One, two. Okay, perfect. Good afternoon, everybody. Hope you're enjoying day two of the Citi Global Technology Conference. I'm Joel Ominu. I'm a research associate on the software team, and I support Fatima Boolani in covering Datadog. So we're very happy to have with us today, Datadog's very own CFO, David Obstler.
Thanks, thanks for having us. Appreciate it.
Thanks. Thanks for joining us. So, David, I think just to start, we'll talk about a brief overview of, Datadog. W hat you're doing for customers today, and how you've gone from the core infrastructure monitoring into the observability and security use cases.
Yes.
Yeah.
Up front, just say in advance that we reported in early August, so all of our comments will be reflective of what we disclosed at that time, with no updates. Getting into the history of Datadog, so Datadog has a unified observability platform that offers logs, infrastructure metrics, APM traces, and logs, all in one platform. We started out as an infrastructure monitoring company, and over the years, expanded the product suite to include all the pillars of observability. Adding on the two I mentioned, plus Synthetics and RUM, which we call the Digital Experience Monitoring Suite.
More recently, adding on such products as network monitoring, CI/CD, or pre-deployment analysis of code- as well as more recently, security. In terms of the progress, one of the metrics we gave out, and I think it was the conference call before the last one, is that we're about over $2 billion of ARR. And what we said was, coming from infrastructure only, about 50% of the revenues now are non-infrastructure. Most of that would be in APM and logs. So from having a single product, over the years, we've expanded the platform in order to have this integrated suite, including about half the company in those other capabilities.
A nd just to stick on that, if you were to try and drill down to a product level- or maybe a category level- security, APM, logs, is there anything you can share in terms of the ARR breakdown or revenue breakdown, across your product suite?
Yeah, so, I mentioned that, we are a little over $2 billion and so about half of that is infrastructure, about $1 billion, and $500 million of that would be the other two major pillars of APM and logs. There are other products in there. They're smaller products. They've been released more recently. So that's sort of an approximate overview.
I'd like to add that essentially, most of our monitoring is of modern applications. The clue there is containers, Kubernetes, microservices, delivered on the cloud, customer facing, where our product is differentiating, that it's used ubiquitously by lots of participants in the DevOps ecosystem. Most of what they're doing is, they're trying to examine the delivery of the applications and analyze and remediate in real time. That's sort of the Datadog end market.
Fair enough. So, just piggybacking off that, when we look at your newer products and your non-infrastructure products, we've really started to see some good traction I think your last earnings, you shared, you know, 30% of customers- adopting one of the newer products- from calendar 2021. Could you talk about what's really working in that part of the business?
Yeah. So, most of our clients are trying to get as many signals about their environment in one place, one single pane of glass. So, the biggest revenue are those pillars. But as we've added on other types of products and signals, network monitoring, security, code analysis, that's what, and then the RUM products, those are the products that account. They're newer, and they're not those first three pillars. That's what we meant by the 30%. Essentially, many of these are pieces of the observability platform and our clients are using more and more parts of it as we create more value in the platform.
Okay. That good. So I think the platform approach is something that's well spoken of today. But the fact of the matter is, there are other vendors who also claim to have these observability platforms even in the cloud. Could you talk about what differentiates Datadog's platform a nd why customers should prefer that rather than some of the other vendors that we hear about?
Yeah. So the market is pretty much divided into three areas. They're born in the cloud platforms like Datadog. Then open source. Open source would be, you build it yourself. That's a choice that's out there. That's probably the biggest competition, meaning you don't buy a package software solution, you build it. And then there's a set of companies that were born more on the on-premise environment. So, we compete most with those that monitor cloud workloads, modern, and the on-premise, and the open source. Now, in terms of open source versus Datadog, it is, if you look at the cost to build and maintain the system, i t's cheaper to buy a service like Datadog than to do it yourself.
If you want that service, if you have a service that needs to be delivered on premise, that has functionality that's unique to you and nobody else, then you might wanna consider building it. In terms of the group of cloud monitoring companies, it's all about the product and the fact that Datadog was built natively with a unified data infrastructure that allows it to bring all the data together, produce reports, correlate it, and a user interface and automation and integrations that allow a client to, without professional services, be able to use it. The other companies that have been trying to do this have not built a native platform that functions this way.
It's either been put together through acquisitions or it isn't able to be accessed by clients in this way. That platform approach from the data, from the components, and from the way it gets used is unique to Datadog, and if you did customer surveys, et c., you would see that's the reason why they buy Datadog.
