Welcome everybody. Thank you for joining. It's a great pleasure to be here with Olivier Pomel, CEO and co-founder of Datadog. Olivier, thank you for joining us.
Great to be here.
I think we'll start off on a, on a great note. This time last year, you were on stage with us. We highlighted that you were one of only four enterprise software companies growing mid-20s plus. We said something very rare was happening at Datadog. A year later, looking back, I think, maybe we understated that a little bit. You're now growing over 30% at a $4 billion scale. Can you help us in layman terms understand the problem that you're solving out there, why it's so critical for customers, and what you think the phenomena that are fueling your growth at scale?
I mean, look, we do observability and security, we sell to engineers and product teams that are customers. We help them understand whether the software, the services that they're shipping are actually working, they're working as appropriate for their customers, if they're fast enough, and if those products deliver the right business value for them and for their customers. That's like fundamentally what we do. We serve every type of company, you know, from the tiniest startup, the newest AI company, all the way to the largest and the oldest enterprises, with pretty much everything in between. The reason why we see demand, I think there's two aspects. One of them is we're in still fairly early in a super cycle of digitalization and cloud migration.
You know, we started the company 15 years ago. It was right at the beginning of the cloud migration. That's still ongoing, and that's still a big driver of our business and still something that's going to keep going for, you know, many, many years. Then the second aspect is, you know, I'm sure it's not lost on anybody in this room that everything is changing with AI. There's a lot more that is being built, that is being shipped in software. There's a lot more interactions that are being automated. All of that creates new kinds of complexities and new kinds of surfaces that and also frankly, quite a bit more infrastructure. All of that needs to be managed, monitored, observed, secured, and that's what we do.
Yeah. Yeah. You know, I want to say you guys made it easy to come up with some of these questions because you had such a good Q1. Yeah, I think one of the best prints we've had this earnings season, 32% revenue growth rate, as we mentioned, that $4 billion scale, accelerating for the fourth quarter in a row, largest sequential ad in a while. A very healthy raise. Really just a lot of strength across the board. I'd love to kind of decompose that a little bit. You know, where are you seeing those pockets of strength? You know, I think it's more than just AI, right? Maybe what surprised you and what do you see kind of persisting going forward?
As we said on the call, I think, on the earnings call, we saw acceleration across every single part of our business. We saw acceleration with the brand-new AI-native companies, whether they're, you know, small or, you know, very big. We saw also acceleration with the rest of our business, which is even more interesting. The non-AI part, all the companies that were around before AI starting taking over. I think the drivers are a little bit different there. I mean, on the AI side, obviously we see that part of the ecosystem blowing up, like the AI is getting into production. Some use cases, such as coding are very real and scaling very fast.
This is feeding a number of large model companies, but also all sorts of application companies that are being built around that. I think drivers are fairly clear there. What's even more interesting for us is that the bulk of our business, the rest of our business that is, that existed before, you know, AI became a thing, that business is accelerating as well. You know, part of that is those companies have to understand they need to modernize and modernize faster so they can be ready for AI. Part of it is just that we're still very early in cloud migration.
There's a number we like quoting, you know, which is that I think Gartner has this report every year on the market share in ITOM and a number of other fields. We're the leader in observability. We're the number one there, but we still have only 13.6% of the market according to them. This tells you how early it is in the market for us, how much opportunity there is ahead of us, even factoring out all of the new developments with AI, all of the new explosion of demand we can see there. We think we're still early in a super cycle there.
Yeah. Yeah, definitely some threads we'll have to pull on in a little bit. You know, one topic that's been out there for the entire time we've covered you all is this concept of customers doing it themselves, you know, using open source now by putting it. It's been persistent even though none of the data points really supported that much. I think your all's retention is stellar. This last quarter, you guys started talking about getting the hyperscalers as customers, right? You know, if there was anybody who could do it'd be these, they do have it, right? They have these tools within their own ecosystem.
