Thank you.

Greetings, and welcome to the Dynatrace Fourth Quarter and Full Year Fiscal 2026 Earnings Call. [Operator Instructions]

As a reminder, this conference is being recorded. I would now like to turn the call over to your host, Noelle Faris, Vice President of Investor Relations. Thank you. You may begin.

Good morning, and thank you for joining Dynatrace's Fourth Quarter and Full Year Fiscal 2026 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 quarterly report on Form 10-Q and our upcoming annual report on Form 10-K that we plan to file later this month.

The forward-looking statements contained in this call represent the company's views on May 13, 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 discuss 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 presentation, 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 for today's call. Dynatrace delivered a strong finish to fiscal 2026, marked by meaningful scale, durable execution and continued innovation. In particular, we surpassed $2 billion in ARR and delivered our fourth consecutive quarter of 16% ARR growth. We drove continued traction in logs, now well over $100 million in annualized consumption, growing more than 100% per year.

We launched major platform innovations, including Dynatrace Intelligence and domain-specific AI agents. We advanced our cloud-native integrations across AWS, Azure and GCP, moving operations from reactive monitoring to autonomous action. We extended our agentic AI ecosystem with native connectivity to Anthropic's Claude Code, deepened our integration with ServiceNow and broadened developer workflow integrations with GitHub Copilot.

We acquired DevCycle, a feature management company as well as Bindplane, an open-standards-based telemetry pipeline company as we entered the new fiscal year. We maintained a leadership position in all major third-party analyst reports for Observability and AIOps -- and we delivered robust operating and pre-tax free cash flow margins.

Our consistent performance in fiscal 2026 underscores the growing criticality of Observability, the strength of our strategy and the value of our platform to customers. Jim will share more details about our Q4 financial performance and fiscal 2027 guidance in a moment. In the meantime, I'd like to cover 4 topics: how AI is reshaping the Observability market, why we believe Dynatrace is unique, Q4 customer highlights and the growth opportunity ahead.

To begin, Observability is entering a new era, one in which Observability is more mission-critical than ever. But a new set of demands is reshaping what Observability must deliver. Observability has already become foundational for enterprises looking to deliver business resilience amidst growing workload complexity and data volumes. And increasingly, organizations are looking to leverage their Observability solution to evolve toward autonomous operations, enabling software to auto prevent, auto remediate and auto optimize.

Adopting this approach requires organizations to trust the accuracy of the data that fuels agents to take action. Deterministic and causal insights from Dynatrace allow our platform to become the system of record so that development and SRE teams and increasingly AI agents can act with confidence to deliver what we refer to as answers, not guesses. Beyond business resilience, organizations now need Observability for reliable AI.

The former addresses the question of, is it working? The latter addresses the question of, is it accurate? Namely, is the content coming from AI models trustworthy in driving action and/or credible in providing recommendations to end users.

AI also adds yet another layer to the software stack, increasing the need for more Observability. Enterprises are deploying new agents, models, orchestration layers and agentic architectures that behave differently than traditional systems. Their environments generate dramatically more telemetry, connect decisions across agents and introduce probabilistic behavior that must still operate safely, securely and at enterprise scale.

Organizations now require continuous validation of system and agent behavior, governance and auditability of autonomous decisions, cost control across GPU-intensive infrastructure and strong security management. As a result, software development life cycles are evolving as well, requiring organizations to operate in 2 modes.

The first is human-led with development teams building and operating resilient systems. These teams are increasingly augmented by AI-powered Observability that drives intelligent automation, agentic workflows and progressively more autonomous operations. We continue to invest to expand our reach in this area by extending left to provide development teams, platform engineers and SREs with the Observability functionality needed to put workloads into production faster.

Second mode is agent-led, resulting in AI-first environments in which agents themselves are primarily acting as the builders and operators. In this environment, Observability insights are consumed directly by the agents in the creation and oversight of delivered software. And those insights are crucial to the effective and trustworthy operation of the environment. We believe the winner in Observability will be the provider that can meet the needs of both human-led and agent-led environments with a shared system of truth that spans both modes across AI, cloud-native and traditional workloads.

This is the moment for which the Dynatrace platform has been built. Serving customers in both modes with the trust and accuracy that autonomous operations demand and the reliability that AI-driven initiatives require is exactly what the Dynatrace platform was built to do. So why do we believe Dynatrace is unique? It is because our advantage is architectural, not feature-based. Dynatrace is built as a real-time context engine that operates at massive scale across millions of monitored entities and exabytes of data, all connected and all in real time.

By combining deterministic AI with Agentic capabilities, we deliver faster, more accurate insights than approaches that rely on agentic AI alone. This level of intelligence, speed and efficiency cannot be achieved with point solutions that offer visibility without causality. And this is why so many of the largest organizations in the world rely on Dynatrace.

As enterprises increasingly operate in agent-led environments, this architectural advantage compounds. Every new workload, AI service and agent added to the Dynatrace platform deepens causal context, strengthens autonomous reasoning and extends the gap between fragmented visibility and the unified intelligence that only Dynatrace delivers.

That intelligence is built on 3 integrated components of our third-generation platform. Grail has an extensible AI data lake house that connects every signal across an enterprise's digital environment. Smartscape has the real-time integrated topology graph and Dynatrace Intelligence delivering both answers as well as action. Together, these 3 elements provide durable competitive differentiation.

For other providers that add capabilities across stitch data stores, it's difficult to reproduce a unified data foundation with real-time causality plus trustworthy automation in the most complex mission-critical environments and certainly extremely difficult to do so in the time frame AI demands. We've now delivered agents across 3 domains, and customers are already using them in production to coordinate agents to take end-to-end action. Our SRE agent handles tasks such as Kubernetes, troubleshooting, infrastructure optimization and automated incident resolution.

Our developer agent supports use cases that surface production context during deployment, validate changes and prevent issues before they reach customers. And our security agent identifies vulnerabilities, triaging threats and accelerating security response, all in real time. That intelligence extends beyond the Dynatrace platform itself. Ecosystem integrations then enable agentic interactions to extend Dynatrace intelligence into third-party tools from ServiceNow and GitHub to the hyperscalers to drive autonomous actions across development, SRE, ITSM and ITOps workflows.

What separates Dynatrace's agents from others is the deterministic foundation underneath, real root cause analysis, anomaly detection and forecasting grounded in Grail. That's not AI that guesses, it's AI that reasons from facts. More than 500 customers are deploying Dynatrace's agentic capabilities to run operations autonomously and extend that intelligence into AI development tools like Claude Code and GitHub Copilot.

At the same time, more than 850 customers are using Dynatrace to observe and trust AI and LLM workloads in production today. Enterprises aren't just managing their environments with the Dynatrace platform. They're using it as the intelligent foundation for AI agents across their ecosystem, creating a critical role for Dynatrace as AI adoption accelerates.

This momentum is increasingly evident in customer wins across multiple buying personas, highlighting 4 examples from Q4. One of the largest banks in Brazil signed a 7-figure expansion and is standardizing on Dynatrace with 100% OpenTelemetry data flowing into Grail, choosing Dynatrace for an open scalable architecture with a clear runway for broader platform expansion. Another large U.S.-based airline selected Dynatrace as a 7-figure new logo through a partner-originated opportunity.

They chose Dynatrace to consolidate a complex multi-vendor environment to improve business Observability outcomes and reduce operational disruption. A leading hospitality SaaS provider consolidated on to Dynatrace as a 7-figure new logo, displacing legacy tooling and invoking end-to-end visibility across their cloud-native platform. And in AI native security platform selected Dynatrace as a 7-figure new logo to deliver end-to-end Observability across AWS. Together, these wins reflect increasing demand for an end-to-end AI-powered Observability Platform in the most complex environments.

Looking ahead, our strategy is to win with both human-led and agent-led operating modes on a single platform. Our product and go-to-market approaches reflect this dual reality, combining enterprise engagement focused on business outcomes with a strong developer motion that enables agentic workflows to expand at scale. This strategy leads us to an expanded set of growth drivers for FY '27.

First, our go-to-market investments in both direct sales and partner enablement have improved productivity and deal quality. End-to-end platform deals are getting larger and more strategic with Q4 annual contract value of anchor deals up 60%, with a record 22 deals with incremental annual contract value over $1 million. DPS, which now represents greater than 75% of ARR also continues to produce double the platform adoption and consumption of non-DPS customers.

Second, cloud growth is an accelerating tailwind with the major hyperscalers now growing at 40% annually. As customers scale hybrid and multi-cloud architectures across AWS, Azure and Google Cloud, Dynatrace's expanding cloud-native integrations and automation drive sustained platform usage as complexity and scale increase.

Third, logs and telemetry pipelines represent a meaningful consumption and displacement opportunity. With our Bindplane acquisition now complete and resulting in expanded ingest from OpenTelemetry, we are simplifying telemetry collection and routing at scale, reducing friction for customers to bring more data into Dynatrace, and we are accelerating time to value plus consumption growth.

Fourth, agentic AI itself is an expansion driver. As agentic development accelerates, customers need more context, precise answers, governance and closed-loop automation, areas in which Dynatrace is structurally advantaged. And finally, developers are a long-term growth engine through the integration of Dynatrace Observability into AI development life cycles, including support for Claude Code, Cursor and GitHub Copilot. With our DevCycle acquisition, we extend this opportunity even further, expanding Dynatrace's footprint earlier in the life cycle and driving durable usage over time.

To close, Observability is already mission-critical infrastructure for AI-driven enterprises. Context and domain knowledge make Dynatrace not only durable, but essential in an AI-first world. We believe we are uniquely positioned with a differentiated end-to-end platform, providing the intelligence engine and AI control plane that produce both insights as well as autonomous action. With tailwinds in cloud and AI plus Dynatrace-specific growth drivers, we are focused on accelerating ARR growth in fiscal 2027 and enthusiastic about the year ahead. Jim, over to you.

Thank you, Rick, and good morning,everyone. As we close out fiscal '26, I want to take a moment to reflect on the execution and underlying momentum for Dynatrace over the past year. At the start of the fiscal year, we laid out a road map designed to put the company on the path to ARR acceleration.

We talked about the growing trend of large enterprise customers seeking end-to-end Observability solutions, the maturation of our go-to-market transformation, the powerful consumption economics of our Dynatrace platform subscription or DPS licensing model, the expanding opportunity in logs and the secular tailwinds driving Observability adoption in an AI-first world.

All of these were strategic underpinnings to stabilize ARR growth in fiscal '26 and position us for future acceleration. Let me walk you through a few milestones that defined fiscal '26. We achieved 4 consecutive quarters of consistent ARR growth at 16%. We delivered double-digit net new ARR growth for the first time in 3 years. We now have over 75% of ARR and 60% of customers on the DPS licensing model.

We exceeded our $100 million log management annualized consumption goal, growing 100% plus year-over-year in every quarter of the year. We delivered a robust 29% non-GAAP operating margin for the year while making targeted investments focused on accelerating growth and improving scale. And finally, we stepped up our share repurchase program, doubling our authorization to $1 billion in February and spending over $478 million in fiscal '26, representing 90% of our free cash flow.

Collectively, these achievements demonstrate the building momentum in the business and our confidence and conviction that ARR acceleration in fiscal '27 is in our sights. Let's review the Q4 and full year results in more detail. Growth rates mentioned will be year-over-year and in constant currency, unless otherwise stated. Annual recurring revenue, or ARR, ended the year at $2.05 billion, representing 16% growth for the fourth consecutive quarter.

This ARR result reflects a foreign exchange headwind of $4 million compared to our guidance. Adjusting for foreign exchange movements, Q4 net new ARR was $81 million, coming in near the high end of guidance. And net new ARR for fiscal '26 was $277 million, representing 12% growth with consistent double-digit growth in the first and second half of the year. This healthy performance was driven by strength in new logo bookings, growing momentum in logs and ongoing success in capturing large end-to-end consolidation opportunities.

We added 126 new logos in Q4, including a record nine 7-figure lands as we continue to target new logos in large enterprise accounts with a higher propensity to expand. The average land size in Q4 remained robust at over $200,000 and helped drive new logo ARR, up 43% in the quarter and up 30% for the second half. Our value proposition continues to resonate with enterprise customers outgrowing their existing DIY and commercial tooling solutions.

They are seeking business value from tool consolidation and coming to Dynatrace for the depth, breadth and automation of our unified AI-powered Observability Platform. Once customers experience the benefits of the Dynatrace platform, they have been quick to expand their usage.

Our average our average ARR per customer is now over $500,000. With cross-sell and upsell opportunities still ahead of us in our enterprise base, we believe the average ARR per customer opportunity could be $1 million or more over the long term. Gross retention rate in Q4 remained in the mid-90s, underscoring the value of the Dynatrace platform as mission-critical infrastructure our customers depend on.

Net retention rate, or NRR, on a trailing 12-month basis was 110% in the fourth quarter. Our DPS licensing model has now become our contracting standard. As I mentioned earlier, we exited the year with over 75% of our ARR and over 60% of our customer base on DPS. With access to the full platform, customers are adopting Dynatrace more broadly across their IT environments, resulting in increased consumption.

We continue to see a broader usage and deeper penetration of capabilities across the platform, notably in log management, which remains the fastest-growing product category, growing over 100% and exiting the year well over $100 million in annualized consumption. We expect fiscal '27 to be another year of robust consumption of the platform.

Moving on to revenue. Total revenue for Q4 was $532 million, and subscription revenue for Q4 was $506 million, both up 16% and exceeding the high end of our guidance range by 200 basis points. Turning to profitability. Q4 non-GAAP operating margin was 27% and above our guidance of 26% -- non-GAAP net income was $124 million or $0.41 per diluted share, $0.02 above the high end of guidance.

In our GAAP results, please note that our Q4 GAAP operating income includes $28 million in restructuring and impairment charges, primarily reflecting actions to align our cost structure with our strategic growth and scale priorities. These actions included targeted workforce reductions and impairment charges from office footprint rationalization.

Turning now to a quick summary of the full year results. Total revenue was $2.02 billion, and subscription revenue was $1.93 billion, both growing 17%. Non-GAAP operating margin came in at 29% -- we continue to drive scalability in the business model while investing for growth and scale with some years driving more leverage than others, while we sequence investments and expected returns. Over the past 4 years, we have expanded operating margins over 400 basis points, and our margin profile is well above peers of similar scale.

We also continue to drive efficiencies in our management of equity compensation. Fiscal '26 stock-based compensation as a percent of revenue was just under 15%, representing a decrease of more than 100 basis points from fiscal '25 levels. Non-GAAP net income for the full year was $518 million or $1.70 per diluted share. Our non-GAAP earnings factored in an effective cash tax rate of 18.5%. Free cash flow was $529 million or 26% of revenue, $4 million above the high end of guidance and roughly 100 basis points above fiscal '25.

As a reminder, this strong cash flow margin includes absorbing 600 basis points of impact due to cash taxes. We are somewhat unique relative to most software companies given our strong GAAP profitability and therefore, pay more in cash taxes. Excluding cash taxes, to provide an operational compare closer to our peer group, pretax free cash flow for fiscal '26 was 32% of revenue.

Moving to our share repurchase program. In addition to doubling the size of our share repurchase authorization to $1 billion in February, we significantly increased the level of buybacks in Q4, repurchasing 5.9 million shares for $224 million compared to roughly $160 million in Q3. This uptick in spend reflects our conviction in the company's operational momentum, long-term growth and cash flow trajectory and view that our shares are undervalued.

For the year, we repurchased 11.4 million shares for $479 million, representing 90% of our free cash flow. As of March 31, we had approximately $849 million remaining of the $1 billion authorization, and we plan to continue to take a disciplined approach to capital allocation, investing in innovation and growth while delivering value to shareholders. Turning to our fiscal '27 outlook. We entered the year with high conviction, fueled by a rapidly expanding market.

The shift towards agentic AI and autonomous operations has made Observability a foundational requirement. Further, intensifying vendor consolidation, inclusive of log management plays directly to our strengths as enterprises trade tools sprawl in favor of an AI-enabled end-to-end platform that can deliver a compelling ROI. We believe our fiscal '26 results prove we are winning and exiting the year in a position of strength. We have stabilized ARR growth, delivered double-digit net new ARR growth and continue to improve our go-to-market execution. With these building blocks in place, we are well positioned to sustain and improve our current momentum.

Now let's turn to our full year guidance. We expect ARR to be between $2.38 billion and $2.4 billion, representing ARR growth of 15.5% to 16.5%. This ARR guide implies full year net new ARR adjusted for foreign exchange movements of $320 million to $340 million, growing 16% to 23% and accelerating from fiscal '26 levels. While we don't provide quarterly ARR guidance, we expect net new ARR to be modestly more weighted to the first half of the year compared to historical seasonality as we entered the year with healthy forecasted pipeline coverage.

As usual, we'll revisit our full year ARR outlook once we get closer to the midpoint of the fiscal year.

Turning to revenue. We expect total revenue to be between $2.32 billion and $2.34 billion. Underlying that, subscription revenue is expected to be between $2.22 billion and $2.24 billion, both up 14% to 15%.

Note, our total revenue and subscription revenue growth rates are impacted by a difficult fiscal '26 compare from the change in accounting for on-demand consumption revenue and other miscellaneous onetime revenue true-ups.

We expect non-GAAP operating margin of approximately 29.5% Unpacking operating margins, we continue to drive efficiency gains across all functions, notably in sales and marketing and G&A. This operating margin guidance includes 150 basis points of additional OpEx leverage versus fiscal '26. This scalability is expected to be partially offset by a headwind of 100 basis points in gross margins from an increase in cloud hosting costs driven by robust consumption growth within our customer base.

We expect this margin pressure to be temporary as we execute on defined projects to improve our cloud cost efficiency with gross margins beginning to recover during fiscal '28. Another important area of leverage is stock-based compensation. We expect stock-based compensation as a percentage of revenue to once again decrease by 100 basis points in fiscal '27 to just under 14%. We expect non-GAAP net income to be $584 million to $594 million, resulting in non-GAAP EPS of $1.93 to $1.95 per diluted share based on 302 million to 304 million shares outstanding. Our effective cash tax rate is expected to be 18.5%.

We expect free cash flow margins of 26.5% and pretax free cash flow margins of 32%. As a helpful reminder for your modeling, due to seasonality and variability in billings, we expect free cash flow to be significantly higher in the first and fourth quarters and significantly lower in the second and third quarters.

Looking to Q1, we expect total revenue to be between $547 million and $551 million and subscription revenue to be between $523 million and $527 million. As I noted earlier, our Q1 total revenue and subscription revenue growth rates are impacted by a difficult compare from the change in accounting for on-demand consumption in Q1 last year.

Non-GAAP operating margin is expected to be 27.5% to 28%. Lastly, non-GAAP EPS is expected to be $0.44 to $0.45 per diluted share based on a share count of 298 million to 299 million shares. In closing, the strength of our Q4 and fiscal '26 performance sets a solid foundation for fiscal '27. The secular growth drivers fueling the Observability market continue to expand and our AI-powered end-to-end platform differentiates us and puts us in a strong competitive position.