Okay. So, you know, just sticking on the platform, you know, some of the feedback that we get is that costs can run high. Right? Especially when we look at that usage factor. You know, with that in mind, we still see customers continuing to adopt the platform. Could you help us understand the dynamic there, where there is potential for customers to incur higher costs but they still prefer to adopt the platform? Or is there further technological differentiation, for instance?
Yeah. I mean, we have a usage-based platform that is correlated to how a client sees value. We've been a very much of a product-led company, and I would say we're fairly recognized as being best of breed. So, our clients, generally for this use case, are choosing what they think is the best and most useful, and most value-added service. Anytime you're gonna be critical to a client, means central to their operations, as they grow, you'll grow, and the price will increase, but we are still a very, very low percentage of the client's cloud expense. Low single digits or lower. Essentially, through value-added pricing, have been able to maintain a gross retention in the very high 90s. Meaning, as you said, our clients are both staying with us, growing with us, and then we can talk later about our new logos landing with us as well.
The clients are finding value in the platform, and its quality, and its usefulness, and continuing with and buying more Datadog.
Got it. So Datadog, you're saying on average, is about low single digits % of its clients.
It ranges quite a bit, but we are about 1%, our revenues are about 1% the sum of the hyperscalers' revenue. Whereas we don't know the cloud spend of all of our clients, we have enough data points to know that we vary from 1% or lower to maybe up to 10%. Generally, our average would be quite in the low end of that relative. Most of the spending, and most of the essentially, cost and the opportunity are really around the cloud and then we monitor that.
Okay. All right, that's interesting to hear. So I guess the financial benefit for Datadog, there's also something over there in terms of customers consolidating on your platform. Yeah. Could you talk about some of the uplift mechanics over here?
Definitely. So we started out with only infrastructure, and then over four, five, six years, we developed the logs, the Synthetics, and the APM product, and got it to the part where, the place where it's, you know, rated best of breed as well. And so it was always recognized that having everything in a single platform allows you to do what you need to do, which is seeing all the signals, see what's happening and remediate fast. So that was always there, and as we sort of improved all the components, clients endorsed that by consolidating on Datadog. And so, that's what's been happening for a while, and then as we've launched more products they've consolidated other point solutions. This, I would say, has been going on, and then in the cost environment we're in, it is also can be quite economical and cost-efficient for a client to have one vendor and one platform. Which means that you can see everything and maybe pay less.
Okay. Got it. Do you see perhaps some sort of uplift, you know, when they're adopting, maybe moving from a security or observability APM? Could you talk a bit about that?
Yeah. So, essentially, a couple different metrics. One is, as I mentioned, half of our revenues are non-infrastructure. So the upsell of all the products, weighted average is 2-to-1. They're equal. So we only had infrastructure, and now we've doubled with the other products. So that's one thing. Two, is with our net retention, we are about 1/3 new products that you didn't own and 2/3 or so increased volume of what you already own.
So that shows the impact of the cross-sell. In terms of security, it's too early to tell. We have good traction. We have 5,000 customers. We have 26,000 approximate customers. We're selling to certain use cases. I think we mentioned on our call the number of customers that are spending $1 million and $100,000. So we have some scale customers. But we're just scratching the surface. We haven't even finished building out the suite. We think that the market could be a doubling in TAM but we need more experience in the market to see if, like the other products, this can be a doubling, like the other products were.
Okay. And we'll we'll talk about security later on, but maybe just to shift to the competitive environment. If you know, with about 19 products I think you have today, there's certainly a multitude of vendors that you would compete with. And then, not to mention, of course, the expansion into security. Could you talk about some of the competitive dynamics? Especially when we get into the security space.
Yeah, so in observability, as I mentioned, you have us, and there are a couple other companies, that were native to the cloud, New Relic, APM. You have open-source. Their names like Elastic, Grafana, and then you have companies that were born on-premise, like Splunk and Dynatrace and AppDynamics. Some of them have expanded into cloud. That's the competitive landscape. In market research and market share, that's been put out by Gartner and others, we're the largest. Of course, we weren't five years ago. We were not even, but we've become the largest, which is evidence of the gaining of the market share.
