Elastic N.V. (NYSE:ESTC) Q1 2024 Earnings Call Transcript
Elastic N.V. (NYSE:ESTC) Q1 2024 Earnings Call Transcript August 31, 2023
Elastic N.V. beats earnings expectations. Reported EPS is $0.25, expectations were $0.11.
Operator: Good day and welcome to the Elastic First Quarter Fiscal 2024 Earnings Results Conference Call. [Operator Instructions] Please note, today's event is being recorded. I would now like to turn the conference over to Janice Oh with Investor Relations. Please go ahead.
Janice Oh: Thank you. Good afternoon, and thank you for joining us on today's conference call to discuss Elastic's first quarter fiscal 2024 financial results. On the call we have Ash Kulkarni, Chief Executive Officer; and Janesh Moorjani, Chief Financial Officer and Chief Operating Officer. Following their prepared remarks, we will take questions. Our press release was issued today after the close of market and is posted on our website. Slides, which are supplemental to the call can also be found on the Elastic Investor Relations website at ir.elastic.co. Our discussion will include forward-looking statements, which may include predictions, estimates, our expectations regarding the demand for our products and solutions and our future revenue and other information.
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These forward-looking statements are based on factors currently known to us, speak only as of the date of this call and are subject to risks and uncertainties that could cause actual results to differ materially. We disclaim any obligation to update or revise these forward-looking statements unless required by law. Please refer to the risks and uncertainties included in the press release that we issued earlier today included in the slides posted on the Investor Relations website and those more fully described in our filings with the Securities and Exchange Commission. We will also discuss certain non-GAAP financial measures. The disclosures regarding non-GAAP measures, including reconciliations with the most comparable GAAP measures can be found in the press release and slides.
The webcast replay of this call will be available on our Company website under the Investor Relations link. Our second quarter fiscal 2024 quiet period begins at the close of business on Tuesday, October 17, 2023. On September 5, 2023, we will be participating in the Goldman Sachs Communacopia Technology Conference. With that, I'll turn it over to Ash.
Ashutosh Kulkarni: Thank you, Janice. And thank you all for joining us today. I'm pleased with how we performed this quarter. We had a strong start to our fiscal year, with our performance, exceeding our stated expectations across both revenue and non-GAAP operating margin. In Q1, revenue grew 17% year-over-year, with Elastic Cloud growing 24% year-over-year. We ended the quarter with more than 1,190 customers with annual contract values over $100,000. As customers continue to adopt Elastic as their data analytics platform of choice for addressing multiple real-time search use cases. And we continue to manage the business with discipline to deliver non-GAAP operating margin of 9.9%. Elastic has always had a singular mission, enabling everyone to find the answers that matter, from all data in real-time at-scale.
The versatility of our platform, the built-in AI capabilities such as the Elasticsearch Relevance Engine or ESRE and our ability to excel at multiple real-time use cases across search, observability, and security on our data analytics platform have all made Elastic a natural choice for our customers as a core element of their IT stack. Our land and expand strategy continues to serve us well and our long-term opportunity remains robust. In Q1, we saw two distinct trends within our business. The first is around generative AI. Generative AI and its intuitive approach to interact with massive amounts of information and generate new content is driving a resurgence of excitement around enterprise search. Businesses are recognizing the opportunity to create new customer and employee experiences and drive efficiencies in various business processes through the use of AI-powered search.
This is opening up new opportunities for Elastic. To build generative AI applications that work within their environment and with their proprietary data, businesses need the ability to provide accurate context in real-time to large language models or LLM. And to do so in a way that doesn't violate their privacy or security policies. This requires a platform that can allow businesses to use their own or third-party ML models to generate embeddings from their data, irrespective of the type of data. Store these embeddings vector store at very large scale and then efficiently search across these vectors in real-time to enhance LLM responses by providing context using retrieval augmented generation. The platform needs to ensure that this vector retrieval enforces data privacy with document-level permissions and takes context, such as user privileges, personalization, geolocation, and other factors into account.
The platform also needs to be flexible enough to enable hybrid search using a combination of vector, symantec, and textual search techniques to ensure the most relevant results possible. Elasticsearch, with ESRE delivers this entire set of capabilities in a single platform. It does so in the same platform that is already being used by tens of thousands of organizations worldwide for real-time search use cases. Our proven scale, performance, and advanced enterprise features like document level permissions, built-in security, and hybrid search with Reciprocal Rank Fusion, makes us a highly differentiated an ideal choice for these generative AI use cases. In Q1, we saw significant activity around generative AI with the number of customers choosing ESRE as their platform for building generative AI applications, using our vector search and hybrid search capabilities.
As an example, a U.S.-based Fortune 100 global media and technology company has integrated as ESRE with their own locally hosted large language model to enable their ticketing system to now deliver contextual answers to questions from their customers. This is projected to enable their team to solve about 50% of their helpdesk tickets through this automation, made possible by the power of generative AI. Another example is a leading file-sharing service that is using Elastic's hybrid search capabilities to power a new AI-powered universal search tool. The combination of vector search and textual search, enables them to bring a significantly superior search experience to their customers across all subsidiaries and applications. With Elastic generative AI and machine-learning capabilities at its core, its tool learns and evolves alongside its users, continuously improving as they use it.