We are committed to maintaining a disciplined approach to optimizing costs and improving efficiency. At the same time, we will continue to invest in future growth opportunities that we expect will drive long-term value. With that, we will open the line for questions. Operator?

[Operator Instructions]

Our first question comes from the line of Matthew Martino with Goldman Sachs.

Maybe to start, and I appreciate the setup on kind of the fiscal 2027 outlook. But when we look at kind of the Q4 net new ARR, it came in around 9% on a constant currency basis. And the fiscal '27 guide implies a fairly meaningful step-up in the net new from here.

Can you walk us through the bridge between the Q4 exit and what's embedded in the FY '27 growth algorithm? And then ultimately, the role the renewal cohorts around DPS are going to play in terms of getting you to that full year guide?

Sure. I'll take that, Matt. That's a good question. I think what I would tell you is that we had a very solid Q4. We had a great half 2. We had a great fiscal year.

So there's underlying momentum building in the business. If you kind of step back and you say, what did we do in fiscal '26? And I outlined it in the prepared remarks, it's a year of milestones in the year first. We stabilized ARR growth at 16%. We showed double-digit net new ARR growth for the first time in 3 years. Logs, well over $100 million, growing 100%.

The go-to-market traction is building. You've seen it in large deals, record new logo lands over $1 million. DPS now over 75% of our ARR, consumption growing at a very rapid rate. I outlined 2 years ago that we were going through a fix, stabilize, accelerate kind of journey. And we're in the -- we just finished the stabilization. If you look at this guide, you're right, this guide implies almost double the net new ARR growth from fiscal '26 levels. And it's all of the building momentum that we've seen. So it's not anything new like we need some new play. This is just a continued execution of the existing play.

So Q4 was a solid finish. You're going to have quarters that are like that. You're going to have quarters that are more robust than that. I can tell you that pipeline is healthy. Forecasted coverage is good. There's just significant interest. So I think it's just a continued building of what we've been doing. We talked about this with our go-to-market changes 2 years ago. They're starting to take root, and we expect that, that will continue.

And I would say, Matt, that AI tailwinds, agent tailwinds, cloud tailwinds are also contributing to our view of FY '27. So it's a combination of internal growth as well as market factors overall.

Our next question comes from the line of Eric Heath with KeyBanc Capital Markets.

Jim, just to continue on the point there. I mean, was there any macro impact in the quarter just given some of the geopolitical volatility? And did it cause some deals to push? And just any additional commentary you can speak to about the DPS renewal activity in the quarter among some of the fiscal '24 cohort of customers?

Yes. I'd say from a macro perspective, you know it as well as I do that certainly, there's a lot going on in the Middle East right now. I would not say that had any material impact on the quarter, something that we're monitoring for sure, for our EMEA business.

But I would say nothing notable. Relative to DPS, again, DPS even beyond kind of -- it is the contracting standard. Now that we have 75% of our ARR on that, -- and as we outlined that when you get customers on this vehicle, you can now team them up with our customer success teams and our SRE teams to drive consumption and consumption continues to grow at a very rapid rate.

As you would expect, consumption for our DPS customers is growing much faster than our non-DPS customers. We continue to see healthy expansions. One thing to note, I think I've mentioned this in the past that fiscal '27 is a year where you're going to see the largest cohort of DPS customers coming up for their annual resets or in some cases, their actual renewal.

And so there's an opportunity, again, depending upon how consumption grows to continue to see healthy expansion. So we're quite pleased with the traction there and the momentum that we're building in that area in particular.

Our next question comes from the line of Will Power with Robert W. Baird.

Okay. Great. Rick, in your prepared remarks, you called out what you all view as some of your architectural advantages. Can you just kind of speak to how that's resonating with customers, what you're doing, what you maybe still need to do to help demonstrate that? And maybe as part of that, anything you could share with respect to AI native adoption of the platform?

And then just maybe more broadly, what you're seeing in terms of agentic usage trends that might speak to? I think you talked about 500 customers using some of your agentic capabilities. But anything you could share on just the broader usage within those architectural advantages?

Yes. A lot of questions in there, Will, we'll attack them. So the architectural advantages, we typically will talk about Grail as a completely integrated, massively parallel processing data lake house. We talk about Smartscape as an integrated topological graph that can provide analytics and context.

We talk about Dynatrace Intelligence, which we launched back in January is covering really 2 elements of the spectrum. First element of the spectrum being around deterministic AI and then the second regarding agentic AI, where we actually can take action. What all of that is driving to is a completely integrated, fully unified platform that enables our customers to move into an agentic world successfully. And that agentic world enables autonomous operations to take hold, which is what they need to be able to manage increasingly complex overall IT environments and software -- and software workloads.

So we believe that, that architectural advantage is durable, and we believe it's sustainable. And that when you get into manual tagging of data stores across multiple data stores, you end up with an environment that is much more fractured. So that was piece number one.

With regard to AI natives, we have been focused mostly in the history of Dynatrace on IT operations, on CXOs. That's how we've ended up with end-to-end Observability, and that's when we get into these larger deployments for large customers where we've been incredibly successful with the go-to-market plan over the last couple of years, which we initially deployed. That is evolving to include developers. And we've made huge strides in the platform beginning 18 months ago with new implementations around elements like we mentioned last quarter like Bedrock agent core integration or Azure SRE agent.

This quarter, we talked about Claude Code integrations. Now Assist headed into AI control tower. So we continue to evolve the platform to enable more developer adoption. That developer adoption opens and unlocks the opportunity with AI natives, which we're driving along with developers now.

And finally, you mentioned agentic. Agentic is certainly a very strong opportunity for us in multiple ways. Number one, as it's driving us toward autonomous operations in our existing core base of enterprise deployments. But secondly, also in, as I mentioned, a brand-new way of development.

We see a sea change in a movement from software development life cycles to AI development life cycles, and we believe that Observability fundamentally is going to be a core foundation of that. We also believe that Dynatrace based on our architecture going back to my first remarks, has an opportunity to be a primary participant in that shift from the software development life cycle to the AI DLC. Three different questions. So I try to attack all 3 of them, but good question.

Our next question comes from the line of Mark Murphy with JPMorgan.

This is [Indiscernible] from JPMorgan on for Mark Murphy. You mentioned closing a record 22 deals with more than $1 million ACV. I think 9 of those were new logos. We had a couple of channel checks over the quarter that mentioned momentum in the Dynatrace business and new logos, large new logos specifically. So I'd love to kind of hear what you think is driving some of the momentum with the large deals and new customer lands at scale.

I'll take that. I think one of the things that we've been talking about, I think I began talking about it probably 2 years ago, and I talked about it as an emerging trend of these very large enterprises that had fragmented DIY and commercial tooling solutions that were looking to vendors for consolidation, both for economic benefits and also just a better customer experience. And so that trend has continued.

And I would say, especially in very large customers that there is huge interest. There is significant interest in integrating fragmented tools to save money. There is money to be had when you can go from multiple vendors to one vendor, and you can get a better experience with Dynatrace for all the reasons that we've outlined. So that trend has continued. I'd say the changes we made on the go-to-market side, we're developing much better and deeper relationships with C-level decision makers. These are the people that make these decisions.

And so it actually is a market tailwind moving in that direction. And we're in a good position to benefit that from the platform depth and breadth that Dynatrace provides. And so I expect that, that will continue.

Our next question comes from the line of Koji Ikeda with Bank of America.

Jim, maybe a question for you. When I look at past transcripts and the way you've characterized the guidance was you always used the word prudent. And I noticed this time you didn't. And I look at -- you definitely talked about much stronger net new ARR growth this year. And so I just wanted to ask, has the guidance philosophy changed with the removal of the word prudent? And if so, why now?

No, our guidance philosophy has not changed, Koji. I would say that the way we've guided in the past and the way you should think about this guide. I think I've built enough experience with you guys that you know how I do guide. I don't think it's necessary to continue to kind of provide that language that you should expect that what I guided here is consistent with how I've guided historically.

Our next question comes from the line of Matt Hedberg with RBC Capital Markets.

Rick, I wanted to come back to some of the first couple of questions that were asked. And referencing your script, there's a lot of positivity about industry trends and why the Dynatrace architecture is well positioned to capture that.

But I think the question people keep asking me is, given those, why aren't you growing faster? I think NRR was maybe a little bit lower than what people are thinking. And I guess I'm just wondering like is there a lag between enterprise AI and inferencing spend and ultimately, your Observability products? Like is it just a timing thing that's holding you back from better growth?

Question, Matt. I do think that there is a bit of timing difference between what we're seeing in the enterprise and what AI natives are seeing. I think that is with regard to, for example, agentic deployment, agentic usage.

As I said in my remarks, we're already seeing 500 customers plus deploying and using our agentic capabilities. We've got now more than 850 customers that are using us to evaluate the reliability and trust of AI and LLM workloads. So in the enterprise, we certainly are beginning to see that kind of adoption.

And we believe that, that adoption will, of course, through the DPS mechanism, get deployed into the numbers over the course of time. APS, as you know, does have a bit of a lag with regard to expansions vis-a-vis a straight consumption model, which is why we see some of the delta between our consumption numbers with those being in the low -- in the 20% plus range relative to ARR. So that's some of what you see. With regard to AI natives, this is where I believe the new evolution evolutionary elements and development capabilities in the platform will facilitate us expanding to the developer, expanding to AI natives more aggressively as we look to FY '27, and we're beginning to see some of that momentum in the AI natives as well.

Our next question comes from the line of Raimo Lenschow with Barclays.

Perfect. Can you talk a little bit to -- on the large customer momentum? That sounded like a very good outcome this quarter. And I was also surprised on the new customers that came up straight into large. Is that something that you're seeing now as your offering got a lot broader, the platform pricing kind of makes adoption really nicely there. Is that something that you're seeing in the pipeline as well?

Yes. I mean, Raimo, I'd simply say it's all of the above. And I kind of answered it previously that large enterprise customers are looking for vendors to consolidate on to be able to integrate fragmented tools and provide a better experience.

And it becomes even more so when you move into an AI-first world. And so we're in a great position to be able to do that. And so I would say the go-to-market changes that we've made have allowed us to go access and penetrate those opportunities. One thing I will tell you is that while we're not making radical go-to-market changes at all for fiscal '27, it's a continuation of what we've been doing.

The traction that we've made in our large strategic accounts, which is think of them as the Global 500, we're going to extend that motion down probably to another 150 or so customers where we're going to improve the density of coverage and get closer to customers to be able to drive either new logos or expansions with these existing customers by getting closer to them. And so I think it's just -- it's a phenomena of what's happening just in the industry that large customers are looking for vendors that they can consolidate on that -- and we have a very unique position to be able to give them both better economics and a better kind of business outcome.

I would just add, Raimo, that I believe that point product Observability is dead or at least dying. And that's why we see some of the ongoing expansion that we saw this quarter and that we see in the pipeline related to end-to-end Observability. And we have a great proven solution there for large enterprises to deploy. And it's at the data layer, it's a domain layer, it's at the persona layer.

All of those layers are looking for end-to-end Observability because that is what is enabling the best outcomes of Observability, number one. And number two, it is a necessity as you look to an agentic future to be able to leverage that underlying fabric in order to deliver the agentic future that's expected leading toward autonomous operations.

Our next question comes from the line of Ittai Kidron with Oppenheimer & Company.

Jim, I had a couple of things I needed to hopefully can clarify on the fiscal '27 guide. You made a comment that net new ARR is going to be weighted towards the first half. I'd love to get some color on this. Again, were there deals that [Technical Difficulty]

You're breaking up, I can't hear you.

[Technical Difficulty]

For ODC. If you could break that down, that would be great.

Yes. I couldn't hear the question. The only thing I heard in the question was the first half. I think you said, could I clarify the commentary about maybe net new ARR being a little bit more weighted to the first half. And I would say it's modest, but I'd say we wanted to make sure we provided some color on that.

I think it's mostly a function of -- we have very strong forecasted pipeline coverage. It continues to build. And so I think what we're signaling is that we expect to have a good start to the year, not just for Q1, but also for Q2. Obviously, your visibility over the next 3 to 6 months is greater than over the kind of beyond that. And I'd say what you see is this is just growing visibility and confidence around what we can deliver.

Our next question comes from the line of Sanjit Singh with Morgan Stanley.

I wanted to get back to some of the drivers that you're seeing in the core business. Clearly, vendor consolidation, tool consolidation is a sales motion that you guys have been executing very well on and has probably been the driver of enterprise large deals for the past couple of years. To what extent is the business seeing the impact yet?

Or do you expect to see the impact from just the big increases in just software development initiatives with these coding agents getting adopted, not even if we just even exclude the AI natives, but even in the enterprise, you're seeing quite strong adoption of coding agents and that should drive more software creation. What do you think that ultimately drives -- comes -- emerges as another core driver for ARR growth for the business?

Yes, I'll take that, Sanjit. Absolutely, as we mentioned in the prepared remarks, I think that whole notion of AI evolution is a core driver. The ability to use AI coding agents is critical. We talked about the integration into Claude code, for example, as a mechanism for that for debugging, root cause analysis, those sorts of elements. So we do see this going through a few stages, whereas today, they're using coding assistance. Coding is done at least in part by AI systems and AI capabilities.

We see that transitioning to an AI DLC where the primary builders of code are, in fact, agents themselves, where agents are building and operating code. And so we are going to go through rapidly this process evolving through a software development life cycle in our perspective, all the way through an AI DLC. We absolutely are seeing momentum in this area in the enterprise with our customers.

That's where I talked about the 500 customers or so already using us to do agentic integrations. So we're seeing this and the expectation during FY '27 is that, that will be a growth driver driving actually increased and accelerated ARR.

Our next question comes from the line of Keith Bachman with BMO Capital Markets.

I wanted to ask a clarification and a question. Jim, if you don't mind, could you just relate what you think the NRR will be for '27? I'm just trying to back into the net new guidance. And then my broader question, Rick, for you is, when I think about Dynatrace, you've made progress in logs, maybe a little less so in security in terms of at least for investors, the visibility we have with that potential.

But when I think about you versus your nearest publicly traded competitor, I think there's been more meaningful product expansion. And so I just wanted to see philosophically with Grail out there, how do you think about the product expansion opportunity over the next couple of years, particularly now with an activist involved who may be looking for more margin expansion. But if you could just speak to your product expansion philosophy over the next couple of years?

Let me take the front end of that, Keith, and then I'll let Rick comment on the broader question. So we don't guide specifically between new logo and expansion or NRR. You should expect that it's going to be similar to what we've had historically, which is net new ARR is going to be roughly 1/3 new logos, 2/3 expansions.

As you've seen this year, sometimes it may be weighted more towards one than the other. And I think that's a function of reps are compensated on maximizing bookings, whether it be new logos or expansions. And so that's one of the reasons we don't guide, but can certainly look at it that if you look at the historical levels, that's roughly what we're going to operate.

And so to your point, if you look at the high end of the guide, which is net new ARR growth of 23% or 24%, you're going to see it both on the expansion side and on the new logo side.

And Keith, on the broad product acceleration front, what I would say is there's been a huge amount of innovation in the Dynatrace platform over the last couple of years. Of course, we've delivered Grail, foundational data lakehouse. We've talked about that. We delivered Dynatrace Intelligence back in January.

This is a major setup for an agentic operations system that can allow for true -- truly autonomous operations, leading to the evolution of overall development through an AI DLC. Dynatrace Intelligence is really a foundational enabler of that. As you mentioned, we have logs. The logs capability is quite substantial, as Jim mentioned in his remarks, now well above $100 million, growing at more than 100%.

So a huge amount of opportunity there, bringing logs into an end-to-end Observability Platform, we believe, to be quite foundational to getting end-to-end Observability right as a foundation for the agentic future. Talked about AppSec. We don't say -- we haven't said as much about AppSec. It is absolutely a relevant ongoing investment thread for us, not growing as fast as the logs front, but we do see ongoing convergence of Observability and security use cases, and so we're focused on those.

And I would say now that our third-gen platform continues to emerge and be more mature, you're going to see a further acceleration in innovation around feature set and functionality for add-on capabilities in the platform as we look at.

Our next question comes from the line of Patrick Colville with Scotiabank.

I guess maybe this one is to both Rick and Jim. If I look at the financial model, we had a lot of questions on the top line. I just want to kind of zoom in on the bottom line, in fact. The guidance, you gave this helpful commentary that expecting GMs to come down slightly in fiscal '27 due to cloud hosting costs.

Do you mind just unpacking why that is exactly? And then also just maybe just zooming out, like what is the philosophy on profitability as of May 2026 because Dynatrace is wonderfully profitable, but we've been at levels that have been consistent now for about 2 years, and you're guiding to kind of flat again in fiscal 2027. So like how are you thinking about that aspect of the business?

I'll start with Patrick, that we continue to drive efficiency in the business, which is why I mentioned that if you look at operating expenses, this guide implies 150 basis points of incremental leverage in operating expenses. We are going through a bit of a, I'd say, a year with gross margins, which is kind of -- I'd say it's a good news, bad.

The good news is what's driving that is robust consumption of the platform. And so all the reasons we talked about that consumption is growing rapidly, growing significantly faster than ARR growth. There is a lag between when you see that in an expansion and when you see that show up in revenue. Whereas when you look at cost of goods sold, you see that immediately because it is recognized as incurred. And so it's truly a function just of growing consumption on the platform. And there's a bunch of initiatives, as you can imagine, that we're driving very defined initiatives to improve our cloud cost efficiency ratios. There's always a give and take between R&D dollars that are spent on those things versus R&D dollars that are spent on efficiency.

And so our expectation is that this is temporary and that as we enter fiscal '28, you're going to start to see margins go back -- gross margins go back to more historical levels. I will remind you that even a bit of pressure on gross margins, we still have incredibly robust gross margins even with a 100 basis pointhead wind. So this is a kind of a philosophy of continuing to drive leverage in the model. As I mentioned in my prepared remarks, some years, we're going to do more than others. Sometimes we sequence investments where the return on those investments comes in the following year. But you can expect we will continue to drive efficiency across the business.

Our next question comes from the line of Miller Jump with Truist Securities.

So the logs momentum continues to stand out. Can you just talk about where you are in your ability to displace competitors for security use cases in logs and the comparative size of the market opportunity that you see for Observability versus security there?

I'll take that, Miller. We have not done an on-prem SIEM. So we have not addressed that with the security use case. We have addressed the Observability use cases, number one. Number two, as we've reported in the past, we are working on and we'll expect over the course of time to deliver a cloud-based SIEM. So that will begin to take on some of the security use cases around logs.

But for the moment, most of the commentary has been around Observability logs.

And that brings us to the end of the call. So let me just wrap up. First of all, thank you all for your engagement and continued support. We really do appreciate it. FY '26 was a solid year of stabilized ARR with us at 16%. We delivered double-digit net new ARR growth through the course of the year. We saw it as a year of exceeding $2 billion in ARR. And of course, we delivered very, very strong logs momentum, as we indicated, now well more than $100 million, still growing at 100%.

We have a number of growth drivers in the business. We're very excited about what's happening by way of innovation. And we believe we have a number of market tailwinds around AI, around cloud, around agentic that will provide further fuel to the opportunity to accelerate FY '27 ARR growth, which is precisely what we're looking for with accelerating net new ARR growth at the midpoint of our guide. We thank you again for joining. We look forward to connecting with you at upcoming IR events, and we wish you a great day.