We've always been the largest company and the market share winner in cloud native cloud deliver types of applications. So that's where we've excelled versus on-premise. That continues to be the case, and probably, over the last few years, I mentioned with the product, has even become more of a, you know, Datadog market leadership. In terms of security, to sort of set some groundwork, there are a number of companies out there, some very large companies like Palo Alto and CrowdStrike and others, that have most of their revenues in endpoint. Network, email. We're not in that business at all. So they're, they're in what they do in their main part, or for that matter, Splunk, in security logs. We're not in that business. So what we are is in cloud security, which has three major components. It has cloud, cloud security, or in the infrastructure side of it, it has application, and it has Cloud SIEM. And most of this is a pretty new area.
It's not very penetrated. You do have Palo Alto and some of the others in there. I would say that where we're selling, which is not the centralized CISO case right now, but as an add-on into DevOps, a lot of it's greenfield. There are, you know, some, some Palo Alto, Prisma, some Wiz, et c.. So a lot of that are new use cases, and what we said is that we're in build mode. Our strategy right now is to attach it to the use cases in DevOps. And as we continue to build, we may attack a bit more of the CISO market, which we're not in today. But that's down the road.
Okay, that's fair enough So, like you said, there's a lot of greenfield in this space.
There is.
But there's certainly other pure play cybersecurity vendors and even large vendors playing here. So, when you go to customers to pitch Datadog Security what are the key points of differentiation that make the products better than the previous ones?
Right now we're only doing attached to our existing customers which means that they have our infrastructure, APM, and logs. The pitch is, one, we already have your eyeballs. Two, we already have all the data in a single pane of glass. Three, we already have your metric traces and logs. You don't have to re-instrument. Our pitch would be as part of the platform. Also, most of the security is not architected for real-time type of use. That's what we're doing. That's the use case we're working on now. That we may evolve to a broader use case in security, but that's the differentiation we have today.
Got it. When you think about your security strategy today. is going after those net new customers, you know, going beyond this pitch to your existing customers, is that part of the strategy?
It's no to existing customers w ho are already using Datadog for DevOps, who are beginning or using or having what's called DevSecOps. And have all our data and our instrumentation. So it's an upsell extension to our existing customers. That's what it is today. That may not be what it is when we finish building it out but that's what we're doing today.
Got it. Okay, fair enough. I think, you know, some people wouldn't be happy if I didn't ask you about AI, but I promise not to spend too much time on it. I think, you know, it's pretty clear that there's a lot of vendors who are coming out and saying that, you know, AI is a positive, exposure to them, r ight? Other vendors that you compete with. I guess the question is, what is the level of differentiation, r ight? Why is Datadog and Datadog's ambition with generative AI? How is that different from some of your competitors?
We are essentially agnostic to the monitoring of applications, however they're created. Our advantage is that we, as applications get created faster and are more data-intensive, we have it. So it's very likely, it's very early, because most of the enterprise clients are just thinking about, they don't actually have the LLMs in their enterprise models yet. But if history repeats itself, anytime there's an acceleration of application creation, new ways that applications are created, and more data, that's Datadog. Because Datadog gets their rewards based on the CPUs, the GPUs, or the hosts, the tasks, and the data that flows in.
We are very likely, if it repeats. Again, it's too early. We don't know how it's gonna shake out on this end, but it's very likely, since we are a follower, we're basically monitoring what our clients do, that if our clients create a lot of new applications that are LLM enhanced, we will be a net winner. So that's sort of the product line. Then, there's our platform. So we are attempting, and we made a number of announcements at DASH. This is about us as a software company, not what our clients are doing and what we're monitoring. We're essentially injecting, w e have been for a while, but we're increasing the pace, large language models and pilots in our platform. We introduced a copilot.
We introduced an LLM suite. These are all in beta. But what does that mean? What does that mean? That means, essentially, that enables our clients to access our platform for whatever they're doing and then automate more functions in our platform using Copilot or automated tasks. Which means they can do their job faster, and that would be the Datadog platform side. And then, the last piece of this is Datadog as a monitoring tool for tools vendors, who are in this industry. Now, why is that interesting? We said 2% of our revenues now, and growing. Why is that? If everything works out, these tools vendors are gonna need to provide a service that is mission-critical and won't go down to their clients and that has to be remediated in real time, and that's a perfect customer for Datadog and a data-intensive one. That's a perfect customer for Datadog. So the third is, do we have an effect from a sector that grows very rapidly? that we're providing products to?
Those are the three ways that Datadog can monetize in AI.
Got it. And that's the 2% of ARR from.
That's the 2%.
The next-gen AI customers.
Yeah. Yeah.
Okay.