If we think about it that way, like if even the hyperscalers conclude that they need to buy from you rather than, you know, use what they have, you know, I mean, was that surprising to you at all? What do you think that says about the value proposition that you have and even the moat that you've built?
I mean, I wouldn't call it surprising, but it's definitely a very interesting proof point.
Yeah.
You know, because the way we see it is, reasons to build it yourself are usually mostly cultural. That's because you want to build yourself. You want to have your team do it, or somebody on the team wants it so bad, and nobody else around them wants to, you know, prevent them from doing that. That's cultural. Typically, you don't get nearly as good of a result when you do that. You know, for one thing, it takes a lot of time and a lot of focus that you should spend somewhere else. You know, typically when you try and build it yourself, you end up with a solution to, in two years, you know, from the, to the problem you had last year. That's typically not great.
You also end up with, despite what you might think when you go into it, much worse economics. That's not a fantastic solution long term. That's something that many of the hyperscalers, again, are happy to live with because for cultural reasons they want to insource everything. What we see happening now is , there’s an extremely competitive situation right now around the development of AI.
I think it really focuses the mind for many companies. You know, when they realize, hey, wait, actually, instead of waiting two years for this, we can have it now, and it's going to run better and cheaper in the end. You know, what are we doing? What should we be doing there? Again, our business model in the end is not to serve the hyperscalers. There's not enough of them. They are too fickle also for cultural reasons, as I mentioned earlier. For us, it's a great proof point. If even the companies that have virtually unlimited access to top talent, and a strong cultural bias for building, use our product, you know, it means that it really doesn't make sense for anybody else to build their own.
Yeah. Yeah, I think that it's an excellent Iron Man argument for what you guys have being differentiated. I recently had a conversation with a partner where he made a very interesting point that I hadn't thought about, which, you know, we're asking about vibe coding, all that, their comment was AI has accelerated the pace of code being generated, it's increased the competition because everybody can do it out there. The effect that it has is the opposite, is now if you don't use something that's really trustable and powerful like Datadog, you might actually be hindering your, you know, development process, that's your bread and butter. In fact, the effect is that it makes people more reliant on something like Datadog. Do you see that dynamic?
Yes, definitely. Look, the, like most of the . Actually, pretty much all of the top eight or 10 coding companies or vibe coding companies, whether you're talking about the models people use in production or the in large companies or the products that are built more for the consumers, all of these companies use Datadog, you know, behind the scenes.
Yeah.
That tells you about the very specific need there is in there. Look, at the end of the day, we see there's so much more stuff that is being built. It's being produced so much faster, that by definition, the folks who produce all of that have no idea, you know, how it actually works.
Yeah.
Like, that's the least understood thing about productivity, is that the more you increase productivity, the more complexity you create because folks manipulate way more things in way less time, and as a result, doesn't go through their brains, and they don't understand what's going on. They need help to actually understand what happened, to actually make sure it works properly, to make sure it delivers what it's supposed to deliver for the business in the end, to make sure it keeps working with the when everything keeps changing around it, to make sure it's secure.
Yeah.
That's what we do.
I think great point to kind of get into the AI-native cohort that you guys have. You know, 22 AI-natives spending more than $1 million, five spending more than $10 million. You know, a lot of the focus tends to go on some of the larger ones, but I think the impressive part is, you know, how diverse even that set is. It's got foundation models, code gen, vertical AI. When you look across your book, where in the value chain do you kinda see the strongest opportunity, and what is the differentiation you have for those customers?
I mean, those customers have a like a high pressure to deliver a lot and deliver very fast. Look, the core of what we do is what they all start with, you know? We cover everything from end to end, from the bits that goes through the CPU, the network, the GPUs, all the way up to the end users, you know, how they're using a product, whether they're coming back, whether they're completing what they're supposed to be doing, how much business value they generate, you know, for you in the end. We cover absolutely everything in between in a way that's fully integrated.