Goodbye. This concludes today's conference call. You may disconnect your lines at this time. Thank you for your participation.

All right. Continuing the session before lunch. I'm Sanjit Singh, I'm the infrastructure software analyst on the Morgan Stanley software research team, thrilled to have the management team from Dynatrace. Chief Executive Officer, Rick McConnell; and Chief Financial Officer, Jim Benson. Rick and Jim, thank you for joining us at the TMT conference once again.

Thanks, Sanjit. Always good to be with you.

Awesome. Before we get into the discussion, for important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures.

I'm going to start out with my comments, I'm a big bull on observability in the category, not just for this year, but going forward, particularly in the agenetic world. We'll dive into those conversations. Just to give us some context, Dynatrace is coming off a couple of strong quarters. Net new ARR is staying at 16%, constant currency for the second quarter in a row. The ARR base is now up to $1.9 billion. You got an operating margin in the high 20s. Trailing 12-month free cash flow margin of 32%. So squarely in that rule of 40 plus territory. We are at a time in the market though, where there's a lot of revisiting of first principle thinking when it comes to software providers and how they pave value for their customers.

So Rick, with that as a context, what are the core problems that Dynatrace is helping customers solve today? And what problems will Dynatrace help customers solve going forward?

The simple way to, I think, begin, Sanjit, is number one, we deliver through observability, resilient software. And at the end of the day, there is no time in history that we can recollect where delivering software that worked perfectly was more critical. So that is a foundationally #1 in terms of priorities. And in the observability space, I would say, overall, we are at the -- we are in an era where observability generally is becoming absolutely mission-critical to essentially every company in the universe that is delivering core software.

The second piece that is evolving and evolving rapidly by way of problem we're trying to solve is reliable AI. Because it isn't just about delivering software that works. It is about delivering AI-first workloads that actually are delivering the outcomes, delivering the content that you're expecting to be delivered.

Yes. That makes a ton of sense. And maybe we can dive a little bit deeper in terms of the product innovation cadence at Dynatrace. There's a lot of buzz coming out of your conference, and that conference is called Perform at the end of January. We'll get into the specific product analysis, but the big theme from my point of view was around Dynatrace Intelligence. And this representing the next major evolution of the Dynatrace platform. So can you give us a sense of what makes up the Dynatrace Intelligence platform. What capabilities and value will this unlock for customers?

So Dynatrace Intelligence, we announced at our Perform Conference about a month or so ago. And Dynatrace Intelligence fuses together the innovations of deterministic AI along with Agentic AI. Now deterministic AI, we look at it, Dynatrace, is really our super power. This is what we would argue we do better than any other observability company on the planet. And the reason is because we've had 20 years of context of building systems based on an underlying data lake house with Grail, a software top logical map using wrapping and Smartscape, elements of artificial intelligence, beginning with causal AI to predictive AI to generative AI, all of which are designed to indicate to a software developer or software provider, specifically what is happening in that software environment at any given moment.

And when something breaks, what broke? With a very high degree of clarity, specificity and accuracy so that you can take immediate action. We do this one of the largest organizations on the planet that have billions of interconnected data points that we are analyzing and providing results and analytics against in real time. That deterministic AI, that foundation and what precisely is happening by way of analytics in your environment then sets up the foundation to be able to take agentic action against that set of analytics.

And that's where the agents come into play. We believe that what the market will hear what customers will hear from basically every observability players. I've got agentic AI. I'm delivering agents, they can take action our supposition when we get into things like proofs of concept and others is that you have to start with reliable trustworthy input to those agents. Otherwise, those agents are going to be taking actions that are guesses. And so the result of that is we believe that Dynatrace Intelligence is really unique in the market space of delivering both what the answers are, what the analytics tell you along with the agents linked into that, that can take action.

And by the way, those agents in that agentic framework are really an ecosystem of agents, not just Dynatrace agents. That can take action, for example, through hyperscalers, through ServiceNow, through Atlassian, through others to essentially enable auto prevention, auto remediation, auto optimization in your overall environment, which gets you back to where we started in your very first question, which is resilient software and reliable AI.

Yes. And I had -- my next question was around some of the things you announced around agents. But before I get to that, if we kind of zoom out and get at least from my point of view, why I'm bullish this category, we think about the attach rate of observability to agentic deployments. Do you feel like that that's going to be as high or even higher than sort of like the cloud-native application era. I mean, these agents are going to be accessing critical business systems. They're going to be calling external tools. They're going to be interfacing with your end users. Like is it sort of obvious that this is all going to be monitored, tracked, logged from your guys' perspective?

We have a theory, which I would say is early stage at the moment, but it speaks to precisely what you're suggesting. And that is, today, we look at the preponderance of workloads that happen across enterprises, the largest enterprises that we typically would sell to. Maybe 30% plus or minus of workloads and traditional workloads are observed by Dynatrace. Why is that? Well, if you need to observe your primary set of infrastructure, you need to observe your primary mobile app, your primary website, whatever those workloads may be.

But I was speaking to a customer recently down in Australia, and their comment was we have 2,000 apps we have this much -- we're not going to -- we don't need -- they don't have the same level of criticality. We don't need to observe all those workloads. In the case of agentic AI, especially, and in an LLM environment that is producing probabilistic outcomes, we believe that you really are going to need to observe darn near 100% of those workloads because it is going to be sufficiently independently operating that you are going to have to do extra work to have systems that can give you the confidence that you're delivering resilient capabilities.

You're also going to have the systems that are mission critical to delivering reliable AI outcomes. And so the result of it is, we believe that in this world that is evolving rapidly to an AI-first world, 2 things happen. Number one, explosion of workloads. So you just have more raw workloads. And then the second is your very point of the question, you need to be able to rely upon those outcomes in such a way that probably drives more observability as a penetration rate against those workloads.

Yes, it makes tons of sense. So let's talk about some of the agentic capabilities you introduced at Perform, your user conference. So you have an agent-first reliability engineers, you have an agents for development, you have agents for security teams. Which of these are you most excited about from a monetization perspective? And how are these domain-specific agents priced.

Sort of like asking what's your favorite kid. It's hard to say. I mean, I think that they are all critical. And the way to think about the agents is, first, you have the foundational agents. These are agents like an SRE agent that would tell you root cause analysis. For example, you need to know specifically what's happening. And then sitting on top of that, you have sort of management and supervisory agents that can direct traffic. So do the agents tell, a, Dynatrace agent to take action to resolve a particular incident maybe to turn off a feature, for example, that we can do internally. Or do you assign that to a third-party agent to like an AWS agent that may provision more storage or whatever it might be or a ServiceNow agent and might take some workflow action.

So that's sort of the next layer. Third layer would be those agents that actually could take action to resolve issues and then you have a series of ecosystem agents that integrate. So it is sort of a very thoughtful stacked map that can define what agents are taking action. But I see that as -- while critical to overall architecture and the architectural topology, foundationally, the more critical piece, I think, for investors and others take away even customers with whom we speak is that start with a deterministic foundation, you then have the confidence to take agentic action, and we, Dynatrace, have produced an architecture through Dynatrace Intelligence that enables you to take that that agentic action thoughtfully either through Dynatrace agents or third-party agents to be able to deliver against those elements of essentially an auto correcting software ecosystem, which is ultimately what we all want to be able to deliver.

Awesome. Let's talk about in terms of sticking on the theme of product innovation. You launched your next-generation real user monitoring service powered by Grail, powered by Smartscape and Advanced AI in the context of that you've gotten to like $100 million consumption run rate with logs. The question here is like, given what the traction we see today with digital experience monitoring and now with this next-gen platform on RUM, how confident are you that this can be your next $500 million, $1 billion plus business?

Well, I will tell you that we already have $400 million-plus businesses. We obviously -- one of them, obviously, most recently at Logs our DEM business is well over $100 million in our Infrastructure Monitoring is actually the second fastest-growing business, interestingly enough next to Logs business. And then there's obviously full stack for APM. So the expectation is across all these categories and some of the things that Rick talked about is more workloads, more workloads across a broader stack, the biggest sales play that we've been able to drive has been end-to-end observability.

These are customers that are looking to consolidate fragmented tools onto one platform. You see in our land sizes for new logos, you see it in the expansions that we're doing. And so these are going to be primary sources of growth now and in the future.

I want to have a discussion with both of you on Dynatrace's defensibility in the era of AI. But let's get an update first on just some of the trend lines of the business. I wanted to walk through this with you, Jim. In terms of the ARR performance, we've seen constant currency net new ARR stabilized at 16% for multiple quarters after years of deceleration. Can you talk us through the specific factors that contribute to stabilization? And do you feel like this is kind of the new baseline for growth as we look forward into fiscal year '26?

Yes. So I appreciate the question. So again, to your point, we've had 3 quarters of stabilized ARR growth at 16%. We've had 3 consecutive quarters of double-digit net new ARR growth, which obviously fuels ARR. Our guide for the fourth quarter, at the high end would suggest this continues. To your point, we haven't done this in several years. You say, well, what has caused that? Well, what has caused that was changes we said we made basically a little -- almost 2 years ago now, where we made go-to-market changes. And we were very clear when we made those go-to-market changes. These were changes to go on the offensive.

So those changes were we oriented more resources around large enterprise accounts, what we call the Global 500, so the 500 largest companies or governments on the planet. We continue to fortify our partner ecosystem, in particular with the GSIs. And so everything we put in place 2 years ago, we knew year 1 was going to be a maturation year. We needed to get the resources staffed. We needed to get the alignment going. We changed some compensation plan designs. And so we knew year 1 was going to be a period of building. What you're seeing this year you're seeing execution consistency, which is exactly what we expected when we built the plan 2 years ago.

So the maturation of the go-to-market model has continued to advance. I expect that will continue going into fiscal '27. And then you look -- even though we're not going to guide here, you look at fiscal '27, there's a lot of momentum in the business that we expect that if we can continue to execute like this you'll see it continue into next year.

To follow on that point, and this is an area of question that I get asked a lot about investors. The underlying consumption in the platform, you guys have purchase going north of 20% and outpacing the subscription revenue. So in terms of the lag between consumption growth and then that materializing ARR, what's the best way to think about those 2 dynamics?

Yes. I mean, I get that question a lot because one of the things we wanted to make sure we shared with investors is what's happening with the underlying growth in the business, which is how customers are consuming the platform. Obviously, our model is a subscription model. And so subscription model as revenue, is ratably recognized. That isn't always how consumption occurs. And so if ARR is growing 16%, and consumption is growing 20%, it will converge. The challenge is the timing of it. There's a lot of dynamics that go into when that will occur.

We do look at something internally, we look at the consumption to ARR ratio. And the way to think about that is the headroom. What is the headroom a customer has before they need to do an expansion. And so for us, it's about continuing to drive more consumption. And one of the things that we've done is even though the model is a subscription model at its heart, the Dynatrace platform subscription model is a consumption model.

Customers commit to a dollar amount, they can commit to a term. And then they consume. And it's a frictionless model, they can consume because they have the rate card for every capability on the platform. They're getting value, and we can work with them on driving more adoption of different product capabilities, they'll consume more. The more you consume, you can consume faster and it will burn through your commitment early and you'll do an expansion.

And so I expect that we will continue to provide that as a metric, like how we're doing around driving consumption. We now have teams of people. They're measured on this. They're compensated on this. We have specific product strike teams for Logs measured on consumption. Product strike teams for DEM measured on consumption. Product strike teams for application security, measured on consumption. And then our CSM teams are also measured on consumption for the accounts that they support. They're not product specific. And so we've advanced a bunch of activities that are very consumption oriented. That wasn't the case 2-plus years ago. It was a very SKU-based model. Now it's a get them on the Dynatrace platform subscription and drive consumption.

That's a great context there. One of the things I've been saying all week, Rick, is that my conversations at the beginning with investors was sort of like, Sanjit congratulations, you cover infrastructure software, you cover data platforms, your companies don't have seat-based models, you're so lucky. And in the last couple of weeks, now everything sort of gets questioned, right, in terms of the defensibility. So I wanted to spend some time with you, Rick, talking about how Dynatrace is positioned in this era of AI and give you some of the scenarios that I get asked about and get your sort of perspective.

One of the questions I've been getting is how does the value proposition of Dynatrace platform changes when the agents are doing the investigating and triaging versus human site reliability engineers or DevOps personnel working through dashboards?

On that topic, Sanjit, specifically, I would say that we expect exactly that evolution we expect over the coming years. And by the way, what the investment community is talking about at the moment, in most cases, is not what customers are talking about with us at the moment. There's a broad disconnect, I would say. But if we look out over the course of the coming years, we do expect that humans, end users, if you will, will become a relatively lower consumer of the Dynatrace platform and agents will become a relatively higher consumer of the Dynatrace platform.

That does not, in any way, translate in my view to any disintermediation of observability by AI. Rather, we see observability as being mission-critical to these AI workloads where agents are taking action that have to result in reliable outcomes. And you're simply, in our view, not going to have a probabilistic system providing input to a probabilistic system on delivering an outcome that is trustworthy or the kinds of organizations with whom we do business.

So it is that sort of notion that we believe that observability and in particular with some bias, Dynatrace, can and should become the control plane or reliable AI. And that really is based on an architectural moat of, as I mentioned earlier, Grail, Smartscape, Dynatrace Intelligence, the various technologies that we have built into the platform to deliver both deterministic AI and certainty of answers that can be trustworthy and that can be acted upon in a reliable way to deliver the AI outcomes of the future.

Whether those outcomes are delivered by an end user or by an agent, in some sense, is not that impacting to our overall business model. In fact, to the extent agents are a bit chatty and they're going to consume more analytics than an end user would then, if anything, we believe that that's a tailwind to Dynatrace just as AI broadly as a tailwind to absorbability and Dynatrace by virtue of generating more workloads.

And one thing I'd add on that, Sanjit, is that we're not a seat-based model. And so because we're not a seat-based model or a consumption-based model, so we monetize through consumption. So get them on the platform again through the Dynatrace platform subscription. We don't even have to change our monetization model for the way we go to market with product packages. It's already in place.

Yes. I take advantage of those elements, yes. And correct me if I'm wrong, one of the marketing messages that you've had for years even before the [indiscernible] was answers -- like literally, answers not dashboards, right? So...

The answers are not guesses. That actually came from Rick.

Yes. answers, I guess, is one of them. And the other thing we would say is, you're right, the answer is not dashboards. It is it is really critical for our customer base to get precisely to answer, not try to ascertain what's happening in the environment through a dashboard and through alerts in that environment.

Awesome. Let's go through the second kind of flavor in terms of investors' concerns on the category more broadly, but also with specific to Dynatrace. So this other angle is that the ability to combine open-source tooling to collect metrics, chases and logs, and combine that with an agent, either for one of the model labs to reason over the data and execute an incident response. And so I guess what investors are getting at is the potential for customers to manage observability themselves at theoretically lower cost or even more nuance, negotiate better pricing when it comes to their Dynatrace renewals and bills. Why is this line of thinking off base?

My response to that is that, look, the primary deployment of observability throughout history of observability has been DIY. It is, relatively speaking, quite recent that companies like Dynatrace and observability companies have come into the fray. And so DIY continues to be feasible that you could use open-source tools, you can use, OTel, Open Telemetry, you can bring in these sorts of elements and you can manage it on your own. The fact of the matter is that is getting more and more difficult to do each and every day.

Now might an LLM decide to do that for their own infrastructure, maybe, why? Because that is core to their business, delivering a resilient LLM that has reliable AI output, as you can imagine, for the LLMs is quite core. In the case, you've an enterprise, the largest banks, the largest health care organizations, the largest airlines, delivering a dynamic end-to-end observability solution that can process billions of interconnected data points contextually in real time. That is super complicated, and it takes this sort of broad-based platform architectural moat that we've described to have constructed that. That is not, in our view, a likelihood of an outcome, certainly for the vast majority of large enterprises.

If we call this category monitoring, and that goes back into the late -- mid- to late 1990s. One of the things about this category monitoring observability has been highly tied to changes in compute cycle. And the history is being that the leader in one cycle doesn't typically stay the leader in the next cycle. I actually think in this category, Dynatrace is one of the true success stories. You guys were a leader, multiple cycles to go when we were building on-prem job applications. You guys innovated, rewrote the platform from a clean sheet of paper, looking like aces today, but in terms of this broader AI debate, what are the ingredients of the business that allows Dynatrace to stay on top of the innovation frontier with potentially a new platform shift ahead of us.

Well, we've talked about some of it. I think the Dynatrace Intelligence is a core part of that. This notion of it's not just about deterministic AI and agentic AI. It is about using deterministic AI and agentic AI outcomes becomes particularly critical and doing so in real time. That dynamic in our view, and we've talked about the shift toward agents as consumers, if you will, observability of observability data. And in that environment, I would say that structure, that architectural context is even more critical to deliver.

So that becomes sort of the next generation, even as AI-first sorts of models evolve, those models are going to evolve in a way in which observability foundationally becomes more critical. But you really do have to have the deterministic piece. And I'd say that is where Dynatrace differs from others in the market to be able to provide that underlying foundation for success.

I think the other thing just covering the space, in the context of investors debating with cost of code going to 0, that customers can now build anything, right? I think what you guys have been doing, what some of your peers have been doing. These aren't tools. These are distributed compute platforms that processes billions, trillions of data points in real time. It's not like we can go out and build this easily. This is pretty hardcore stuff.

So some of what you're suggesting, I think, is exactly right, which is -- it is -- at least in our view, it is much easier for an LLM as you vibe code something to rebuild something that has a standard workflow. Our workflow at any particular customer, at any given moment is highly dynamic, highly variable depending on what's happening at that moment in time based on a data plane and contextual data as input to that system that is inherently different than it was seconds ago, let alone minutes or hours ago.

And that dynamic element means that the platform always needs to be learning. And that is a shift that doesn't result in, I'm producing a piece of code for a moment of time, and it takes that sort of domain expertise of the individual environment in the context of the overall platform and its generation that delivers meaningful value.

Yes. That's great context. So we've talked about the secular debate. Let's get down to the field level and talk about some of the things that are going on in the ground in the business. Starting with kind of market to market in terms of the go-to-market progress. We're about -- I think, Jim, you mentioned almost 2 years into the go-to-market changes. You stated that visibility and confidence is greater now than a year ago with pipeline also accelerating. Where are you seeing the most success and which elements are still maturing.

So I'd say it's playing out about as we expected. So again, when we outlined this almost 2 years ago, where we are in our journey is about what we expected. I'd say the #1 sales play, I think I may have mentioned it earlier, is end-to-end observability. So we have 3 sales plays. We have end-to-end observability. We have an APM land play where you land, you do a POC and you expand from there. And then we kind of have a cloud-native play as well. I would say, universally, the most successful sales play has been end-to-end observability. That we have a sales organization that knows how to sell it. The value proposition is very clear. We can actually allow them to save money and they consolidate tools and they can get a better outcome.

