Ashutosh Kulkarni: That’s a great question. And the way I look at it is effectively gen AI and the very differentiated capabilities, we are able to provide is really helping us in a significant way with just brand recognition, with a C-level audience that generally in the past we’ve not had too many conversations with. And that is a wonderful thing because that then allows us to continue that discussion of the platform story and how there are so many things that, when it comes to real-time search use cases, whether it’d be for observability or security as well, we can add tremendous value to their organization by becoming a key element of their IT infrastructure, and that’s really something why I believe that the gen AI tailwind as this builds up in the coming quarters and years is going to be very meaningful for us because it will affect everything that we are able to do with the platform including the work that we do in observability and security.
Matt Swanson: That’s helpful. One other thing you talked about was the time to value being kind of a core value proposition and when thinking about new products or advancements, like the time series data streams and the cost savings for storage, could you just talk about in this macro how important ROI in customer conversations and maybe if that’s influencing either product development or the broader go-to-market?
Ashutosh Kulkarni: Yes, absolutely, is incredibly important. So we have seen as Janesh mentioned stabilization in sort of the consumption optimizations that we had seen in prior quarters. But the reality is that customers even today care about making sure that they are spending wisely, they are being thoughtful about how they spend, but they want to do it without sacrificing innovation. And that’s really the place that we are leaning in with. Our – the value that we offer for our price tends to be incredibly strong. We heard that over and over again. We offer tremendously differentiated capabilities. We allow you to bring in all of your different data types, analyze it in real-time at scale, the performance is fantastic. And the more we do to help you optimize your costs with things like time series data streams, with things like searchable snapshots.
Even the improvements that we keep making in the platform itself that allows you to reduce your overall cost, whether it’d be for vector search or something else, like all of these things matter, because that helps them reduce their infrastructure spend on hardware or what they’re leasing from cloud providers. And that is incredibly important to them and to be able to do that with a single platform, is just a great value proposition. I’ve talked about this now for a couple of quarters that we’ve been leaning into this, both from the product side and the go-to-market side because we see this as an opportunity to take share in the market in this time and we are absolutely doing that. That’s what’s really driving a lot of the large commitments that we’re seeing, because we were able to get a customer to see how they can do more with Elastic and that results in a displacement of some incumbent and over time, that just increases our share.
Matt Swanson: Thank you.
Operator: Thank you. And our next question today comes from Raimo Lenschow with Barclays. Please go ahead.
Raimo Lenschow: Hi, thank you. And I might have missed it earlier, but like – can you speak to – if I think about vector search and semantic search, obviously with vector search there is a lot more compute involved than before, like what do you – what are you guys seeing in terms of consumption trends and ad customers are working with it? And what does it mean for you guys in terms of the momentum there, because clearly customer signs one contract and then customer is using them to resources, that it seems to be like a bigger resource consumption that you get from these newer technologies? So could you speak to that please? And I have one follow-up for Janesh.
Ashutosh Kulkarni: Yes, sure, Raimo, this is Ash here. I can touch upon that first one. So you’re exactly right that when you’re dealing with vector search and or even semantic search, really semantic search is all about doing search based on the meaning as opposed to just text and what that requires you to do is to take the data that you have and run it through an ML model and that ML model doing that work is effectively significantly higher in terms of compute than just storing that data But effectively, you have to take all your data, run it through an ML model, create vector embeddings, and then use everything that you’ve built to then search against whatever that information might be that you’re searching for using that semantic information as opposed to just the textual information.
And that tends to be much more compute-intensive than traditional textual search. And as you know, for us, it’s not just about consumption, but it’s also the fact that ML is in a higher paid tier. So you either need Platinum or Enterprise, and that is the second way in which we monetize. So both of those come into the picture for us for either vector search or semantic search and that’s really where we’re seeing the progress. So like I mentioned, we have hundreds of customers now that are using us for our vector search capabilities and that’s really very exciting for us.