That's the most definitive thing we said on the call. Because why? That's servicing clients who are already doing it. The other parts of it are, one, we launched a bunch of products, but they're in beta, and people say "How's it going?" Well, there's enthusiasm, but clients aren't really using them. They're beta. And then the last part is probably the biggest part, which is the knock-on effect from what we all believe, I think, is gonna happen here, which is that if the dream is realized, that in the injection of all of this, this is gonna improve the speed and the innovation of application launch. That is where Datadog wins.
Okay, perfect. And then I think just the last one on AI. When you think about the investment that's needed to capture this opportunity right? The hype started recently but we also understand Datadog has been pursuing AI for a while. Are there incremental investment dollars, that you need to put into the model in order to compete and to capitalize on this opportunity?
Yeah, like I said, we are, we have been, so we've been. Our R&D is our largest cost element. It's if you look at it versus the other, we're a product-led company, it's, you know, in the thirties. This is a component of it, and yes, we're basically prioritizing between all the different demands, and this demand is getting, you know, a higher percentage than it did a year ago. Within the envelope of our R&D spend.
Okay. So today, you know, AI investment's higher priority, would you say? Or higher proportion.
There's, you know, our priorities include, include our platform. About half of our. Part of this is AI as our platform. S ecurity, being built. AI, so there's a number of different priorities, but within that envelope of sustained innovation and R&D, launch, is AI. We're, we haven't, and we're not gonna basically go over our head count of what's doing what, but it's a component of that.
Perfect. Fair enough. So, I think that's a good segue into the financial model which I wanted to talk about for a little bit. You know, you've had this strong sales efficiency and I think that's helped by the, you know, higher R&D. C ould you talk about some of the drivers, even as the top line sees these macro pressures that are enabling you to maintain respectable levels of profitability?
Yeah. Yeah. Y eah, yeah. So a couple different things. One is that we invested substantially in the previous two years, but not so much. We didn't binge. We didn't have to do risk, but we had a good amount of investment, and we're now harvesting that. Like, salespeople. We hired a lot of salespeople. Retention's getting better. They weren't ramped. So now they're ramped. So essentially, we've been able to, in this environment, make productive the investments we made in the previous years. We've also prioritized R&D, as I mentioned. So that's been the area where we've protected the most. And because in sales and marketing we did do this capacity expansion, we are essentially a little bit more this year in productivity enhancements, in improving that. So that's why we've been able to rebalance this a bit.
Yet, we've always said, big long-term opportunity, we're gonna continue to grow the investment a lot, but, in a prioritized way. Another thing is, we've never invested. I've been there five years; since I've been there, never invested above the top-line growth. So when you essentially look at conservative top-line growth, and then you develop your investment envelope, that provides you a lot of flexibility, but protects margins as well.
Okay. And maybe a specific question on profitability. So gross margins. I think the past couple of quarters, or at least last quarter, you spoke about cloud cost efficiencies was just hoping to dig deeper and talk about where these efficiencies are coming from. Are you renegotiating contracts? Are you optimizing your own cloud usage? Just talk about that.
Definitely. So, first of all, we are re-architecting the platform on an ongoing basis to make it more efficient. We announced our log store, Husky, that's an example of that. We are, in our own instances and platform, investing in efficiency. Two , as we get larger, there's economies of scale in negotiating with the cloud vendors. Three, I think we've gotten better in capacity planning. We've always been pretty good, but I think we, in terms of how much you want to be on demand or not, we think we've gotten good at it, through analytics and stuff, and things. So all of that has allowed us. But we know that there might be times when an investment in the platform will have a very strong ROI, but you have to leave the existing platform up while you're innovating.
That's why we've been careful to caution everybody that there could be fluctuations. Had we not done that a year or two ago, we wouldn't have this margin and this way of sort of getting scale to our customers in Flex Logs and others.
Got it. I think another key theme around Datadog is these cost optimizations, right? If you just spend, you know, a few seconds or a few minutes here talking about the trend of cost optimizations that you've seen. And then also recently, we of course heard about some normalization in usage growth. Talk about that as well.
For a long time, we had a set of cohorts that operated, I think we gave metric above 130% growth. And that was, I would say, and we've had a pretty weird set of events in COVID, and then . And so then we had a caffeinated level of spend. We had essentially an escalation of spend. This is a number of reasons. I mean, low interest rates, the prioritization of growth versus profitability ., very strong demand environment. So essentially, what happened started when the Fed started raising rates, and everyone started getting more cautious, is that we started, just like inflation unwind, we started to have a little, some unwind here. And that's what's been weighing on the net retention.