So I'd say we're still -- even though I talked about this 2 years ago, Sanjit, that this was an emerging trend. This is a prevalent trend now. This is more and more enterprises are looking to consolidate fragmented tools onto one platform, not unique to our particular industry. You're seeing it in security in other places as well. So I'd say it has been a source of growth, and I think it will be a continued source of growth because it's still many companies are not doing this. I'd say the area that we're making great traction, and we're very proud of the fact that we hit our $100 million milestone for Logs.

But that's just the tip of the iceberg. So to your point, we have a lot more growth to be had within Logs. 40% of our customers leverage our Log solution, continue to grow the cohort classes. And so they start on the platform smaller, and have grown significantly. So between end-to-end observability, Logs in this growing use cases, that you will see this also with AI-native workloads that these are all areas that will continue to mature. And the good news is the go-to-market motion is we're year 2 which is where we thought the productivity improvements would begin. We expect those productivity improvements to even accelerate further in fiscal '27.

I mean, to your point on like the consolidation buying behavior, there's -- if I just kind of look at this market, it's been crowded. There's a lot of players. You can probably name a dozen different players. But there's probably not that many they can pull off the consolidation deal, right? And so when we think to that and then I look at the success you've had in winning large deals on the pipeline being constituted with deals over $500 million, kind of speaks to your ability to really win those consolidation opportunities.

When we think about managing the timing of variability inherent with these large enterprise deals and while maintaining guidance accuracy, what rate of success are you seeing converting the pipeline needed to see, not only your goals for fiscal year '27, but just making sure that you get more confidence into your guide?

So I would say 2 years ago that we were growing pipeline or closing pipeline 2-plus years ago, with an or. And so I'd say the consistency around what we drove for net new ARR quarter-to-quarter varied a bit. I'd say what we have now is we are growing pipeline and closing pipeline at the same time. So pipeline growth is very strong. I'd say the quality of the pipeline is also very strong. And we measure that by just inspecting the pipeline and looking at kind of where we're at from a sales stage perspective. So -- and I think that comes from the go-to-market changes that we made.

Again, we may go-to-market changes to get closer to customers that one of the big changes we made around the enterprise accounts was to go from maybe 10 accounts per rep to say, 4 to 5, which means they're a lot closer to the specifics of their customer base, which means when you're looking at pipeline and deal flow, there's a lot more intimacy around what exactly is happening with that brings confidence now around closing. Now your timing might vary a little bit, but I would say even though we continue to see end-to-end deals keep growing in number and growing in size.

I'd say our confidence in our ability to not have to land all of them. Some are going to maybe close one quarter, some will close the next quarter. I'd say we have building confidence that the consistency in the go-to-market execution just continues to advance, and I expect it will continue to.

That's definitely encouraging. I think from my seat, when I think about the last 12 to 18 months. It felt like you guys would execute on quarters, knock down some of those bigger deals, we'd see good ARR results, but because the pipeline was weighted toward those deals. We had to be more conservative on the forward quarter. Is that also part of the solution maybe getting a higher velocity, more transactional blocking and tackling business that generates some of that ARR and that revenue as you guys go and penetrate your larger enterprise customers? Maybe Rick, you can speak to the potential to build a higher velocity sales motion, maybe it's mid-market or upper mid-market, just your thoughts on that.

I actually think, Sanjit, you're going to see in a cloud-native-AI-first world is you're going to see not a pivot, not a transition, but an evolution toward more departmental selling, in particular, the development audiences associated with cloud-native deployments. So I wouldn't walk away from the session thinking, wow, Dynatrace is going to go to SMB because our value proposition really wins most often in the larger enterprise. But it doesn't always have to win in centralized IT. I think it is just as pervasive, just as impactful in smaller groups within that larger enterprise, which then get aggregated. So I think that is some of the transactional volume that we can imagine evolving into the future.

Awesome. Let's talk a little bit about capital allocation. So you announced a new $1 billion share repurchase program. Can you maybe give us why re-up for $1 billion? What's the signal that you're looking to send to the market?

So I'll take that. So we were -- I think it was probably -- I think it was May of '24 that we put the $500 million authorization in place for the first -- in the first go. We tripled what we spent on our buyback in the third quarter because we actually thought it was a significant value dislocation for the company based on what we believe the prospect of the company were and what we were valued at. And so we exhausted it. We doubled the authorization. Again, going back to our philosophy. Our philosophy is, one, invest in the business. So that is the first use of capital.

But because we have $1 billion of cash, and we generate $500 million of free cash flow, we're in an advantageous position where we can both return capital through a buyback and opportunistically grow and look for opportunities from an M&A perspective. And so you can expect that we will be buyers at current prices, probably at an increasing level to even more so than what we did in Q3. So that's what we're going to do. I'd say from an M&A perspective, we are active shoppers, but disciplined buyers. And there are things that need to kind of fortify the platform and fortify what we think is an opportunity to kind of grow and broaden the observability use cases.

Maybe last question to wrap up. In terms of stock-based compensation, how you're managing that, what level of share dilution should investors expect? And in terms of getting is more GAAP profitable? How would you sort of rank those priorities?

Well, we got a unique company that is GAAP profitable. So we're not becoming GAAP profitable. We are GAAP profitable, which is unique in kind of the software space. But relative to stock-based compensation, we've always been a very appropriate user of stock-based compensation. This year will be around 15% or 16% of revenue. And my expectation is that we'll probably -- we'll drive some more scale from that going forward. And so while others are trying to catch up relative to the profitability, look at the profile of the company, the company has almost 30% operating margins. Because we're a cash taxpayer, where most tech companies are not, we generate, on an equivalent basis, call it, 32% free cash flows on a pretax basis.

So it's been a focus on the things that we're doing have all been about how do we accelerate growth in the business, accelerate growth in the business. You'll find that it's -- again, you will actually drive more leverage when you accelerate growth in the business. You'll actually get it at the same time. And so we're quite optimistic about the opportunities ahead for the company, where we're at, where we're positioned, both on the go-to-market side, where we're positioned on the product side and the platform side. And so I'd say this is an exciting time for Dynatrace because I think a lot of what we've put in place is actually kind of something that we're going to see this momentum continue into fiscal '27.

With that, we're out of time, Rick, Jim, thank you for giving us the update on Dynatrace. Best of luck going in Q4, and thank you for coming to the TMT conference.

Thanks, Sanjit. Thank you all.

Welcome to Dynatrace's Fiscal Third Quarter 2026 Earnings Call. [Operator Instructions] Please note, this conference is being recorded.

At this time, I'll turn the conference over to Noelle Faris, Vice President, Investor Relations. Thank you, Noelle, you may now begin.

Good morning, and thank you for joining Dynatrace's Third Quarter Fiscal 2026 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 quarterly report on Form 10-Q and annual report on Form 10-K. The forward-looking statements contained in this call represent the company's views on February 9, 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 discuss 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 presentation, 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 today's call. Dynatrace delivered very strong third quarter fiscal 2026 results, exceeding our guidance across every metric. Through this fiscal year, we've driven a stabilization of ARR growth at 16%, 3 quarters of consistent double-digit net new ARR growth, annualized log consumption that has now surpassed $100 million, a strong balance sheet and a healthy cash flow generation that have allowed us to double the size of our share repurchase program while still investing aggressively in innovation, and finally, an increased ARR guide of 125 basis points at the midpoint that puts us on track to achieve $2 billion in ARR by the end of fiscal '26.

Our sustained strength underscores the increasing criticality of observability to the software ecosystem, accelerated demand for our end-to-end observability platform and successful execution of our go-to-market strategy. Jim will share more details about our Q3 financial performance and guidance in a moment.

In the meantime, I'd like to start with some highlights from our annual customer conference perform illustrating the substantial evolution and differentiation of the Dynatrace platform in capturing the AI opportunity along with several customer and partner advancements.

Perform 2026, which took place just 2 weeks ago, was an invigorating event where we hosted roughly 2,000 people in person, including customers, prospects and partners plus thousands more virtually. If you weren't able to join us, I encourage you to watch the replay of our main stage presentations. Each year, Perform offers the opportunity to examine the larger forces shaping our industry. And this year, the shift is more profound than ever.

I shared 2 primary takeaways from my opening keynote. First, Observability is entering a new era, one in which it is foundational to resilient software and dependable AI environments. Second, Dynatrace is unique in its ability to combine trustworthy deterministic AI with agentic AI to deliver reliable autonomous outcomes. And this is why we see Dynatrace as the AI-powered observability platform for autonomous operations. Let's unpack this a bit.

To start, in 2023, the AI market was estimated to be less than $200 billion and it is now on a path to be nearly $5 trillion in the next 7 years. Meanwhile, cloud and AI native workloads are exploding. Hyperscaler growth continues to climb, now approaching $300 billion in annualized revenue from AWS, Azure and GCP alone growing in the high 20s. That level of growth at this kind of scale is simply unprecedented.

But this astonishing scale and growth are also accelerating the challenges our customers face every day. As we have often stated in the past, workloads and data are exploding along with a massive increase in their complexity. Tools are fragmented and it's often difficult to know whether AI results can be trusted. And without trustworthy insights, organizations understandably are hesitant to automate.

One ramification of all this has become abundantly clear, AI-powered observability has become essential in an AI first world. The question then becomes, how do organizations harness the value of observability to help deliver on the promise of AI? Dynatrace exists for this moment. We are already helping organizations realize increased value from AI. We have been driving to this point for many years, from initially enabling organizations to be reactive to issues to proactive through automated root cause analysis, to predictive by adding machine learning and anomaly detection allowing customers to anticipate and resolve issues before they become customer impacting.

This is our quest to help customers deliver software that works perfectly. Our third-generation platform is fully available and built for the complexity and incredible scale of modern cloud and AI native environments. Its advancements allow us to look ahead, predict issues and build resilience directly into the fabric of an organization.

So what makes Dynatrace unique? Our differentiation is integrated deeply into the platform's architecture. And it comes down to 3 things: Grail, Smartscape and AI. First is Grail, our massively parallel processing Data Lake house and the only analytics engine purpose built to process exabytes of observability and security data in real time while preserving full context. This isn't a general purpose data store retrofitted for observability. Grail was architected from the ground up for modern software, where a single transaction can reverse hundreds of services across multiple clouds.

In the agentic era, where every AI-driven transaction can be unique and unpredictable, real-time contextual processing is essential. Logs are a great example of why this architecture matters. When logs are unified with traces, metrics, events, sessions and other telemetry in the same platform, they don't just add volume. They add decisive context. Once customers experience the increased value and lower cost of having logs in context with other data types, they are eager to replace their legacy logs tooling. That's why we are delighted to have exceeded the $100 million logs consumption threshold with current growth of over 100% year-over-year.

Our second major differentiator is Smartscape, our real-time dependency wrap that continuously maps the entire technology stack. Smartscape builds a living topology model that understands not just what exists but how everything connects and impacts everything else. So the platform always has the current context of the environment itself.

And third is AI, and this is where the first 2 converge. Grail provides unified data at scale and Smartscape provides the topology in context. Together, they enable our causal and predictive AI, proven in the most demanding enterprise environment and continuously evolving to deliver optimal outcomes. This leads to our announcement at Perform of Dynatrace Intelligence, one of the biggest innovations in our history. Dynatrace Intelligence is the industry's first agentic operation system built for modern software ecosystems. By fusing our determinantic AI foundation with a agentic AI across an ecosystem of agents, Dynatrace Intelligence delivers AI-powered observability that organizations can trust.

With Dynatrace Intelligence at its core, our platform is purpose-built for the agentic era, enabling a future where AI-powered observability can help customers auto prevent auto remediate and auto optimize. Similar to Grail, Dynatrace Intelligence is embedded in the platform and is not sold as a separate SKU. It is available to every customer today. We expect to monetize it in 2 ways. First, through increased platform usage as customers adopt AI assistance across teams driving greater use of Grail. And second, through usage-based agentic execution where AI-driven actions are delivered through workflows and ecosystem integrations. The durability of infrastructure software comes from deeply engineered data planes and AI control planes that operate in highly dynamic production environments.

Dynatrace has developed broad domain expertise and is uniquely positioned with Grail, Smartscape and Dynatrace Intelligence that is built directly into the backbone of the platform. Each of these capabilities is complex in its own right, but magnified in the Dynatrace platform as they are designed to operate as one system, applying AI to deliver answers and automation in environments that constantly shift in composition and workflow. This foundation has been built over years of operating at scale in real-world production environments, making the Dynatrace platform, both highly differentiated and difficult to reproduce quickly or safely. We, therefore, strongly view broad-based AI expansion as a tailwind for Dynatrace.

AI-assisted development with our [indiscernible] code copilots or AI-driven software delivery, compresses release cycles and increases the rate of change across already complex enterprise stacks, that raises operational risk unless teams have an observability control plane with closed-loop feedback to protect reliability and user experience. And as AI-driven systems become more probabilistic, outputs vary and issues can be harder to reproduce. AI doesn't reduce the need for observability. Rather, it makes observability essential for trusted insights and automation so organizations can operate with confidence.

I'd like to turn next to our customers. At Perform, more than 70 customers shared how the Dynatrace platform has become an indispensable component of their software environments. Here are just a few of their incredible stories. One of the largest airlines shared how they're using Dynatrace to help them deliver 31% better reliability, 75% fewer incidents and a 10% reduction in mean time to resolution, leading the industry in on-time departures and arrivals for 2 years in a row.

Canadian communications giant, Telus, shared how they're using AI to move from firefighting to proactive reliability, reducing the average time to resolve issues from 40 minutes to 5 minutes. Vodafone shared how they're using AI to modernize operations at massive scale, migrating more than 2,500 users, 8,500 dashboards and 8 terabytes of daily log ingest from their legacy logging provider to Dynatrace in just 2 months.

And Nationwide share how Dynatrace has helped them reduce Priority One incidence by 74%. In addition, customers expressed very strong interest in our strategic collaboration with ServiceNow to advance autonomous IT operations and scale intelligent automation. At Perform, ServiceNow's EVP and GM of technology workflow products reinforced our Better Together vision, highlighting use cases of how customers can integrate with Dynatrace to get greater efficiencies in their teams, accelerate agentic initiatives and drive automation with confidence.

In an agentic world, engaging in and integrating with an ecosystem of partners, including our long-standing relationships with global system integrators and hyperscalers, will be mandatory. And we are investing to deepen and broaden those relationships. In Q3, we announced deeper technical engagements with all of the major hyperscalers. We're integrating with Amazon bedrock aging core embedding Dynatrace with Azure's SRE agent and serving as the launch partner for GCP Gemini command line interface extensions and Gemini enterprise.

Finally, as the velocity of software development continues to accelerate, we have advanced our strategy to extend left to developers with the acquisition of DevCycle last month, built on open feature, an open-source initiative originally created by Dynatrace. DevCycle is a feature management platform that helps developers, SREs and platform teams bring progressive delivery for AI native applications directly into the Dynatrace platform. This solution will enable customers to accelerate their ability to release features in a controlled manner and remediate issues faster.

To wrap up, we've seen impressive customer momentum over the last several quarters with dozens of 7-figure wins, rapid logs expansion and customers standardizing on our platform for end-to-end observability. And we have delivered extraordinary innovation with Dynatrace Intelligence and the evolution of the Dynatrace platform to deliver significant customer value. This momentum is a testament to our unique ability to provide precise and trustworthy answers that serve as the foundation for autonomous operations in delivering resilient software and reliable AI.

As I hope you can tell from my remarks, we are highly enthusiastic about our opportunity ahead.

Jim, over to you.

Thank you, Rick, and good morning, everyone. Q3 marked another quarter of strong execution as we once again surpassed the high end of guidance across all our key metrics, showcasing the growing demand for our leading AI-powered observability platform and the durability of our balanced business model. As Rick mentioned, we achieved 3 consecutive quarters of double-digit net new ARR growth, record new logo ARR, and we surpassed our goal of $100 million in annualized consumption for our log management solution. The combination of our ongoing go-to-market maturity and execution, the increasing necessity of observability in an agentic AI world, our leadership position in the market and our unified platform differentiation give us confidence that the momentum in the business will continue as we look ahead.

Our conviction in the business is further evident in the Board's authorization of a new $1 billion share repurchase program that is double the size of our inaugural program. I will share more information on that later in my remarks.

Now let's review the third quarter results in more detail. As the business has evolved with the introduction of DPS, we have continued to look for ways to provide investors with the best KPIs of our performance. Given what we have learned as DPS has matured, and with the changes in the on-demand consumption accounting treatment, going forward, we will focus on ARR and its underlying growth driver, net new ARR. This is the primary metric that drives our subscription revenue, and it's how we measure and track the business internally.

Turning now to ARR. We ended the quarter at $1.97 billion, representing 16% growth and demonstrating stabilization of ARR growth for 3 straight quarters. Q3 net new ARR was $75 million, adjusted for foreign exchange movements coming in well above our expectations. Net new ARR was up 11% from a year ago and represents the third consecutive quarter of double-digit growth. This strong performance was driven by both record new logo ARR and steady expansion ARR, including a number of 7-figure end-to-end observability deals.

In Q3, we added 164 new logos to the Dynatrace platform. The average ARR per new logo was over $160,000 on a trailing 12-month basis. We continue to target landing with high-quality new logos that have a greater propensity to expand. The average land size in Q3 was particularly robust at over $200,000 and helped drive new logo ARR over 21% off a robust Q3 of fiscal '25.

Our value proposition continues to resonate with enterprise customers that are outgrowing their existing DIY or commercial tooling solutions. They are seeking business value from tool consolidation and coming to Dynatrace for the depth breadth and automation of our unified AI-powered observability platform. Once customers experience the benefits of the Dynatrace platform, they are often quick to expand their usage.

Average ARR per customer continues to grow and is now nearly $500,000, highlighting the continued adoption of the platform and value we provide to customers. As we have said in the past, given the significant cross-sell and upsell opportunities in our enterprise customer base, we believe the average ARR per customer opportunity could be $1 million or more over the medium to long term.

Gross retention rate in Q3 remained in the mid-90s, demonstrating the strategic relevance for the Dynatrace platform as mission-critical to our customers' operations. Net retention rate or NRR on a trailing 12-month basis was 111% in the third quarter, consistent with the prior 2 quarters. As Rick mentioned, we continue to see broader usage and deeper penetration of capabilities across the platform, notably in log management, which remains our fastest-growing product category and surpassing the $100 million annualized consumption milestone we set for ourselves. With our log strike team in place now for 9 months and our selling motion continuing to mature, we expect logs to be an ongoing source of significant ARR growth.

Moving on to revenue. Total revenue for Q3 was $515 million, and subscription revenue was $493 million, both up 16% and exceeding the high end of guidance by 150 basis points driven by strong net new ARR.

Turning to profitability. Non-GAAP operating margin was 30%, exceeding the top end of guidance by nearly 100 basis points, driven mostly by revenue upside flowing through to the bottom line. Non-GAAP net income was $135 million or $0.44 per diluted share $0.02 above the high end of our guidance.

We generated $27 million of free cash flow in the third quarter. Due to seasonality and variability in billings quarter-to-quarter, we believe it is best to view free cash flow over a trailing 12-month period. On a trailing 12-month basis, free cash flow was $463 million or 24% of revenue. As a reminder, this includes over 600 basis point impact related to cash taxes. Pretax free cash flow on a trailing 12-month basis was 30% of revenue.

