Elastic N.V. (ESTC) Earnings Call Transcript & Summary

All right. Good afternoon, everyone. I'm Sanjit Singh. I run the infrastructure software coverage on Morgan Stanley software team. Super pleased to have the management team from Elastic. We have CEO, Ash Kulkarni; and CFO, Janesh Moorjani. Thank you both for coming to the conference.

Thank you.

Thanks for hosting us.

All right. Let me get through some disclosures, and we can start the discussion. For important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/researchdisclosures. If any questions, please reach out to your Morgan Stanley sales representative.

To maybe kick off the conversation, Ash, just to start with you. If you look at the 3 core sets of opportunities that you're going after in security, observability and search. As you look at where your customers have been and where they're going, I guess the high-level question is, how early or mature are you in each of these core opportunities? And in terms of the kind of where do you sort of stack rank on the priority list going forward, what do you -- how would you sort of frame that out for us?

Thanks. So as I look at Elastic as a whole, like fundamentally, like I think of us as a search analytics platform. So the core we are a data platform. What we're really good at is bringing in any and all kinds of data into the platform really quickly. We do incredibly well when it comes to unstructured data, which is quite messy, logs, any kind of unstructured data. We're able to index it, make it all searchable. We have pretty rich developed set of machine learning capabilities that allow you to run all kinds of algorithms on that data and then visualize that information. And the use cases that we've always tend to play in are ones that play to those sweet spots, right? So it involves a lot of unstructured information, involves being able to deliver results in real time. And that's the reason why security, that's the reason why observability. As we look at the spaces that we play in, obviously, when it comes to search, enterprise search, that is -- that was the genesis of the company. So that is an area where we are very well known. You look at the Gartner Magic Quadrant for Insights engine sets the space, they consider us to be a leader in sort of the farthest most to the right on the X-axis. When you look at observability where we tend to land, our core sweet spot is in log analytics. We always start with log analytics. That's where we not only have a very mature solution. But in terms of our competitive win rates, they are very strong. We are able to do well, both in terms of new opportunities, but also expansion opportunities. And once we land with log analytics, we expand from there. So whether it's application performance monitoring or metrics or real user monitoring, we'll expand from that core position of log analytics. And in security, it always starts with SIM or security analytics. So that's our core landing spot. Once we are in there, then we expand from there, not just the detection but protection, so endpoint security and more recently, the work that we've done around cloud security. That's how I think about it. So in terms of maturity, where we have the greatest strengths are in these areas of log analytics and SIM and then obviously, enterprise search.

Makes total sense. I wanted to circle back to Q3 results, Janesh. And you had a solid quarter. You grew revenue growth, 27%. Cloud business grew 40% in constant currency. In terms of Q4, which is your next quarter, you're calling for high 18%, I think, 18% constant currency growth. And then for the forward, you gave sort of an initial view of sort of mid-teens growth. Can you walk us through the underlying assumptions on this more difficult environment that we're in, why we can more or less sustain growth fiscal year '24 or fiscal year '23?

Yes. Happy to talk about that. So for us, given that we just finished Q3 and are entering Q4. Obviously, like you said, it's a difficult environment out there, relatively uncertain. So at this point, we at least wanted to provide people with a general framework of how we are thinking about the future, even though we don't formally guide until our next earnings call and what we said was we expect fiscal '24 growth to be in the mid- to high teens in terms of revenue. And there's a few different things embedded within that. And maybe I'll start with what's not in that. We're actually not baking any kind of assumptions in there about some improvement in the macro environment. We generally talk about the world the way it exists today and the way we see it today. So we're not anticipating any significant changes or improvement. It's a wide enough range at this point that we still have some ability to withstand variability, frankly, in either direction, even if there's some level of downward pressure in that number. The other piece is that fiscal '24 really comes down to, first off, it's our own internal execution and Q4, it's our biggest quarter. It's seasonally the largest quarter for us. April will be the biggest month in the quarter for us. We had a really good Q3 from the standpoint of how well the sales team executed. So we were very pleased with that, came in well ahead of what we were expecting. But Q4 is still a big bounce in to climb. And so we'll execute here in Q4, and that will allow us to form a more thoughtful view of fiscal '24 as well. And then the other piece that we've talked about is consumption patterns that we've seen for people that might not be familiar with it, our cloud business, which is now 40% of revenue is predominantly consumption based. And certainly, within that, we've seen customers continue to optimize consumption in the near term. And as we look at fiscal '24 and what drives the growth in fiscal '24, again, we're not assuming any kind of inflection or a sudden increase in rates of consumption. From the standpoint of how year-over-year growth rates get measured, you will see that, but that's more of a function of the math of difficult comps in the first half of this year versus easier comps in the back half. Operationally, we are not assuming any kind of significant inflection. So when we look at the strength of the customer commitments that we signed here in Q3, and that's reflected in a number of the different kinds of measures that people look at, but when you look at the strength of those customer commitments that will translate into revenue. It's just a question of time because those are contractual commitments. So those are some of the things that went into it. And then, of course, as we get to the next earnings call, we will formalize the view in the form of a specific guidance number with a narrower range.