Another example is the leading AI platform Labelbox that uses Elastic to power one of its most popular tools, Labelbox catalog, enabling teams to accelerate and streamline machine-learning model development through optimized search experiences. With Elastic fast and rich search capabilities, Labelbox customers can undertake unstructured data searches in a fraction of the time compared to its previous search solution, which ultimately helps them to capitalize on the possibilities of AI. Similarly, companies are also using Elastic to enable things like forensic video analysis at scale. One leading telecom equipment company is using their own large language model coupled with Elastic vector search capabilities to power their cloud-based video Search solution, enabling them to better identify bad actors and provide real-time security.
These are just a few of the many examples of customers using us for generative AI today. Elasticsearch is the most popular platform for search and as customers build contextual generative AI applications, they are naturally choosing Elasticsearch and ESRE to provide relevance and context based on their private data. Today, we have hundreds of paying customers using ESRE for vector search. And the conversations we're having with our customers gives us confidence about our continuing traction in this space. We anticipate that as customers start to put more and more of these use cases into production, generative AI will be a real tailwind for our business. The second distinct trend in our business is the continued push by customers to consolidate onto the Elastic platform for multiple use cases.
In Q1, customers continued to make large multi-year commitments as they sought ways to lower their total spend without sacrificing innovation by bringing more workloads from other incumbent solutions onto Elastic. We continue to leverage our competitive strengths in our core areas of search, log analytics, and security analytics to drive our land and expand strategy. As an example, in Q1, we closed a multi-year deal with Texas A&M University for Elastic Cloud on AWS. The university previously deployed a competitor's solution, but moved to Elastic for security and observability. The customer chose Elastic for ease-of-use of a single platform without needing multiple licenses. And search results in high speed and relevance for all their data, enabling them to rapidly and effectively solve their business challenges.
They use Elastic to search through analyze and secured all of their data from a unified platform, while optimizing costs and meeting compliance requirements. We also closed a multi-year deal for Elastic observability with one of the largest multinational communications and entertainment companies in the world. They started with a small deployment of Elastic next to a competitor's solution, but consolidated onto Elastic to become the enterprise standard for its observability platform. This company chose Elastic for its flexibility and scalability across different data types and leverages advanced features such as searchable snapshots and machine-learning to help them taken AIOps approach to the data they're ingesting into Elastic. This quarter, we also renewed and expanded business with one of the world's leading Internet domain registrar and web hosting companies.
A long-time Elastic customer, the company previously used a competitor solution, but moved to Elastic and in Q1 signed a multi-year contract for Elastic Cloud on AWS. The company has consolidated multiple tools across logs, metrics, and APM in Elastic observability to effectively monitor thousands of online services for customers, while reducing meantime to resolution and streamlining operational costs as its business continues to scale. As we have discussed previously, our customers routinely tell us that our platform delivers a much higher value than competitive offerings and these advantages along with our innovative AI-power data analytics platform are enabling us to compete very well in this environment. Now, onto our products, in Q1, we continued our focus on innovation and delivered on several key capabilities to our platform and our solutions.
One of the most significant announcements in Q1 was the release of the Elastic AI-Assistant powered by ESRE. This AI-Assistant, which helps guide analyst investigations and remediation is in beta for security and in technical preview for observability. We continue to enhance capabilities in ESRE and delivered new hybrid search capabilities with the industry-leading implementation of Reciprocal Rank Fusion or RRF to combine vector, keyword, and semantic techniques for better results. We're also continuously improving the speed and performance of the Elasticsearch platform. And we did work in Q1 in this area, that resulted in faster and more relevant outcomes for search aggregations for cross-cluster search and for dense vector search. This included support for native implementations of vector search using hardware-accelerated SIMD instruction sets, which yields even faster queries and 30% greater indexing throughput.
In the area of Elastic observability, we integrated our Time Series Data Streams or TSDS capability with popular Elastic observability integrations, such as Kubernetes, Nginx, AWS Kinesis, and Lambda, enabling the potential to reduce storage needed for metrics data by up to 70%. In the area of Elastic Security, we extended support for advanced entity analytics with the general availability of lateral movement detection. On the go-to-market front, we continue to focus on our partnerships with the major cloud hyperscalers, and I'm pleased to highlight that we recently earned top accolades from each of the three hyperscalers Microsoft, AWS, and Google Cloud. Specifically, we were named the Microsoft Commercial Marketplace Partner of the Year and the AWS U.S. ISV Rising Star Partner of the year.
And just this week, we were honored to receive the Google Cloud, Global Technology Partner of the Year award. These awards from all the three cloud hyperscalers are a reflection of the strength of our relationships with these cloud partners. The deep product integrations, we have built with them and the success we are achieving together in driving growth for our businesses in the market. Customers are making significant multi-year commitments to our platform through these cloud marketplaces as they leverage Elastic, as an AI-powered data analytics platform for multiple real-time use cases across search, observability, and security. Finally, I would like to again highlight that Q1 was a continued demonstration of our commitment to managing the business with discipline.