Finally, as of today, we have substantially completed the $500 million share repurchase program announced in May 2024. In Q3, we increased the pace of our repurchases, buying back 3.5 million shares for $160 million at an average price of just over $45 per share. We believe the strength of our balance sheet and cash flow generation afford us the ability to continue to strategically invest in R&D innovation for our customers while also returning capital to shareholders.

We announced today our Board has authorized a new $1 billion share repurchase program. This program is the largest in our company's history and doubled the size of our previous program and underscores our confidence in the business, our conviction in the long-term growth opportunities and view that our shares are undervalued. We intend to be active buyers in the market at current levels.

Moving now to guidance. The demand environment for observability remains robust, and the growth drivers fueling the business continue to trend positively. The landscape is benefiting from secular tailwinds of end and observability, cloud modernization and AI workload proliferation. Our go-to-market strategy continues to build momentum and consistency as evidenced by 3 consecutive quarters of consistent double-digit net new ARR growth.

Our DPS licensing model continues to enable broader adoption and increased usage of the platform. Logs continues to be a significant source of growth for both our installed base and new logos. The combination of these strong underlying growth trends give us conviction in the ongoing momentum of the business. As a result, we are raising full year guidance across the board.

Starting with ARR, we are raising ARR growth guidance 125 basis points to a range of 15.5% to 16%. And expect to surpass our next milestone of delivering over $2 billion in ARR. The high end of this ARR range implies another quarter of double-digit net new ARR growth.

Moving to revenue. We are raising total revenue and subscription revenue growth guidance by 75 basis points at the midpoint to 16% growth. Turning to the bottom line, we are raising full year non-GAAP operating income guidance by $9 million and free cash flow by $13 million. This translates to a non-GAAP operating margin of 29% and free cash flow margin of 26%. Finally, we are raising non-GAAP EPS guidance to a range of $1.67 to $1.69 per diluted share, representing an increase of $0.05 at the midpoint of the range. This non-GAAP EPS is based on an expected diluted share count of 304 million shares.

In summary, we are very pleased with our Q3 and continued momentum through fiscal 2026. We are focused on executing to close the year out strong.

And with that, we will open the line for questions. Operator?

[Operator Instructions] And our first question is from the line of Raimo Lenschow with Barclays.

My question was if you think about what you see in the client base in terms of understanding how your automation stories coming together, how it's important to kind of bring all the decent data sources together in one place, what does it mean in terms of engagement levels decline, et cetera? Is that -- are we seeing an ongoing kind of bigger kind of the [indiscernible] -- or bigger momentum they are building? What do you see in the pipeline in terms of deal size, et cetera? Just kind of seeing how that momentum is ongoing.

Raimo, thanks very much for the question. I guess where I'd start is end-to-end observability is what we're seeing as being very, very strong in terms of the sales play and momentum at the moment, this is where our customers are looking to expand. They are realizing that there is extraordinary tools for all that there is lack of expense management in that and poor outcomes. And so this is, I'd say, the #1 area where we're seeing momentum in the business is precisely that. And as you look to an AI-first world that's coming it becomes even more pervasive that end-to-end observability is a mandatory foundation to be driving those kinds of outcomes.

Our next question comes from the line of Sanjit Singh with Morgan Stanley.

Congrats on the stabilization in AR growth for the last several quarters. I wanted to ask a follow-up on Raimo's question. In a world where agents are doing a lot of the investigating and the triaging on incidents versus human site reliability engineers, there's sort of 2 questions there. What's the pace of that change? Like how realistic are we going to see that sort of environment in the next a couple of years? And then two, from a product perspective, how does that change observability? And how does that change how customers will use Dynatrace?

Sanjay, so in terms of pace of change, I think that there is still a lot of apprehension about just how much conviction organizations have in driving AI and AI outcomes. So I think it's going to evolve over a period of time. Having said that, I do think that there is a significant change in observability as we look to the future and that it really becomes, in our view, the control plan for enterprise. And what I mean by that is simply that observability becomes foundational through this agentic action without deterministic AI without assuredness of understanding what the baseline problem is. And of course, we use the notion of answers, not guesses here, you simply can't take agentic.

So the steps that we see that organizations have to go through are: number one, you have to get the end-to-end observability because that's where you get the broadest, most concrete outcomes and answers. Number two, you then use that deterministic AI to develop an understanding of what actions need to be taken. And then number 3 and only then and agentic AI take over [indiscernible] probably only with a portion of the actions required for auto production, auto remediation and auto optimization. So it is going to be a journey, but that journey is beginning today and that journey begins with end-to-end observability followed by the leverage of deterministic AI as a mission-critical foundation for agentic ai to follow.

Our next question comes from the line of Gray Powell with BTIG.

Okay. Great. So yes, it's really good to see log monitoring consumption past the $100 million mark this quarter. I guess a couple of questions there. Like how quickly has that materialized over the last year? I know that there was sort of a new iteration that you came out with in late '24? And then what are your -- what are the next milestones for the product?

Thanks for the question, Gray. Again, we're very pleased. We told you last quarter that we were on the precipice of hitting this milestone, we exceeded it. Our logs business is continuing to grow north of 100%. So by far, the fastest-growing product category in the business that -- you're right that we've seen a significant step function increase in the acceleration of the business from last fall to now, last fall was when we were able to get the log product use case complete. We had the product packaging and pricing right, that evolved a little bit in the following quarter. So you saw a step function increase and it continues to exceed our expectations.

Relative to the next milestone, we haven't necessarily set our next milestone publicly. I can tell you that it will be a significant source of new ARR. We're seeing this already. Rick mentioned these end to end observability deals. Nearly all of them have logs embedded with them. So we expect this to be a huge source of ARR growth for the business going forward.

Our next question is from the line of Will Power with Baird.

Yes, I guess I'd echo the congratulations on the results. Great to see the ARR growth, stabilizing the strength in net new ARR. As we think about the net new ARR trends, the consumption commentary you just provided, the logs opportunity, the AI opportunity. Maybe help us think about kind of the puts and takes that could contribute to ARR perhaps accelerating as we move into fiscal '27? How do we think about the outlook there?

I'll take that, Will. Good question. Again, we're focused on continuing the momentum. As I said in my prepared remarks, third consecutive quarter of net new ARR, double-digit net new ARR growth, stabilizing ARR at 16%. If you look at the high end of our guide, I'd suggests that continues into Q4. So it will be 4 quarters of stabilizing growth for ARR and 4 quarters of double-digit ARR growth. We've said that our objective is to show an acceleration in ARR. Obviously, we'll have to continue to execute like we have. The go-to-market momentum continues. We continue to benefit from large-scale end-to-end observability deals. So the changes we made are manifesting themselves in the results. We're seeing traction with partners. I'm not going to provide guidance on this call. I can tell you we're very optimistic about the momentum in the business. We'll have to see come May what the guide is, but our objective is to build an acceleration in the top line growth.

The next question is from the line of Koji Ikeda with Bank of America.

Jim, in your prepared remarks, you mentioned net new ARR and ARR the core foundations and the building blocks of growth. But last quarter, you did give a new metric, the annualized platform consumption dollar growth rate of 20% plus. I wonder if you could share how that metric compared this quarter compared to fiscal second quarter? Is it directionally higher? Is it the same? Or is it lower than the 20% plus that you gave last quarter?

It's a good question. We've shared a lot of KPIs, and we've tried to share KPIs as investors try to understand our journey with DPS, which would certainly get them on the platform allow them to access the platform all offerings, and that's playing out nicely. So relative to consumption, consumption continues to grow north of 20%. So consumption is growing very healthy, consistently higher than ARR growth.

The next question is from the line of Ryan Mac with Wells Fargo.

[indiscernible] on for Ryan Mac. This year, you've talked about some of the strength in large deals and large deal pipeline. Is there any update you could provide on how those large deals are progressing and maybe whether your incremental confidence on those deals coming through has changed in the past couple of months? I have a follow-up if there's time.

Sure. I'll take that. I think what I talked about in Q2 was we continue to see a robust pipeline. That pipeline is very weighted to large deals, deals over $0.5 million, deals over $1 million. And that makes sense relative to the go-to-market changes we made last year that we were focused on these accounts that had a large -- or a high propensity to spend. I think what you're seeing, you started in the Q3 results that we built consistency in close rates of these large deals. Our expectation in Q4 is you'll see that again.

So I think what we've done is we build consistency in our execution. You're seeing that through the first 3 quarters. The expectation in the fourth quarter at the high end of our guidance, you'll see that again. And it's heavily driven by this continued trend of very large enterprises looking to vendors to consolidate on. And Dynatrace is in a very good position. You saw even new logos, new logos, 5 of our new logos were over $1 million. So this is both existing customers and new logos, looking to Dynatrace to be their provider of choice as this trend continues.

I would just add to that also, that we see AI as an enormous tailwind to the observability business overall. You have to have the observability in order to drive an AI first world. We believe that fundamentally. And the result of that is that you have to have end to end observability to get the best outcomes, the best underlying analytics and insights to be able to take agentic action. So as the world shifts toward a move of autonomous operations, observability becomes foundational and end-to-end observability is fundamental to maximizing the success of that type of [indiscernible].

Our next question comes from the line of Eric Heath with KeyBanc.

I guess, what stood out to me that was most impressive was the fiscal 4Q net new ARR guidance, there's a strong lift from what you're expecting previously. So just maybe an extension of the prior question there but...

Eric, could you just speak up a little bit?

Yes, any better?

That's a little bit better. Thank you.

Okay. Yes. So just to touch that question. So the fiscal 4Q net new ARR guide was a strong lift from what you were expecting previously. Just an extension from the prior question, your response there. Just any more detail you could share on what has given you the confidence in the outlook, maybe some assumptions on the close rates in those large NN deals?

Yes. I mean one of the things I talked about in the last call is the -- we had very healthy close rates in the first half of the year. We saw a healthy close rates in Q3. I'd say our visibility of the pipeline here, especially near term, is quite strong. That doesn't mean we expect we're going to get every deal. But we do believe that we have a very good line of sight to be able to deliver against this guide, the range that we're providing. So I think some of it is the visibility and I think it's also what I think is a continued improvement in our go-to-market maturity around having an ability to call the ball on some of these large deals, I think our ability to do that continues to improve.

The next question is from the line of Keith Bachman with BMO Capital Markets.

I wanted to ask about new logo growth. Jim, you had called out that I think you had 164 new logos, solid growth there. And yet if I look at the numbers, it appears you're still getting about 70% of your ARRs coming from existing clients, 30s -- 30% is coming from new logos. And I'm just wondering, is that the way we should be thinking about as we look out over the next year, I think investors have some worry that the existing customer base may not sustain growth for a period of time. And so trying to understand how you might be able to expand your new logo growth and particularly maybe going -- not the small business, but maybe expanding the TAM a little bit.

As you talked about, AI is foundational. So maybe that presents some opportunities for a broader customer audience. At the same time, there are solutions out there that are less expensive that require more work. And I'm not sure how DPS would fit into, is that a facilitator of new logos? Or is it a hindrance in that you got to make a little bit larger commitment? But just maybe talk about how we should be thinking about new logo growth over the next couple of years.

So I'd say near term, Keith, but -- one, I would say we're very pleased with the new logo momentum that we're having, in particular, that there is a lot of momentum with just customers that are looking to Dynatrace to consolidate fragmented tools on. So we continue to make good traction there. You should expect in the near term kind of over the next year, that it will be a, call it, roughly 1/3 new logo, 2/3 expansions. I would clarify, we are not even close to exhausting our ability to expand within our installed base.

We continue to grow. We almost have $0.5 million now per customer. So -- and we have many, many million-dollar customers. So the propensity to expand is still pretty material. But near term, I think we're going to be roughly 1/3, 2/3. We do look at that, Keith, relative to what are the different velocity motions you can do to maybe land a little bit lower, I think, in this cloud AI native world, that's probably an area we continue to get some traction in. But near term, you should expect kind of the mix that I just mentioned.

Keith, I would just add that with sensibly the completion of the third-generation platform that is now out there, it does enable us to do a much better job of tuning and targeting to different personas, SRE, platform engineering, particular developers at our Perform conference a couple of weeks ago, we had a full developer day with a ton of developers working on the product and the solution overall. We just did the dev cycle transaction, which enables us to extend a further with regard to feature management.

So you can expect us to be leaning into development teams and developers as an added persona with a primary intent of generating over the course of time, new logo momentum in unit volume not just in ACV, which is what Jim was referring to by way of a record quarter in Q3.

The next question comes from the line of Matt Hedberg with RBC Capital Markets.

Congrats on the consistency, really good to see. I guess for either of you, I wanted to ask about the competitive environment. I guess, both from smaller vendors like [indiscernible] getting acquired. But I know investors are also worried that large language model providers at some point might do everything in software. I mean just given that concern, how do you think about the competitive risk from some of these frontier model providers as well?

Matt, a lot in that question. Let me start with the first part, which is some of the acquisitions in the industry. And I would just say, as we look at [indiscernible] and observe others, we really don't see them in the market very often on a direct competitive basis for us, simply because of our target segment typically being in the global 15,000, the largest accounts on the planet and what they're looking for is end-to-end observability and many of these solutions that you mentioned really have point solutions either focused on logs or focused on metrics, but really not an end-to-end solution that's at all comprehensive to be able to compete against Dynatrace at that level.

So we always are paranoid about competition. We're looking at moving chess pieces, especially as larger players get into the market. And so we're very observant of that, but at the same time, the smaller players haven't really been competitive in the past. With respect to this dialogue around LLMs replicating, observability, that's a longer answer and something that we've been spending a lot of time thinking about to try to give you a succinct response to it, I would say, number one, we view there to be a very sizable difference between enterprise software with standard workflows and infrastructure software like ours with highly dynamic workflows, requiring variable evaluation of billions of interconnected data points.

Secondly, as I mentioned earlier, we really do see observability as the control plane for enterprise. We don't believe that you can easily replace observability through vibe coding or an LLM instantiation that can do the same thing that we do.

Third, we really see our focus and our moat really is being architectural and not code based. We have a platform with Smartscape, with Grail, with Dynatrace Intelligence, not just individual point products and the interaction of those becomes quite sophisticated. We have AI increasing complexity across LLM and agentic systems, not reducing it.

And then finally, we've built an extraordinary amount of domain expertise over more than a decade of valuation of AI workflows or workflows based upon the AI analytics that we complete. And we do believe that grounding AI actions in a deterministic and explainable system is much more powerful than using and relying only on probabilistic models. So there's a lot to be had in there in terms of additional conversation. But the net of that is we see Dynatrace has a very, very durable solution over the course of the long term for the overall environment we see for managing enterprise AI [indiscernible].

The next question comes from the line of Patrick Colville with Scotiabank.

And I thought your answer just then -- it was really fascinating. I guess I just want to pivot back to the question earlier on the 4Q guide. I mean I'm calculating the updated guide implies 22% net new ARR growth in constant currency in 4Q. Which -- if I'm calculating that correctly. I mean, that would be the biggest net new ARR guide for a long, long time. So just -- can you just circle back, what gives you confidence there? Because I know that's the #1 question we're going to be receiving like why does Dynatrace have confidence in that number?

Well, I'll start with why we have confidence in the number, and then I'll clarify what the guide is as far as the growth rate. We have confidence in the number because our pipeline is in incredibly robust. We continue to have significant demand for observability. And so the change we made in the go-to-market side are playing out. So we have -- this is just we have good visibility. So our visibility is what drives our conviction, that's one.

I'd say relative to the guide, and this probably just puts and takes relative to FX. At the high end of the guide, it would imply another quarter of double-digit growth. It's not 20, it's like in the kind of 10% or 11% in the fourth quarter relative to the guide. But still quite robust. Again, 4 quarters of double-digit net new ARR growth which is continued in building momentum in the business.

Our next question is from the line of Ittai Kidron with Oppenheimer.

A couple of small ones. First of all, if you could give us an update on how your security strike teams are doing, if there's any progress there? And then, Rick, I want to go back to your answer about LLMs and what they can do to observability. The opportunity is very clear. Again, just on the cost side, you just announced Dynatrace Intelligence, which sounds fascinating. I guess logically speaking, it would seem that customers would find this as the way to interact with your system longer term.

I guess the question is, again, could agent -- third-party agents just intermediate you and actually replace Dynatrace Intelligence and third-party agents can do that? And somehow, again, you get step removed from the customer system working in the background and customers only engaging with third-party agents to interact with your system. Wouldn't that this intermediate the value that you bring to your customers?

Thanks, Ittai. On security, it continues to be an important business for us. It's growing nicely. We said that it would take us longer to get to the $100 million level than logs a few quarters back, but we see ongoing progress there. We do see the ongoing convergence of security and observability, so no change there. And our security strategy remains to be focused on observability buyer as opposed to the CISO where we have relationships today. So that continues to be our strategy, our focus and our evolution of security and the security strike team.

On the overall LLM environment, I think I covered it relatively completely. I would only -- I guess I would only add that no, we don't see it as highly likely that an LLM is going to be able to replicate Dynatrace for all the reasons I described. In particular, we see that in a highly variable environment that is playing out on a day-to-day basis with all of these interconnections within that environment in large enterprises that it would be very, very difficult for any AI piece of solution or a piece of software to then replicate that environment for all the reasons I described.

The next question is from the line of Mike Cikos with Needham.

It was great to get some of the data points around average land and new logo growth, et cetera. But maybe for Jim, I want to ask about DBNR with the stabilization we've been able to demonstrate here at the 111. Can you help us think through where we are in driving an improvement in this metric, just given the go-to-market maturity that you are talking to in these observability deals as well as where are we in that DPS renewal cycle? That would be terrific.

Thanks, Mike. So again, we're pleased to see a continued stabilization in dollar-based net retention rate at $1.11. I think I mentioned this in the past that our sales organization is focused on maximizing bookings. So you're going to have some quarters where you're more new logo heavy, you're going to have some quarters where you're more expansion heavy. But I would say, Mike, we are still in the building phase. You mentioned EPS that in fiscal '27 will be kind of your first full 3-year cohort classes coming, so that you'll have a 1-year, 2-year, 3-year cohort classes. So there is a building momentum going into fiscal that to the extent we can continue to execute, you should see an inflection in that metric.

The next question is from the line of Brad Reback with Stifel.

Rick, in an increasingly agentic world, do you feel you'll need to evolve your pricing kind of paradigm in order to properly monetize?

It's a good question, Brad. The way that we articulated in my prepared remarks and the way that we see it evolving is that we monetize the agentic world in 2 ways. Number one, through increased workloads and increased overall usage of the platform through Grail, through Smartscape and in delivery of the analytics that we provide on all day everyday basis. Secondly is we do expect to monetize directly agentic workloads. So with a combination of those 2 elements, we do believe that there is direct monetization of the agentic AI element.

And if you look at Dynatrace Intelligence, overall, it really does have both components that number one, it has the component deterministic AI to evaluate what's happening in your environment with certainty and through causation and context. We then take those answers and then deliver those into an AI environment that is agentic in nature where either a Dynatrace agent or a third-party agent through an ecosystem that is essentially orchestrated by Dynatrace can take action. And it is through that agenetic workflow that we can provide additional monetization.

