All right. Good morning, everybody. Day two, 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.
Thanks for having us. We are always happy to be here.
Do we need to get you a napkin?
I probably need to move to new pants. Yeah.
Very well.
With my exploding Perrier.
Yeah.
I think we'll survive.
All right, good. Rick, let's start with you. The observability market feels like it's entering a new phase. 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 AI. In an AI-first world, we would submit that the die has been cast, that observability is more critical each day. I remember six months ago, nine months ago, when we were in the midst of SaaSpocalypse, when it was deemed that the vast majority of software could be rewritten. It has evolved since then. I think we've evolved to the point where there are clearly categories that are winners and maybe some other categories that will struggle more in an LLM-driven world. I would submit to you that observability is one of those categories that is going to win. We are entering a new era. In observability, we have all the traditional software observability that we've been driving today.
This is a 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. The goal in this traditional business is that 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. AI observability is the observability of AI workloads and of agentic models. 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.
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. What you need is context to be able to do that, and that is what observability systems provide in real time by analyzing billions of interconnected data points. 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. 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 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 1500 organizations. Near as I could tell, the inhibitor to broader-based deployment of AI is confidence. What I mean by that is once 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 chatbot sitting in their mobile app and somebody says, "Well, where should I invest $10,000?" 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. The calibration of the input coming from the LLMs to deliver to end users is really critical, and it is that confidence level that I think is required to get over the next hump. 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.
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 billion-dollar business that I talked about of Dynatrace's, is 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, 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, so you start with the same question, but you then expand to another question. The next question is really around is it accurate?
Meaning that are the LLMs 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. 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? The point is, in a traditional observability sense, is it resilient, is it working, is a pretty good start. In AI workloads, you need to supplement with a couple of additional questions related to the accuracy of the data flow. 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 the maturity of the product capabilities to meet that moment relative to 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. Arize is the category leader in AI experimentation and AI observability. So the result of it is that we're betting with our pocketbook, 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 as two 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 pre-production to more sophisticated workflows. 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?
Yeah. Just to level set for everyone, the statistic was that we went from our fourth quarter to our most recent first quarter, from little over 800 customers, where we were monitoring some LLM workload, to 1,000 in one quarter. Then we went from 500 to 850 customers that are leveraging Dynatrace for some of our agentic capabilities. Kind of two unique cohorts. 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. What we found is the characteristics of these cohorts is they consume the platform at a much more significant rate and pace.
Their consumption growth is 50% higher than customers that are not leveraging us for an LLM workload or for agentic capability. We view it a little as this is a precursor of what we think is going to happen when others start to do it, and it will build. You say, "Well, why is that the case?" One, I think, Matt, what ends up happening is they start leveraging more of the Dynatrace capabilities. Those customers tend to use Dynatrace for more of our capabilities. Think of it, the breadth of the platform that they're consuming is not maybe just using us for application performance monitoring or infrastructure. They're usually using us for application performance monitoring, infrastructure monitoring, digital experience, logs, and in some cases, security. They use more capabilities of the platform.
I think it's also true that the LLM workloads tend to be chattier. The characteristics of those workloads is you consume at a higher rate. 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. 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 in consumption of the platform.
It sounds like you actually are starting to see maybe an accelerated curve on the consumption side.
We are.
Okay. Let's double-click on the DPS opportunity. This is the first year the major DPS cohorts 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 how has that relationship developed as you expected?
Yeah, I think we've said in the past, and you're right, that this is the first year that it all converges, where all your cohort classes come up for their annual reset or renewal. That will be the new normal every year, Matt. This is the first year you'll see that. I'd say that the general characteristics that we've seen, most of our DPS contracts are three-year. Not all, but most. I think the general characteristics that we saw is customers that are maybe earlier in their life cycle, maybe year one, they may have a predisposition, if they're consuming at a pretty significant rate, to go on demand. They had just done a renewal maybe a year ago, and it isn't worth necessarily going through the bother of an expansion. Then in year two, year three cohorts, more inclined to do an expansion.