We delivered a non-GAAP operating margin of 9.9% for the quarter, which was significantly better than our expectations and we remain on-track to deliver on our non-GAAP operating margin target for the full fiscal year. In closing, I want to thank our team for their dedication and continued focus in execution. I also want to thank our customers, partners, and investors for their continued support and confidence. Our conviction in the long-term opportunity in front of us remain strong. It is based on the strength of our relentless innovation and continued customer confidence in Elastic. Generative AI is opening up new opportunities for us that we expect to capitalize on in the coming quarters and years. And as cloud optimization is stabilizing, we expect to continue making progress on our stated goal of driving growth with profitability.
With that, I'll turn it over to Janesh to go through our financial results in more detail.
Janesh Moorjani: Thanks, Ash. We are very pleased with the strong results that we delivered in the first quarter, marking an excellent start to the new fiscal year. We once again came in above the high end of our guidance for the quarter for both our topline and our bottom line. In Q1, we delivered 17% year-over-year growth in total revenue with Elastic Cloud yet again driving our results with 24% year-over-year growth. Importantly, we delivered non-GAAP operating margin of 9.9%, demonstrating both our strong investment discipline and the operating leverage inherent in our business model. As Ash mentioned, we saw increased engagement around generative AI use cases in the first quarter, which led to customer dialogue at the highest levels with the C-suite being deeply engaged on the topic.
Our advanced capabilities enable customers to build generative AI solutions, leveraging the benefits of our data analytics platform including its native vector database capabilities as they address multiple real-time search use cases. And this positions us exceptionally well to be a leader in generative AI long-term, which we believe will ultimately drive meaningful revenue for us in the coming years. We also saw continued strong contractual commitments during the quarter, particularly among our larger customers as they consolidated use cases on Elastic and benefited from the value of our platform. In addition, we began to see signs of improvement in consumption patterns as customers increase their consumption against the commitments that they had previously made.
We will monitor this ramp against the backdrop of broader consumption optimization trends, which still might take a couple of quarters to play out, but we are pleased with the early signs we saw during the quarter. As we look out over the rest of the year, we continue to expect that the compelling value proposition for customers of our platform, combined with the strong engagement we've seen for generative AI will drive our overall business momentum. Let's get deeper into the results for Q1 and our outlook. Total revenue in the first quarter was $294 million, up 17% year-over-year on an as reported and constant currency basis. Subscription revenue in the first quarter totaled $270 million, up 17% year-over-year, or 16% year-over-year in constant currency, and comprised 92% of total revenue.
Within subscriptions, revenue from Elastic Cloud was $121 million, growing 24% year-over-year on as reported and constant currency basis. Elastic cloud represented 41% of total revenue in the quarter, up from 39% a year ago. Elastic Cloud revenue based on month-to-month arrangements contributed 15% of total revenue compared to 16% in the prior quarter. Professional services revenue in the first quarter was $24 million, growing 29% year-over-year on an as reported and constant currency basis. Although professional services may fluctuate across quarters based on the timing of services delivery, we do not expect it to vary significantly in mix over time. To add more context around overall deal flow. EMEA grew fastest during the quarter, followed by the Americas, and APJ.
We continue to see a healthy balance across the business based on geography, solutions, and verticals and this diversification reflects the breadth and popularity of our platform. Moving on to customer metrics. We ended the quarter with over 1,190 customers with annual contract value is more than $100,000. Looking at customer additions more broadly, we ended the quarter with over 4,170 customers above $10,000 in ACV and approximately 20,500 total subscription customers. Our net expansion rate, which as you know is a lagging indicator was approximately 113% in-line with our expectations for the quarter and consistent with our prior comments. Overall, customers continue to adopt Elastic as their AI-powered data analytics platform of choice for addressing multiple real-time search use cases.
Customers across industries and across the globe are adopting and growing on Elastic, particularly Elastic Cloud and we remain excited about the opportunity ahead of us. Now turning to profitability for which I'll discuss non-GAAP measures. Gross margin in the quarter was 76.5% versus 76.3% in the prior quarter. I'm very pleased with how the team managed discounting in the field during the quarter and also drove efficiencies in running our operational infrastructure. Our operating margin in the quarter was 9.9%, which was better than expected. The strong operating margin performance was driven by our revenue outperformance and our continued focus on managing our expenses. Diluted earnings per share in the first quarter was $0.25. Free cash flow on an adjusted basis was $49 million in the quarter or 17% adjusted free cash flow margin.
This represents our highest adjusted free cash flow margin to date, as we continue to drive operational focus in the business. The strength in adjusted free cash flow was partly due to timing benefits of approximately $20 million, primarily related to the timing of cash collections and payments that we had previously expected in the second quarter, but occurred during the first quarter. Looking at the adjusted free cash flow outlook for the second quarter, this timing of cash flow, shifting from Q2 to Q1 will impact adjusted free cash flow in Q2. Additionally, we anticipate $13 million of one-time payments that relate to previously completed acquisitions that will be due in the second quarter. Although we don't usually provide a specific quarterly outlook on cash flow, given some of the puts and takes in Q2, I'll share that we expect adjusted free cash flow in the current quarter to be in the range of approximately negative $10 million to breakeven, reflecting these two items.
