Elastic N.V. (NYSE:ESTC) Q2 2025 Earnings Call Transcript

Elastic N.V. (NYSE:ESTC) Q2 2025 Earnings Call Transcript November 21, 2024

Elastic N.V. beats earnings expectations. Reported EPS is $0.59, expectations were $0.38.

Operator: Good day, and welcome to the Elastic Second Quarter Fiscal 2025 Earnings Results Conference Call. All participants will be in listen-only mode. Should you need assistance, please signal a conference specialist by pressing the star key followed by zero. After today’s presentation, there will be an opportunity to ask questions. To ask a question, you may press star then one on your telephone keypad. To withdraw your question, please press star then two. Please note, this event is being recorded. I would now like to turn the conference over to Anthony Luscri, Vice President, Investor Relations. Please go ahead.

Anthony Luscri: Thank you. Good afternoon, and thank you for joining us on today’s conference call to discuss Elastic’s second quarter fiscal 2025 financial results. On the call, we have Ashutosh Kulkarni, Chief Executive Officer, Janesh Moorjani, Chief Financial Officer and Chief Operating Officer, and Eric Pringle, our Interim CFO. 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 this 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, or expectations regarding the demand for our products and solutions and our future revenue and other information.

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, including the press release that we issued earlier today, including 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. 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 Relations link. Our third quarter fiscal 2025 quiet period begins at the close of business on Friday, January 17, 2025. We will be participating in Scotiabank’s Global Technology Conference on December 10th and the Needham Growth Conference on January 16th. With that, I’ll turn it over to Ash.

Ashutosh Kulkarni: Thank you, Anthony, and thank you everyone for joining us on today’s call. Elastic delivered a strong second quarter supported by solid sales execution and customer commitments. In Q2, we meaningfully exceeded guidance across all revenue and profitability metrics. Revenue grew by 18% year over year. Cloud revenue grew by 25% year over year, and we delivered a non-GAAP operating margin of 18%. We also increased the number of customers spending over $100K with us to 1,420. At the start of this fiscal year, we made sales segmentation changes to increase the focus on our key enterprise and high potential mid-market customers. After some unexpected disruption in sales performance in Q1, we are now starting to see the benefits of the changes we made.

Our performance in Q2 reaffirms our confidence in our strategy and shows that we are well on our way to returning to the strong pace of sales execution that we have demonstrated in the past. In Q2, we saw strong customer commitments, with key wins across all of our solution areas, especially in search, powered by generative AI. We also saw continued consolidation onto the Elastic platform for security and observability with many customers displacing incumbent legacy products and migrating onto our search AI platform. Turning to generative AI, our momentum in this area continues to build. In Q2, we saw strong demand for our vector database as customers increasingly adopted Elastic for building semantic search and retrieval augmented generation (RAG) applications.

Our clear product differentiation and our relentless pace of innovation is helping us become a natural choice for customers building Gen AI applications. We are seeing adoption and winning deals across many different industries and for use cases that seek to automate a wide variety of business processes. In Q2, we saw continued acceleration in our search business with significant tailwinds from Gen AI. In Q2, new customer commitments with Gen AI almost doubled in dollar volume as compared to what we saw in Q1, with three of the deals we signed being greater than a million dollars in annual contract value. We now have over 1,550 customers on Elastic Cloud using us for Gen AI use cases, with over 240 of these amongst our cohort of customers spending $100K or more with us annually.

For example, this quarter, a US-based global leader in the automotive industry expanded its relationship with Elastic in a multiyear seven-figure deal by selecting our Elasticsearch AI platform. The company has standardized on Elastic’s vector database as the backbone for their retrieval augmented generation and chatbot applications. Elastic’s vector database powers over 30 chatbot clusters used for both internal employee support and customer-facing interactions to enhance efficiency by providing real-time relevant answers and driving improved productivity for the organization’s workforce. Beyond chatbots, the company is leveraging Elastic’s hybrid search capabilities, combining keyword and semantic search for broader applications. We also signed an expansion deal with a leading sporting goods retailer in North America to support their omnichannel experience.

Using the Elasticsearch AI platform, the retailer will improve search relevance by adopting semantic search and using advanced AI relevance capabilities, like learning to rank, to improve margins and profitability in-store and online. The company chose Elastic for our deep expertise in retail search transformation and our integrated machine learning and search AI capabilities all within a single platform. In addition to Gen AI, the other secular tailwind that we have been benefiting from is customer consolidation onto our platform for multiple use cases. Our ability to help customers reduce complexity and drive efficiency at a lower total cost of ownership by consolidating onto the Elastic platform for multiple use cases is helping us secure strong customer commitments and become an increasingly strategic part of their IT infrastructure.

And we continue to invest in capabilities and incentives that make it possible for customers to migrate easily from incumbent solutions to Elastic. Last quarter, I talked about the Elastic Express migration program and our search AI-powered automatic import functionality. And I’m pleased to say that we are seeing significant momentum from customers who are leveraging these to migrate off of legacy offerings onto our platform. These incentives and offerings were critical in helping us win over 40 competitive deals in Q2 when we either displaced incumbent solutions or onboarded new workloads through platform consolidation. This quarter, an online marketplace for short and long-term homestays selected Elastic Security to replace its existing SIEM solution, marking a strategic shift towards a more scalable and AI-driven security approach.

This seven-figure expansion deal involves replacing a complex and inefficient solution unable to keep up as the company’s threat landscape and data footprint grows. The Elasticsearch AI platform, including SQL and our AI assistant, will help the company streamline its security operations and ensure faster, more accurate threat detection. Cost efficiency at scale, seamless integration, and advanced AI features were significant factors in their choice of Elastic, positioning us as a key partner in their next-generation security infrastructure. In another seven-figure deal, we signed a new agreement with an insurance provider displacing two competitive solutions for their cybersecurity operations. The company chose Elastic Security, leveraging our AI assistant and attack discovery to strengthen their threat detection, incident response, and vulnerability management as part of their broader digital transformation efforts.

Now turning to product innovations in Q2. We introduced a steady addition of new AI capabilities, including a number of features that significantly improve our performance as a vector database. We continue to innovate to maintain our position as the most downloaded vector database on the market. Elasticsearch now supports bit vectors, SIMD acceleration, and int4 quantization to improve performance. And now, Elastic is the first vector database to offer better binary quantization, now in tech preview. BBQ, as we refer to it, offers a 32x lower memory footprint compared to storing and searching full precision vectors. It also surpasses traditional methods like product quantization, delivering faster vector search at lower costs without compromising accuracy.

