Right, well, good morning, everyone. Thank you for joining us today. I'm Howard Wilson, the Chief Financial Officer of PagerDuty. We're delighted to be able to share with you a little bit about PagerDuty, the company, what it is that we do, and also why we believe that we have a very exciting opportunity ahead of us in this AI-enabled enterprise and environment that we're moving into. I'm sure that all of you too, this morning, when you started your day, you used some sort of technology. Using your phone helped you connect to a whole series of applications, whether it was checking the weather, or listening to music, or doing some banking, or booking a trip, or checking in for your flight.
All of that technology is part of a very complex environment that we rely on and that companies rely on every day in order to support their business. It's how companies think about their growth. It's how they think about delivering the right customer experience. This is all an integral part of how we live today. But behind that technology that you're using on your mobile phone, for example, is a very complex web of infrastructure that consists of databases, hardware infrastructure, software infrastructure, applications that are often spanning the globe and showing up in different parts of the world. Now, for you as an end user, having a perfect experience is really important, and what PagerDuty does is really about ensuring that we are there to help companies manage their digital operations so helping them manage that infrastructure that sits behind delivering that perfect customer experience.
Now, the way that this shows up a lot of the time is that companies are wanting to ensure that not only are they able to respond really quickly when there is an issue, an actual problem that you encounter, but also they want to be able to see that they are able to respond really quickly before an issue arises. So that is why PagerDuty has been a global leader in digital operations management now for over a decade in helping companies being able to provide the operations management and operations resilience that they need. So what does this look like? So I have a chart here which gives you a view on some of the complexity that's involved, and just to help people understand a little bit about what that environment includes.
So today, on the left-hand side, you'll notice that today we're dealing with a situation where you have a number of ways in which applications are being used, customers are interacting, you have development tools that are all in play. And these effectively become the mechanisms that are often used either to build or the mechanisms that are used to deliver certain experiences. And what we've done is we put a whole number of different mechanisms in place to help companies monitor what's going on in the environment. So we talk about systems of visibility. And today, we have products that fall within the category of monitoring and observability, security. We also have Signals that we're collecting from customer service. And then also there's the activities that are occurring as applications and code is being built and developed.
And so PagerDuty really sits at the center of this as a system of intelligence and a system of action, really orchestrating across the environment. So our ability is unique in terms of being able to ingest Signals from over 700 different integrations, both API integrations and more recently using the Model Context Protocol, being able to ingest integrations from other AI agents. And we are able to then ensure that we can orchestrate the right response, essentially getting the right people, or the right machines, or the right software all engaged in terms of being able to deliver that right experience. We've been around since 2009. Our history started off as a company that was focused very much on on-call management.
But over the years, we've been expanding that to include incident response and automation and customer service operations until we eventually reached the stage where we today have this full platform that will allow you to manage your operations. When we think about the PagerDuty Operations Cloud, what makes this really valuable to customers is the fact that we are able to integrate both automation and AI into our environment, but on a platform that is extensible, that has a large number of integrations, where we have a proprietary foundational data model, where we have embedded AI and machine learning into our product, and also where we're able to support different mechanisms, both the no-code and pro-code environment, but also with a high level of resilience and a high level of scale from an enterprise perspective.
When we think about the Operations Cloud, there are effectively four pillars to this. The first is incident management, which is really about being able to say when there is an issue that has been detected, that you're able to respond really quickly to that issue and orchestrate the right people, and you can operate at machine speed. We have PagerDuty AIOps offering, which is really about managing events and being able to make companies more efficient in their ability to deal with the large flow of Signals that are coming into the environment, and then we have automation, which is able to be called on demand or be triggered by certain events, or that's able to be initiated by humans or by agents with a view to being able to automate certain actions. Excuse me.
And our most recent product family is PagerDuty Advance, which is really our generative AI offerings, which includes a number of different characteristics, both for helping responders or operations teams work within an environment using generative AI. But also then we recently launched four agents that actually operate just as humans would, getting certain activities done, working in combination with other responders. And then we have a bunch of other add-ons. One of them, most notably, is our customer service operations, which really allows us to not only capture the Signals that are detected by observability products, but also capture the Signals that would be coming from a customer who might be using a chat application or email to be able to interact around an incident that they see or a problem they might have.
So today, if we look at the way in which we sell the product, we have these different individual elements that can be acquired by our customers in terms of either the incident management component or our automation, AIOps or Customer Service Ops, and PagerDuty Advance. What we are looking to do with some of the recently announced pricing changes is create the ability for customers to be able to, in fact, purchase the entire Operations Cloud. So essentially, what they would be doing is getting access to all those different products and being able to then use those products in a way that makes most sense for them to get the most value within their organization. And that means that you end up with a high degree of flexibility for customers. We've been testing this out with a number of customers over the last few months.