As we've said before, cash flow on a quarterly basis will fluctuate given timing issues around inflows and outflows, as well as seasonality impacts. So we continue to look at cash flow primarily on a full-year basis. For the full fiscal year, there is no change in our prior outlook and we continue to expect free cash flow margin on an adjusted basis for fiscal '24 to be slightly above the non-GAAP operating margin for fiscal '24. We continue to maintain a strong balance sheet. We ended the first quarter with cash, cash equivalents, and marketable securities of $957 million. Turning to guidance, while we were very pleased with our outperformance in Q1, we continue to be prudent as we plan for the rest of the year. The macroeconomic climate has been stable, so we continue to assume that macroeconomic conditions will remain unchanged.
Additionally, although we are seeing customers ramp their consumption for the new workloads we're consolidating onto Elastic, we believe it is appropriate to anticipate that consumption patterns may continue to fluctuate in the near term. Accordingly, we are raising the low end of our total revenue guidance for the full fiscal year by $4 million at this time, resulting in an increase of $2 million at the midpoint. Since it is still early for us in the fiscal year, we are going to monitor these trends for another quarter before further evolving our outlook. In terms of operating expenses, we continue to invest with discipline in the business. Over the past several quarters, we've continued to drive efficiency in the business and that focus will not change.
At the same time, we see an opportunity to invest in development and marketing around generative AI as we solidify our leadership in this space. We continue to balance investing for growth against profitability and we'll carefully monitor our progress each quarter. We are raising our non-GAAP operating margin guidance by 25 basis points at the mid-point at this time. For both fiscal '24 and fiscal '25, we expect to grow revenue faster than overall expenses, expanding our non-GAAP operating margin each year. With that background, for the second quarter of fiscal '24, we expect total revenue in the range of $303 million to $305 million, representing 15% year-over-year growth at the midpoint or 13% on a constant currency basis. We expect non-GAAP operating margin for the second quarter of fiscal '24 in the range of 9.5% to 10% and non-GAAP earnings per share in the range of $0.23 to $0.25 using between 101.5 million and 102.5 million diluted weighted average ordinary shares outstanding.
For full fiscal '24, we expect total revenue in the range of $1.242 billion to $1.25 billion, representing 17% year-over-year growth at the midpoint or 16% on a constant currency basis. We expect non-GAAP operating margin for full fiscal '24 in the range of 10% to 10.5%, and non-GAAP earnings per share in the range of $1.01 to $1.11, using between $102 million and $104 million diluted weighted average ordinary shares outstanding. In summary, we had a strong start to the year, we are executing well and we are excited about the rest of this fiscal year and beyond. And with that, let's go ahead and take questions. Operator?
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Q&A Session
Operator: [Operator Instructions] And our first question today comes from Pinjalim Bora with JPMorgan. Please go ahead.
Unidentified Analyst: Hi guys, this is [Noah] on for Pinjalim. Thanks for taking our questions. Just curious if you could maybe expand on the performance of both the observability and security practices and how that's stacking up against the competition at this point and I just had a quick follow-up. Thanks.?
Ashutosh Kulkarni: Yes, this is Ash here, I can take that. Thanks for that question. So in terms of our ability to compete and differentiate in the market on both observability and security that continues to stay very strong. Just - even in this quarter like I talked about, one of the trends that we're seeing in the business is continued consolidation onto our platform, a lot of those consolidations tend to be around observability and security. The examples that I gave in my prepared remarks were around that. The capabilities that we've been delivering in this area, especially the - some of the newer AI-Assistant functionality for security, what's in beta now and it's an early preview for observability, those are driving a lot of excitement in our customers as they see not just the functionality that we've built, but the way we can help them take advantage of generative AI and do things in a very differentiated manner, really analyze all their data and get insights that other platforms aren't able to give them like that's continuing to play well towards our strengths.
So the win rate continues to be very strong and I'm very excited about both these segments for us.
Unidentified Analyst: Thanks. And then just a quick second question, it sounded a little bit more incrementally positive around the consumption trends you're seeing so far and totally understand - the understanding that there could be still some fluctuation for the remainder of the year, but how are you sort of thinking about that as you update the guidance for the rest of the year? Thanks.
Janesh Moorjani: Yes, I can take that. So, look, the way we think about the guidance for the year, as I mentioned, it's relatively early for us. We are very happy with the momentum that we saw in Q1. The quarter played out nicely for us across the topline, across the bottom line. But as I said earlier, because it's still a little bit early in the year, we think the best thing to do at this stage is just to continue to be prudent. We continue to guide based on what we know and we also continue to build in some protection for the things that we don't know. And although we saw some positive signs of customers ramping here in Q1, it's conceivable that consumption may fluctuate in the near term. So we just think it's best to consider that possibility in our guidance.