Initial benchmarks are showing 30x less quantization time. This is a game changer for navigating the usual vector search trade-offs between cost and accuracy and is only available from Elastic. We also announced the general availability of AutoOps, the outcome of our acquisition of Opster, which significantly simplifies Elasticsearch cluster management with performance recommendations, resource utilization, and cost insights, as well as real-time issue detection and resolution. By analyzing hundreds of Elasticsearch metrics, configuration, and usage patterns, AutoOps recommends operational and monitoring insights that deliver real savings in administration time and hardware costs. In security, AI continues to transform the SIEM landscape. With the SIEM fast evolving to an AI-driven security analytics solution for the modern SOC, we expect this new generation of solutions to not only subsume traditional SIEM functionality but also consolidate extended protections for various parts of the IT infrastructure, which today require separate tools.

Cloud detection and response, or CDR, is one such area of extended protections that we recently integrated into our AI-driven security analytics solution, providing threat detection and response and contextual investigation to protect cloud environments, all within a unified set of workflows already familiar to our SIEM users. As part of this capability, our users can benefit from detection rules that combine cloud telemetry with other relevant logs collected by the SIEM and from context gained from correlating other events and entities to perform streamlined yet informed investigations. Since this capability is fully integrated into our SIEM, CDR users can now benefit from all of Elastic’s unique differentiators in the areas of query speed, data management, and relevance-focused AI.

In the area of observability, Elastic is now 100% OpenTelemetry (OTEL) Native. As you know, OTEL enables observability users to move from proprietary data ingest mechanisms to an open standard format. As of Q2, all OTEL-compliant data is now stored in Elastic without data translation, which removes the need for SRE teams to worry about data formats. Our entire observability suite now works out of the box for OTEL-compliant ingested data. In addition, we introduced our OTEL-based Kubernetes integration and dashboards, providing users with instant visibility into clusters and application metrics, logs, and traces, all without the need for any manual configuration. Elsewhere, we expanded our LLM observability capabilities to include Amazon Bedrock.

This adds to our previously announced support for Azure OpenAI. With this, we provide comprehensive visibility into the performance and usage of foundational models from Bedrock, as well as dashboards and detailed insights into model performance, usage patterns, and costs. On the go-to-market front, we kicked off our Elasticon events in Q2 and to date have held events in San Francisco, Bangalore, Munich, and New York. Our Elasticon events have drawn thousands of attendees, and we are looking forward to hosting six more events across the globe during fiscal Q3 and Q4. Elasticons give us the unparalleled opportunity to meet with thousands of customers, partners, prospects, and developers to share ideas and showcase Elastic innovations. Partners play a critical role in our success.

This quarter, we launched the new Elastic AI ecosystem as part of our vision to transform, simplify, and accelerate how enterprise developers build and deploy generative AI applications. Working with leading technology providers including Alibaba Cloud, Amazon Web Services, Anthropic, Confluent, DataRobot, Dataiku, Galileo, Google, Hugging Face, LangChain, Llama Index, Microsoft, Mistral, NVIDIA, OpenAI, ProtectAI, Red Hat, Unstructured, and Vectorize, we have built a comprehensive set of integrations with our Elasticsearch vector database to help developers speed up the time to develop Gen AI applications. Now switching to some organizational news. Today, we are announcing that Janesh Moorjani will be leaving Elastic to pursue a new opportunity, and his last day with Elastic will be December 13th.

Janesh has been a key part of our leadership team over the past seven years, first as CFO, and more recently as CFO and COO. His tenure here has included a number of major milestones for the company, including leading our IPO back in 2018 and more recently helping guide the business across the billion-dollar mark. Personally, to me, Janesh has been a trusted colleague and a friend over the years, and I want to thank him personally for all that he has done during his time here. I’m looking forward to seeing all that you can accomplish in the years ahead. With this change, I’m happy to have Eric Pringle, Elastic’s Group Vice President of Finance, taking on the role of Interim Chief Financial Officer, effective December 14th, while the company conducts a search for a permanent replacement.

Eric has been with Elastic for the past two years and has already made significant contributions to Elastic with broad responsibility for various FP&A and business partnership functions. Prior to joining Elastic, Eric spent nearly ten years at JPMorgan in various investment banking roles, and he was involved with Elastic even then, having worked on our IPO. I have worked closely with Eric during his time here, and I’m confident in his disciplined leadership and ability to excel in this role. As such, not surprisingly, Eric will also be considered as a candidate in our search process. In closing, I’m pleased with our strong performance this quarter. I want to thank our team for their focused execution, and I also want to thank our customers, partners, and investors for their continued support and confidence.

We are seeing positive signs that we are well on our way to returning to historical levels in terms of our pace of execution, and our Q2 performance is a strong indication of this progress. The innovations we are building into our search AI platform, the momentum we are gaining around generative AI, and the traction we are seeing with customers consolidating onto our platform give us great confidence in our future and in our ability to build a multibillion-dollar business over time. With that, I’ll turn it over to Janesh to go through our financial results in more detail.

Janesh Moorjani: Thank you, Ash, for those kind words. It’s been a privilege to have been part of Elastic’s growth journey for the past seven years. I am proud of our successful track record, and I firmly believe that the future for Elastic has never been brighter. It’s been an incredible partnership with you personally, and I am deeply grateful for your trust and friendship. While I’m sad to leave behind such a talented team, many of whom are now good friends, I am excited about my next opportunity and confident that the business will be in great hands with Eric. He has been my right hand for almost two years and has proven himself to be an exceptional and disciplined leader. I’ll let him say a few words before I get into our results.

Eric Pringle: Thanks, Ash and Janesh. I really appreciate it. In the almost two years I’ve been at Elastic, it has been a pleasure to partner with both of you. From my time working on the IPO, I was impressed with the differentiated technology and strong culture that Elastic has. Since joining, it has become even clearer to me that we have an opportunity to be a truly generational company. Janesh, you have shaped so much of Elastic, and I very much appreciate that I got the opportunity to work closely with you. I wish you the absolute best in your next role, and I’m excited to be stepping into this role with all that is in front of Elastic.