And that allows us to also transition to more of a usage-based model. So when you look at this, the overall picture around the PagerDuty Operations Cloud, this gives you some idea of what that ecosystem looks like that we end up being at the center of. So you'll notice that there are a variety of different categories of integrations that are reflected on the slide. Monitoring and observability is obviously one that's well known to folks in terms of products like Dynatrace, Datadog, New Relic. We also have integrations to security products, DevOps and lifecycle, which is really around us being able to get engaged before code is deployed or being able to help deal with tracking root cause. Cloud infrastructure providers, you'll see on the customer service side that, again, we have integrations, workflow applications to ServiceNow, Salesforce, and Zendesk, and a bunch of others.
And also then in terms of being able to facilitate communication, things like ChatOps collaboration. We integrate into the likes of Zoom and Teams and Slack. Of course, we often integrate into systems of record like the ITSM products. More recently, our efforts around integrating into AI products like Arize and Glean, where we're able to then, in fact, ingest those Signals in order to, again, drive action. The way that I always like to think about it is PagerDuty really ends up being that system of orchestration and system of action, which is really about trying to ensure that we are allowing our customers to be able to provide the best operational resilience that they can in order to make sure that they're always ahead of any particular issues that may arise.
So when we look at the opportunity for us today, we've looked at our total addressable market in terms of the number of potential personas or users of PagerDuty. A number of different users. Our core group historically has been developers, and then infrastructure and operations resources. But certainly, customer service and security operations are other popular users of the PagerDuty platform. We have over a million users on the platform today. But if we had to count up all of these users, there's 87 million potential users of PagerDuty. Why this is important is often, although there have been many incidents that have the high-profile incidents get publicized when they sort of maybe slow down with respect to airlines being able to operate or any of those types of issues. But just for the average enterprise company, an incident is a very expensive event.
So if there is a customer-facing incident, this could easily cost up to $800,000 per minute. In fact, in some companies, it's a lot more than that. But when we did a study, we were able to determine that across a range of enterprise companies that this is what that cost could be. And if companies are running into a few of these incidents, and these incidents do occur, they're not always visible to customers, but these incidents occur. There were just a couple of those that were happening each month. You would see that that could very easily add up to a large number. So there's a very important value proposition associated with this in terms of the ability to, one, protect revenue and reputation for the customer, and also be able to help them drive efficiency within their organization.
Forrester published a total economic impact study, and this highlighted that for a large enterprise, that choosing PagerDuty would help them very quickly get to a payback position and a strong return on investment. So the study revealed that there was easily a 249% ROI over three years with a payback in less than 12 months. So the economic value for our customers is clear. Often, what they're paying for PagerDuty, the cost of PagerDuty would be covered by them simply having one incident that they're able to avoid or manage more effectively. And what then sets us apart from the competition? One, our history and legacy has been very firmly rooted in understanding operations management and what is required to be able to drive operations resilience, which has become so critical. And that intelligence becomes a key part of how we continue to innovate and expand our platform.
The other aspect that is key for us is really about being able to support enterprises. So today, we do have over 34,000 companies on the PagerDuty platform. Of those, 15,400 roughly are paying customers, but we have a large number that are free. We have customers from very small SMB all the way to the largest Fortune 50 companies. And even within that mix, it's key as we focus on the enterprise that we're able to support their requirements. So that enterprise-grade level of offering in terms of being able to scale to tens of thousands of users, being able to have a strong governance framework, being able to support the likes of SOC 2, also FedRAMP, and a number of other characteristics that we use to deliver our infrastructure in a very robust way. We also have a unified ecosystem from an AI perspective.
Not only do we have our API integrations, but the work that we've already done with our MCP allows us also to ensure that we are able to facilitate agent-to-agent interactions, and as I mentioned, that really powerful time to value. One example here that just shows a little bit about how customers can grow with us over time. This is a large financial services company, and this really just illustrates a little bit over a period of years how that growth can happen, so this was a customer that initially made a purchase back in 2019. Their focus then was really around managing an application that was being used by high-net-worth individuals within their wealth application space. They were then increased.