If you go to any of our competitors' website, they all say they do it. The reality is it's actually really, really hard and really differentiated to do it well, and that's why those companies all use us. You know, when you think of the investments they're making, whether it's on their engineering teams, their research teams, their, you know, GPU fleet, all of that goes to waste if you do not understand how to build the right thing. Then ship an experience that works for your end users.
The need to perform is the highest for them.
Yeah.
I think one of the most striking comments that you had on the Q1 call was the change in posture with regards to training. I think the quote was, "Last year we said training was not really a market for us yet. Now we're actually starting to see it become a market." You know, that's a big step function change, I think, in people's minds. When did you start to see this happen? Was it, you know, maybe you don't like the word surprise, but was this surprising to you guys, and what do you kind of see about that opportunity looking forward?
This one was actually a bit of a surprise, yes. It made intuitive sense to us before that training might become a market.
Yeah.
Because we saw , look, a while ago, training was mostly pre-training. It only made sense for 5-10 companies in the world to do it. It was large scale. It was completely homegrown. Not a great fit for building a product, typically. We saw that the technology changed quite a bit. You know, models went from being mostly pre-trained to being largely post-trained. The post-training was becoming increasingly, like specialized to different types of verticals. The stacks that are used for post-training also are becoming richer and richer.
You know, now when you post-train a model, you're going to run all of those different environments. Basically, you're going to run all the applications you can run, simulate behavior in those applications, capture what comes out. You end up running way more complex stacks for doing that. Then we saw, you know, instead of having, you know, 5-10 companies doing that, now there were 50-100 doing that.
Yeah.
You know, intuitively it made sense to us that something might be more interesting there. We were surprised, though, to see a number of different customers, including multiple hyperscalers, come to us for training at about the same time.
Yeah.
Which tells us like there's something happening and there is a, there's potentially, like a, the emergence of a new market there.
Yeah.
Again, too early to call it because the technology's changing fast, the markets are changing fast. If we get into a situation where we go from 50- 100 of these companies to 500- 1,000 to maybe more then that becomes a really interesting market and there's a real problem to solve and, you know, and we can do it. The other thing that's interesting if you look at the evolution of the technology is, we're probably going towards models that can learn on an ongoing basis. You know, instead of training a model, whether that's pre-training, post-training, if you keep improving your model with online evaluations and online improvements, that becomes really very much an ongoing production concern.
Yeah.
Something that you can repeat, you know, in many different companies over there. We are hopeful, interested in that.
Yeah.
It's not the core, like, it's still small compared to the inference business, compared to everything else that happens in the stack Datadog customers are running. It's an interesting new green shoot, I would say.
Do you have a sense You said they kind of all came to you at the same time. Do you have a sense of what the trigger was from their perspective? Was it a lot of this stuff going to post-training? Is that just something that makes more sense for you guys, they don't have solutions for it? Like, what was the catalyst?
I think, you know, a lot of these companies are on a bit of a similar clock, you know?
Yeah.
You know, everybody woke up to ChatGPT at some point, and then there were a number of internal efforts started in a number of different places. You know, some of these efforts work, some of these don't, you know. You have these cycles that companies live through and, you know, when something didn't work exactly the way you want initially, then you try and reset and do things differently. I think part of it is that. Part of it is companies being somewhat aligned on that, on the same because they all compete in the same market.
Yep. Okay. Let's think about the fact that, you know, blossoming out of AI, right? Where we're seeing this move, like you said, beyond just a handful of companies. I think there's been a lot of indications from people across the stack in the software space talking about that, hinting at it. You know, I think maybe that's underappreciated by a lot of people. Like I said, a lot of focus just goes to those kind of very well-known LLM companies. Could you provide your thought on that? You said, you know, it might democratize quite a bit more, right? Both on the training and inferencing side, whichever one you want to dig into, like how are you seeing that democratization happen? Are we, you know, literally in the very first inning of that? How do you see that curve growing?