Raimo Lenschow: Okay. That sounds very exciting. Thank you. And then Janesh, if I think about the evolution on the profitability side, like how do you think about investments, as we think about going kind of maybe – you talked about stabilization on the macro side a little bit, how do you think about like investments into sales and marketing, et cetera? Because lot of these need to be much earlier than kind of the real revenue coming through because people need guys, for example if you go live, et cetera, like how should we think about the progression of investments as we go through this year? Thank you and congrats from me as well.
Janesh Moorjani: Yes. Thanks, Raimo. So look, as I look back at Q1, we are very pleased with the operating margin result in the quarter. I think that just reflects the hard work and focus of all of our employees to ensure that we manage the business with discipline and as we’ve shared before, we have natural operating leverage that’s inherent in the model and that was visible here in the Q1 results. We’ll continue to grow expenses slower than revenue on a full year basis, as we invest in the business and that will be sufficient to then help us achieve our near-term goal for fiscal ’24. But in terms of investments in the business, we entered this year with right amount of selling capacity for this fiscal year and that was a focus area for us a couple of quarters ago as you’ll recall.
And we continue to selectively invest in enterprise and commercial selling capacity. We’re also investing appropriately here against the gen AI opportunity as I mentioned. So we’re continuing to make investments throughout the business in areas that we feel are appropriate and that are best-positioned to drive growth the rest of this year and into fiscal ’25. We obviously don’t want to compromise on topline growth, but it is about ensuring balance growth and profitability and that’s what we’re committed to doing.
Raimo Lenschow: Okay. Perfect. Makes a lot of sense. Thank you.
Janesh Moorjani: Thank you.
Operator: Thank you. And our next question comes from Andrew Nowinski with Wells Fargo. Please go ahead.
Stephen Schwartz: This is Stephen Schwartz for Andy. Thanks for taking my question. I wanted to ask, in terms of the vendor consolidation that you’ve talked about seeing, is there any commonality among the customers who tend to consolidate with either in terms of size or vertical in geo?
Ashutosh Kulkarni: There is no specific trend in any geo. I mean, we’ve seen that trend playing across multiple kinds of verticals across multiple geo’s. What tends to be the typical driver is when these customers are paying extremely high rates for incumbent solutions and then they see what Elastic is able to do, which is not only much more differentiated, much more capable in terms of performance. The kinds of data, we can handle. The kinds of analytics that we can do. The machine-learning functionality that we’ve built in now with the generative AI capabilities like it just becomes a no-brainer. And usually what is needed is. That inflection point where they see the value they would get and the price that they would get it is meaningful enough that it justifies the effort to do the conversion of the move from their incumbent vendor to us.
And that’s really the inflection point in the current environment where customers are continuing to be thoughtful and mindful of their spend areas and they want to make sure that they’re driving innovation, but also with cost controls and constraints, it’s a perfect setup for us.
Stephen Schwartz: Got it. Thank you very much and congrats.
Ashutosh Kulkarni: Thank you.
Operator: Thank you. And our next question today comes from Brent Thill with Jefferies. Please go ahead.
Bo Yin: Hi guys, this is Bo Yin on for Brent. Thanks for taking the question. So, I wanted to ask about optimizations and customer behavior. Can you talk about the dynamic between customers focusing on optimizing their near-term consumption, but also bring on more workloads to Elastic to drive TCO saving? Is that increasing workloads being offset by optimization on these workloads or how should we be thinking about these dynamics?
Ashutosh Kulkarni: You know it’s – this is Ash here. So it’s really difficult to sort of tease apart the exact dynamics in any one customer on how these things are moving, but in the aggregate – what I’d say is, what we’re seeing is that the majority of customers are generally when it comes to the consumption optimization that people were doing, they’re generally where they want to be at this point. It started a few quarters ago as you know, we’ve talked about it and it’s gotten to a point where customers have done all the things that they generally believe that they need to do. Data continues to grow, the kinds of use cases that we play in, tend to be incredibly important. So it’s not like they can just walk away from them.