Janesh Moorjani: Thanks, Eric. I’m sure you’ll knock it out of the park. Let’s get into our Q2 results. With a challenging first quarter behind us, we were pleased that we improved our sales execution in the second quarter with strong customer commitments as Ash described, and also outperformed against the high end of both our revenue and profitability guidance. Total revenue in the second quarter was $365 million, up 18% year over year as reported and up 17% year over year in constant currency. Subscription revenue in the second quarter totaled $341 million, up 18% year over year as reported and in constant currency. Elastic Cloud represented 46% of total revenue in the quarter. Aggregate consumption trends in the second quarter remained healthy, with enterprise and commercial customers generally continuing to consume as anticipated against their commitments, and with stronger than expected consumption among some of our larger customers.

Revenue from our Elastic Cloud month-to-month motion, which is driven mainly by self-service SMB customers, was consistent with our expectations and remained flattish in dollar terms, coming in at 13% of total revenue. Consumption revenue can fluctuate across quarters, and we’ve seen such fluctuations in the past, so we will continue to monitor this closely. Professional services revenue was $25 million, growing 7% year over year as reported and in constant currency. As a reminder, professional services revenue may fluctuate across quarters based on the timing of services delivery. To add more context around deal flow during the quarter, we had solid sales execution with improving performance compared to the prior quarter. We are pleased with the progress we have made so far and intend to continue our focus and diligence around sales execution.

We saw healthy growth across our solutions, where search grew the fastest year over year, given the strong traction we’ve seen in Gen AI that Ash described. The quarter’s strength was also balanced across geographies, where the Americas grew the fastest. Customers continue to make strong, multiyear commitments to us, reflecting their preference for Elastic as they consider platform consolidation and reflecting our increasing relevance to their business. We did not see any significant changes in the competitive environment during the quarter. Turning to customer metrics, we ended the second quarter with over 1,420 customers with annual contract values more than $100,000. We continue to be focused on customers with a higher propensity for growth and target such customers in the enterprise and commercial segments.

These larger customers provide a strong foundation for our land and expand motion as we continue to scale. Looking at customer additions more broadly, we ended the quarter with over 4,480 customers above $10,000 in ACV, and approximately 21,300 total subscription customers. Our net expansion rate was approximately 112%, which was in line with our expectations. Our customer retention rates during the quarter also remained strong. Now turning to profitability and cash flow, for which I’ll discuss non-GAAP measures. Gross margin in the quarter was 76.9%, consistent with the past several quarters. Our operating margin in the quarter was 17.6%, which was significantly better than expected, driven primarily by our strong revenue outperformance and continued discipline in spending.

With some of our expense efficiency actions that we had previously discussed coming in larger than expected and also taking hold earlier in the quarter than we had anticipated. Diluted earnings per share in the second quarter was $0.59. Adjusted free cash flow was approximately $38 million in the second quarter, which translated to a 10% adjusted free cash flow margin. Cash flow on a quarterly basis can fluctuate given timing issues and seasonality, so we continue to look at this primarily on a full-year basis. Although we don’t formally guide to cash flow, we continue to expect adjusted free cash flow margin for fiscal 2025 to be slightly above the non-GAAP operating margin for fiscal 2025. As you know, our adjusted free cash flow is on an unlevered basis.

Turning to guidance, looking ahead, our market opportunity remains large. The Elasticsearch AI platform is highly differentiated, our Gen AI traction is strong, and customers are continuing to consolidate workloads onto our platform. Given the revenue outperformance in the second quarter and our conviction around our opportunity ahead, we are raising our full-year total revenue outlook. As we look into the second half of fiscal 2025, we assume that the current business environment will remain similar to the first half of the year. We remain focused on execution and believe that we are well-positioned for long-term growth and profitability. In the third quarter, we expect both self-managed and annual cloud revenue to grow slightly in dollar terms compared to the second quarter.

I’ll highlight some of the factors we considered in our guidance. First, while our most recent performance gives us confidence in our improving sales execution, the shortfall on customer commitments we experienced in the first quarter of this year will remain a headwind to year-over-year revenue growth in the back half of this year. Second, we have considered that consumption revenue on both annual contracts and in a month-to-month motion can fluctuate. Since we’ve seen such fluctuations before, we have been prudent in our assumptions on consumption rates on annual contracts for the remainder of the year. We also continue to expect revenue from our month-to-month motion Elastic Cloud will remain somewhat flat for the rest of this year. Third, we have considered the revenue headwind from the recent strength in the US dollar.

Finally, in terms of seasonal effects in the third quarter, professional services revenue is typically impacted by the holiday season. With respect to Gen AI, as Ash described in his remarks, customer interest around generative AI use cases remains very strong. The strength of our technology and our pace of innovation underpins our position as a market leader in this space. We are still in the very early stages of capturing the substantial and rapidly growing opportunity, and we continue to believe this will be a significant growth driver for us over the long term. As we consider investments in the business, we plan to continue to balance investing for revenue growth with profitability. Given the improvements we saw in our sales execution in the second quarter, we are selectively increasing some investments in the second half of the year.

We will continue to prioritize investments towards areas intended to drive growth, particularly in Gen AI. With the operating leverage inherent in our business model, we are also raising our profitability guidance for the year. We remain well-positioned to drive higher operating margins as we scale the business in future years. With that background, for the third quarter of fiscal 2025, we expect total revenue in the range of $367 million to $369 million. This represents 12% year-over-year growth at the midpoint on an as-reported basis and 13% year-over-year growth at the midpoint in constant currency. We expect non-GAAP operating margin for the third quarter of fiscal 2025 to be approximately 15% and non-GAAP diluted earnings per share in the range of $0.46 to $0.48, using between 106 million and 107 million diluted weighted average ordinary shares outstanding.

For full fiscal 2025, we expect total revenue in the range of $1.451 billion to $1.457 billion. This represents 15% year-over-year growth at the midpoint both on an as-reported basis and in constant currency. We expect non-GAAP operating margin for full fiscal 2025 to be approximately 13.5% and non-GAAP diluted earnings per share in the range of $1.68 to $1.72, using between 106 million and 108 million dilutive weighted average ordinary shares outstanding. In summary, we are pleased with our performance in the second quarter and remain confident in our ability to continue to drive profitable growth going forward. And with that, let’s go ahead and take questions. Operator?

Q&A Session

Operator: Thank you. We will now begin the question and answer session. Please pick up your handset before pressing the keys. If at any time, your question has been addressed, first question comes from Matt Hedberg with RBC Capital Markets. Please go ahead.

