We’re increasing capacities. We’ve increased capacity last year, even though it was a slowdown year in the economy and we have plans to increase the capacity again this year. And we think there’s plenty more markets for us to be had, both in terms of the — inside the segments in which we’re already very present, but also in a number of new categories — sorry, new segments of the market, new geographies that we don’t cover very well yet.
Ray McDonough: Thanks. Maybe just as a follow-up, can you talk a little bit more about your pipeline construction? Specifically, how are larger opportunities weighted in your pipeline at this point? You talked a lot about more multiyear deals. You talked about kind of the construction of billings and CRPO. But maybe alongside that, can you talk about how long it takes typically to close an opportunity like the nine-figure deal you mentioned? Obviously, your go-to-market motion is more of land and expand motion, but I’m just wondering if as you see larger opportunities in your pipeline, deals may be taking a little longer to close still than typically.
Olivier Pomel: Yes. Because of the way we do business, where we try and land fast and small, we expand after that on an ongoing basis. We don’t really — we haven’t seen — even in last year, what’s been a tougher year for most sales organizations, we haven’t seen an elongation of sales cycles. It remained very, very stable. In general, we always felt that our sales pipelines were very solid. As a CEO, when I start the quarter, I can trust what I see in the pipeline and the forecast is going to materialize at the end of the quarter. It typically grows during the quarter, it doesn’t go up, it doesn’t go down. So it speaks, I think, to the quality of the work the sales team is doing and the quality of the motion we have put in place.
When you talk about this specific customer, like a customer that pays us eight or nine figures, these deals are growth deals. They are expansion deals. And so we get those deals by making those customers successful over the last period of time and then engaging with their teams as we ship new products, making sure they get their hands on these products. And by the time we get to have a sales conversation, typically, the usage is there. The customers have tried the product. They understand them. They’ve spoken to our product teams about them or support teams about them. And then it’s more a matter of understanding how we package that commercially and how we make a good case for it with the customer including understanding what other products they might retire as part of that.
When you think of sales times for brand-new customers, the time it takes is highly dependent on the size of the land. Most of our lands are small and can go very fast, which is for a large enterprise, it could be a quarter or two, which is very short for an enterprise deal, could be longer for some larger deals.
Ray McDonough: Great. Thanks for the color. Appreciate it.
Operator: Thank you. One moment for our next question And it comes from the line of Ittai Kidron with Oppenheimer. Please go ahead.
Ittai Kidron: Thanks. And Oli, I wanted to dig into your comments on security. I think you mentioned that you have 6,000 customers that are now using one or more security product. Can you give us a little bit more color. First of all, who is the buyer usually do you see for these solutions initially? And then second, maybe you can kind of parse out a little bit more color on ranking order kind of the more popular versus least popular security products right now? Would love to get more color on that.
Olivier Pomel: Yeah. So the buyers, it depends. For infrastructure security and application security, this tends to be the DevOps teams that start buying with some involvement on the security teams on their end. For our cloud SIEM product, the buyer tends to be the security team. So we have a little bit of both. And it turns out we’re successful in both ends. So both types of products are growing in a similar way. The focus has been over the past year on developing the products, getting them to maturity, getting them into the hands of as many customers as possible and also getting as much usage of those products as possible. So the metrics we look at internally with our security products are even more than the revenue which can vary with usage and things like that.
We look at the activity. How many of those customers are actually using the product, how many issues are being tracked are being solved for the products. And these are the North Stars we use internally as we develop those products.
Ittai Kidron: Got it. And then as a follow-up, maybe just to dig into this a little more. When customers buy these, I mean, how often is it, there’s a vacuum there. There’s no solution versus a displacement? Or how often do you kind of sit side by side by other solutions that do the same thing like in the same way the companies had multiple monitoring solutions? Will they just have same security solutions from other vendors? Or this is a complete replacement or greenfield? Would love to get more color on that.
Olivier Pomel: Yeah. In most situations, we start side by side with other things because customers are going to have other security solutions, and they typically have a patchwork of a lot of different things, which is not very different from the situation when we started selling our infrastructure and APM products, I would say, seven, eight, 10 years ago. So very similar. The customers that spend more with us, so we have a number of customers that spend more than $1 million a year with us on security because a large number of customers has spent more than $100,000 a year with us on security. Those tend to use less other products and consolidate more into what we do. And obviously, the playbook here is the same for us as it has been for observability.