Our next question is from the line of Miller Jump with Truist Securities.

I know we aren't guiding to '27 right now, but I was just hoping to understand how you're thinking about hiring as we zoom out? Specifically curious if there's AI efficiencies that you're potentially getting on head count or if some of the initiatives you've talked about are causing you to lean in further on hiring maybe in the year ahead?

Yes. We're not going to guide here for fiscal '27. But I can tell you that your point about leveraging or internal productivity is something we continue to leverage within Dynatrace. And so in that regard, that will continue. That will certainly be a source of kind of moderating hiring in certain areas, whether they be customer support, G&A, driving more sales productivity. I don't think it's necessarily going to be a driver of us not hiring in the R&D space, even though we're leveraging heavily AI within that realm as well to make our developers more productive. So we'll continue to hire in R&D notably. And then I'd say hiring thereafter is going to be kind of more moderated.

The next question is from the line of Andrew Sherman with TD Cowen.

Jim, great to hear of the 20%-plus consumption growth continued. How should we think about the gap of that between ARR and revenue growth? What drives the convergence of those 2 over? And what time frame would that look like?

Yes, it's a good question. We've gotten that -- that was one of the reasons why in our prepared remarks, I didn't comment on consumption growth. It's very healthy. DPS is playing out the way we expected. I would say there is going to be -- there's certainly a lag between consumption growing at those rates and seeing it manifest itself in an expansion. Sometimes you're seeing early expansion, sometimes you do not. So I'd say that there's no simple answer to that. I'd say there is an elongated time period between consumption growth and then seeing ARR acceleration that comes thereafter.

But again, the whole premise of getting customers onto the platform for DPS is get them to consume more. Consuming more means they are getting more value. More value means likely an early expansion because they're trialing something that maybe they weren't using before. And so even when consumption growth is healthy, there are other reasons why customers expand. They expand because they're entirely new use cases that they're looking at. And so again, look at our performance around driving double-digit net new ARR growth, our expectation is we want to continue to do that going into fiscal '27.

Our final question will be from the line of Brent Thill with Jefferies.

Just on the go-to-market side, if you can maybe talk through the plans on the sales hiring front rep capacity, how you're seeing, obviously, you're getting great production, good to see the good results. But maybe if you could just walk through what you're seeing there and what you're expecting to do in terms of the overall distribution adds over the next year?

I'll take that. We continue to drive improvements in sales rep productivity. So we will continue to hire reps. It's important to remind investors that, it's not just reps that drive this productivity. We've continued to get significant traction with leveraging our partner channels, notably GSIs and the hyperscalers continue to be a source. So continued improvement in productivity per rep. So hiring will continue. We'll have more to say about what we're going to do in that space in the Q4 call, but that's what you should expect as you think about fiscal '27.

Thanks very much to you all. That brings us to the end of our call. We very much appreciate your gauge questions, your ongoing support. To close, we believe that observability is becoming increasingly critical to the overall software ecosystem in delivering reliable software and AI. We have very strong conviction as we entered Q4 with momentum headed into FY '27. And we are enthusiastic about what we see ahead from a customer standpoint and from the number of personas engaged in and requiring observability solutions to deliver the outcomes they seek. We very much look forward to connecting with you at our events over the coming months, and we wish you all a very good day. Thank you.

Ladies and gentlemen, this concludes today's conference. You may disconnect your lines at this time. We thank you for your participation.

Time is running. Okay. Welcome to our next session. Really happy. I didn't realize, Rick, that you were just based here, so that's even better.

Yes, based in the Bay Area. So this is very approximate.

So it's a question I'm asking everyone at the moment. And it's, how does it feel out there in terms of end demand? Because I guess the -- is it stock market? And we can debate that all day long. But like difficult to find answers at the moment, but how is the real world?

From my perspective, not a huge amount of change in macro, obviously, incredible amounts of deployment of capital being spent on data centers. We're all very aware of that. I haven't really noticed or observe much change in the overall spend environment for enterprise-oriented software at the moment.

And the -- if you think about it, like the bigger question -- so the big thing going on for you guys is that you're talking more about strategic deals that the customer conversations are changing, et cetera. Talk a little bit about what's driving that from a customer perspective? Maybe start with where the industry is as a whole and then how you can help the customers on that?

Sure. Well, the evolution of the selling environment for us has evolved immeasurably even in my 4 years of tenure at Dynatrace. When I began, it was very silent. You had the APM vendors and then you have the infrastructure monitoring vendors and you have log management vendors and application security vendors.

And over the last 4 years, really, that has consolidated in a material way. And the real question is, well, why? Why is it? Why is it consolidated? And my simple answer to that is outcomes are better that in a highly complex environment, especially with our target customer set in the Global 15,000, really even the Global 2,000 or 3,000 at the high end of the pyramid. Huge amount of complexity, hundreds of applications, huge amounts of infrastructure, which are growing immeasurably, even more so in an AI world.

You have a log management coming into the fray now, which is getting integrated into the overall observability framework. And the result of it is you can't manage it manually. You can't do what you used to do. When I was at an oil and gas company and the principal walked me into the network operations center, and there were hundreds of people staring at hundreds of screens, trying to do manual processing of alerts.

That doesn't work anymore. You have to automate that. It has to be oriented around an AI world, and it's got to get integrated into an overall view of your software environment to deliver the best answers and the best outcomes. And that's why it's consolidating. And that end-to-end observability trend uniquely -- maybe not completely uniquely, but it certainly benefits Dynatrace.

And do you -- on that note, though, because like we've been talking about full stack observability for a few years now. And I was like, yes, I want to do that. I remember like I wrote my first big note on observability like 5 years ago and it was like that's what everyone is doing.

It was brilliant.

Yes. Yes. Thank you. Yes. But it is only now coming kind of together. Is that kind of driven by product maturity? Or like back then it was all marketing. We knew you need to get there, but the products weren't there? Like how do we have to think about that?

It really is an evolution, I think, of capabilities. It is the product capabilities. Like, for example, at Dynatrace we have, over the last 5 years, evolved our platform to our third-generation platform. This resulted in a couple of years ago, the delivery of Grail.

Yes.

Grail for us is an underlying foundational massively parallel processing, data lakehouse. It stores logs, traces metrics, behavioral analytics, business events, user data, that sort of thing. That data lakehouse is overseen by a fully integrated, completely AI-oriented or AI-powered platform, which is Davis, which does causal AI, predictive AI, Generative AI, headed to agentic AI. We can talk about that if we wish in a bit.

And that has resulted in delivering answers that are trustworthy, incredible to then enable automated action. And it is the stack of the data plane, the AI plane for answers and outcomes that are able to deliver automated action that's really come together over these last years. That sort of capability didn't exist for Dynatrace or really anybody else 4 or 5 years ago.

Yes. Okay. Perfect. And then so you have -- if you go to your customer, you have it, but now like, look, take Box as an example as well. But like everyone, we're still on-premise, we're still messy. How do I kind of go from that messy world to your kind of new fancy world?

This is one of the advantages, not overselling it. This is one of the advantages of Dynatrace in that our capabilities exist on-prem, in the cloud, they exist in multiple cloud environments, AWS, Azure, GCP, et cetera. So you can manage your migration from on-prem workloads to cloud workloads to AI native workloads seamlessly with Dynatrace and you can oversee all of them using our Observability framework at your discretion and at your rate of expansion or migration.

And so by enabling that, we really do facilitate a mix, a hybrid environment that continues to evolve. And in the largest of enterprises, that really matters because the largest banks on the planet, the largest commerce companies and the largest pharmaceutical companies, they're going to have a mix of environments forever or at least for a very, very long time to come.

Yes, yes. So then -- so if I'm like a big enterprise on-premise cloud, so I can start consolidating at home towards Dynatrace, as I move to the cloud, I can use Grail or can I use Grail on-premise as well? Or is that more cloud core? And so as I move to the cloud, I just do more, more Grail...

Well, one important part of -- one important distinction, Raimo, is that -- even if you have an on-prem workload, you can observe that in the cloud. And that enables Grail.

Yes, yes.

So yes, the observability needs to be happening in the cloud, but that can apply to all sorts of different workloads.

Okay. Perfect. Okay, makes sense. And you mentioned log as a thing that came up a lot. That was probably the last leg to bring all the stuff together?

Yes.

You start talking more about logs like, where are you on that journey? And I'm asking because it does look like there used to be a log company that was very big that might -- where a lot of feeds might come up for renewal, like what are you seeing there?

Well, logs has been the fastest-growing business that we've seen. We really got to the point of enabling logs. I'd say that we're ready for prime time at enormous scale beginning in about October of last year, so October of '24. At that point, logs was a very small sort of single-digit millions of dollar business for us. That has grown to the point where in our earnings release for our second quarter earnings that we just completed a month or so ago.

We talked about that number being almost $100 million in consumption. So it is -- logs is essentially a business that has grown from 0 to near $100 million in 1 year, and it's growing at well more than 100% per year. Well, 100% growth on a $20 million number is a lot smaller than 100% growth on a $100 million business. And so that really is fueling a good amount of growth. And I would say it is driven by 2 factors.

One is priced relative to incumbents in the market. But it's not quite the way that you would think about it and without overly belaboring it, I would simply say that it isn't that we're just coming in and saying, well, we're a lot cheaper. It is that if you incorporate logs traces metrics, other analytics user data, you actually don't need to store as many logs in order to get better outcomes, better answers.

Yes, yes.

So the results of it is that for enterprise customers, you can actually get more efficient in your overall cost without us coming in as Dynatrace and just say, well, we're going to give you a 30% discount over what you're doing today. Just continue it the same way. So the 2 drivers of logs business for us are: number one, overall cost of managing a logs environment, as I just discussed.

And then the second is delivering better outcomes. And that better outcomes piece is, I would say, much more relevant especially as you head into an agentic world for autonomous operations because this is where you want all data types to be delivering the answers that you need to be able to take that action. So you can think about it, end-to-end observability really consists of 3 different layers, integrated data layer, integrated domain layer. This is application performance monitoring, infrastructure monitoring, log management, et cetera. And then the third layer is the persona layer.

You want developers, IT ops, SRE, platform engineering, even executives all to be able to access that same data. That is what end-to-end observability is. That is what enables the best outcomes, and that is ultimately what will enable automation of that environment.

Can I -- because I'm the financial guy, so I look at numbers. The..

I don't believe that. I think you got way more -- way more to offer...

If I just look at numbers, the big log guy, who is like massively bigger than all of you guys. In this new world, and I'm doing less logs like, is that same size up for grab? Is it going to be a bigger market? Is it going to be a slightly smaller market? How do you think about that?

Gosh, Raimo, I look at this as -- I look at this as sort of like the mainframe market that was always going to disappear.

That never did. It was supplemented with client server and then it was supplemented with cloud and then it was -- and I think this is the same. We can get more efficient with logs. But gosh, as AI workloads grow almost in an unbounded way, what's going to happen. The overall workflows to be observed is going to increase immeasurably.

So okay, yes, yes. Okay. That answers it. And then the -- since we talked about players in the market, we just saw -- obviously, it's an interesting market. And if something is interesting, and other people want to share of that. We just saw like Palo Alto to like a kind of step into the -- with Chronosphere into your market. How do you think about kind of how this will play out? Because like to some point, they look at the same data set for some of it. On the other hand, not fully, I think your data volume is much bigger. Like how do you think about how that's kind of going to play out?

I would say several comments on this. First, it provides absolute validation of the observability market being ready for prime time. Again, 4 years ago, when I began at Dynatrace, it was -- you go talk to customers, and it was difficult for me to get a meeting with the CIO. It's sort of observability. How do you spell that?

That is no longer the case. I assure you. I -- in the last 30 days, I have done dozens of meetings with CIOs, CTOs and others. Observability is ready for prime time.

It is broadly understood that you cannot do it manually. You cannot engage in the same way that you used to be engaged. So that was one element. The other element is just the evolution of AI is exploding these workloads and requiring more and more observability as we go. And so as we look at these components, I think all of these are factors and how the market is evolving.

And it's evolving very, very rapidly. And relative to the power deal specifically, I think it also validates the emerging of application security with observability. We've been talking about this for some time. I think it is indelible that we see that convergence. And we've looked at that as well.

The one thing I would say is that I do think it's not without its challenges and the reason for Palo, and the reason is because the buyer profile is a little bit different. The selling to the CISO where the CISO organization is very different than selling to the CIO organization, I believe that the buyer for observability is different than the traditional buyer for Palo. So cross-sell is not something that is easy to get done there.

Yes. Okay. Perfect. Hey guys, could you in the back of the room? All right. Could you just kind of quieten it. Thank you.

The next thing is like you mentioned GenAI already a little bit. Can you just -- like you guys have been doing AI like for a while and with more kind of anomaly detection, et cetera? Like how is GenAI kind of play into this [large ML]?

Yes. Let me -- let me answer it this way. What I would say is that we sort of view the observability market having evolved through several phases in a way back when a decade or more ago it was really reactive. It was, wow, something broke, what happened and then you start doing research. It moved from there to maybe what I would call Phase 2, which was proactive, which was automated root cause analysis. Something went wrong, you had immediate root cause, you took a look at it, you knew what was going to happen and then you resolved it as quickly as you could.

The third, which is sort of where we are now is predictive operations, which was anticipating issues but still resolving them manually. So you were using causal AI, predictive AI, predictive AI was adding machine learning, anomaly detection, those sorts of elements to anticipate problems so that you could get them resolved before end users would see them.

Where we are going next is to a world of autonomous operations. And this is where it really gets exciting, which you take predictive operations to the next level, and that is to not just see it anticipated, cut it off before end users see the impact do so manually. But rather through an agentic world, an agentic ecosystem, you could actually handle those sorts of elements on an automated way through MCP servers, auctioning out through various different other agents, it could be a ServiceNow agent, it could be an Atlassian agent, it could be a GitHub agent, a hyperscaler agent.

And then resolving issues really in real time before they become an issue. And what's amazing is of the companies I've talked to recently, they get the fact that, you don't need autonomous operations on 100% of predictable incidents in order to matter. If you get to the ability where you can autonomously, react to respond to and resolve 20%, 30% of the potential incidents before the largest organizations on the planet. That can save them tens of millions of dollars in annual cost savings. Not to mention the user experience benefit that they get by not having the challenges that they might otherwise see.

So that's one aspect. The other aspect I wanted to speak to is like, in theory, with GenAI, like the whole idea is like there's going to be a lot more software, there's more applications, et cetera, which basically means there's more observability that is needed. Is that a fair statement?

Absolutely. I think that's exactly right. You're going to -- to the extent that you can write code so much faster, deliver so much more in the way of applications, you end up requiring or mandating a lot more infrastructure. The result of all of that is an explosion of workloads.

The more workloads you have, the more you need to then be able to observe those. And by the way, there is an added element. You take a look at what we would refer to as AI observability. And AI observability is what we refer to as observing AI workloads. You have to do all of what you do today with regard to all of the other workloads.

You have to observe applications, you have to observe infrastructure, you have to do log management, you have to do all of what you do today. But you also have an added mandate, an added ask from customers. And that is, by the way, could you please make sure that the content that is delivered is accurate.

And this is where you get into hallucinations, you get into guardrails, get into these sorts of elements of, please tell me also, can you validate the data that is coming out of the answers that are being delivered and that makes it even in some sense, more complicated, but also more mandatory, that observability becomes a requirement.

And so the -- that sounds all really interesting, like at the moment, if I talk to the industry players, that's a little bit of a bragging competition going on. It's like, oh, yes, I work with this -- what's your kind of...

Differentiation?

Yes, differentiation position, you talk about like AI customers or whatever, what's the situation here for you?

The picky phrase I've been using to denote the answer to this question is really, answer is not guesses. And what Dynatrace delivers is answers, not guesses. We always have. This is why the biggest and the big, the most complex of the organizations around the planet tend to buy Dynatrace.

And they do so because in Grail, we can handle billions of interconnected data points in context, manage them across all data types, across all domains and give you an answer that is highly contextual and that is causal, not based on correlation, but causation instead.

The result of that is with a very high probability, we can tell you precisely what the answer to the problem is. Why did it occur? And we will tell you precisely what that answer is. We don't guess based on correlations. We know based on the environment that we've structured.

And for those of you that have used Generative AI and some of the overall LLMs and you've sent out data, you've asked questions and you've gotten wrong answers back. You know that there are issues with that underlying trustworthiness of data. In our case, in order to take autonomous action. You must trust the answer as to why the problem is occurring because otherwise, you're solving the wrong problem.

And the last thing that you can do is have some sort of autonomous agents or agent ecosystem take action on solving the wrong problem. You have to trust the answer. And if you trust the answer, you can then enable autonomous action. And what Dynatrace does uniquely, it is our superpower is to be able to tell you precisely what the action is. Not to live you a dashboard that it's red, yellow, green, not to tell you these are the things that are happening in your environment that you may want to look at based on correlation, but rather this is the answer. This is the cause.

And that enables you to have the trustworthiness of that answer to take action, and that is what has enabled in an autonomous world. And it is obvious that every vendor and observability, if you take a look at our Gartner Magic Quadrant, they are 20 of them. Why is Dynatrace in the far upper right of that? It is because of our ability to deliver answers.

Foundationally, that's a great starting point. And as you move into agentic world, while every one of those vendors is going to be talk, well, I've got an agenetic -- have agentic system because you have to say that. The ability of those agentic each systems to actually take the right action is predicated on the ability to deliver the right answers. Then we would submit that Dynatrace is better than anybody else.

Okay. Okay. Perfect. And then shifting gear to the last -- the next few minutes. So there's a product and the product has evolved. Now let's talk about the organization because the organization needed to be evolved as well. And there's 2 aspects. It is the platform and platform pricing and there's go-to market. Those are like about a year to 2 years both of those initiatives. Can you speak to kind of how this is coming together for you on both kind of vectors?

So go-to-market was one and the other one was....

Pricing, pricing, yes.

Pricing. So the -- let me attack go-to-market first. So -- on the go-to-market side, we simply realize that it is the largest of largest organizations, the most strategic organizations that needed observability from Dynatrace the most. That is where we have the greatest differentiation. And those are the ones that are moving to end-to-end observability. Those are the ones that are driving AI observability. Those are the ones driving business observability. This is where Dynatrace wins.

So we restructured at the beginning of 2024 our overall go-to-market to really focus and attack that. And that's resulted in substantial benefit. For example, our pipeline we reported this last quarter in strategic accounts, these large accounts was up 45% year-over-year. The number of 7-figure ACV deals that we closed last quarter was up 53% year-over-year. So we are seeing enormous traction in the biggest of the big because that's where the complexity is, and those are the ones coming for end-to-end observability from Dynatrace.

So I'd say that, that has been a major evolution in a positive way for the overall environment. Pricing has been part of that. So covering the pricing component, it was funny. But when I first began and I would ask customers, geez, what do we need to do better? One of the elements that came back was, you need to do pricing and licensing better.