Matt Hedberg: Great. Thanks for taking my questions, guys. Congrats on the results. Really nice bounce-back quarter. You know, there’s a lot of things that stood out to me. I think, you know, the success in cross-selling, but also Janesh, you said something about larger enterprise, particularly their consumption patterns accelerated. I’m wondering if you could double click on that. Was there any commonality there that drove that trend?

Janesh Moorjani: Hey, Matt. So just digging into that consumption number. Overall, consumption was quite strong across Q2. We are very pleased with the results that we saw, and that was across enterprise and commercial. It was across the different geos as well, across the solution areas. We did see some strength among some of our larger customers that accelerated their consumption, and that was really encouraging because we’re continuing to use the Elastic AI platform for both new and expanded workloads. In terms of whether there was any particular commonality there or similarity to the past, we have seen consumption be stronger in prior periods as well, but it’s not necessarily the same customers that are always stronger, so there can be some variations there.

So I wouldn’t read too much into that. We were obviously pleased with the result, but we’ll continue to monitor it carefully to see if there’s any particular patterns or trends that emerge. From our standpoint, as we built our guidance for Q3, although we are very pleased with the consumption rates that we saw in the second quarter, we have seen these rates fluctuate in the past. So we built our guidance quite prudently, and just for clarity, we’ve not actually seen any change in the business so far. We’re simply considering the possibility that consumption patterns may revert to what we previously experienced prior to Q2. So that’s the way we approach it.

Matt Hedberg: That’s great. Well, it seems like there’s others that are also talking about better consumption trends from last night. Maybe just to ask for you, it was great to see that Salesforce back here. I’m wondering, is there anything that you put your finger on that drove to that better sales execution? There was a lot of changes in 1Q, but curious if you can understand sort of what the positive change factor was this quarter. And I know Janesh is being conservative in back half guidance, but the opportunity for continued improvement there, just sort of curious on your thoughts there.

Ashutosh Kulkarni: Yeah, Matt. Thanks for the question. So, you know, even in the last earnings call, in the Q1 earnings call, like I’d mentioned, we felt pretty confident that the changes that we had made at the beginning of the year were the right changes. And what we’ve seen is, with rigor as we’ve been inspecting the pipeline and making sure that we are doing all the right things with discipline, a few things have already happened or are happening at the pace that we want them to. First, we have seen that the pace of pipeline creation and pipeline progress has come back to normal levels. So we are really happy about that. Second thing that we have seen is that just the changes, the reason why we made changes to begin with, were all about focusing more on the enterprise segment, on the high propensity mid-market segments, and the benefits of those are now starting to take hold.

And just as our reps have had more time to work on their accounts, to work with their accounts, we have started to see all the right kinds of behaviors that we were expecting to see, and that’s what gives me a lot of confidence going forward. And, you know, as we’ve said, the thing that happened in Q1 was a near-term impact, and I’m really happy that the team really delivered in Q2. And we are moving in the right direction.

Matt Hedberg: Great. Thanks a lot. And, Janesh, it’s been great working with you. Best of luck in future endeavors.

Janesh Moorjani: Thank you, Matt.

Operator: The next question comes from Pinjalim Bora with JPMorgan. Please go ahead.

Pinjalim Bora: Great. Thanks for taking my questions. Congrats on the quarter, Janesh. From me as well, great working with you and all the best. And, Eric, congrats on the interim role. I wanted to ask you, Ash, on binary quantization. Seems like it’s a differentiation for Elastic from a vector view perspective. But my understanding is quantization is kind of a trade-off between accuracy and cost. Help me understand how pervasive is that going to be across different Gen AI use cases?

Ashutosh Kulkarni: It is something that I am personally very excited about. Our team’s very excited about it. Like you correctly pointed out, quantization is always a trade-off between accuracy and efficiency or performance. And, you know, better binary quantization, we are the first to really deliver this capability, and what it does is it delivers an absolutely amazing level of efficiency, everything from how much memory is required to the time it takes to do the actual quantization to even query performance. And all of those with very little impact on accuracy. And that’s what’s so great about this algorithm. Like I mentioned in my prepared remarks, you know, this, we feel, is a true game changer. And at this phase, you know, we are still in the very early phases of the whole AI revolution, the way I see it, the way we see it, this is the time when we want to make sure that we are delivering the kinds of asymmetric innovations that will allow us to really gain maximum share in this market.

And so I’m personally very excited. The applicability is pretty broad. And, you know, we are just going to drive this hard.

Pinjalim Bora: Yep. Understood. Janesh, one question for you. It seems like a very strong cloud consumption quarter. But when I look at the RPO numbers, especially on the sequential trend, it seems a little bit muted given that a number of deals pushed out of last quarter. You had noted you had closed a lot of those. So maybe how do you characterize kind of the booking cadence at this point versus historical norms, you know, given the changes that you see it’s coming it’s improving, but we are not there yet.

Janesh Moorjani: Pinjalim, we were really happy to see that the team bounced back and the additional measures that we had taken following Q1 are having the desired effect, and sales reps have also had more time to engage with their newer accounts. So I think all of that played quite nicely for us. While there were many deals that moved out of Q1 that we closed in Q2, those were not the main driver of strength for us in the second quarter. But, you know, overall, looking ahead, I think we feel pretty good about the outlook for the rest of the year. With respect to the RPO timing and the quarter-over-quarter changes in RPO, that was consistent with what we’ve delivered before, and in fact, probably even slightly higher than the sequential change that you may have seen in the year-ago quarter. We felt pretty good about that as well.

Pinjalim Bora: Got it. Thank you.

Operator: Next question comes from Tyler Radke with Citi. Please go ahead.

Tyler Radke: Yeah. Thanks for taking the question and Janesh, it’s been great working with you and all the best. Good to see you leave on a high note here. Ash, I wanted to go back to some of the Gen AI use cases. It sounded like, you know, very strong quarter in terms of commitments, the doubling quarter over quarter. But I’m curious how you’re kind of seeing those types of use cases unfold. It sounded like you referenced some use cases where you’re kind of the backbone, you know, in terms of being the vector database, supporting 40 different use cases with agents and chatbots, and then some other use cases just around semantic search. So maybe help us understand, are you kind of orienting the Salesforce around kind of discrete go-to-market motions, particularly around Gen AI to serve those use cases? Just give us a flavor of kind of how you’re standardizing the Gen AI go-to-market and use case evaluation here.