And that's what we've done here for Q2 and for the full year. And beyond Q2, as I look out to the back half of the year, we don't anticipate any worsening in the second half. We simply want to be measured in our approach to the full year outlook, since it's early in the year. So given the results that we had here in Q1, which were quite strong, we raised the guidance by $4 million on the low end as you saw, because we no longer likely - we no longer see the likelihood that will end at that lower-end of the range. So that's why we raised the bottom end of the range. We're looking forward to Q2 and the rest of the year and we'll update you again on the next call.
Operator: Thank you. And our next question today comes from Tyler Radke at Citi. Please go ahead.
Tyler Radke: Yes, thank you very much for taking the question. Great to hear about the hundreds of customers using the new ESRE product. Could you just talk about the monetization of that? I know there's kind of several ways you can monetize in terms of being on the enterprise and having to turn on some of the ingestion capabilities. But how significant could that be in terms of driving revenue? Would you expect to kind of see the impacts this year or is this more of a next year event? Thank you.
Ashutosh Kulkarni: Thanks for the question, Tyler. I can - I'll all address that. So, this is Ash here. So, as you think about our consumption model, the way you should think about it is, as customers use the ESRE functionality, they're doing multiple machine-learning tasks in there. They're effectively taking their data and then turning them into Vector Embeddings, they're storing that data and then using our vector search functionality. They're using things like Reciprocal Rank Fusion for hybrid search, combining that vector search with semantic search and textual search So all of this tends to be - especially the machine-learning stuff tends to be quite compute-intensive. So that is one aspect. The second aspect is for machine learning, you have to be on one of our premium tiers, so either the Platinum or the Enterprise tier.
So both of those tend to be ways in which the consumption grows. The second is what I'd say is, as you think about where customers are in this journey, first and foremost, it is beyond exciting, right? So generative AI and the possibility of the kinds of experiences that you are able to deliver to your employees to your customers and to do it in such a way that improves the efficiency of business processes, reduces your cost, this is absolutely a C-Level discussion, the kinds of conversations that we've been having, it's accelerating. It's really driving a resurgence for search in so many different ways and just in the conversations that I'm having with customers, it's becoming very clear that everybody is looking for ways to do this in different domains across their company.
Now where they are starting in most cases is with internal facing applications, applications that their employees might be accessing, which gives them a little bit of control over things, allows them to make sure that they really get comfortable with the large language models in the generation functionality, all of these capabilities are relatively new. So that's where we are. As more and more workloads go into production, as the volumes of data in these systems, grows all of that is also going to drive the consumption and the revenue that comes from it. So in terms of the way we are looking at it, the way I look at it, we are very early in the journey. The hundreds of customers that we have today just gives me tremendous confidence and the traction that we're seeing, gives me tremendous confidence in our ability to continue to be a strong leader and to see this become a real tailwind for us in the coming quarters and years.
Janesh, I don't know if you want to add anything in terms of how you're looking at this?
Janesh Moorjani: No, I'll just reiterate the same level of excitement around the opportunity that you mentioned. When - I hear from customers, our salespeople, folks out there, the level of excitement around gen AI is just tremendous. So I think it's a great long-term potential revenue opportunity for us.
Tyler Radke: Great. And Janesh, just on the macro environment, it sounded like you saw some encouraging signs of stabilization and improvement. Could you just give us a sense on how the linearity of the quarter played out and have those stabilizing or improving trends continued in August? Just any comment on kind of the timing of when you saw that and obviously, we're not calling the bottom, but just if there a kind of been consistent in August? Thank you.
Janesh Moorjani: Yes. Tyler as you know, linearity within the quarter can always be affected by the timing of specific deals and this time was no different. I think we did really well to close the business that was on the table before the end of the quarter. With respect to the consumption patterns that we saw, again, we saw consistency during the quarter. In any given month, we did see some customers go up and down, but looking at consumption trends on a monthly basis, it can be a little bit noisy. But overall, the themes played out as we described in terms of the customers starting to consume nicely against the contracts that they had previously committed to. And in terms of August. I think it's just too early to tell, we haven't even closed August yet, but I will share that the general tone of customer conversations that we've seen has stayed similar to Q1 with a lot of interest in gen AI.
Tyler Radke: Great. Thank you.
Operator: Thank you. And our next question today comes from Koji Ikeda with Bank of America. Please go ahead.
Koji Ikeda: Hi guys, thanks for taking the questions. I wanted to ask a question digging into the Elastic Cloud revenue growth here of 24%. Really strong number there, but when looking at the monthly cloud revenue that grew looks like about low-single digits. And then the net new customer ads the $100,000 ACV, that number was a bit lower than in prior quarters. So just kind of thinking through Elastic Cloud, does that mean that the growth or the strength there is coming from customers below $100,000 or is it customers that are well above $100,000, that's driving that cloud tread, maybe it's a combination of both, just looking for some more color here?