They expanded that into other groups and into other technologies successively over the following years and eventually made PagerDuty the standard in terms of incident response within the organization. And so that model is how we've managed to grow customers. We have more than 850 customers that are spending more than $100,000. We also have a large growing cohort of customers that are spending over a million dollars with us. And all of that is through their ability to progressively grow with using the Operations Cloud platform. So very quickly, just in terms of our PagerDuty, our Q3 overview, these are the results for Q3. Revenue was $125 million, which represented 5% year-over-year revenue growth. We delivered non-GAAP operating margin of 29%. We have continued to demonstrate our ability to improve our operating margin and deliver strong free cash flow.
And then I'll just skip on to giving folks a little bit of view in terms of the most recent update we did in terms of our long-term model, which is really a view of being able to maintain gross margins within the 84%-86% range, which has been our target since we went public. But also then, as you can see, we've been progressively improving our operating margin on a non-GAAP basis with a view to our target margin being 30%. We have also said publicly that we will be GAAP profitable for the full year this coming year. And so we continue to work through how do we drive efficiency within the business and continue to drive leverage through the organization. So just a summary of a few investment highlights before we get to questions.
So first of all, there is no other company that is able to provide what PagerDuty does in terms of that real-time operations management, being able to go all the way from detection through to the triage, through the management of an incident, through to the actual learning of that incident, and being able to drive a continuous process of improvement. There's a large opportunity ahead of us. We're still early relative to the opportunity, and especially within this environment where complexity is growing as companies are deploying more technology through the use of AI, the opportunity for the fragility that could exist in that environment and bring about failure means the ability to respond is really key.
We have strong adoption in terms of our enterprise customers, well-known name brands, more than half of the Fortune 500, large percentage of the Fortune 100, growing number of the Global 2000, or companies that are using PagerDuty every day. And we've really picked up our own pace of innovation. With even the release that we did within the H2 of this last calendar year, we were able to deliver over 150 capabilities to our customers, including the delivery of the four agents. And we continue to work to drive growth within the company, but doing so whilst also ensuring that we are delivering strong operating margins. So with that, I will pause and open up for questions. And I know we'll see if we have a few questions here as well. How are you doing?
Thank you. Thank you.
Good to see you again.
For the group, I'm Mike Cikos here with Needham. Thank you to the PagerDuty team for coming. I know you guys have been supporters now for multiple years. Apologies for running late. I think the unsung hero of our conference every year is the elevator. I have some questions to run through on my side over the next 15 minutes. If you guys have any inbounds, please make this as interactive as possible. The first thing I wanted to tackle was the product innovation. I know, no surprise, it's going to skew towards the AI agents that you guys have under that, call it Advance pillar. Right? I'd love to get feedback. First, as a reminder, when was that launch? Secondly, what has the initial feedback loop been from those customers?
Yeah, sure. So in terms of our agentic offerings, those were progressively rolled out into early access through 2025 and went generally available in the back half of the year. The response has been really positive from our customers. We also, because we're a platform that companies rely on, we have to ensure the quality of what we're doing from an AI perspective all the time so that they can really rely on what we deliver. So it's done in close partnership with hundreds of customers so that we're able to ensure that by the time we make this GA, that we have a high degree of confidence that it's going to operate as expected. And if you look at the agents that we put out there, we have one agent that is a Shift Agent, which might sound like a relatively trivial thing.
But when you are managing an operations team in an operations environment where you're having to ensure that you have the right people available at the right time, the scheduling activity can become fairly complex. And using our Shift Agent, we're actually now able to just allow folks interacting through an interface like Slack to be able to update their details around their availability and automatically integrate to calendaring functions, which then means that you always have this QA across, for example, your on-call schedule. So that's one of the uses that we have.
The other is our SRE Agent, site reliability engineer agent, actually almost takes on the role of another person on the team that is then able to perform certain functions, whether it's running diagnostics or doing the initial analysis on a particular incident or providing a summary of what's going on as that's happening, or in some cases, initiating the automation to close it, so we use this concept of when you think about the problem space that we work in, there are problems that are often well understood that repeat themselves often within technology environments. There are problems that are partially understood where there are elements of it that are well known, and then there are others that are new or novel, and what we found is that when you have problems that are well understood, that's an area where our agents can just operate independently.
So a Signal can come in, an incident gets created, the agent can actually pick that up, and they're able to work through what's happening, recognize it, and close it, get it done. If it's partially understood, it's a situation where maybe the agent is initiated first in that process and then recognizes that, look, this has got some of the similar characteristics, but now it needs some human in the loop. And in the case where it's new or novel, this might be an area where it gets identified by our platform as being, hey, this is something we haven't seen before. And so a team gets involved, and they may then initiate using AI or an agent to be able to help them in different parts of that situation. So we have a variety of different mechanisms through which we're using AI.
