Olivier Pomel: We think we’re ideally positioned for that. It’s actually — one of the things maybe we — if you attend our Investor Day, like we’ll share some of our thinking on the topic. But we — so we’ve been actively building on our Bits AI assistant. We’ve been interacting with customers based on that. There’s a number of ways for us to build on that and to do more to automate work for our customers, and that’s something we’re working on. And we also see a lot of demand and expectations on the customer side for incorporating Generative AI in the product. So I think from a positioning perspective, we feel great about that. We don’t have much more to share today, but we are just definitely top of mind.
Frederick Havemeyer: Sorry, I don’t mean to ask about the Investor Day, too early here. So look forward to that this week. Just quickly then also, I understand the perspective on complexity driving more usage of observability and DevOps tooling. But I think last quarter, we got an update on where GenAI-related operations were contributing to the business. Would you have into Q4, any data or points you could share about how much Generative AI in these use cases are contributing? Thank you.
Olivier Pomel: Yeah, we said — so we said 3% of our ARR comes from the AI native companies. And look, it’s hard for us to wrap our arms exactly around what is GenAI, what is not among our customer base and their workload. So the way we chose to do it is we looked at a smaller number of companies that we know are substantially all based on AI. So you have companies like the model providers and things like that. So 3% of ARR, which is up from what we had disclosed last time. I know one number that everyone has been thinking about is one cloud, in particular, Microsoft disclosed that 6% of their growth was attributable to AI. And we definitely see the benefits of that on our end too. If I look at our Azure business in particular, there is substantially more than 6% that is attributable to AI native as part of our Azure business.
So we see completely the — this trend is very true for us as well. It’s harder to tell with the other cloud providers because they don’t break those numbers up.
Frederick Havemeyer: Great. Thank you. And congrats on a good quarter.
Operator: Thank you. One moment for our next question please. And it comes from the line of Patrick Colville with Scotiabank. Please proceed.
Patrick Colville: Hi there. Thank you for taking my question and it’s really great to be on the call. I would like to actually double-click on this kind of from ARR from kind of AI native companies. I mean my question is, like, are the product SKUs, these kind of GenAI companies are adopting, are they similar or are they different to the kind of other customer cohorts? And then I guess when I think about GenAI, I think we can all agree that these are pretty — typically pretty computerly intensive workloads. So how does GenAI — how do these companies kind of impact the financial model in — versus traditional kind of your other customer cohorts? Is there any kind of differences to call out?
Olivier Pomel: Yes. There’s not many differences today. I think — and today, this is largely the same SKU as everybody else. These are infrastructure, APM logs profiling these kind of things that they’re, or release or monitoring these kind of things that these customers are using. It’s worth noting that they’re in a bit of a separate world because they’re largely the builders of the models. So all the tooling required to understand the models and — that’s less applicable to them. That’s more applicable to their own customers, which is also the rest of our customer base. And we see also where we see the bulk of the opportunity in the longer term, not in the handful of model providers that anybody is going to use. In terms of the economics, look, we — so there’s two parts to the AI workloads today.
There’s training and there’s inference. The vast majority of the players are training. There’s only a few that are scaling with inference. The ones that are scaling with inference are the ones that are driving our ARR because we are — we don’t — we’re not really present on the training side, but we’re very present on the inference side. And I think that also lines up with what you might see from some of the cloud providers, where a lot of the players or some of the players that are scaling the most are on Azure today on the inference side, whereas a lot of the other players still largely trading on some of the other clouds.
Patrick Colville: Okay. Very helpful. I guess the other question I want to touch on is, for me, the standout comments from your prepared remarks that were just fascinating were about the kind of intensity of optimization having dissipated. I think you also called out the headwinds over the last few quarters have slowed materially, which is really great to hear. I guess with that in mind, looking at the guidance, the guidance for 4Q was 22%. The guidance for 1Q is the same. And for the fiscal year is 21% growth. So I guess, just help us understand the puts and takes between the commentary about kind of optimization having dissipated and guidance and what kind of the levers you pulled thinking about that guidance?
Olivier Pomel: Yes. We stick to our guns for guidance, which is — our practice is we look at the trends for the past two quarters, we discount them and we carry that forward. This is where we build guidance. The beauty of our model is that it is usage driven and we benefit from the move to the cloud of our customers and the way they scale. The only problem with that is that we do not drive that, and it is hard to time it. It’s hard to understand if the customers have the intent to scale and they want open the application, it’s hard to time it to understand whether it’s going to be next quarter or two quarters from now. So that’s why we look at the past trends, and we try not to think too hard about what we hope might happen in the near term. Obviously, we’re in a much stronger set than we were last year, though.
David Obstler: Yeah. I think if you repeat in a little bit different words, what Oli said, I think if you look at our history, we always take the most recent performance trends and discount that. So even if we are seeing improvement what we do in our guidance in conservatism, again, because of the consumption model, is we do discount that. So that would be consistent with what we’ve done throughout our history as a public company.
Olivier Pomel: Yeah. And just to give you a better example, like we had a number of customers sign large long-term commitments with us that are well above their current level of consumption. But we do not know when those commitments are going to turn into revenue. We trust that they will, customers trust that they will. They spent a lot of time over the past year thinking about optimizing and what they actually needed and where they were going in the future, where I think they are less prone to overcommitting that they might have been a couple of years ago. But again, we do not — we cannot call that on to the revenue yet.
Yuka Broderick: Carmen, we’ll take our last question.
Operator: Thank you. Our last question comes from Keith Bachman with BMO.
Keith Bachman: Hi, thank you very much and congratulations on a really solid set of results for the quarter. I’ll ask my question jointly. And could you talk about, first, how you see the net retention rate unfolding for the year? And I realize that’s a backward-looking metric. But I just wanted to understand how you’re thinking about that as we progress through the year even directionally. And the second broader question is about diversification. One of the interesting stats you gave was growth of outside of your core APM and infra growing 75%. Just, is there any metrics you can give on what percent of that is your base? Or how we should be thinking about the diversification of the portfolio? In my opinion, Datadog has certainly the broadest portfolio in the category. I’m just wondering how that diversity is contributing to your growth as we look out? Again, if I’m pulling from the Investor Day, then I apologize.
David Obstler: Yeah. So we do not provide guidance on net retention. I think we said that for the first time in Q4, we had more ARR add than we had in the year-ago quarter, and that would indicate the stabilization of net retention, but do not provide guidance. It’s all incorporated in our revenue guidance. As far as platform, yes, totally we’ve given a number of metrics about this. Remember, last quarter, we talked about the three pillars, and we said that we had essentially the [$1.5 billion and $500 million] (ph) and that’s been a huge driver. In terms of this metric, this is in addition to that, a metric that the expansion, as you said, of the platform is further than those three pillars and is an additional product and is a significant driver of our growth.