By the way, you did ask me to stack rank the security products. Didn’t forget about that. It’s easy in order of introduction. Like the cloud team has the most usage and was introduced first and then the other products are still below that.
Ittai Kidron: Very good. Appreciate it. Thank you.
Operator: Thank you. One moment for our next question please. And it comes from the line of Mike Cikos with Needham. Please proceed.
Mike Cikos: Hey, guys. Thanks for taking the questions here. I guess first, a question for David. Again, just coming back to the guidance here, wanted to get some more color, if we could, on December into January. So it’s great to hear that January this year seems to be trending better than the seasonal drop that we saw in January of last year. Just wanted to get a better sense. The holidays seemed to play out the way that you guys expected but can you provide some more color or parameters on that December holiday slowdown and then how that is playing out in January versus where we stand today in mid-February?
David Obstler: Yeah. It’s as you said, we’ve seen, and we’ve tried to flag that in the second half of December, we have a slowdown of usage, particularly in our sort of our more use oriented products like logs. It played out very similarly to what we expected. And I think we caution everybody, it’s too narrow of a data point. But the bounce back from that and the growth in January was stronger than the bounce back last year, and we’ll have to see how the rest of the quarter plays out.
Olivier Pomel: Yeah. And in general, I think we — throughout the rest of the company, we’ve seen a slowdown in December, I mean for good reasons, like there’s all sorts of environments get turned off developers, some companies close shop altogether for one week at the end of the year. So we — it’s become more pronounced starting last year, I think, because companies put — wanted to see some cost control and maybe they automate some processes to downscale or shut things down at the end of the year. And this year was consistent with last year. It’s hard to compare year-to-year exactly because some of that, for example, depends on which day of the week the holidays are or in terms of how much the impact of usage. But overall, what we saw was very consistent with what we had last year.
Mike Cikos: Great. [indiscernible] Understood. And thanks for that, Oli. I do appreciate the additional color. And just for a quick follow-up here, I know that you guys are citing the expanded penetration of the Fortune 500, and it’s great to hear the five points of tick-up when you think about the 42% of Fortune 500 customers using it today. Wanted to get a better sense. I know you guys are saying that, I guess, those customers on average are still spending less than $500,000 with you. So I know it’s a bit of a point in time here, but a two-parter. First, can you help us think about if you had 37% of the Fortune 500 last year and 42% this year, like how has that average spend per customer within the Fortune 500 increased over the last year?
And then the second part of that question is, if the 42% of Fortune 500 customers with you are on average under $500,000 at this point, where does Datadog ultimately see that opportunity going to — just given — I know you guys are talking about the 400 new features and capabilities launched in the last year, but where does that spend increase to over time?
David Obstler: Just to give you — we’re not sort of giving specific data on the expansion of that group. We have — if you look at our larger customers and you just look at the trend over time, you can see that in the land and expand model, we have had an increase of average customer size with us in the group over $100,000. And we said previously that we have customers in the tens of millions. We have those customers within the Fortune statistic as well as outside. And so we said in the past that we see buying patterns in the tens of millions. We think that in many of the larger, more traditional enterprises, they’re just getting started, and there’s a lot of upside is what we’re trying to communicate.
Olivier Pomel: Yeah. And definitely, actually, customer on the Fortune 500, that’s in the hundreds of thousands should be in the millions to tens of millions with us in the end. There’s no question about that.
Mike Cikos: Thank you.
Operator: Thank you. One moment for our next question please. And it’s from the line of Frederick Havemeyer with Macquarie Capital. Please proceed.
Frederick Havemeyer: Hi. Thank you very much. I wanted to ask a bit more of a forward-looking technological question here, perhaps to Oli. There’s been quite a lot of, at this point, let’s say, testing development, but a lot of interesting development with like autonomous agents for DevOps-related task. So I’m curious, as you’re considering the opportunity and potentially some of the risks also around Generative AI and perhaps like agentic usage of large language models, how are you thinking that Datadog is positioned strategically, both from a and perhaps pricing perspective around this technology trend?