Ashutosh Kulkarni: Yeah. That’s a great question, Tyler. So, you know, the first thing that says as we look at the market broadly across pretty much every industry and every vertical, what we see is that there are a lot of processes, business processes that depend upon unstructured data being manually processed and moved from one step of the process to the next step, and all of those have the ability to be automated through generative AI. So we are looking at that, and effectively, our strategy is to be that runtime platform for retrieval augmented generation for anybody building those kinds of generative AI applications. Now some of those will begin first and foremost by somebody just implementing better search through semantic search, you know, techniques like hybrid search, to get the most relevant precise data for their use cases and their applications, then they’ll evolve from there to building the kinds of chatbots and generative applications that then automate that process.

And we just want to be the underlying layer, the underlying platform for all of those things. And so instead of taking a vertical by vertical approach, you’re basically taking a platform approach, you know. And as you know, that’s been really something that has worked very, very well for us in the past. And our motion around this is very simple. You know, our field just goes in and talks about semantic search, and the simple question is, how are you thinking about vector databases? What vector database are you using? And it just leads from there naturally to either a semantic search sale or a hybrid search sale or a retrieval augmented generation search kind of opportunity. And we are starting to see, you know, like in the past, I’ve said that a lot of these tend to be internally focused applications.

But now you’re starting to see a blend of both internal focused and external as people are getting more and more comfortable. I think that’s exciting.

Tyler Radke: Yeah. Thanks, Ash. And Janesh, for you, just going back to the commentary around the large customers’ consumption in the quarter, I guess, as you think about the stronger than expected trend that you saw, would you say that that was kind of one-time in nature or was this kind of onboarding new use cases? And just to clarify, you’re saying that you’re seeing these strong trends continue. You’re just not embedding it into the guidance. Just wanted to clarify that. Thank you.

Janesh Moorjani: Hey, Tyler. Yeah. So in terms of what we saw, absolutely, we saw the strength be broad-based. I wouldn’t characterize it as one-time. It was just some of our larger accounts consumed at a faster pace, and that was really encouraging to see. I think that’s reflective of all of the things that we’ve been doing and driving over the past several quarters, that ultimately get reflected in customer commitments that then translate into consumption. We’ve talked about the momentum that we’ve seen in Gen AI. We’ve talked about the platform consolidation. So I think that there’s a lot of goodness in terms of what the teams have been driving, which has translated into higher consumption rates. So it was higher than what we expected, but I wouldn’t characterize it as one-time.

In terms of what that means for the future and the assumptions that we’re using for the future, you’re right. We have not seen any change in the business. We have not seen anything adverse, but we’re just being prudent in terms of how we build our forecast because we have seen historically consumption rates can fluctuate, and we think we just want to be careful with that and not get ahead of our ski tips.

Tyler Radke: Yeah. I guess you’re guiding before Thanksgiving for the first time. So more days in the quarter. Exactly right. Right.

Janesh Moorjani: Alright. Thanks, Tyler. All the best.

Operator: Next question comes from Koji Ikeda with Bank of America. Please go ahead.

Koji Ikeda: Yes. Hey, guys. Thanks for taking the questions. Hey, Ash. Janesh, thanks for everything. All the best to you. And, Eric, looking forward to working with you. So I have a question on the sales organization, and I guess the question is on a scale of one to ten, how would you rate where you are today with the sales org? You know, ten being back in business and fully ramped on the new strategy. Where are you at today? And if it’s less than ten, what needs to happen to get it to ten? The reason why I ask is the fiscal second quarter results are really strong. I keep staring at that implied fiscal fourth quarter exit growth rate of 11%. So I’m just wondering if we could still see effects of the sales org change playing out over the coming quarters or is there just massive conservatism embedded in the guide?

Ashutosh Kulkarni: Hey, Koji. Thank you very much for the question. So, you know, I’m not a huge fan of putting anything on a scale, but first of all, I’m really happy not only at the way we performed, but the metrics that I look at internally are effectively are we creating pipe at the right pace and are we progressing pipe at the right pace? And in both of those areas, I’ve seen that, you know, our pace of pipeline creation and our pace of pipeline progression has come back to normal. Also, our win rates continue to be incredibly strong, and that’s really, you know, those are the fundamentals. Right? When you think about your sales strategy, all of those things are working as they should. And, you know, the Q2 results from that perspective give me a lot of confidence about, you know, where we are. In terms of the guide and so on, why don’t I ask Eric and Janesh to maybe weigh in?

Eric Pringle: Yeah. Sure. I’ll take this. Thanks, Koji. What I’d say is, you know, as Ash has said and as Janesh said on the call, we’re very pleased with our revenue outperformance in our second quarter, which was driven by both stronger commitment and strong levels of consumption. And as you think about our guidance for the rest of the year, in terms of the year-over-year revenue growth, you need to keep in mind that the effects of the shortfall on customer commitments in the first quarter are going to have an ongoing impact on the year-on-year growth of the business. For self-managed, that’s already playing through in the second quarter, and so it’ll be similar. But for cloud, there’s going to be an increased headwind in the second half given it usually takes several months to fully ramp cloud consumption.

This dynamic is something we anticipated, and we’ve considered, and it is in our outlook. As we’ve shared it. So that’s the way I’d think about it. There’s also a slight headwind year-over-year on year-over-year growth in the back half from currency effects. Most importantly, I think that our focus is to maintain our sales execution the way it was in Q2 so we can continue the positive momentum that we saw and continue to secure customer commitments.

Janesh Moorjani: Yeah. Koji, I’d add to that, obviously, it’s too early to talk about 2026 specifically, but as you think about that exit growth rate, our revenue next year will largely be determined by sales activity from here on out Q3 and Q4. And we feel pretty good about where we are in that trajectory as Ash said.

Koji Ikeda: Okay. So thanks, guys. And just one follow-up here. Ash, in your prepared remarks, you talked about three deals, generative AI deals being one million dollars in annual contract value. Super impressive there. The question here is, are these for specific generative AI projects? And what I’m trying to understand is that we’ve been waiting for these big commitments to come. And so if these are for specific generative AI contracts, does that really imply that as experiences, generative AI experiences come to fruition, Elastic could be a beneficiary of lots of these big, big six, seven-figure deals for very specific generative AI projects? Thank you.