I think we're still super early. I think the focus for most companies or most of the users of AI today is to make sure it works.
Yeah.
Everybody is getting from, like step one is let me make it work once, and then step two is, okay, so now let me deploy that at scale, you know, across my company, across, you know, similar use cases and things like that. I think we're still very much in that phase. We're not at all in the phase of, okay, so now what else can I build on that? How do I rationalize it? How do I optimize? That's for later.
Yeah.
That later might come, you know, sooner, you know, if we see the explosion of the AI company's revenue, you know, continue at the rate it is right now. That money's coming from somewhere and, you know, it's, there's probably going to be a push for rationalization sooner rather than later. The mode right now is still very much let me get it to work.
Yeah.
Our mental model for what the market looks like in the end is that it's similar to what you see, you know, in the overall cloud infrastructure or even the database market. Like database market, you have a number of options out there. You can have a, you can buy a closed source database that you're going to run yourself. You can buy closed source cloud databases that you don't run yourself that are completely black boxes or turnkey and, you know, and if somebody else is running them for you. You can use open source ones that you run on infrastructure somebody else provides for you. You can run the open source ones on your own infrastructure. You can build your own databases.
All of those actually coexist. There's reasons for all of those to exist, and customers typically are going to mix and match a number of those. They're going to have different cloud providers, they're going to have different database providers. They're going to have a bit of everything in there. My guess is we're looking at a market that looks like that for AI inference.
I wanna get to non-AI cohort accelerating, which I think is again, a big part of the story but one last one. A couple years ago, we kind of gave you this analogy, at least that was our thinking, which, you know, training is like a bottle rocket and inference is gonna be like a steady compounder. I think at the time you kind of agreed with that framing. You know, we touched on it a little bit, but do you think that maybe that framework has changed a little bit where the training might be a little bit of more of a steady compounder on its own as well?
It's inching closer to being an ongoing recurring thing.
Yeah.
It's still a little bit one-off. Like what we see still, you have these large training runs or these small and large training runs, but they're still episodic runs. Like you do a run, then you do another one, and then maybe nothing happens for a few days, then you do another one. These also tend to be still somewhat, like custom coded. Like, you know, you don't have like a standard way of doing it that every single company out there is using, you know? It's a big improvement from the way it was, like a couple of years ago where again, only a handful of companies were doing it. It was extremely, you know, hand-coded. Absolutely not production-minded in general.
Like it was, you know, kept up basically by people babysitting those training jobs, you know, night and day. You know, being like very large pre-training runs and then nothing after that. We're still not yet at the point where it's an, it's an ongoing, always on, every day of the week. Live with customer data kind of operation. I think we still don't know whether the market's going there. We think it might be, but that's for the future.
Got it. Moving to the non-AI cohort, the, you know, core customers, they've been accelerating, I think, every quarter for a little while now. As you've said, as we've said many times, you know, your story's about a lot of customers and a lot of things going in the right direction. It has been accelerating, so I'm sure the pieces are kind of similar, but what's changed? I mean, you know, a y ear ago to now, what is going on in those core drivers that's pushing that a little bit higher?
I mean, look, we do have some proof points that those customers are starting to adopt some AI in production. It's still small relative to the size of those customers and still a small driver of our growth there. You know, we see traffic, for example, in our MCPs and traffic also to our LLM Observability product. We see a very strong growth. I think we release some numbers in the earnings call on that. That's still a small amount, just gives us an idea that these customers are moving there. The bulk of it for these customers is that they're still modernizing, moving to the cloud. We're getting to more of them because we have successfully built up sales capacity. We go to market in more regions and more segments. We get great return on investment on that.
We've been expanding the product, the set of products we can sell and the categories we sell into, and we have enough of those products that are reaching product market fit. Getting to inflection points in their growth and where we, you know, we basically see great adoption and consolidation from customers in our products. You know, I would call it the boring side of the business. You know, it's not, you know, it's not AI revolutionizing everything.