The other agents that we have, we have a Scribe Agent, which effectively sits like somebody, a note taker, if you like, on an incident, helps drive the summarization of those things, also helps drive the ongoing. We have technology from an AI perspective to help drive stakeholder notifications and things like that, status updates as part of that. And then we have an Insights Agent, which is really around sort of sitting in the background, reviewing what's happening with your operations and coming up with recommendations how you can improve your operations.
Excellent. And if I think about those agents specifically, we're a couple of years out of the chute since ChatGPT hit the airwaves. I feel like agents are still relatively new. Where are we in terms of the adoption curve? And I'm more thinking about it almost from the cybersecurity side of the house, but what are you guys doing from a security compliance standpoint to help alleviate maybe some of the pushback you would get at the corporate level?
Yeah, sure. So as I mentioned, because our whole story is really about resilience and making sure that what we do, our customers can really rely on it. I often say that trust is our strongest value proposition because customers feel that they can rely on us. So that's why from the agentic perspective, we have taken a very collaborative approach with our customers. We do our own monitoring in terms of how our agents are functioning and operating. We use products like the products that we integrate to that our customers will use to integrate to in terms of being able to validate for things like model drift, all those sorts of things.
So there's a fairly robust infrastructure around how we actually manage those agents. I would say that the adoption of the straightforward generative pieces was easy for people to go to because they went from ChatGPT to being able to interact with our platform via a messenger type of messaging platform. But the agents, of course, it's been a more gradual adoption. But certainly, because we've had so many customers involved from early on in the piece, they've been able to really validate and test that with us.
Right. And you're building up your flywheel of customer testimonials as well.
That's correct. Yeah.
When advanced or those agents are introduced, does that in any capacity slow down the sales cycle because more constituents are brought to that procurement table?
Yeah. So I think at the moment, it's been interesting. As you say, over the last two years, there was this initial reaction from procurement teams around, we don't touch anything that has AI in it. I think that has had to shift. And so there is a more open posture with respect to that discussion with procurement teams around that. We're also able to be very clear around exactly what our AI does. And so it's all about giving a level of transparency to customers that helps build their confidence in exactly what you're doing. So I think that's an area that will continue to evolve and we'll have to navigate through as an industry as AI continues to evolve and become so embedded in everything that we do.
One of the things I've been spending more time on, I feel like SaaS, we almost took it for granted, but it's become much more prevalent, is the idea of how do you go through price discovery in an agentic era where we do need to think about what those behind-the-scenes costs are from an infrastructure standpoint. So can you walk us through how you guys went through that process?
Yeah. Yeah. So this to me is really fascinating because if I think about the world that we operate in today, we fully expect that companies will use more and more of AI and more and more technology in terms of even managing the operations environment. And so hence our shift to being able to deliver the AI that they needed and these agents because we see a world where agents will be working in combination with humans. And the agents will gradually take over more and more work over time. So from our perspective
We've had to think about this with respect to how do you ensure that you're creating an environment where these things can operate together, and how do you ensure that you're also creating a future mechanism to be able to monetize your offering. Part of what we've had to look at there is also ensuring that you can do this in a way that is scalable from a cost perspective. We have a well-tuned, finely tuned infrastructure which has been operating at high gross margins for many, many years. We've taken that into account. There's a lot of thought that goes into selection of what we would use as underlying technologies to help us deliver that AI.
Good to see that you guys are able to, it looks like, at least maintain the gross margins that you guys have. You're talking about the 84%-86% facility.
That's correct. Yeah.
Okay. Sorry. If I shift gears over to the flex pricing, full access to Ops Cloud, as a reminder again, when was that rolled out? Do we have initial evidence in support of, I guess, broader adoption of the platform? What has customer reception been on that as well?
Yeah. So we feel that any pricing change needs to be done in consultation with customers, so with a view to being able to deliver a good experience and ensuring that there is a fair exchange of value. What we've done is an approach where our flex pricing was essentially the first model that we introduced on the road to being able to get to full operations cloud pricing. So what we do with flex pricing is essentially give customers access to the full platform. And this has worked out well in terms of driving product discovery because previously, customers had to buy, they'd buy incident management, and then they'd buy AIOps, and then they'd buy automation.
And each of those was separate, and they were not visible to them until they bought them. Now with the flex pricing, what we do is we give them access to all the products, and then we essentially allow them to manage their usage across all of those products, so reception has been good. I think the first prize for our customers is the flexibility, so the fact that they're now able to have that broader access, and it removes some of the friction around, well, oh, I need to go and add a user, or I need to go and add that product.