And it was because we had SKU-based pricing. And if you bought application performance management from us and you wanted to buy infrastructure management, we'd have to come back and add a contract and sell more, and that was painful and God forbid, you want log management because that was another contract, more painful.

So what we did was we put in place our Dynatrace platform subscription, DPS, as we referred to it. This is basically a -- this is an annualized commit. It could be a 3-year contract, but essentially an annualized commit that covers the entire platform. We've seen enormous traction in it, as you know, Raimo, and it has resulted now in 70% of our ARR just over the last 3 years, becoming DPS in orientation, more than 50% of our customers, 70% of ARR.

And it has substantially broadened the access to the platform. So for example, one of the reasons log management, I would submit to you, has grown so rapidly for us as we've eliminated the contract overhead of that. If you have a DPS contract, you have access to log management, just that simple. Turn it on, enable it, begin to use it and watch it grow and it makes it seamless for customers to then replace other third-party solutions.

Can I -- as we go through this now, like there's one thing that I'm talking with investors about that's kind of always raises the question, it's like, your product story is coming together. Your go-to-market is coming together. The pricing is coming together, but the overall growth numbers haven't changed.

Yes.

So what's the disconnect?

Well, the good news is we look at leading indicators, and consumption is one of those. We see consumption in the low 20s. And while we don't want to make the overall metrics more complicated than they need to be, which is some of the feedback that we've clearly gotten is, geez, don't provide too many metrics because we want to make sure that we're tracking the ones that are most important.

Make no mistake about it. Ones the most important is ARR growth. And the fuel to ARR growth is net new ARR and net new ARR comes from a number of metrics, but inclusive of new logo ACV and expansion, NRR and those sorts of elements. One metric that we did think was relevant to share was this consumption metric because as consumption grows, that provides the fuel to then lead to future upgrades and future expansion.

So we see consumption growing over 20% as a relevant leading indicator of where ARR growth should converge to. Certainly I'm not suggesting that ARR growth immediately moves to north of 20%. But we believe that, especially with logs and application security and some other elements growing well faster than that, that provides the fuel to future ARR growth.

And make no mistake about it, our objective is to reaccelerate ARR growth as we head into FY '27. And we're certainly not at the point of providing FY '27 guidance, but from overall strategic and conceptual perspective, that's what we're looking to achieve.

Yes, because at some point, it needs to show up like -- full...

Has to.

Yes, yes, I see it also in my client conversations on the Dynatrace clients like they love it. Yes. So yes. Okay. The one thing that you probably got feedback as well in terms of communications or where you're different is, if a client over uses on the platform side. Some of your peers would say, well, then I'll give you like the full like a higher price. And then that way, I encourage early renewals, have bigger discussions. You don't punish clients. And on the one hand, it's good, like because you want adoption. On the other hand, it kind of maybe takes away a little bit one off the upsell opportunities.

Takes a little [indiscernible]

They take a little bit. Like how -- talk a little bit about your thinking there.

And in the largest of enterprises, it has been our observation that you don't hammer customers with a stick. It just doesn't work very well. So we obviously are going to charge you overage if you go if you go over, but we haven't been punitive in that regard. So we don't charge 120% of your cost to force you to do an upgrade. We have customers that have 3-year contracts. And if they're 1 year in or 10 months into their first year and they're running at overage levels, the last thing that an IT ops team wants to do or CIO wants to do is go back to the legal department and say, I did a 3-year contract. Could you please renew this contract after 10 months?

So we have -- we -- our account execs obviously have all the incentive in the world to come back and tell you an expansion. So they can do that. And in many cases, that will happen. And in some cases, they'll just pay us overage for a month or 2 and then...

Give you a turn.

Just rotated into the next year. Either way, we're largely indifferent as to how that goes. Ultimately, that is going to show up in ARR. And that's -- we've played a long game, and the long game is ARR growth, and that's what we want to achieve.

Okay. And then what drove the decision to kind of try to smooth that overage out a little bit because it was...

Accounting, is the short form. We -- for those of you not tracking the short form, it is where we were showing ODC revenue, on-demand consumption revenue on an as-incurred basis, and it showed you precisely what the overage was in that quarter. Because we do rack on an overall ratable basis, given that as opposed to a direct consumption basis, that would suggest that we would have to do ODC in the same way. So we had to move it to an accrual basis, which flattened the ODC. So now you can't see the variability in ODC as you look at it, takes away one of those metrics. But on the other hand, it smooths the curve.

Yes. And then last question for me before I let you go. Like -- how do I think about that margin versus growth envelope now for you guys going forward?

We've committed to maintaining the margin level, but our #1 focus is accelerating growth. So we want to achieve both. You're not going to see or at least our current plans don't suggest a lot of margin accretion. We want to be able to use that powder to accelerate growth. We believe that the market is ready for prime time. AI is driving it. We're in the midst of this sort of tornado. We want to take advantage of that, and we want to lead in observability as we look at.

Perfect. That's a great closing statement. Thank you.

Yes. Thank you so much.

Thank you. Perfect. Thank you. Thank you. Good to have you have you again.

Yes. Thanks, everybody. Appreciate you coming.

Thank you.

Okay. Welcome to the [indiscernible] everybody. We're very happy to have Rick and Jim here to talk through the Dynatrace story. Thank you, gentlemen, and Noelle for attending our conference. Again, we always love to have you. We are delighted to be here. It's a great venue for our conference, isn't it?

Yes. And it's -- the good news is shareholders and investors like to come to it, and it's a great place to see. Thanks for having us.

Thank you so much. So let's start off with some good news because I think in this last quarter, you guys reported some numbers and some comments that were quite constructive. Obviously, 16%, 17% revs ARR growth, 20% CRPO growth. Your net new ARR of $70 million was up nicely year-over-year. So obviously, something is feeling good in the business to give you these kind of numbers. Maybe you could put your finger on 1 or 2 things that went right during this most recent quarter.

Well, I think, Karl, you just pointed a number of them out. We delivered strong performance in net new ARR growth at 14% for the first half, 16% for the second quarter. That is, of course, the fuel to drive overall ARR growth. We increased in addition to exceeding the high end of the guide for Q2, we further increased the guide for the back half of the year for ARR growth. So that was positive.

And in our view, we derisked the second half as well. So from the standpoint of sort of core metrics that organizations look at as to performance, we believe that we contributed in a number of those areas. On top of that, there were numerous other growth drivers, which we do speak about that are really the leading indicators, maybe a level down of how does the business feel a level below the core metrics of new ARR.

Let's talk about some of those.

And that gets you into, for example, our Dynatrace platform subscription DPS customers, that's now 70% of overall ARR. We see double the consumption growth of DPS customers as non-DPS customers -- so the greater the percentage of ARR on DPS, the greater the opportunity for further NRR accretion in the future and ARR growth. So that's expansionary. Logs, huge performance on logs now in just the last year, having essentially built a $100 million-plus consumption business or it is, let's just say, rapidly approaching $100 million or so in that region, growing at more than 100% year-over-year.

And that has been also expansionary as part of the overall end-to-end ecosystem. And then you end up with pipeline growth amidst the strategics very positive and then consumption growth underlying that, which we view as a leading indicator of future ARR growth. And that was in the 20% range, 20% plus range. So a number of these, let's say, core underlying factors we felt very good about in the quarter.

And Rick, let's hit on that consumption strength because as you talked about on the call that's one of the things that's driving early expansion, so what is driving that consumption strength in the quarter and hopefully since.

I'll give you that one, if you want it.

So I mean I think it a byproduct of getting customers onto a Dynatrace platform subscription contracting vehicle. As we've said from the beginning that once we get customers on this vehicle, it makes it much easier for them to consume the platform. They commit to a dollar amount. They get a unit price based on their commitment. And so they have full access to the platform with a full rate card. So there's no longer an issue of having a sales engagement anytime you want to have trial a new product.

And so now having that many customers, and it's been growing, as you know, over the last 3 years, now the core underpinning is now they can leverage more of the platform, and they are leveraging more of the platform.

Rick provided a statistic that customers that are on a Dynatrace platform subscription consume at 2x the rate of a SKU-based customer. They trial 2x the capabilities of the platform. And so I think what you're seeing manifested themselves in the half 1 results were changes we made in fiscal '25 to go on the offensive from a go-to-market perspective.

You're seeing it show up now. That wasn't going to happen overnight. And I would say the fastest-growing product that Rick mentioned with logs, you're seeing a lot of our expansions that we saw early, but those were customers that maybe weren't necessarily thinking of leveraging logs as a use case. They were able to trial it on their platform subscription, and it's led to some very significant expansion. So I think we're seeing the manifestation of that now in the results.

Okay, so let's hit on that log performance a little bit more. That was one of the highlights from this call that struck my attention. Similar question. What's behind that? To what extent is that incumbent log management companies like Splunk giving share to rivals like Dynatrace. I'm sure it's partly the investment that you've made in that log management product such that it's just getting better and better, more customer traction. Can you unpack that?

And how big could that be?

Well, there are 2 primary drivers to keep it simple to our logs growth. One of them is to reduce cost. I have seen, I don't know how many dozens of customers around the globe in the last few months. And the adjectives they use range from out of control to meteoric to these kinds of numbers on log costs. Why? It's because cloud is exploding. AI is exploding. It is exploding AI workloads. It is exploding cloud-native workloads that is creating more logs.

More logs mean more cost associated with those logs and an ability to manage that is important. And if you have an ability to optimize the use of logs, traces, metrics, all observability data types as opposed to only looking at logs, then you can get better outcomes for less money. So cost is a piece of it. The second I just alluded to, which are outcomes. Most of the -- well, many of the log vendors traditionally have not had end-to-end observability capabilities.

They've had logs. So how are you going to find a root cause analysis, right? You are going to perform a root cause analysis when you only have logs, you're going to use logs, but maybe that's not the right tool. Maybe that's not the right data set. Maybe you need traces, metrics and having all of that data combined in context to provide analytics then delivers a better outcome. And so it isn't just a cost equation. It is really the combination of cost plus outcomes that's driving end-to-end observability. That is driving tool consolidation. And in our target segment, which is Global 15,000, so the largest of the largest companies around the planet, that causes them to move to consolidating these elements with Dynatrace.

I'd add maybe something to provide additional context for it that -- so we really got the product and the packaging right last fall. So while we had a log solution, I'd say with the product and the packaging and the pricing, we got that right in the fall of last year. And then I think we've augmented that with the addition of the strike teams. So we have these product strike teams that are focused on particular product areas. One of them is logs.

And I would suggest to you that we have both the product, we have the pricing, we have the packaging. And we now have teams of people that are allowing our sales force to be much, much more productive around having these discussions, either leveraging logs on their own, meaning it's an extension of what they're already buying or in some cases, to Rick's point, broadening to an end-to-end observability story. So a lot of the building blocks that you're seeing for things that we've been putting in place has been very purposeful.

So, Jim, Maybe a question for you. You and Rick have talked about a lot of things that are going right in the business. Yet, how do we bridge that goodness with the guide for the second half, which you guided to a deceleration? Rick, you used the term derisked. Is it just conservatism, Jim? Or are there any factors that informed that second half guidance?

No, it's much more just -- the term I've used is prudent. And I'd start with what does the demand environment look like? The demand environment is incredibly healthy. Our pipeline is the strongest it's been in 5 or 6 quarters. So the pipeline is extremely robust. A consequence of some of the go-to-market changes we made, which is orienting more resources around these high propensity to spend customers, which are large, call it, Global 500 customers is, by definition, you're going to have large deal sizes.

We're seeing that play out. We saw that in the first half of the year. Our pipeline is relatively weighted from a mix perspective to these very large deals. And so end-to-end observability, which tends to be the play that is the most successful. There is a lot of pipeline in that area. The timing of that is difficult to judge. It doesn't mean we won't win it. It just means pegging it, does it happen in the fourth quarter? Does it slip into Q1? So we thought that we should build some kind of prudence into that. What I would tell you is that if we have close rates similar to the first half in the second half, we will deliver a much better outcome.

Okay. Jim, on the call or at least the call back, you also mentioned that there were some year-over-year compare headwinds this quarter or next. Can you elaborate on what those were? Is there any way to quantify like on an adjusted basis what growth might have been?

So that was more oriented around subscription revenue than necessarily ARR that -- as you know, that we used to recognize ODC revenue as incurred. And so you saw building over last year. From an accounting perspective, we had to change that in Q1 to a more ratable model. And so the variability that you were seeing in ODC revenue has gone away. It's now smooth.

So you're seeing a bit of a headwind just on a year-on-year compare perspective from ODC revenue. And then every company now again that even with a subscription revenue business, some customers may be what we call on hold, meaning maybe they weren't paying you. So you stop recognizing revenue for them. And then they get off hold and then you start recognizing, you do true-ups. And so last year, we had more of those in the back half of the year than normal. And so what I've suggested to people because they've looked at the back half of the year guide and they say, jeez, that seems like a pretty significant deceleration in subscription growth. And my comment back to them is that if you do adjust for those things that I just mentioned, I'd say the normalized growth rate for the business is more mid-teens, call it, 16%. And that's the way you should think about it exiting fiscal '24.

Okay, that's helpful. Let's switch subjects a little bit to the competitive environment. And I think one of the issues in the last couple of weeks that this group has been thinking through, there's always consolidation in your space, mostly to private equity, not so much strategics. But Palo Alto acquired Chronosphere, a much smaller player than you guys. But obviously, that created a little bit of concern in the Street that weighed at least temporarily on your shares as well as Datadog. Rick, what did you think of that acquisition? What's your perspective as CEO?

Who's Palo Alto Networks? I am kidding. Chronosphere certainly is a visible competitor. But from a direct perspective from Dynatrace, we just don't see them. In the market, they are oriented to metrics. They do not have a broad base of end-to-end observability as a solution and they have traditionally sold metrics increasingly into AI native companies and we're selling into the Global 15,000.

So partly because of the end-to-end observability nature of the solution and the breadth of our solution and selling into the kinds of customers we're selling to and to the personas within those customers to whom we're selling, it really is not aligned as a direct competitive threat. So we don't really see it.

Now having said that, of course, Palo has an ability to invest in that business. And as with all competitors, we will approach it with paranoia and a clear view as to what's happening over the course of time, and we'll make adjustments. One thing that it does certainly validate or one thesis that it certainly validates, of course, is the thesis we communicated a couple of years ago, which is that observability and application security are certainly converging.

So we do see that, but they have some headwinds and the headwinds are going to be breadth of portfolio. Another headwind is going to be the buyer persona of the CISO buyer for Palo Alto Networks versus an IT ops or CIO-type buyer for observability traditionally. And so that's what they're going to have to work through, I think, as they go forward.

Can we talk a little bit about some of your own efforts to step into the security space, not so much Palo Alto going the other way. But what has Dynatrace done to add security features to your already world-class observability platform?

Our application security business -- so our logs business is growing faster than any other business for us at the moment. Second fastest growth is application security. So we are investing there and investing in a significant way because we do believe that convergence. Having said that, we have been very disciplined in our strategy of application security. It is not our intent, I should say, hasn't been our intent to go compete aggressively with all the security vendors. We don't believe that, that is a winning strategy. The winning strategy for us in application security, we believe, to be investing in areas of security in which real-time runtime observability data matters to the security outcome.

And so that has resulted in areas and investments like runtime vulnerability analytics, which is our largest application security portion of the business. It is cloud security posture management, Kubernetes security posture management. It is cloud SIEM. These are areas where runtime analytics coming out of a broad-based data lakehouse consisting of security data types add enormous incremental and immediate value to the security outcome. So those are the areas in which we've been focused as part of the strategy to really evolve and leverage the 2 businesses together as opposed to try to construct a separate security business.

Okay. That makes sense. Rick, let's also talk about some of the ways that Dynatrace differentiates from some of these rivals, many of which sort of as you describe Chronosphere are very cloud-native, AI native. They don't have a presence with Fortune 200s where a lot of their infrastructure is on-prem. So can you elaborate a little bit on the on-prem cloud mix, how that perhaps gives you an edge and which way the wind is blowing in terms of that mix?

Sure. It is clear that in -- and I'll just pick a vertical for the sake of argument, but in financial institutions where we have very, very deep penetration really across the financial organizations, it is the case that they're going to have on-prem workloads for a lifetime to come. They're just going to continue to exist indefinitely.

Now of course, they're also bringing up cloud workflows and those are getting supplemented or they're supplementing the on-prem workloads. So it really is a combination of factors, but the on-prem workloads don't dissipate. And so the result of it is that we believe that this is a significant advantage for Dynatrace by being able to do both with a common underlying platform.

And that does give an advantage because now you're looking at the same dashboard, you're looking at the same capabilities, you're expecting the same answers irrespective of where the workload resides. And as it moves to the cloud or as you move some of those workloads from on-prem to become hyperscaler workloads, then you can use the same set of observability tools, capabilities and platform to oversee that transition.

Okay. I think another differentiator would be on the pricing front, where I think Dynatrace has been a little bit further along in being flexible on your pricing with DPS to customers, whereas some of the other large observability vendors are -- have a reputation for being quite expensive. So to what extent has that let's call it, more flexible pricing structure giving you a head-to-head edge?

I would say that it has. I'd say that the feedback that we get from customers almost universally of the DPS model is it is the most flexible contracting model that's out there. We don't charge overage rates. So if you exceed your commitment, we don't charge you a premium rate when you exceed that, others do. And now obviously, we will talk to them about if you're exceeding your commitment, it means that you're probably getting value.

And oh, by the way, we may be able to offer you a better unit price. So there is this kind of give and take around whether they want to do an early expansion or whether they want to go on demand. And so we get very good feedback around that pricing model. They like the fact that they can trial the platform. They don't have to have a sales engagement. So it's just that it's a kind of a perfect match for what the customer is looking for and what we're looking for.

One of the things that we've done that Rick started out that this notion of consumption and driving consumption, that's not a muscle that we've had, Karl. That historically, we have not been a very consumption-oriented company that I'd say now we have incentive structures focused on consumption. Our customer success teams are focused on consumption. They have a compensation at risk based on consumption.

Our strike teams, their compensation goals are consumption of that particular product that they're supporting. And so this muscle of get them on DPS, get your teams of people that work with customers around driving consumption, driving consumption will ultimately lead if the customer is getting value and they're consuming at a rapid rate to an early expansion. And so you're starting to see that play out. And I would suggest to you that some of the performance that we saw in the first half of the year was a manifestation of that.

Okay. Let's talk a little bit about AI. There's a couple of aspects to it that are interesting. I think one topic that our team is thinking through, and I'm sure investors are, is when will the AI phenomenon be a real pull-through at the observability layer? I think I'll share the consensus and then Rick you can rebut it if you don't agree, that most of the AI applications inside large enterprises are today, still early stage, somewhat lightweight.

They're chatbot-based, they're coding tools. They're not robust enough, and they're not really at scale in production to really need a lot of monitoring pull-through. So you haven't seen it yet. But you will, once the likes of UBS start building more robust enterprise-grade apps, then you'll see the pull-through. Does that feel right to you? Or are there ways in which you're actually seeing a benefit even today?