Yeah.
But it's a predictably high return on investment, very buildable part of the business where we keep building those products. It makes sense to our customers, we keep investing in the sales capacity because we are still early in what is a very large market opportunity.
I wanna touch on the Bits AI SRE. Even before you guys had launched that, you know, I talked to a few people and they're pretty excited about it. Saw a lot of value in it. I think you guys have 100,000 investigations since launch, 2,000 customers, you know, really going well. Love to hear what you see on that front in terms of customer utilization, and one thing we do hear is that the more autonomous the technology is, the more powerful it is, customers do have a bit of a challenge of how do you integrate that into the workflow. Right? You kind of have to change how you run your business. Is that something you see as a real hurdle? Are there customers out there who are kind of really pushing the edge there?
I do. We see a ton of pull for it, and it gives us a lot of areas to develop in the product. You know, one thing we keep hearing from customers is, "I want it to go faster into auto-resolution." You know, initially we were worried that if it does too much, it creates a trust issue, you know, and it's hard for customers to control. You know, with AI taking hold pretty much in every single part of our customers' businesses, I think that train's leaving the station now, and folks are getting comfortable with automation, they're pushing for more and more end-to-end automation with it. Maybe, okay, it's fine. You told me what was the issue, why it was there, who introduced it, how to fix it.
Give me a button to fix it, or maybe even better, you know, fix it for me and then tell me about it. We're working on that. A second area of pull we hear from customers is, "Okay, this is great. This works really, really well, but I want it to work across my other systems." It should work to solve issues within Datadog, but also I'm using some other logging system. I'm using some other security tools. I'm using all these different things, is I have a lot of open source. Can it work across all of that? We're also pushing so that Datadog actually connects to all those, and Bits can investigate across all of those, you know, different system. Another area we're investing in, is in getting more proactive and predictive. You know, we don't have to wait until you run into an incident.
Yeah.
To, you know, investigate, maybe solve it or prevent it even. Like there's quite a bit of R&D that goes into that, you know. We're, we're developing our own models for that. We've released actually 10 days ago our the second version of our time series foundation model. It's called Toto, and you can look it up. There's a blog post and some results that we've published there. This model is open weight, but we're also, so it's being adopted by the community as well.
What's really exciting there is, it's a time series model. It's general purpose, even though it was trained almost exclusively on observability data, it performs extremely well on everything else. Actually, it's state-of-the-art across all time series use cases, not just observability, it is a competitive field. You know, every single large company has time series models. It's very exciting for that reason. It's also very exciting because it's a first time series model that shows scaling, meaning we can train it with more data and train larger models for longer, we get better performance.
It used to be that, you know, it didn't work for time series models. We all know that what started the current AI revolution we're living through is the fact that we saw large language models scale. We saw, and I think it was, started with GPT-2, we saw that you could, you know, throw more GPUs and more data at it, and you'd get better results. I think we’re getting to that point. It's very exciting for us because that's the path for us to get from, okay, we run one investigation after you had an incident to we're going to be predictive. We're going to understand what's going to happen next in your systems, and we can run AI directly within our data plane, without having to get outside of Datadog, which is very exciting.
Wow. That change in kind of being able to feed in more data, that could really kind of bend the curve on the capabilities.
That's what we're working on, yes.
Yeah.
That's exciting. And again, it's research, you know, but its research is good enough that we can open weight it. We can open it up.
Yeah.
We see large amounts of adoption from it.
What do you think that would look like once you start being able to do that? I think, you know, is it charging per, like, you know, issue that you've proactively resolved? Or how would you think about that?
I mean, right now we charge per investigation for this.
Yeah.
Long term, we don't actually know what the model's going to be.
Yeah.