Janesh Moorjani: Yes, Koji, there's a couple of elements in there that you talked about in terms of monthly cloud as well as the customer sizes. So let me try and unpack both of those for you. In terms of monthly cloud, the monthly cloud business as you know is predominantly our self-service motion and the majority of that is SMB. That continued to be stable compared to the past couple of quarters. It was not meaningfully different, so neither better nor worse, and that was as we expected. So we anticipated those results. What you're really seeing in the mix there is that our annual cloud subscription motion has actually performed really well. We talked about the increase in consumption that we started to see from the consolidation of workloads and that's all reflected in the annual cloud revenue and then that then drives the mix of revenue So our strategy to focus on customers that have a higher propensity for growth is working quite nicely.
We are seeing those customers expand, we are seeing that make larger commitments and I think that's actually been working quite nicely for us. And then if I look at the customer metrics for more than $100,000, again, when I maybe just step back and look at the overall results for the quarter, the overall numbers were really strong for us in terms of both total revenue as well as cloud growth. But in terms of the customer accounts, we saw as Ash, mentioned earlier that customers are continuing to make strong contractual commitments to Elastic, although the additions to that pool of greater than $100,000 ACV was a little bit lighter. We did see strong expansion in the larger accounts and that reflects those commitments. So from a dollar perspective, we saw strength there too, and the number of net-adds in any quarter, it might move around a little bit, but the underlying drivers continue to remain strong and so that's very consistent with the theme of consolidation that we talked about in continuing commitments from these customers, it gives us a lot of confidence in our outlook for Q2 and the rest of the year.
So, I think we've had good consistent growth in our customer metrics historically, and we expect that that trend will continue over time.
Koji Ikeda: Got it. Thanks so much, Janesh. And just one follow-up here looking at net revenue retention of 113%. Is it too early to call a potential bottom or how do we - how should we be thinking about visibility into the bottom of net revenue retention going forward?
Janesh Moorjani: Yes, the net expansion rate in the first quarter, it moved as we had expected it would. You'll recall that we had covered this on the prior earnings call as well, and we had said that we would expect it to go down. And I think the decline in the net expansion rate reflects the two themes that we had mentioned earlier on commitments and consumption. So for cloud contracts commitments generally don't count towards the net expansion rate, while consumption does. So the strong committees are not in the number, but the slower consumption is. And then over time as the consumption against the committed contracts ramps that will naturally help the net expansion rate over time. If I think about that from a different angle, our gross retention rates remain very strong and we didn't see any change versus the prior quarter.
So the slower net expansion rate has been from slower expansion. So as I mentioned as that the consumption against committed contracts ramps, that will naturally help the expansion number. And finally, as you know, the net expansion rate is a lagging indicator, it's a trailing 12-month measure. So as consumption ramps, it will take some time for that to be fully reflected in the net expansion rate. We will continue to monitor that as we go, but so far it's playing out as we had previously predicted.
Koji Ikeda: Super helpful. Thank you so much for taking the questions.
Operator: Thank you. And our next question today comes from Jacob Roberge with William Blair. Please go ahead.
Jacob Roberge: Hi, thanks for taking my questions and congrats on the great results. Just going back to those hundreds of paying customers using ESRE in vector search, how would you characterize kind profile of those customers? Are you seeing that more from existing customers kind of upgrading platform SKUs and lifting expansion rates or is that actually starting to drive some new logo activity of - to Elastic as well?
Ashutosh Kulkarni: Yes, so we're seeing a mix of both, right? So keep in mind that our motion has always been a land and expand motion. So, new customers come to Elastic typically onto Elastic Cloud using the monthly subscription motion, as they start to use us for new use cases and as they grow, seeing the propensity to grow, we will engage with them and then move them to an annual contract and they sort of continue to expand from there. That motion remains consistent, even in the monthly cloud use cases that we're seeing. We are seeing customers use us for the generative AI use cases that I mentioned. The examples that I gave were all customers using us with annual contract rates in my prepared remarks. And keep in mind that when you think about the value that we bring in the area of generative AI, like what I'm hearing from customers is that really there are four key reasons why we tend to win and why we tend to do very well.
First is, we simply have really, really good vector database implementation. It performs very well, it scales incredibly well and that's something that is key to success, and so our customers are appreciating it. The second thing is, we've invested a lot in making sure that we don't just stop at a vector functionality, a vector implementation, but we have invested in capabilities like Reciprocal Rank Fusion for hybrid search. We've invested in functionality that allows you to incorporate context in all the search functionality needed for retrieval augmented generation, using personalization, using geolocation, et cetera, as context. The third big reason is we keep hearing about the fact that the enterprise-class capabilities like document-level permissions, like built-in security that we've implemented is something that is critical for actually putting these use cases into production.
And that's an area where many others are - it's an afterthought for them or they haven't implemented it. And lastly and this maybe comes back full circle to the point the question that you'd asked, we have a position in the market where most customers - all our customers obviously, but also a large number of people outside of our customer base are already using Elasticsearch for some form of search or another. And that just means that there is tremendous familiarity that data is already sitting in some Elasticsearch instance. So if you are in Elasticsearch user, it's just a natural thing for you to look to us for this kind of functionality. So that doesn't mean that we aren't getting new logos like I said, because there is a massive Elastic Search community out there that might not be paying us today, they might be using the free version.