I'd say that behavior remains. What I would tell you is that 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. Only 30% are happening now. I'd say the characteristics that we're seeing now are consistent with what we expected, but your volume of them is significantly less. 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%-17%.
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. I think the back half of the year, if that continues, we expect that we will see an increase in the expansion rates and that ARR will then inflect.
I guess just to put a finer point on that, for the 30% of renewals that came due in the first half of the year and what you have already seen inside the business today, is that commensurate with the 20%+ consumption rate that you guys have been talking about for the last several quarters?
It is. It is just a small-
Small bit.
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. Reps are incented on maximizing their quota, and so 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 expiration.
Okay, great. Rick, let's move to another growth pillar for Dynatrace, tool consolidation. 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 onto 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.
To keep running. Even once you find out what's wrong, it takes you too long to fix it. 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 for digital experience, and synthetics, and application, and the list goes on and on. 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.
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. The trend, and this is sort of irrespective, Matt, of Dynatrace even.
The trend in the industry that we would say is toward end-to-end consolidation of observability tools. 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 those answers can be acted upon by an agentic system and trusted. That's what's driving it, and I would say that end-to-end observability is getting driven at three levels. Number one is the data level, logs, traces, metrics, behavioral analytics, business events, all in one integrated data lakehouse. 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 at 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. Yeah, 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.
Yeah. Exactly.
But at the same time, it does seem like the AI startup ecosystem, you're seeing almost like a re-fragmentation in the observability category, right? There's no shortage of companies trying to build the AI SRE layer from AI native startups to the existing observability platforms, ITSM vendors. 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 observability 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 its 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 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, in observability.
In 16 years running, Dynatrace has been a leader in that Magic Quadrant, just as one example of the staying power of Dynatrace in a market that's evolved in an immeasurable way over that period of time.
I think this segues nicely into Arize. You kind of gave 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. What were customers telling you that reinforced the decision? How large could that opportunity become across the two installed bases?
Yeah, I'll let Jim take the how large the opportunity is. Here's what I would say. The three 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 as I could tell, is trying 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. 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 in a notable way. Secondly, we looked at over a dozen vendors in the space of AI observability. We analyzed tools and capabilities, sophistication of solutions, and so on, and ultimately found Arize as the category leader. 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, yeah, we're already using Arize on our workloads here." We saw that a large automotive manufacturer, a large e-commerce provider, a large bank. So Arize was already making substantial inroads there with their product.
Yet what Arize was getting asked to do is, "Could you please move from pre-production to production?" Just as we're getting asked to move from production to pre-production. So putting those two together was really very synergistic. So product and product synergy was another. 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 a developer on a credit card, and done so on a product-led growth motion bottom-up.
Well, Arize, through its Phoenix open-source solution, does more than 3 million downloads per month to developers, AI developers specifically. 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 fast-growing market that we wanted to take advantage of.
What I'd say on the growth side, it's still a very new emerging area, right? Rick talked about a $10 billion market opportunity. When you think about Arize as the category leader, and we mentioned when we acquired them, we thought they would contribute two points of ARR growth for us. So you do the math on that. 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. The good news is Arize also sells, as Rick said, to enterprise customers. So they're relevant with enterprise customers, which is the sweet spot of Dynatrace. They also sell to cloud-native customers, which is certainly an area that we've aspired to get into. 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 it's a great cross-sell, up-sell opportunity. Gives us a cloud-native push that they sell to AI developers. To Rick's 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. Sometimes companies do acquisitions because maybe their core business is slowing. That's not the case. So 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 expect that that combined is going to lead to an acceleration in Dynatrace growth rate well beyond FY 2027.
Okay, great.
Matt, the Q1, as we reported it, provides a bunch of proof points to that really show that acceleration are demonstrable of it. 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 two quarters ago to 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.
Yeah. Let's talk about logs, Rick and Jim. I thought it was really compelling because when you go from $0- $100 million, it took several quarters, right? $100 million-$200 million .
Yeah.