In part because the broader market is still figuring out how to package intelligence. It's not clear yet, you know, what customers will relate to the most there. I guess we'll see. The short of it though is for us it doesn't really matter because we have a usage-based business model in general.
Yeah.
It doesn't matter whether we have a new dimension in our usage that relates to this particular type of investigations or whether that gets attached to other parts of usage we have, you know, whether it's on the data volume we process, the number of events, the footprint our customers have in the cloud. Like, there's a number of different ways to look at it. You know, for us it's not a big deal either way.
You know, let's talk a little bit about your R&D, your head count, and kind of how you're handling that. You guys are obviously kind of very forward-leaning. We had a discussion last night at dinner about how you guys are kind of thinking about, you know, putting productivity and, you know, R&D through people and then through codes, tokens, however you wanna think about it. Can you just frame that discussion and how you guys have set it up?
I mean, the short of it is we're currently . Like, if you think of what drives or limits our growth.
Yeah.
We're limited and driven on, like, on two sides. One is, the sales capacity we have, and we're still early there compared to the number of markets and segments and customers, we need to be talking to. We still have to grow that sales capacity. That one is still largely human driven, like the buyers are humans. Maybe, you know, for, at least for the foreseeable future the humans will be buying. We'll see if that changes at some point, but that means the sales capacity is largely driven by humans, and we're still growing that as fast as we can. We get great return on investment on that. We're also driven and limited by the number of products we have in relevant categories with the right amount of functionality and get quality, and that is driven by our investments in engineering and R&D.
Historically, that's been mostly about human labor and how many people are on the team. Now, maybe there's a bit of a mix shift. Maybe we'll have the same overall R&D investment, which right now is around 30% of the top line. You know, some more of it will go to, you know, tokens or GPUs, and less of it will go to labor. I think it's unclear, but we know we'll keep investing, and we also know that right now we're also we keep hiring. We think we can still scale. Still need to scale. There's still a lot more we need to build for our customers and a lot more demand out there.
When you say that you're growing as fast as you can in terms of the sales and marketing side, is the limiting factor just your ability to hire people?
Well, it's, you know, when you talk about sales marketing, and sales capacity in general, it's, you can't just think of it in terms of a big pool of labor.
Yeah.
You need to think of it in terms of the right people with the right territories in the right segments everywhere around the world. It's way more of a bottom-up kind of a thing than a top-down.
Yeah.
That's actually really challenging to do well and at scale.
Zooming out a bit at the end here, everybody's kind of seen the all the models. There's an LLM du jour it seems like, you know, ChatGPT, Gemini, Claude, so on and so forth. Do you see that leapfrogging effect? Do you expect that to continue? You know, what is your view on how that's evolving?
It's a super competitive market right now, you know, which is fascinating because these are products that on one hand look like commodities because you can hot swap them pretty easily, and, you know, from afar it's hard to tell which one is better, you know, from the consumer. At the same time, it's an incredibly competitive market where, you know, improvement is very rapid.
Yeah.
You know, similarly to what we've seen with the cloud, like, 10 years ago, like, there was a fear at some point that, you know, Amazon was going to run with everything.
Yeah.
They would compete with everyone on, in every single field. We've had a little bit of the same, and it didn't turn out that way, right?
Yeah.
I mean, it's a very healthy, multi-vendor, very innovative, marketplace where, you have these large scale vendors that provide things that kind of are, like, largely commodities with some differentiation but not all that much. It's a very dynamic marketplace. My guess is we're gonna see the same happen with the, the AI models. I think we're well on our way to that already. There's multiple vendors. They compete. There's a lot of innovation. There's a range of frontier paid versions. There's a range of open source versions that are lagging behind but not crazily behind. We, my guess is that we're not gonna be in that situation either.