But that's really the source of our success. And in some of the conversations that I'm having, a Fortune 100 company, we just recently talked to them and they told us that they evaluated vector capabilities across all the vendors out there. They did a very detailed evaluation and they were just blown away by what they saw Elastic bringing to the table. So lots of good traction, lots of good momentum across the broad market. Many of these are existing customers, but also new customers that have had familiarity with Elastic Search looking to us for this.
Jacob Roberge: Great. That's very helpful. And then when we think about the numbers, when do you think that those apps will actually go-live into production and drive potentially more meaningful consumption of the platform? Do you think that's a Q4 story or more of a fiscal 2025 dynamic? And then on the margin side, are there any incremental AI investments that we should be just thinking about when it comes to modeling?
Ashutosh Kulkarni: Yes, so let me maybe touch upon the first question first. In terms, of many of these use cases are already in production, right, so the examples that I gave in my prepared remarks, but even beyond that, there are customers who have spoken publicly together with Elastic on behalf of Elastic, just earlier this week at the Google Next event, Cisco presented with us and talked about the work that they have done on building an internal search application that goes across over 50 internal applications. They had some wonderful stats of what they were able to do in terms of saving hours for their support engineers and making their job a heck of a lot easier and better. So there are lots of production use cases already.
I think what's important to understand is, when does this become a big enough customer group that's in-production with data having grown to scale that, this shows up as a major tailwind. And I - we feel very confident that that's going to happen in the coming quarters and years. But discreetly, we are not looking at that as a significant impact for fiscal '24.
Janesh Moorjani: Yes, and just to touch on the investments real quick. I mentioned earlier in the prepared remarks that we are increasing some of our investments in gen AI particularly oriented towards marketing and development. And that's all reflected in the operating margin guidance for the full year that I provided. Within that you'll see that if you look at the total spending that was implied in our model for the full year at the start of this year and you compare that to the total spending implied in the guidance in the current guidance that we're providing, it's about the same in the aggregate. So what we're really doing is being efficient and saving in some parts of the business to create room to invest in growth areas like gen AI and that's what you see reflected in the guide.
Jacob Roberge: Great. Thanks for taking my questions and congrats again on the great results.
Ashutosh Kulkarni: Thank you.
Operator: Thank you. And our next question today comes from Matt Hedberg with RBC Capital Markets. Please go ahead.
Matt Swanson: Yes, thank you. This is Matt Swanson on for Matt Hedberg and I'll echo my congratulations as well. You know Ash, we kind of focused on two things during the prepared remarks, it was the gen AI and then the consolidation. And I was wondering if you could just talk to maybe how interrelated these two things are now or maybe in the future? Basically, when you're having those C-level conversations, how much this gen AI and things like vector search come up in terms of who they want to consolidate to when they're trying to kind of future-proof these decisions?
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.
And so we see that kind of stabilization in the consumption optimization that people have been doing. At the same time, as we've talked about with you in the last several quarters, we have been leaning in this current environment to really drive consolidation onto our platform, really showcase all the things that customers can do on our platform and that's reflecting - that's resulting in customers bringing newer workloads consolidating multiple things. And those new workloads are also now starting to ramp up. So both those factors are playing in and that sets us up quite nicely as we look ahead.
Bo Yin: Thank you.
Operator: Thank you. And our next question today comes from Rob Owens with Piper Sandler. Please go ahead.
Ethan Weeks: Hi, thanks for taking my question. This is Ethan on for Rob. I just wanted to ask around how important the channel is, when it comes to selling these - selling ESRE and gen AI functionality? Especially if you think about you going into these really large organizations and when the fees we get evolve? Thank you.
Ashutosh Kulkarni: Yes, so the channel tends to be important, but keep in mind that for newer areas like this, the reality is that these are areas where we tend to have direct conversations with these customers and the channel can often help us broker some of those conversations. But it's we tend to be the experts, so we tend to have a lot of these conversations. Where the relationships - the partner relationships really become wonderfully important and are helping us, is the relationships we have with the cloud hyperscalers. As you saw from some of the accolades that we won that we talked about in this earnings call. We have - our relationship with Google, with Microsoft, with AWS, they are strong and they're continuing to grow stronger.
And that just means that when customers are talking to them, it allows us to go and jointly with these partners and have these kinds of conversations, where customers are looking to do things there are on the leading edge. And those are the relationships that we believe are really critical at this phase, and that's where we have a lot of strength. So I'm excited about that.
Ethan Weeks: Thank you.
Operator: Thank you. And our next question comes from Kingsley Crane at Canaccord. Please go ahead.
Kingsley Crane: Hi, thanks for taking my question. I wanted to talk through consolidation wins. Are these picking up for you? And then how often is the case that logging is leading in consolidation motion? And then, how often would you see a customer potentially adopt APM during this process?