It's because it's there. It's available, and I can now use that, and then I'm able to use that within the annual subscription that I have, and we drive sort of the usage through that model, so we're looking to a more broader, and we've done that for a smaller set of customers. So we've almost kind of been hand-picked customers as we've gone through that process. But we're looking to a more general release of that pricing model in the early part of this year.
And just to make sure I'm clear, because there's a number of companies that we're working with that are doing a similar contracting vehicle, if you will. But is it, hey, I am going to commit to a subscription, so it's still a ratable subscription for you guys.
Correct.
Let's say it's $100 a year for a two- or three-year term. Then I have a rate card that I can execute against, and XYZ product is going to charge me $0.10, whereas ABC product is going to run me $0.05. I'm going up against this rate card.
That's exactly the right concept because we have different things that are measured. So we have actions in terms of PD advance. So whether you're running generative AI queries or using agentic, you are essentially using actions. And then if you're using our automation products, then you're driving workflow execution or tasks. If you are using elements that deal with event management, then it's processed events. So there's a variety of different metrics that are behind that. And then there is almost like a rate card in terms of how you would effectively be utilizing your overall subscription fee.
And then the hope is Mike Cikos or Team Needham sees the value, we consume faster than planned. And then let's say it comes to October, and then we have to top up on it.
Then you buy it. Yes. You add co-term to the end of your agreement, and part of what we've done is the work to instrument the platform so that it's really visible to customers so they can see exactly what they're using and what progress they're making so that they're able to then proactively move to extend or manage their usage.
Are these expected to be sold only on a multi-year time frame? And the reason I ask is to what extent we can see customers burn through credits and then we get that early renewal is the wrong term, but they get that top-up effect, which would then benefit them.
So the model that we have is that even if they do multi-year, they effectively would have an amount per year. So it doesn't expand through that whole period. So there would be a mechanism around topping up if needed while they're within that subscription term relative to kind of each individual year as they're making progress through that. But the expectation is that we try and make sure that ahead of time we work with customers around what their needs would be to this stage, trying to make it as accurate as possible given what we know about how they're operating today.
It's more of a feels like a pull and push as far as like, hey, if I burn through my credits, there's not necessarily punitive pricing, but if I make a larger commitment.
That's correct.
We know customers don't like these large surprise overages. So we've intentionally thought about how we build a mechanism where we can then actively work with them when they're either approaching their limit or getting to their limits to be able to then naturally extend that with an incremental purchase. Okay. And we have time for maybe one or two questions. Otherwise, happy to keep going. All right. On the model, good to see what was it, the Q3 highlights, 5% revenue growth, 29% OM, targeting 30% OM. Can you just talk about how you think about balancing that growth versus profitability?
Yeah, sure. So for us, from a growth perspective, we've certainly our focus on how do we continue to accelerate growth into the future. We, as a company over the last couple of years, as we've like a number of SaaS companies, have seen some of the user or seat-based contraction happening in terms of companies staying with us but reducing their number of users because they've had headcount reductions or along those lines. But what we've been able to do, and hence the shift even from our pricing model, is really about giving us resilience with respect to that. So our focus really is using our pricing model to be able to help drive top-line growth.
And we're getting closer and closer to our target operating margin. And our view is that we continue to see ways in which we can drive some leverage from a sales and marketing efficiency perspective and some G&A. We're wanting to maintain a high investment in R&D because we think that the innovation that we are delivering still warrants us continuing to focus on that. But we certainly have a long-term view that is now we're shifting not only to being profitable from a non-GAAP perspective, but also being GAAP profitable.
Excellent. And then probably the last one we'll have time for here. But if I'm looking at the amount of M&A across the broader landscape, just because you guys touched so many different pieces, Palo Alto announces the pending acquisition of Chronosphere. You have ServiceNow acquiring Veza, Armis. CrowdStrike just announced Seraphic, Signal. There's a lot of M&A. You guys have $500 million plus on your balance sheet at this point. How do you think about M&A or potential tuck-ins?
Yeah. So we've always taken a view on M&A around thinking about what will expand our product footprint, what is going to be. And the acquisition we've done to date has largely been driven around seeing how do we expand the offering to our customers and often things that are on our roadmap. And we'll continue to take that, look at it through that lens in the first instance, is looking about how do we drive the enhancement of our platform. So more technology-driven.
Excellent. And with that, we'll leave it there. But thank you very much.
Thanks so much. Thanks, Mike.
Appreciate it.
Cheers. Thank you.
Thank you to everyone for joining us.
Thank you.