Yes, it makes total sense. You hit on a topic that was also the theme of the call on Q3, which was customers making more substantial larger commitments to the Elastic platform. For the investors in the room, how much in terms of thinking about the focus on the near-term revenue trends versus the performance on commitments. What's the right way to -- for investors to sort of assess the health of the business across both near-term revenue and on the committed side?

Yes. I think there's a couple of different pieces within that. So generally, when we think about the commitments, as I said a minute ago, those commitments will translate into revenue, right? We have firm commitment. So you'll see that reflected in some of the balance sheet and backlog-oriented measures and so forth. We've generally been of the view that revenue is the best indicator of the health of the business because it naturally considers all the different formats in which our technology is used or consumed. It naturally normalizes for duration and so forth. But some of these other elements are important measures to look at as well. So we do look at those, and we do think that, that's a good reflection of how this will all eventually translate into revenue over time.

For -- just a follow-up on like the customer commitments is just sort of an example. If a customer is signed up for a minimum commitment or a purchase commitment. And they're not ramping as fast as they had expected. How do you sort of handle that customer? Do you sort of roll over that unused capacity into another contract? Or is it more of a take-or-pay type construct? How do we think about customers who may be falling behind against executing their commitments?

Yes, it's a great question, Sanjit, because consumption models are still relatively new out there and they're evolving. So for us, when a customer signs an annual contract or a multiyear contract, they -- even those multiyear contracts with annual breakpoints and there may be ramp embedded in that if a customer is assuming that their consumption will grow over time, it could be linear, it really depends on what the customer wants. By and large, what we've seen is that customers don't overcommit. And they are more thoughtful about making sure that they don't end up with a large overcommitment and the additional points of discount that they might get for a commitment may not be worth that risk for them because our contracts are generally structured as use it or lose it contracts. And so what we've generally experienced is that customers will actually tend to consume faster than the rate of commitment. And so we've -- but in the handful of cases where we do see customers consuming slower, then in those instances, we'll either work with them to arrive at some sort of alternative approach. And in a handful of cases, they do actually have minor bounds that are actually featured under the contract. But as I said, for the most part, I think what we see as customers actually consume faster than what they were contracted to consume for. I think the other piece, by the way, from a consumption perspective that as a dynamic that plays into the numbers is around the net expansion rate because our -- on the cloud business, all of our contracts, the commitments that customers make don't show up in the net expansion rate because the net expansion rate is measured based on the actual consumption. So when you've got these large contracts, which are still waiting to be consumed or the consumption is ramping slowly, that adversely impacts the net expansion rate because the lower level of consumption is in there, but the higher level of commitment is not. And I think that just plays itself out of a time.

And I guess the point there is that ultimately, those customers, the vast major of time get to that consumption level.

And one way or another, it becomes revenue.

Yes. Makes total sense. So what are the themes in the broader cloud ecosystem has been around this topic of cloud cost optimization and rationalization. We're seeing it at the major hyperscale levels. We're in seeing in parts of the independent software ecosystem. When it comes to Elastic Cloud, what are the various levers that customers have to optimize their spend as they look to preserve budget in a tough environment? And to what degree are you guys helping them to achieve that?