Ashutosh Kulkarni: Yeah. That’s a great question. And what I’d say is, first of all, generative AI is sort of the fastest-growing part of our business at this point. Like, we are seeing a lot of traction in it. You know, keep in mind, Koji, that we don’t have a discrete generative AI SKU as you know. And because of that reason, you know, the metrics that we look at and the metrics that we are sharing with you are sort of the directional metrics that we’ve shared. Right? The fact that we are now seeing commitments really grow very nicely because, as you know, it starts with the design wins. We’ve talked about those design wins in the past, and we’re now even in cloud, we have over, you know, 1,550 customers. Then it moves from there to customers actually making, you know, meaningful commitments, and now we’re giving you some color on that.

And then that translates into revenue. So this is exciting. This is why, you know, I feel really good about the long-term trajectory for the business. And that’s the reason why, like, our focus now is to continue to execute like we did on the sales front in Q2 so we can keep building that up and, you know, keep going.

Koji Ikeda: Thanks, Ash. Thanks for taking the question, guys.

Operator: The next question comes from Brent Thill with Jefferies. Please go ahead.

Brent Thill: Thanks, Ash. I’m on the go-to-market, I guess, I’m confused. You say that you’re pleased with your sales team, yet your RPO is flat quarter over quarter. Your competitors are growing RPO twice the speed of you. And so I’m just trying to reconcile this. Is it you’re pleased with the pipeline that’s building back? Because it hasn’t converted into backlog yet, so I just want to make sure we clarify what you mean by that.

Janesh Moorjani: Brent, maybe I’ll jump in and then Ash can add. In terms of the math behind the RPO, if you look at the RPO in year-over-year terms, it’s roughly flat. But in year-over-year terms, keep in mind that RPO is a point-in-time cumulative measure of all prior activity. So the Q1 shortfall from commitments does impact the Q2 year-over-year growth rate in RPO. But if you think about more current activity from Q2 and you consider the sequential growth, it is up a little bit. And usually in Q2, sequentially, RPO has gone down. So if you look at last year, for example, RPO was a little bit lower in Q2 than in Q1. So we were actually quite pleased. And, again, we’re not declaring mission accomplished on the sales execution side.

We’ve said that we’re pleased with the progress that we made in terms of our return to that. And you see that reflected in these numbers, and you see that reflected in all of the other indicators of the business as well, RPO being just one of the several indicators of the business that we all look at collectively.

Brent Thill: Okay. And you think about search as a percent of total revenue versus broader observability, is it still roughly at a 30/70 split, or did that change?

Ashutosh Kulkarni: Yeah. Let me maybe touch on that, and I’ll ask Janesh and Eric to also weigh in. But look, the overall mix of the business changes from quarter to quarter, but at an annualized level, it takes a while for mix shifts to happen. Search has definitely been accelerating for us, and I’ve talked about that. In Q1, it was the same in Q2. This quarter, all parts of our business grew. You know, when I look at the actual customer commitments that we got and the way we are seeing our business grow, I feel good that in all three solution areas, we are taking share, but search is definitely benefiting more given the trends that we are seeing in generative AI.

Brent Thill: Great. Thank you.

Operator: Next question comes from Raimo Lenschow with Barclays. Please go ahead.

Raimo Lenschow: Perfect. Thank you. Can I stay on search, maybe one more question? Ash, if you think about the offering there and, you know, you have a very large installed base in search because that’s kind of where you guys started out. How do you think about the opportunity there in terms of going back to all of them or to a lot of them and just kind of seeing, okay, like, look, we have hybrid search now. You get to better outcomes, kind of a starting point for doing more stuff around Gen AI then anyway. But, like, where are we on that journey of the whole client base understanding what you can offer now and kind of what on that one? Because it does feel like it’s still very early.

Ashutosh Kulkarni: Yeah. Raimo, what I’d say is that that is the natural motion for us. And as you can imagine, you know, for our customer base, the data is already in Elasticsearch. So for us to go to them and, you know, make that case that now they can get even better, more relevant results, is a pretty straightforward thing. So, you know, for sales teams, when you see a motion, when you see a sales motion that is natural, that your customers relate to, where you already have a good setup because the data is in Elasticsearch already, like, that’s just a natural place for them to go, and we are seeing that happen. We are seeing our sales teams do that naturally. A big part of the success that we are seeing in generative AI is because of that.

So, you know, where are we in that overall journey? We’ve got a large customer base, as you know, and if you see the progress that we have made, we now have 240 customers on Elastic Cloud that are part of that greater than $100K cohort, and we’ve gotten there this quickly because that is a motion that our sales team is driving. So lots more to go, but very excited and happy about the progress.

Raimo Lenschow: Yeah. Perfect. And then one follow-up from me was you mentioned, Ash, earlier about this SIEM opportunity. Obviously, there has been a lot of disruption in the market with some of the takeouts, etcetera. What are you seeing there in terms of customer engagement with you guys around SIEM? Thank you. And, Janesh, it’s been an honor working with you.

Ashutosh Kulkarni: Yeah. Thank you for that question. Even on SIEM, like, you know, even in my prepared remarks, I talked about a couple of consolidation deals that we won. They were both security-related. In SIEM, to your point, like, what we’re really seeing happening is AI is now becoming not just a nice to have, but almost a must-have when people think about security analytics and SIEM. And people are looking at ways in which AI can be used to fundamentally improve the entire detection and remediation process. And given the early foray that we made into this, like, we were ahead of most in terms of leaning in with AI just given our core strengths in vector database and so on, you know, we’ve seen a tremendous opportunity and momentum build.

So I’m quite excited about it. Things like the express migration program that we launched are also helping because, you know, some of these are greenfield opportunities, but many of them are ones where there is a displacement opportunity. So I would expect that, you know, you should expect us to continue pushing hard in this area and security. And also, you know, leaning into the whole AI space to allow us to win.

Raimo Lenschow: Okay. Perfect. Thank you.

Operator: The next question comes from Ittai Kidron with Oppenheimer. Please go ahead.

Ittai Kidron: Thanks. And Janesh, my thanks to you as well and good luck on the next move. Ash, I wanted to ask you again about the search. Yeah. I’m sorry. Going back to this. 1,550 customers. It’s quite impressive and quite nice. But can you tell us how many of these customers were historical search customers, meaning just added the AI capabilities, or were net new to the company and perhaps, what’s the penetration right now of AI within your search base?