It took two quarters.
It took several years.
Several years.
It took a while.
Right. 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 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. 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. There were probably some product gaps early on several years ago. You start working on that. We always believed 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. We always thought we had a better solution, but there were gaps.
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. Call it your product was right. Then we worked to make sure we got the pricing and packaging of the product right. I think we were at a point where you could then put your foot on the gas. One of the things that we did was having the product and the pricing and the packaging right, we introduced what we call strike teams. 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.
I think we were able to bring it all together, Matt, and you get the product and you get the pricing and the packaging right, great value proposition. Then on top of that, you introduced, I'd say, a selling motion with. They're not a specialist team in the sense that some companies have specialists, they just sell a particular product category. These are people that drive consumption. I'd say we 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. It's a pretty easy conversation to have.
Would you be interested in trialing us?" The benefit of DPS is you don't have to have a separate sales contract, that they're able to do that. These strike teams can help with that. I view $200 million as a milestone. This isn't the destination. This is a billion-dollar-plus category for the company. This is going to continue to be a very, very fast-growing category.
It comes down to, in logs, 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.
Mm-hmm. Yeah.
You get better outcomes from fewer logs if you combine them with other data types. The result of that is 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 vendor B. Some of the logs vendors in the market are delivering meteoric increases in logs prices anyway, so the bar is not so hard to get over in terms of coming up with a lower price point. The second is you want to deliver integrated value. If you have all of 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 at 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, which is why you need to combine them to deliver better outcomes, 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 of investment. Just give us a mark to market as to where we are on sort of that go-to-market productivity.
Yeah, you're right. It was two years ago that we had outlined for you that we were making pretty substantive go-to-market changes where we were investing more in the top of the pyramid, your largest customers, where Dynatrace really shines. We reduced the number of accounts per rep in the top of the pyramid, we call it the Global 500. That has been a huge success. Our fastest-growing area are in our, 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- 250 more accounts with a similar model. Think of it as same density, same level of resource alignment. 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.
You have dedicated teams of people that are focused on these large accounts. I expect that that will continue to be the fastest-growing product category for us. Having said that, we're not ceding what we call the enterprise, which is, think of that as the accounts that are below the 750 global accounts, to call it 5,000. 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 5,001- 15,000, because we target the Global 1500. 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 smaller teams, lower ASP, easier kind of onboarding process. 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.
Would you just say, broadly speaking, we are in a better place in terms of sort of the baseline around sales productivity, pipeline generation? Talk about those a little.
100%, that we had outlined very clearly when we put this in place that we thought sales productivity in our FY 2025 would probably drop. You move a lot of accounts, you cause a lot of disruption within your customer base with accounts moving. Actually, for us, that disruption was less than what we expected, but there was a sales productivity drop, Matt, if you look at it around how much bookings or ARR was generated per rep. Then we said in FY 2026, you would start to see some productivity improvement and that we would see ARR growth rates stabilize. We saw that. We also laid out, we said in FY 2027, if we are successful with this journey, you will see an improvement in productivity and you will see an acceleration in the growth rate of the business. That is where we are at right now.
I think to Rick's point, we have shown some proof points. I think the better measure of progress is what I call trailing 12-month net new ARR. Sometimes when you look at a quarter, quarters can be lumpy just because of large enterprise accounts that we have had four quarters in a row of trailing 12-month net new ARR 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 is going to continue.
Okay, great. In the last two minutes that we have, Rick, I want to bring it back to you. The market is evolving very rapidly. Dynatrace has advanced the platform significantly over the last two years. You have improved things operationally. When you look out over the next five years, what would define success for Dynatrace?
Well, the principal financial indicator that Jim and I look at is ARR growth. The ingredient to that that drives it is 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 2025. FY 2026, we stabilized it. FY 2027, we want to see that ramp. 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, things like pricing and packaging adjustments that we believe can add new ARR growth. So I think it's really that simple, is go deliver accelerated ARR growth as a core metric for success.
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.
All right. Thanks, everybody.