Yes, maybe I'll touch upon that. So we've seen different patterns, right? So for example, customers might sample some of their data -- they -- we've seen some customers retain data for shorter periods of time. So instead of a year, they might bring it down to 9 months. In some cases, we've seen them move more data to low-cost object storage like in the frozen tier that reduces their overall cost a bit. But there are a few elements to this that are important to understand. And probably the first one is that fundamentally, data is still continuing to grow, right? So they are doing all these optimizations, but there's a limit to which they can do these optimizations. And the way we are approaching this is rather than trying to be against this we are leaning in and having a conversation with them on, we'll help you -- if you have an existing workload on the Elastic and you're looking for ways, like these are all trade-offs that you're going to have to make, obviously, you don't get the same outcomes. If you sample the data, you don't get the same outcomes if you're storing it on lower-cost object storage because the performance vector there is different. But that's fine. We are helping them with it. But in that same conversation, we are also trying to figure out what more can we do for you? Because clearly, we are demonstrating that our pricing model is a lot more advantageous to them than a lot of others. And in this point in time, like the worst thing that you can do is be seen as inflexible or high cost. And we are able to come in and help them actually reduce that cost and have the conversation of, oh, you're doing APM with some other vendor. Oh, you might be using some other vendor for SIM how can we help you reduce that total cost by moving some of those workloads to Elastic, which you're already seeing as having the ability to reduce your overall costs. So that is a big part of what is helping us bring new workloads onto our platform and a lot of the Q3 activity that you saw in terms of the strong contracts from customers reflected that kind of activity. And fundamentally, we see this as an opportunity in this current time when everybody is concerned about their total budgets to see how we can take market share. I mean, obviously, we can't control the fact that there is optimization happening across the board. So consumption is slowing down. But what we can control is the activity that we drive from a sales perspective to try and take share in this time, and that only sets us up to be in a stronger place as we come through this, however long it takes.

Yes, on the pricing point, do you want to just, Ash, Quickly just describe Elastic's approach to pricing and how we may differentiate versus the other players in the space?

So our pricing model, we basically do not sell our products separately. There's -- you just effectively are buying the platform. You're buying Elastic. And there are a few tiers, and in each of those tiers, we have incremental functionality, but that functionality exists for observability, security, search, all the use cases that you can drive with Elastic. And our pricing model is purely consumption based. Now obviously, if it's self-managed, where somebody is just purchasing the licenses from us. They are purchasing it on a virtual CPU basis, and then they are running it. We don't have the ability to meter it on a second-by-second basis. But if it's Elastic Cloud, we are measuring and monitoring and metering everything, and we are charging the customer based on their actual consumption. So that's the model. And what that lets people do is very flexibly try out using Elastic for additional things. So a lot of times, somebody might start using Elastic for log analytics. And when we have these kinds of conversations, it's very easy for them to maybe instrument a few of their applications and try out our APM functionality or try using Elastic for cloud security. And then once they see that not only are we functionally very capable and have a rich set of capabilities, but also the pricing model scales a lot more gracefully, like that becomes easy for them to then start bringing more workloads on to us. And that's a big differentiator. We're able to -- the value for price equation tends to be very favorable for us.

Going back to the topic of sort of cloud cost optimization, from your perspective and what you've seen at your customer base, how long does a cost optimization project initiative take? And what percentage of the base are sort of actively involved in these types of initiatives?

Yes. You've been seeing the optimization efforts from customers across the board, across all sizes of customers and segments of customers. The moment somebody decides that they want to do something to optimize their usage, whether they are sampling or doing something different it shows up immediately in our metering, right? So the impact is seen immediately. And at this point, we're seeing it quite broadly. So it's not -- and even now, some customers are doing it more than others, like it's -- what you saw in Q3 was sort of everything balancing out. But I think cost considerations are on everybody's minds right now. .

Yes. I guess the point is here is that it's relatively quick to implement. It sort of goes to the point is like when this is done, we're in a better environment, the spike is potentially turned back on more meaningfully once we get into a better budget environment.

Yes. For the last many years, budgets weren't the most important thing for most CIOs. Right now, their budgets are like the first and second and third thing that everybody is thinking about. And so there is a tremendous willingness to look at how do they do more with less. And that's the opportunity that we see. That's what we're leaning into.

I want to combine like 2 of the questions that I had written down into one. But I guess, big picture, sort of explain to us why does Elastic win more -- like more broadly? And then in this current environment, I think a lot of players in this space is talking about the opportunity around consolidation; vendor consolidation and tool consolidation. Can you give us the case why Elastic is well positioned to be a sort of net consolidator of spend in the categories you played in? I mean you have touch the pricing point, but if there's anything else besides the pricing point...