Meaning, we're not having customers land differently. We're not having them, those ones that land, ramp them differently. We're not losing customers. It's the rate of expansion of the customers. And so what we said was, we had a group of customers that were identified by being cloud native, having grown very, very fast, potentially overspending, and maybe that was because of the prioritization on growth versus profitability. And we said, starting in Q2 last year, and in affected industries. Maybe their industry and demand. That was the place we got hit hardest. We said this now for over a year, and we've been through a year cycle with them. I think what we said on this call was that, that group of most affected customers seems to have stabilized. We don't know that there's not gonna be other waves.
Some of the reasons we can see that they've stabilized, that a number of them have signed commitments, sometimes for three years, at this level. Now, they haven't begun to grow substantially yet, but the pain from them unwinding. So that is sort of the most, the most pain. So that is a sign that we may be getting later stage in this optimization. Now, we still have, in our broader customer base, a cost-focused world of one that is constrained relative to what it had been in the highest growth. We have different degrees of freedom. Since this is not something that we control, this is what our customers. We try to be always cautious in both our planning and in our guidance. And that's why we said, "We may be getting to a trough, but we can't tell you when the rebound's gonna happen."
We expect that we're gonna have a cost-conscious environment in the rest of this year. In terms of what's happened in the quarters, I think we've gotten this question a lot. There's a lot of variability in a frictionless model, a cloud model. And what we saw last quarter, we've been very transparent, is we saw a stronger start to the quarter and then we saw in the middle of the period, the most intense optimization. And then we had a little better towards the end of the quarter in July. But we wanted to say that given we don't control this, we essentially have not said, well, we're not declaring victory or anything. We're saying: This is gonna continue, this is gonna be volatile, so let's take the weighted average and then discount that for guidance. That's a question that's been asked quite a bit.
Got it. Maybe just to try and dig into something here. You know, if these customers that were most impacted, as you said, with the optimizations . a year ago, if one year later, you know, we're seeing them sort of normalize, right? M aybe what are the reasons why we couldn't take that and say, "Okay, if there's other customers that are less impacted then maybe they take a year to come back and normalize." Why not?
We can see that. We basically, w e've been using cohorts. We have other cohorts where it hasn't been extreme. It wasn't as tough for them. They maybe didn't do it right away. They might have been more hopeful, but there are waves of this, and we, we don't think we're through it. Also, customers, and this is in Datadog throughout its history, we have a rolling set of optimizations, always going on, and so, you know, it's harder to predict. You know, we think it's a mixed, but there's some, you know, light at the end of the tunnel. But we don't feel comfortable predicting that, you know, our customer base broadly has finished doing their work.
That's fair. And then just one more on this issue. I think, you know, if we look over the past couple of years, right? T here was the onset of COVID, which had a little bit of a negative impact on the business. And then post-COVID t here was this COVID-driven demand strength, right? From people going to cloud. And then we get hit with the macro. There's been a lot of volatility, when you think about the model and the usage growth rates. How are you thinking about, you know, a sustainable medium to long-term, you know, usage growth rate. Or how do you think about, you know, usage growth for Datadog over the next couple of years?
Yeah, I mean, we essentially look at longer term that only 20-some percent of the workloads are in the cloud. That many of our clients, particularly as you get into enterprise, are very immature in their cloud journey. That we're landing great new customers and consolidating, building out more product, and we have a weighted average time series that before all this happened, was more normalized. We don't know for sure, but there's data points that, you know, that when, you know, when we said this above 130, that, that wasn't in net retention. That wasn't, like, just random. That was because the data was sort of centered around that, and that was maybe a conservative version of it, over a longer period of time.
But yes, things got volatile. And, you know, and it isn't gonna be a straight line, but we use that type of evidence. We also have 26,000 customers, and companies like AWS have 1 million. Even if you wanna discount that down, we have very low penetration in the end customer base.
Okay. Fair enough. We're coming up on five minutes here. I'm just gonna pause, see if there's any questions from the audience for now. Otherwise, I'll keep going. Okay, perfect. So I also wanted to talk about the contract structure d ynamic that you have, right? So you have this situation where customers commit to a certain amount a nd then if they go past that, they'll pay you overage, right? Just curious how you saw that change during the macro.