Ashutosh Kulkarni: The consolidation trend has definitely been strong in the last couple of quarters, like we've talked about. And a lot of it, in my opinion, has to do with the fact that as people have - as CIOs and companies have become more thoughtful about their spend envelopes. They are looking to see where they can save and we have a value proposition that allows them to do that, while at the same time doing even more and getting the kinds of innovative technology that we've been bringing out in market. So it's working very nicely in our favor. Now what we typically tend to lead with, like we've talked about in the past, tends to be Log Analytics, tends to be Security Analytics, and tends to be Search. And then we progress from there, right?
So we have lots and lots of customers now that are using us. Thousands of customers that are using us for APM. And that's all - many of those customers started with us with logs, to begin with, and then they moved from logs and realized that they could get a much better picture of their overall observability landscape by not just bringing logs into Elastic but also bringing APM traces and then they eventually will bring on metrics and other things deal user metrics and so on. And that's the pattern that we see over and over again. So the consolidation pattern tends to be one where we might start with Log Analytics and Security Analytics and Search, but then will invariably, go onto other elements of observability and other elements of security and so on.
Kingsley Crane: Great. Thank you, Ash. And then one brief follow up for Janesh, if I may. It makes sense to be prudent on the guidance, but could you walk us through how consumption has trended month-by-month-in Q1 and then how does that fare so far in August?
Janesh Moorjani: Hi, Kingsley, as I've mentioned a little bit earlier, I think it can vary from month to month. There is always going to be a little bit of noise in the monthly data. And so what we generally saw was consistency through the course of the quarter. And, overall, I think the themes just played out as we described over the course of Q1 and then specifically for August, as I mentioned, we aren't done with August yet, so hard for me to talk about that, but. I will share that the general tone of customer conversations was similar to where it was in Q1.
Kingsley Crane: Okay. Thank you.
Operator: Thank you. And today's last question comes from Shrenik Kothari with Baird. Please go ahead.
Shrenik Kothari: Hi, thanks for taking my question. Great quarter. Congrats. So since you touched upon the annual cloud subscription motion performing quite well, Ash, and highlighted some of the prominent organizations, the Fortune 100 and the leading share service, leveraging our capabilities. So it appears that your sea-level engagement strategy and to your earlier point on the hyperscaler partnerships working quite well. But then, I think Janesh also mentioned that the net adds to the [100,000] was a bit lighter. So just if you guys can kind of help reconcile and help perhaps unpack like how much of the businesses is currently being driven by this top down or enterprise-focused motion that you guys are strategically kind of moving towards that you highlighted few quarters back and then I have a quick follow-up?
Janesh Moorjani: Yes, I'll just touch on that real quick in the interest of time. We've continued to see strong contractual commitments to Elastic and we saw that here in Q1. It continued to trend. We've seen for a few quarters now. So overall, I'd say, our investments in the enterprise selling motion and everything we bring to that including the partnerships and so forth that you touched on - I think that's actually working quite nicely. Keep in mind that in our land and expand motion, the initial lands tend to be very small and then we tend to expand with customers over time. And so within that pool of over 100,000 customers that you mentioned, although the number of additions was a bit lower, we did see a very strong expansion in the larger accounts and it reflects those commitments.
So we felt very good about how this all played out. And I do expect that we will have consistent growth in our customer metrics over time. It's been good - a good driver for us in the past and I do expect that trend will continue over time.
Shrenik Kothari: Got it. Thanks a lot, Janesh. And just very quickly, I mean, I know there was earlier question around consolidation correlation with multiple use cases, including gen AI. I was just wondering like, are you also seeing kind of direct correlation with this kind of enterprise focus top down motion and consolidation as well? And are you guys kind of going about in a strategical fashion, not just like as I said, targeting industries or verticals, but just kind of in terms of kind of larger commitments and trying to target accordingly?
Ashutosh Kulkarni: Yes. I mean, so - like we've talked about, right in prior calls as well, our focus on enterprise selling has been strong and we've talked about the fact that we've been very strategically focusing on customers that have a greater propensity to grow with us. And that includes customers in the Enterprise segment, that includes customers in the Commercial segment. And one of the levers that we have been really focused on is our ability to deliver highly differentiated value at an incredible price for our customers because of the strength of our platform and its ability to deliver on multiple real-time search use cases, whether it be for search observability or security incredibly well. And so we've been leaning into that as a motion, irrespective of vertical and geography.
So we've been driving that motion with our sales organization and it's paying off. It's paying off in larger commitments. It's paying off in more workloads coming onto our platform and that's taking share and it's reflecting now in some of the benefits that we're seeing in revenue.
Operator: Thank you. And ladies and gentlemen, this concludes our question-and-answer session. I'd like to turn the conference back over to Ash Kulkarni for any closing remarks.
Ashutosh Kulkarni: All right. Thank you all very much for joining our call today. We had a strong start to our fiscal year and I'm really excited about the opportunity and our position in the market as the leading AI-powered data analytics platform for multiple real-time search use cases. We will be hosting our Elastic on AI Conference in San Francisco in a few weeks, and I am looking forward to seeing our customers there as we talk about all the exciting things we are doing in this space. Have a great rest of the evening. Thank you.
Operator: Thank you. This concludes today's conference call. We thank you all for attending today's presentation. You may now disconnect your lines and have a wonderful day.