Yes. I'll come back to the pricing front maybe at the end, but probably like when I look at all the reasons why we win, when we analyze our win rates and what are the reasons why we tend to succeed, probably the first one is in terms of scale and performance, we do incredibly well. So just the technology, the ability to scale very nicely. I mean that's a huge strength for us. You've got lots of customers who are bringing in hundreds of terabytes of data per day into our platform, and we are able to deliver really fast responses, and this is relevant for search use cases, for observability, security. It has meaning across the board. The second reason is when you think about what we are, we started life as a data platform and fundamentally, there is no edge to the map, right? You can bring in any and all kinds of data into the platform. So we see customers, especially as observability and security are becoming a lot more mainstream, think about how do they want to do observability. Like they want to -- we have lots of customers who basically want to be able to look at the performance and availability of their application based on their end customer journey. So they want to be able to view it in a particular way. We have customers who want to do things like analyze the performance for our most important clients. That means now correlating your observability data with your business data because your most important clients are typically -- it's a subset of your customer base, depending upon your customer [ ideas ], you're pulling in some cohort like if you want to do that with any other observability product, you've got to take both those data sets out, put them in a data warehouse and do that analysis offline somewhere. In the case of Elastic, because we had a data platform, there is no limit to what kind of data you can bring in. We have these customers just bringing in their business data into Elastic and then doing the correlation right there. We have customers who, for insider threat analysis are joining their physical security data, the card scans that people do with their application logs and their access and identity management logs and traditional SIM platforms just aren't able to do that, but we can because fundamentally, we are a data platform. So there's no limit to the kinds of data and the kinds of analytics that you can do with Elastic. And that's the second most important reason why we tend to constantly win. We talked about price. The pricing model tends to be very permissive and it really aids in that kind of viral adoption. And probably the last reason is we've been investing a lot in machine learning. So we talked about -- last summer, I talked about vector search and all the work that we've done in vector search, that makes not just search a lot more powerful but it has a lot of relevance to observability and security use cases, and that's also driving all kinds of interesting use cases and enabling us to compete even better. In AI Ops, as an example, which is all about machine learning for observability, we are now considered to one of the really strong players in that space, and that's all coming from these investments in machine learning. So I think those are the core reasons why we tend to win. And the way I see them, it's -- these are strengths. These are pretty significant moats that we can continue to build upon.

One of the more popular questions I get from investors is that they say something to the end of "I like the market opportunity. I like the product category that Elastic is in, but the market is so crowded in terms of the number of players, at least saying that they can sort of compete in the space between commercial solutions, open source solutions and even the hyperscalers have their own set of functionality." as CEO, how do you think about competing in a market that does have multiple alternatives in a way that will translate to durable growth and profitability. Sort of talk to you like that sort of challenge. Or is it a view that the market is so big that can support multiple players?

I think the market is big enough that there are -- it's definitely going to be able to support several players, right? That doesn't mean that it's going to be able to support dozens and dozens of players necessarily over a long period of time. The way we look at the markets that we play in is do we see a viable path to be 1 of the top 2 to 3 players in each of the core categories over a period of time and sustain that. And hence, the focus on log analytics, hence, the focus on SIM and XDR, hence the focus on search. And those are areas where you just look at security. In security analytics, we are -- like Forester put out an article recently, we are 1 of the top 3 players. When you look at AI Ops, we are doing incredibly well in that area. When you look at search, we're kind of like the de facto name out there. And so in the places that we land with where you need to have a very competitive offering to ensure that you can win the RFP, you can show -- demonstrate really well in these enterprise accounts and win those accounts, we've got tremendous strengths. And the competitive moat is one that we believe will hold. And then once we land, then we expand from there. And the second part of this is how do we do this profitably. I think that's a really important question. And to me, the most important thing there is the platform approach that we've taken. So the bulk of our R&D efforts go into the platform itself. So when we talk about features like searchable snapshots, we talked about that as being one of the major drivers for customers upgrading to our enterprise tier. That searchable snapshots functionality helps all our solutions with its security, observability, search. The same thing for things like vector search. Vector search allows you to get better relevance results. That's true for search. It's just as true for behavioral detections and security. It's just as true for observability. So a lot of our investments actually go in at the platform tier. That really helps us make sure that we are being very efficient in how we service this market. And that also helps us from a go-to-market perspective because it's not like our sales team is now having to learn 4 different products or 5 different products. It's one product with multiple use cases. And the effort for a presales engineer to ramp up, the effort for account executive to ramp up, is that much better because of that. So -- we're big believers in the platform approach for that reason. We believe that over the long run, it ends up being a heck of a lot more efficient. And that's really why we stay so disciplined to that approach. Even in the acquisitions that we do, that tend to be really technology tuck-in acquisitions.