Ashutosh Kulkarni: Several of these are new customers, and as you can imagine, you know, a large number of them are existing customers. At the end of the day, when you think about the sales-led motion for our sales team, the expand motion is the most natural thing for them to do. It’s the most efficient way for them to go in and, you know, meet their numbers. So in that sense, like, we are naturally seeing that happen. But then because of our self-service motion, we see, you know, new customers come to our platform, and this is an important reason why customers have the opportunity to come to our platform, just the vector search capabilities. So it’s a mix. That’s the best way to describe it. And in terms of the opportunity for us to go across our entire customer base and get them to start using, you know, this capability, especially, you know, the search part of our installed base, there’s still, you know, a lot of room for continued expansion and growth.

Ittai Kidron: Very good. And then maybe on the competitive front, can you elaborate a little bit more, you know, what do you view as your toughest competition here on the Gen AI side and on the SIEM? Like, who are the one or two vendors that you see most frequently? And when you talk about the displacements, I think you talked about it more in the context of SIEM on the call. Where are the greatest displacement opportunities that you see right now?

Ashutosh Kulkarni: Yeah. Look. When it comes to displacement opportunities, you know, there’s a whole host of incumbent vendors. Like, the way I like to think about it is sort of the early generation of SIEM players, you know, everybody from a Splunk to a QRadar to ArcSight. I mean, there’s a pretty large set of players that have been in this space. And for us, like, the way we describe ourselves, you know, we are a modern next-generation SIEM. We think of ourselves as a security analytics product. The way our customers are able to do ad hoc AI-based analysis on our platform is something that’s incredibly differentiated. And, you know, because of the flexibility of our backend, we are able to bring in data from just about everybody, and that’s a big part of why we are succeeding in this area.

And when it comes to Gen AI, look, we don’t have our own large language models. Right? So we partner with just about everybody. You saw the announcement that we made about the AI ecosystem. So we are partnering with a lot of players that provide their own large language models. All four places, the way we want to position ourselves is as the platform for retrieval augmented generation. We want to be the vector database of choice and then also provide some of the capabilities around it. So, you know, beyond the only vector database in town, there are pure plays out there that you might have heard of. And then, you know, in my opinion, every data platform is likely going to have their own vector functionality. And, you know, our core strength is when it comes to unstructured messy data.

Right? Whether it’s logs, whether it’s word documents, whether it’s product descriptions, that data is always best suited to bring into Elasticsearch, and we have, you know, the best vector database to support those kinds of use cases. And that’s a massive, you know, amount of data that exists in every company. So I feel that, you know, as long as we focus on what we are doing in that area and continue to innovate the way we have been, the market opportunity is huge.

Ittai Kidron: Appreciate it. Thank you.

Operator: The next question comes from Howard Ma with Guggenheim Securities. Please go ahead.

Howard Ma: Great. Thank you. My question is for Janesh and/or Eric. So it sounds like you did not make up for the Q1 shortfall in customer commitments in Q2 despite the bounce back. But when you factor in the acceleration in consumption trends, it sounds like maybe you were back on track with your revenue targets before the cut last quarter. That you’re just being a bit conservative with your back half assumptions. So is that true or we’re going to get to ask another way. Had you known you would achieve the Q2 performance that you did both commit and consumption, would you still have lowered your full-year guide last quarter?

Eric Pringle: Thanks, Howard. And I think what I’d say to that is that given the headwind that we saw in Q1, it’s going to impact the full year, and I think we still would have lowered our guidance. Q2 was obviously we’re very happy with the outcome. And Q2, but what happened in Q1 is unfortunately going to play out over the rest of the year. And particularly, as we said before, cloud is going to have a little more of a headwind in the back half specifically versus what it had in Q2 given the timing of the customer commitments. So while we were very happy with the performance in Q2, we would have still made the same decision that we did.

Howard Ma: Okay. Thanks for clarifying that, Eric. I just had a follow-up for Ash. When you look over the last twelve months, how would you describe the pace of adoption of vector database, semantic search, hybrid search for Gen AI use cases? And compare that to how you expect the pace of adoption will play out over the next twelve months. And on a related note, could you in the next few quarters, you know, next quarter or the quarter after, could we start seeing a disclosure of AI contribution to total revenue, you know, maybe when it hits 2%, 3%, 5%, like, you know, is that perhaps in the works? Thank you.

Ashutosh Kulkarni: Yeah. So let me touch upon the first first and then I might invite Janesh and Eric to also talk about the second piece. So, you know, in terms of the adoption, the phases of adoption that I’m seeing are not that different from the phases of adoption of other technologies in prior generations like cloud computing and so on. It all starts with people sort of trying to build prototypes first. There’s a lot of design work that goes in. You want to be baked into those designs. People are still trying to figure out how many of those, you know, go into full-scale production versus not. We saw a lot of that about, you know, eighteen months ago and so on. Since then, we’ve started to see people actually take many of these into production.

At first, it was internal-facing applications. Now it’s also external-facing. This is very similar to the pattern that we would have expected. What I’d say is it’s happening in a much more compressed time frame. So, you know, the time it’s taken from this being sort of early to now, you know, people building these applications and deploying them, it’s happened much faster than anybody would have expected. So that’s been the exciting part. Now what we see is customers actually being very, very thoughtful about things like efficiency, things like performance, things like scale, because they really want to not only put it in production but put it in very large-scale production. And so the kinds of innovations that we are working on are all related to those kinds of things.

Right? We are past the basics, and now it’s into the more sophisticated stuff, which you see from us even in terms of the capabilities that we are delivering like better binary quantization and so on. In terms of the metrics, you know, your second question, I’ll just say one thing that, you know, keep in mind that we don’t have a separate SKU. Right? But with that, like, Eric, any thoughts, Eric or Janesh?

Eric Pringle: Yeah. To Ash’s point, we don’t have a separate SKU. And, you know, what we have done is we’ve always tried to give appropriate indicators and color around the traction that we’re seeing in generative AI. We shared that we have 1,550 of our cloud customers who are using generative AI, that of those 1,550, 240 of them are $100K plus customers. Ash also shared some of the traction that we saw this quarter with the generative AI commitments more than almost doubling relative to what we saw in Q1. So it is something that will be evolving what we share. But it is not something that we have a specific SKU on. To that point.

Howard Ma: Okay. Great. Thanks. Thanks, and great to see the bounce back.

Operator: The next question comes from Austin Dietz with UBS. Please go ahead.

Austin Dietz: Great. Thanks, guys. Maybe just another question on RPO. Janesh, it sounded like you felt good on the trajectory of the business in Q3 and Q4. Could you maybe talk about the second half, you know, what you’re seeing, and is it possible, especially as we have more time for that sales execution motion to continue to improve, could RPO see an uptick kind of on a sequential dollar growth basis in the second half, especially relative to last year? Thanks.