I think that is right, Karl. I think your articulation is correct. It is the case today that we have hundreds of our customers that have deployed us for AI workloads. So they are already using infrastructure monitoring. They're using log management. They're using metrics. They're evaluating their AI workloads today based on Dynatrace. What will absolutely accelerate is when those go regularly into production and begin to generate production level workloads, I think we'll see an acceleration of that.

On the side, you then have the AI natives who are the LLMs, for example, that are doing their own infrastructure monitoring and others with their AI workloads. And those are already moving at a very fast clip, and that's where the infrastructure monitoring really comes into play for those kinds of companies. But I think you have to look at it both from an AI native perspective as well as AI workload perspective within the enterprise, and you have to think about both in order to really understand the evolution of that space.

Can you talk about your outlook for Dynatrace to penetrate that AI native community? I think you called out some successes on this last call. But can you elaborate and describe to the audience exactly what you're doing to become -- essentially gain mind share in that cohort?

Many things. First, the Dynatrace focus has traditionally been the CIO down through IT ops. We're selling an enterprise-wide end-to-end observability solution that delivers answers, and those answers deliver insights to enable very rapid root cause analysis and analytics and resolution of problems. And that's fundamentally what we've done.

The evolution of the current environment is suggesting that developers are becoming much more involved in the observability decision, and that is abundantly clear in AI natives. So what we've done is over the last year, we've really spent a ton of time on our road map developing or expanding the Dynatrace platform to become developer-ready, number one.

Number two, we've expanded connections into the overall ecosystem. So this past quarter, we announced integrations with Atlassian, very important one with ServiceNow, with GitHub, NVIDIA. So the result of all of these is that we are now built more fundamentally into this ecosystem of agents. And that really results in an ability to deliver autonomous operations in the future through an Agentic ecosystem that others can't provide.

Yes. I think that gets at another question I had about this, and that is all the large successful incumbents like Dynatrace are going to have to worry about essentially, in your case, an AI-native observability player coming at you, but you're not going to sit still and wait for that day to happen. Your R&D team, I'm sure, in a lot of interesting ways, are AI enabling your core product such that you can retain that incumbency. Can you talk a little bit, Rick, about ways in which Dynatrace is getting in front of that risk and essentially innovating to make sure that you indeed are the AI-enabled platform going forward and not some scrappy start-up?

Sure. The most fundamental answer to that question is we're not starting from today looking forward to develop AI sort of capabilities. We have been utilizing our AI-powered platform for more than a decade. We didn't come up with it 2 years ago. We didn't start work on it 18 months ago. We've been doing it for more than a decade.

We build a completely integrated, massively parallel processing data lakehouse called Grail with all observability data types built into it in a causal way with context. That then provides the ammunition for our AI engine to analyze that to derive and deliver very specific answers. We believe that those answers are quintessential to enabling an AI ecosystem of agents to then take action.

If you cannot deliver answers that are deterministic and trustworthy, I would commit to you, you cannot take action on those outcomes because they are simply guesses. So the way we like to think of it is that Dynatrace delivers answers, not guesses. And by delivering answers through this integration underlying data foundation with an AI engine, we set ourselves up to actually add value into an AI ecosystem that others cannot do.

Okay. Let's finish with maybe a quick thought on all the go-to-market changes that the 2 of you have spent a number of earnings calls discussing. Rather than ask you to go back and describe all that, maybe I'll phrase it a different way, and that is you've now had a year to 2 years. So looking back, what in your judge -- give yourself a report card on those go-to-market changes. What do you think went really well? And where do you think you've still got a little bit of work to do?

Well, I'd say where it went really well was, one, I think the weighting of investment to large strategic accounts. That is the customer base that we really shine in. So fortifying resources in that area, I think, was a smart move. It resulted in pipeline growth. It's already resulted in deal closures. So I feel very, very good about that. Two, I would say we've made great traction in the partner ecosystem, notably with GSIs. The GSIs are now a source of pipeline, and they are enabling even pipeline that they didn't source, enabling us to get exposure to C-level leaders through their involvement.

So I feel very good about the weighting of resources to strategic accounts. I feel very good about the partner expansion. I also feel very good about this introduction of strike teams, evolving away from what was a specialist model with security now to the strike teams focused on consumption because at the end of the day, this orientation of consumption, I think, is an important measure for the health of the business. So I'd say if I were to say 3 things now the last -- obviously, all of these things are underpinned by having DPS as a contract vehicle.

Yes, got it. Okay. Any questions from the audience? We've got about 1.5 minutes. We successfully ran through everything. Gentlemen, thanks so much for coming to the event. You've helped to make it even better than it was last year. So I'm super appreciative. Thank you...

Yes. Thank you very much, Karl.

Okay. Thank you all.

I appreciate it.

All right, guys. So Ryan Mac, small and mid-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 the stage is we should open the doors up. So I mean you just get a view of the ocean.

It'll be much better. 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 still used to perfection of how great it is here. This is my first year here. So I'm just like taking them all in. I'm blown away, but...

Pretty nice. You need to do the bluff path along the water.

Yes. And people have been telling me about the hike and everything. So I'm coming here for the weekend before next time. That seems like the pro move.

I think that sounds great. Now everybody is going to leave the room and go for a [ ride. ]

I mean...

Can't leave until we're done.

Maybe 1 year, we can just do a hike fireside.

[indiscernible]

Yes, I'll walk and talk. So look, guys, we'll be taking questions directly from the room, but if you have them, you can e-mail me at [email protected], and we get them in. Here from Dynatrace today is Rick McConnell. Rick, thanks for being here.

Just to start, we'd love to kind of hear about your year so far, a lot of change with AI, a lot of change with some of the go-to-market changes, but -- and something like the ODC and DPS. But overall, I would just love to hear kind of at a high level, like maybe what surprised you? What are you pleasantly surprised with for the year so far?

Well, first of all, very strong first half of the year. And 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; and number three, largely derisked that second half. So 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. Well, when logs was growing north of 100% per year at $20 million on a $2 billion business, it has limited impact. But $100 million consumption business growing at 100%, a much greater impact as we expect going into the future and a huge beachhead for us as we look ahead.

The overall strategic pipeline, up 45% year-over-year, very strong. So we feel good about the pipeline at the largest end of the segment, which is where we're really seeing the end-to-end observability and the tool consolidation. Our pricing method of our Dynatrace platform subscription continues to grow. That's now over 50% of our customers, 70% of our overall ARR. So we feel great about that.

And consumption growth is super strong, more than 20% and that we believe to be a leading indicator of future net new ARR. So core growth drivers are in place. The business is strong, very healthy as we look into the second half and customers are expanding. And I suppose the macro [ uber ] comment at all is that observability is becoming more and more critical day by day, especially with the expansion to AI workloads, you just -- you need observability. It's no longer optional. It's become mandatory.

Yes. A lots to work with there, but I think the last part is really interesting. So can you just tell me in your customer conversations, like how do you feel like there's a higher priority place on observability? Like what does AI have to do with that? Like how does that change, I guess, versus like a year ago or in prior years?

Well, many different ways, I would say AI is changing the calculus on observability. The most basic one is that more workloads mean more observability. So you really as a -- 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.

So the result of it is you need to be driving toward a methodology of automated processes to be managing software workloads. And 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.

And I've been to -- I've been privileged to have been in, I don't know, a dozen countries in the last 3 months, meeting with many dozens of CXOs. And 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 would be a great place to start on the AI theme is in terms of the Dynatrace architecture, you've been doing observability at the largest of scale for some of the biggest of customers. So as people think about like where can they start on their own AI journey with the products that they're coming out with, how are your existing customers, 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?

Yes. A partner asked me the other day and this particular partner was of the mode of, geez, we're -- 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. And his question to me was, what percentage of Dynatrace customers use Dynatrace AI? And my answer is 100%. And it isn't 100% using agentic AI with autonomous operations where everything is auto correcting. That's still to come.

But the foundation of AI, causal AI, predictive AI, generative AI, all analyzing an integrated data lake house of content, including logs, traces, metrics, behavioral analytics, business events, et cetera. 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.

And that is becoming more and more crucial day by day. And so -- and it is that AI engine of Dynatrace that hasn't existed for 1.5 years or 2 years or since AI became a thing, an exciting new thing. 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. And we can go there if you wish.

The short form and to keep it as simple as possible is observability has gone through a number of phases. And I would say we're now approaching Phase 4. And to cover the first 3 phases, simply, I would do it in sort of single adjectives. Phase 1 was reactive. Something broke, you tried to fix it or you tried to figure out what was wrong and then you try to fix.

Phase 2, 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 3 was predictive, and that's really where we are today. And that is taking all of the AI elements that we had before and applying additional machine learning and anomaly detection to anticipate issues to be able to resolve them before they became user impacting.

Phase 4, though, is really where we all get super excited at Dynatrace. And this is around autonomous operations, as I mentioned. Well, 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 3 things. And 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.

But we aren't always the solution to fix it. 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 a GitHub or a Jira. You may need a workflow integration, and that would be with ServiceNow. So in this 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.

And maybe a final point on it is you say, well, you're never going to 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.

And who doesn't want that? I mean, as you go through the phases of observability historically, right, as you kind of outlined where you went from 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? Because it seems like to me in the dynamic you outlined on a go-forward basis, right, you almost want to have like one end-to-end platform in order to do those use cases. So do you think that changes with more focus from AI?

Well, this is a lay-up question that's going to sound self-serving an 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 and another one for infrastructure and so on.

And we -- there's a large airline comes to mind that we closed last year in the summer, and they had one of everything. And when they started on the journey with Dynatrace, the principal there even told me, gosh, I can't even imagine deploying Dynatrace because I've already got one of everything. How is that going to solve the problem? And in the end, they weren't getting the outcomes they wanted. So they ended up with an end-to-end observability road map that basically was Dynatrace, and it eliminated or supplanted all these other tools, deploying Dynatrace as a single integrated AI-powered platform instead.

And that gave them much better outcomes than they otherwise would. Well, why is that? Well, if you have the underlying data types of observability, things like logs and traces and metrics and really 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. And the more complex the business, the harder it is to drive those insights and the harder it is to piece things together. And God forbid, you have multiple incidents at the same time. And then what do you do? And how do you cross-correlate this data? It's impossible.

With the Dynatrace platform, we have a single integrated data lake house, 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. So the result of this is by providing a completely integrated platform, you have a comprehensive integrated picture that you can then act upon.

And in fact, this is the motion that we see our customers driving and especially the larger the customer, the more complex the environment. The more complex the environment, the more they need a single integrated platform. And this is the directional heading.

And 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 present in the leader quadrant in the far upper right. And it is because, first, that's what customers are actually doing, they're betting with their wallets. And secondly, that's where you get the best outcomes.

Yes. I mean you always hear in software like, oh, we want one pane of glass. And it makes sense in theory and why it would be nice. But when you have like a more reactive use case, right, like archeologist, then it's okay to go platform to platform or vendor to vendor. But 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. But just on the consumption rates that you talked about, around 20% consumption growth rates that's higher than the current revenue rates. I'd love to hear how that's trending and what's kind of driving that higher level of consumption?

Well, consumption rates are up quarter-over-quarter. So our consumption rates are growing in part fueled by the log business that we discussed earlier. And those consumption rates are, we believe, precursors of the opportunity for acceleration of growth in ARR and subscription revenue. And that is what I wake up every day thinking about is how do I help the organization as a whole to reaccelerate top line.

And that is really with a keen focus on expansion of net new ARR. And 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, a 16% net new ARR growth was very, very favorable result in the second quarter, 14% for the first half, very strong also.

So those -- that really is the fuel to a reacceleration of top line. And why consumption is important is it's important to remember that we report revenue on a ratable basis, not a consumption basis. So consumption growth doesn't translate to revenue growth precisely for us. It is with a lag. So 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 in, for example, the mid-20s of consumption while they're only growing revenue 15%.

I mean, at some point, it converges. And that's the expectation is that the faster we can drive consumption, the more demand there is for the platform. The more demand there is for the platform, the greater the opportunity for ARR growth down the road.

And that's the leading indicator you're probably most focused on, right? Like how much people are...

Yes. We've got 1,400 people in our services organization who don't 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. So 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 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 more servers, more logs, more whatever. Second is the inclusion expansion in new workloads, and those could include core level new workloads, consolidated workloads from other vendors or AI workloads, net new AI workloads.

And then a final category might be new products and logs would fit in, in that category, for example, of where we see logs growth at 100%-plus growth year-over-year in terms of consumption, all of a sudden becoming material, and that really is driving new opportunity for consumption growth as well.

Excellent. So I mean, consumption definitely seems like the metric on a leading indicator you should focus on. Investors have been looking at net new ARR and ODC revenue and subscription revenue. But 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 investors were looking at. But that's okay. I mean you're encouraging the right incentives for your larger customers. But 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 got investors that have come back and that have said, wow, ODC was light, which is your on-demand consumption revenue, which is essentially overage billings. And I would say our response to that is sort of twofold. Number one, light meant $1 million to $2 million on almost $0.5 billion of quarterly revenue. So...

We're nitpickers here. That's how we go.

That's rounding here. And you always want to exceed expectations, and I've got it. I understand that. But we blew out ARR, net new ARR relative to even internal projections, let alone guidance. And 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.

Well, every cohort in every quarter is going to be a little bit different. And we can't predict who's going to decide to pay overages and -- for a couple of months before going into the next year cycle and who's going to 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 onetime event and ARR is recurring.

So an expansion -- an early expansion by a customer, let's say, 1 or 2 years into a 3-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 2. So if we had to pick, we'd like to see the ARR conversion versus the ODC. But ultimately, it's the customers' choice as to whether they want to pay a month or 2 of overage versus expand, and we give them that option.

And so what do you think is driving that decision to renew early? Is it just they've realized, okay, we're going to have a lot more utilization? So...

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. And the reason is because they're going to get lower unit price. So it certainly is the case, the more volume you do, the lower the unit price. Now the commit will go up. So maybe your original DPS contract was for $1 million a year, and now you're consuming at a higher rate, you go to $2 million.

So you're going to spend more, but you're going to get more than double the capabilities off of that 2x improvement. So 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 going to move to an ARR expansion as opposed to an ODC single payout.

And so as I look at ODC versus the new ARR, that's just like a timing thing in regard to...

Just timing, I mean, ultimately, ODC as long as we -- ODC obviously ends up in subscription revenue. But to the extent that you continue to expand, that is a sign in and of itself that you have exceeded your commit for that 1 year. By definition, if you're paying overage in that year, you exceeded the commit for the year. And that is a healthy indication any way you look at it.

That's a pretty healthy signal. Yes. And since your DPS customers, 50% of your customers are on DPS, but 70% of your ARR, as you think about who's doing these renewals, those could be bigger customers as well. So like that would exacerbate the timing dynamic there if they had an early renewal.

Absolutely. And the largest -- 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. And I mean it's timely because we're coming into a period now of the holiday selling cycle with Black Friday and Cyber Monday. Well, you look at our large commerce customers and their requirements for observability explode in the calendar fourth quarter.

And if you have exclusively a server-based pricing model, then how do you manage that? I mean do you quadruple the number of servers you need for a quarter and then -- I mean you can't 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 may be -- calendar Q4 maybe a large multiple of the other quarters, and then that enables you to grow, to expand and then contract. It gives you much more flexibility. And that drives greater consumption as well. That flexibility enables our customers to expand way beyond what they would have done otherwise.

I'm going to 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 is already running pretty hot, it might make more sense to like, okay, let's 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.

Yes. That makes sense to me. So just on one of the comments 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 it in a couple of years. And then now we're here. So -- but still...

That was our fastest business to $100 million. So I mean, we really just began selling logs in earnest in October of 2024. So it's really been just about a year that, that business has grown to that size.

So can you talk me through like what you think the next 6 to 12 months looks like for that logs business? Like it's a higher ticket item, right? So are you winning from a competitor here or your customers looking to do like get their logs under one roof with AI? Like what's kind of driving like the next leg of growth for logs?

There are 2 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 didn't feel like they were driving any additional value from that. They're storing more logs, they're ingesting more logs, they're querying more logs, but not a lot of incremental value. And they very much wanted to get a handle on that. And 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, which is I'm not sure why, and I've been in this industry with Dynatrace for 4 years, not 10. But I have no idea why logs grew up on the one hand and observability data types and others on the other. It makes no sense. And 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 lake house 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.

And to do that, you really want logs because logs add value to that. Moreover, it contributes on the cost front, but you actually don't need to store as many logs if you have trace symmetrics 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.

So 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. And the result of all that is that our log business is growing because of those dynamics. And 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 going to be inclined to move in that direction, and that's what we're seeing customers do.

So they probably have a logs provider today. And so 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 1 year of people shifting away from different vendors.

Yes. And it is because as you move to an integrated platform, it becomes much stickier. So if you're simply replacing like-for-like logs to logs with the sole objective being cost improvement, you're going to potentially run the risk of down the road, another vendor coming in and doing it for yet cheaper. And that then doesn't deliver the stickiness. In our case, it is -- 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.

So 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 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. So you become much more embedded and therefore, much stickier as you look forward. So I think the likelihood that it -- a log workflow comes to Dynatrace and then leaves later on is much lower as a result of that platform-based approach.

Excellent. And maybe due to logs or maybe due to more broad-based strength or something else, but I thought it was interesting that your new logo land size increased 30% in the quarter. I know you guys had a big shift towards upmarket in your go-to-market. But, yes, anything that can call out for why those new deals are landing bigger at this point?

It is -- 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 [ proficient ]. And whether it is a large bank, a large insurance company, a large package delivery company, many others I can think of, these are landing bigger. And the reason they are doing so is because they are resulting from a pretty substantial tool consolidation and the tool consolidation is driving the bigger land.

So 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 and then I need to attack it in that way. And that's what we're seeing, and that's driving the larger land size.

Excellent. And you talked about some pipeline strength with larger customers as well, but it's difficult to wrangle the timing on these things. Like -- is that just like enterprise buying, it's tough to pinpoint...

Yes. This provides some of the challenge in our guidance, to be honest, which is we delivered a very strong Q1, very strong Q2, a very strong overall first half. We raised guidance. But some investors have asked us, well, why not more strength in the second half guide? And the answer is well, trust, number one, that our internal plan is higher than the guide, obviously, without a question. But number two, that 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've done an excellent job of managing that uncertainty heretofore with very good results. And so as I mentioned at the outset, we believe that we have radically derisked the second half as a result of this. And with the strategic pipeline growth at 45%, we believe that we've got the pipeline to cover some of these machinations of these large deals, but it is a reality of our business.

And some conservatism around close rates in the second half part of these deals?

That's exactly it.

That makes sense. Two more AI questions, 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?

I would say in the AI native, it's a more logical use case to start with metrics and logs and probably infrastructure. So that's probably where they land.

You started first. And then large language model self observability seems like a very early innings for that. But 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 or AIOps for enterprise-wide deployments. We have, unlike some others in our market, not typically sold to developers. But 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. And 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.

So we're very excited about it, and 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. So they're already using it today. So we're super excited about that. And we think that, 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.

And with developers more focused on those as a customer, your shift to DPS and consumption makes a lot more sense in terms of they can scale more quickly on the Dynatrace at this point.

Exactly.