What's interesting though is that for customers and users of those models, it's really hard to understand how well they work, how they compare, whether they still work the same way they used to, whether something has changed, whether something would be more appropriate for them. We think it's a big opportunity for us and for observability in general. Like, you know, our job, among many other things, is to tell them, "Hey, that thing you're using, it's actually not performing as you thought it was or it changed." Something else changes better, or this is where you can mix and match them. I think it creates a long-term opportunity for us. The same when we've had an opportunity with the various kinds of clouds being mixed and matched by our customers.
Another vector for growth. I think at our 2024 TMC conference, you had this comment where you said, "It doesn't take a lot of imagination to see how we can get to 5-10 times the size we are today." I think you were about $2.5 billion run rate then, and now you're at $4 billion. You know, the components back then were, listen, you know, the ITOM market is growing at a healthy rate, and we're winners in that market taking share. We're gonna grow even faster. We've talked about AI in a bunch of different ways today.
Yeah.
Do you think that's transforming that TAM that you have? I mean, does that change this concept of how big you can get?
I mean, on the first part of it, the part about the lack of imagination, We can still grow the same way without imagination.
Yeah.
We're still in a similar position. I think I remember that time. I remember the Gartner number we quoted at that time was around 10%, we had around 10% of the market. Now we have around 13.6%.
Yep.
You know, the math that gets you to the 5x is pretty much the same as it was at the time. That's without you know, entering massive new categories or without huge tailwinds in AI.
Yeah.
The thing that's new now is this explosion of AI, and we think it opens up a number of other opportunities. In particular, we can do so much more, and we should do so much more in automation, which allows us to deliver a lot more value to our customers. We think observability was only ever 1/2 of a solution.
Yeah.
Yes, okay, you've observed, but now what would you do? I think there's a lot more we can do to automate.
Close the loop.
Yes. That's the exciting part.
We're coming down to the last few minutes here. We love to ask this question, which is, you know, when you're hopefully back here in 12 months, what do you think that the audience is gonna know or see come to fruition that you're kind of seeing right now? Like, what's gonna surprise them that you're kind of already seeing in the pipeline and, you know, not discussed as much as it should be?
I mean, look, again, I don't know if it's going to be truly surprising to anybody here, but the amount of stuff being built and the way the development cycles are collapsing is creating so much opportunity for us. On one hand because of the sheer volume of stuff that's coming up, but also on the other end because the value is moving from the act of writing code to everything that comes before and after. You know, what should I work on? How do I know it's working? How do I know it's safe? How do I know my users actually find value in it? All of that creates such an amount of such massive opportunity that again, I don't think it's always understood by the market, you know.
Yeah.
Especially on days where, you know, SaaS apocalypse trends, you know, definitely misunderstood at those times.
Yeah.
I think it's a, it's a, for us, it's a huge driver of, you know, short and long-term growth.
Yeah, we've had a lot of such days, between now and the start of the year. We got just a few seconds left. Anything on security? I mean, it's one of those areas that's growing very fast in the background. Where do you see that going?
It's exciting. I think there's two areas in particular where we find great satisfaction over the past, you know, couple quarters. One is code security. I mean, that whole field is upended by coding models. There's so much more to be done there, and we find a great amount of demand for it, that's exciting. The second area we find a lot of success in is our Cloud SIEM product. What resonates really well with the market there is it's a combination of two things. One is it's a really top-of-the-line event management storage, log management system that lets you stream data from anywhere to anywhere, storage and all sorts of extremely efficient way, query it very well. That's because it comes from our observability product, so it's already, you know, way above what a pure play security company would do, for example.
Yeah.
We combine that with the surprisingly good Bits AI security assistant. I say surprisingly because, you know, we were surprised by it.
Yeah.
We find that it constantly woos customers when we show them that. It's fairly unique. You don't see anybody in the market that has this combination of the super good data backend with the super flexible, with the AI on top of that. Most companies try and focus either on one of the two.
Yeah.
That resonates very well in the market.
Full solution. Thank you so much for your time and your insights. Really appreciate it.
All right. Thank you very much.