Janesh Moorjani: Hey, Austin. Yeah. Happy to talk about that. So look, as we continue to execute in the back half of the year, especially if we execute well, you will see that reflected in all of the metrics we talk about. And as I was talking about the guidance and the specific assumptions I’ve used for building the guidance, I laid them all out earlier, so I won’t repeat them necessarily here. But fundamentally, you’re right. If we execute well, you will see that reflected not only in RPO but in all the other metrics, including in revenue.

Austin Dietz: Okay. Great. And then just last, Ash, as a part of this go-to-market changes, it seemed like there was more of a focus on greenfield opportunities, new logos. Can you update us on how that’s progressing? Thanks.

Ashutosh Kulkarni: Yeah. Thanks for the question. So, like you said, there was one of the things that we did was we took a lot of the accounts that, you know, weren’t really being worked, and we turned them into distinct greenfield territories and assigned sellers to go pursue those. As you know, typically, it takes a lot longer to build pipeline in greenfield territories and to convert those opportunities. So what I’d say is that we are still in the early days. But when I look at the overall pipeline creation and progression, I feel very good about the pace coming back to what we’ve seen historically. And, you know, the area of greenfield territories, we’re going to just keep tracking it and make sure that we continue to build the momentum in those areas.

Austin Dietz: Okay. Great. Thanks, guys.

Operator: The next question comes from Kash Rangan with Goldman Sachs. Please go ahead.

Kash Rangan: Hey. Thank you very much, Janesh. Good luck with the next gig here. One for you, Ash, maybe two things. One is when you talk about the generative AI progress, what are the metrics that you are tracking? So granted that you do not have a revenue to disclose to us, you talk about use cases, sort of, but how would you define Gen AI success in a way that we can say, okay, you know what, that makes sense relative to say Microsoft or ServiceNow that seem to be talking about Gen AI bookings or AI bookings. Also another question for you. When you look at the go-to-market motion, you sell a platform, so you do not really know the use cases instance by instance. How does that percolate into having the right go-to-market strategy, kind of that, Gen AI is merging as a use case?

Yes. Do we not know what the Salesforce is hunting for and then they come back and tell you and then sometime later when the implementation happens, you find out that it’s implemented for search versus observability versus SIEM versus AI whatnot. So trying to understand how if we do not know what the sale is for, how do you articulate and fine-tune the go-to-market strategy? Thank you so much.

Ashutosh Kulkarni: That’s a great question. I’m glad that you asked it, Kash. So let me clarify. Yeah. So first and foremost, the internal, let me address your first question. When we look at generative AI, the metrics that we look at. First and foremost, we see how many customers are using us specifically for generative AI in the cloud because there, we have perfect telemetry. And we know exactly how they’re using us because we can see that they have created, you know, indexes specifically for dense vectors and so on. The second thing that we look at is, you know, the customer commitments that are being made that are related to generative AI use cases. Now there, it is all based on the information that we get from our sales teams at the time of the deal closure.

Right? So, you know, we know that a particular deal is related to semantic search or it’s related to somebody in that cohort that I talked about, you know, building a chatbot or several chatbots. So at the time of the sale, we know what that customer commitment is related to. Now that is customer commitments, but when you look think about revenue, once the customer has purchased something from us and they are, you know, running, they’ve stood up a cluster of Elasticsearch, and they are, you know, now let’s say doing semantic search or RAG on that cluster, at some point of time, they might decide that they also want to do some observability workload on that same cluster. If they do that, we would not know exactly how to parse out what percentage of that compute, of that actual consumption is related to, you know, the Gen AI piece versus the observability piece.

So all the data that we have when it comes to things other than cloud effectively or at sale time. And even on cloud, if it’s in the same cluster, because we don’t have a separate SKU, it’s hard to disambiguate. So what we look at is customer commitments. And that’s true even for our other solution areas. So, yeah, that’s why when we talk about the solution mix, we always say that it’s based on, you know, reported information from our sales teams. And there, we absolutely do have that clarity. That also guides our go-to-market motion. So based on that information, we know exactly what is working, where we are seeing success, and that’s how we guide our sales teams to make sure that we’re maximizing the opportunity ahead of us.

Kash Rangan: Got it. And we can save this for another discussion maybe at your analyst day or whatever. But I’m curious in the long term, how does the go-to-market strategy evolve and the product marketing and the product management evolve so you can actually go to market with a bundle use case bundle. But we don’t you don’t have to address this now, but I’m just throwing it out there. But the real question I wanted to follow-up with was at what point do you feel like you need to add or accelerate the sales capacity growth rate? Because clearly, if things work out the way you would like to, the growth of the company can be better. For that to happen, you need to also have another round of investing in sales capacity go-to-market. Just curious how you look at the trade-off of faster revenue growth rate versus enjoying the margin structure that you do currently. Thank you so much, and that’s it for me.

Ashutosh Kulkarni: Yeah. Thanks, Kash. So, you know, we see our growth opportunity as continuing. And just to be very clear, you know, even when at the end of Q1, we said that we are going to be taking some near-term cost control actions, all of them were such that we did not affect our selling capacity. We are continuing to, you know, make sure that we give ourselves the room to continue to grow in the best way possible. But to talk about some of those investments and so on, let me actually turn it to Janesh.

Janesh Moorjani: Hey, Kash. So as Ash mentioned, we are seeing the course on investments. And in fact, as we look at the second half of this year, just given our revenue outperformance in the second quarter, we are actually increasing our investments in the second half a little bit. And given the strength of the operating leverage inherent in the model, we’re able to do both add to the margin as well as increase the investments. And those investments will go towards a variety of functions, including selling capacity, which, you know, as you know, is important for us to make sure we enter fiscal 2026 with the right amount of productive selling capacity as well. So definitely keeping an eye towards that growth in the future and investing appropriately towards it.

Kash Rangan: Awesome. Very good to hear. Thank you so much.

Operator: This concludes our question and answer session. I would like to turn the conference back over to Ashutosh Kulkarni for any closing remarks. Please go ahead.

Ashutosh Kulkarni: Thank you all for joining us today. We are extremely excited about the continued momentum in our business driven by generative AI, and we look forward to continuing our strong execution. For those of you who will be at AWS re:Invent, I look forward to seeing you there. Have a great day.

Operator: The conference has now concluded. Thank you for attending today’s presentation. You may now disconnect.