Makes sense. I definitely want to see if the audience has any questions. So get ready to ask your questions. I do have to get to the obligatory AI question first, though, right? It's like I can't do a conference in 2023, we talking about AI. So it's a 2-parter. The first part is, as it relates to your enterprise search business, do you believe things like OpenAI and ChatGPT [ ask ] like capabilities pose a threat to Elastic in the search category? How do we think about those new large language models?

It's actually incredibly complementary because if you think about what we do, we provide a foundation for you to run your ML models on your data. So the way -- when we came out with vector search, what we enabled was the ability and around that same time, we made it possible for you to bring your own models, whatever, PyTorch models that you might want to bring on to Elastic, run those models natively on your data in Elastic Search, right? We made that possible. When you look at these large language models like the one from OpenAI or even what Google has, they all depend on something called embedding that allow you to create like pretty long vectors with lots of dimensions in them. We delivered that support on our platform as well. So you can now bring in these kinds of models. Now you would never want to run -- I don't -- most customers, let me put that way, wouldn't want to run like a ChatGPT like the OpenAI model on their own infrastructure, it will be just prohibitively expensive in terms of the compute cost that you would need. So my expectation is that most customers will have their own models that they will then augment by making API calls to something like an OpenAI or Google in the future. But I think that's going to be a really interesting way to make your search queries that much more interactive, that much more interesting. And you talked about enterprise search, but I believe that there are applications of this even in observability and security. So we've been investing in this area for a while. We are not going to be building the models ourselves, like that's -- OpenAI is doing that, Google is doing that. Facebook is doing that. We're going to make it possible for you to augment your own ML models with whatever you want to get access to from these large transformer models.

Great. I'm going to skip my second and third question and let's go to the audience. There's someone here right in the front?

I appreciate the time here. I appreciate the conversation. I actually wanted to -- let me bring a few topics that we've discussed here together. I know we went through the customer commitments, which are very great to see optimization around cost. I know that's, I guess, just the reality of the world that we're in. And then our pricing model. I know we touched on those 3. I guess, the question I'm thinking about, I know in the way we price, if customers commit ahead of events, they sometimes get better -- they can get better pricing per hour. And so in like a world of optimization, does -- when customers commit more, does that help them basically get more pricing per hour? Is that possible to think about how customers are committed or why -- one reason of why we've been successful in commits as well?

Yes. Maybe I'll start and you can add to that as well as you like, Ash. So fundamentally, if a customer makes a larger commitment, they do get better pricing than a customer that makes a smaller commitment, right? And I think that's fairly straightforward. But I think the important piece for us is as customers think about making these larger commitments to Elastic, what they're really doing is moving additional workloads on to Elastic. And in many instances, those may have been workloads running on competitors. . So the way we think about it is actually increasing our footprint overall in the account. And from a pricing perspective, what we have not seen is any sort of undue pressure based on deal bands and so forth. So it's not like we're giving away the farm to attract greater commitments or anything of that sort. In fact, if anything, the sales team was really -- was really well disciplined in how we executed in Q3 in terms of protecting ASPs and deal sizes and discount rates and so forth. ASPs actually got bigger compared to the year ago period as well as compared to the prior quarter.

Yes. And just to put that in a little more context as well, right? So if you have a customer that's running at a run rate where they consume up to, let's say, $0.5 million over a period of a year. If that customer is looking to get a much significantly larger discount for committing to 600,000, like we'd never do it. Like that just makes no sense whatsoever. And like economically, that would not really like make any sense for us, so we would never do it. Now if that customer commits to $5 million a year, they're not doing it for the discount. They're doing it because they can actually burn $5 million a year because, as Janesh said, these contracts are written to be use it or lose it. And that's when they are intending to bring some additional workloads onto our platform. In today's environment, if they're bringing some additional workloads onto our platform, it's because they are taking that from somewhere, right? And they're taking that from somewhere who might not be giving them the same kind of economics, overall benefits, whatever as Elastic is able to give them, and that's really how it plays out.

Unfortunately, we have to stop it there. Thank you so much, Ash and Janesh, for great conversation.

Thanks again for hosting us.