It didn't change in that customers still committed. They still went into overage, and because of the burst nature, because their service is not the same every day, they will always do that. What did change is that they, a nd we do. So basically, we recognize revenues largely on usage, but underneath of it is a take or pay commitment. So if they don't use it, we have to recognize it. But what was happening, with growth being higher, they were getting into another commitment situation faster. So they might get into something that was beyond the no overage. They might. A lot of customers operate with, I don't know, 5%-15%, 20% overage because they wanna do that operationally, but they got beyond that faster, and they did it in their contract.
What's happened, it's taken longer to get to that period. And that's what's been going on in the contracting. When we looked at the RPO and the billings, which is not exactly correlated, but it was relatively strong, it is a sign probably that that they are now getting to the point where they can predict their business more and can contract in a confident way as I mentioned, with the most affected.
Okay, so and speaking of customer, you know, customer behavior and customer expansions, now, historically, expansion has been a very strong driver for your business. I think a couple quarters ago it was about 80% of the year-over-year increase in revenue. I think more recently it's about 60% now. Right? So when you look at that dynamic, how much of it is, you know, as a result of pressure on expansion, of course, because of the macro versus how much of it is actually internal execution improving and your team just getting better at landing these new logos?
The new logo. So essentially, what's changing is the net retention. So the new logo, as we said, has been fairly consistent. If new logo stays roughly the same and your net retention goes down, your new logos will be a higher percentage. So most, that tells you that most of what's going on has been the economic environment. Some other things that tell you that is gross retention in the very high 90s. Upper 90s. Continued growth, weighted average, meaning the customer base is continuing to grow. The customer base not leaving us. The flow, the balance of things flowing towards us, meaning consolidation.
What that tells you is that we believe, and I think we're, is that most of what's happened has been the either the deflation of escalated spend or an overall cost environment, largely caused by macro.
So therefore, when we think about, you know, the medium term and the long term, is the idea that, you know, as we exit the tough macro environment, that Datadog will return to these, you know, higher weighted towards expansion, this type of growth? Or do you see this new level as a comfortable place for the business?
No, I think that would be our greatest prediction. We have said when we were 70%/ 80%, which implies a net retention quite a bit higher than that, sorry, than today is, we said: "You shouldn't expect this. This is not normal." We basically said that. So, I think that's right, that's what is likely. Of course, of course, we are working on, in many ways, to control what we can control, which is grow our sales team, grow our partnerships, grow our alliance, you know, everything, to try to get more and more lands. In fact, I think we've done very well holding that because the economic environment certainly, in fact, affects weighted average landing. It's just we've gotten bigger and better on the other side of that, and it's balancing out the stability in lands.
Okay. Got it. Well, and with two minutes left, I just wanted to toss in a question on the Cloud Cost Management, be cause it's something we've heard a lot of conversations about. Could you talk about the advantage of adopting, you know, the Cloud Cost Management use case with Datadog? Especially because, you know, hyperscalers tend to have their own native capabilities for this use case. So why would customers choose Datadog?
So it's like a lot of things for Datadog. So we put all the clouds, all the cost on that. We have all of your usage, so we already own your usage data. And then we also are putting in all of the clouds, so you can see a unified cloud. The groups of people that are responsible are not in the same department, but work with the DevOps. That would be, those would be our advantages of it. But you're right. I mean, we haven't come out and said: "Oh, expect $1 billion of revenues." We think this is a good product. It's healthy, it helps our clients get healthy. But we don't know yet whether it's gonna be one of, you know, the main pillars or smaller.
Okay, perfect. I think the question, you know, perfect question to cap, the discussion is just when you look at the near term, right? Near to mid-term product portfolio, you look at the market, what are some of the areas that are low-hanging fruit that can really help you drive value?
That's a very good question. I think some of the low-hanging fruit are, one, being responsible to our clients so that we help them optimize, so that we have them being Datadog advocates on the back end, which we're doing. Two, consolidation. That's already happening, but I think we can even do more of that, because of how we're set up in our product and our platform. Three would be, we've seen areas that we haven't really penetrated very well, some of the emerging markets, et c.. So put our money there, and, o r government, as an example. And basically lean into more things. And lastly, would be to work on productivity and enablement of our sales team, and continue to, to build out the product.
Okay, great.
Yeah. Thanks!
Well, David, I think that's a perfect place to cap the discussion. Everyone, please have a good rest of the conference.
Okay.
Yeah.
Thanks a lot. Thank you, everybody, for listening. Thank you.