Elastic N.V.

Good afternoon and welcome to the ELASTIC Third Quarter Fiscal 2026 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 Eric Pringle, Global Vice President of Finance. Please go ahead.

Thank you. Good afternoon, and thank you for joining us on today's conference call to discuss Elastic's third quarter fiscal 2026 financial results. On the call, we have Ashkel Carney, Chief Executive Officer, and Navali Hinda, Chief Financial 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. These forward-looking statements are based on factors currently known to us, speak only as to 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. Disclosures regarding non-GAAP measures, including reconciliations with the most comparable GAAP measures, can be found in the press release and slides. Unless specifically noted otherwise, All results and comparisons are on a fiscal year-over-year basis. The webcast replay of this call will be available on our company website under the Investor Relations link. Our fourth quarter fiscal 2026 quiet period begins at the close of business on Thursday, April 16, 2026. We will be participating in the Morgan Stanley Technology, Media, and Telecom Conference on March 2. With that, I'll turn it over to Ash.

Thank you, Eric, and good afternoon, everyone. Thank you for joining today's call. Elastic delivered yet another outstanding quarter, beating the high end of guidance across all key metrics and showcasing the power of the Elastic platform in our business model. Sustained platform demand, strong sales execution, and our relentless focus on customers drove Q3 momentum. As LLMs rapidly evolved their capabilities around inference and reasoning, it is becoming increasingly clear that context is the most important ingredient in making these models useful within an enterprise. With that backdrop, in Q3, we continued to see enterprises choose Elastic to power context for their most critical AI needs. Translating the success to our performance, we achieved 18% total revenue growth and an 18.6% non-gap operating margin. Sales-led subscription revenue accelerated to 21%, alongside our growing cohort of 100K ACV customers, which now exceeds 1,660. Q3 marked our sixth consecutive quarter of strong field execution, driving solid customer commitments and supporting healthy CRPO growth. That execution is also translating into a strong pipeline as we head into Q4. The lifeblood of organizations is the proprietary data that they create, manage, and analyze every day to drive business decisions and operations. This data is massive, often many petabytes in scale, and simply cannot be moved for cost and security reasons outside of the organization's control. For businesses to use agentic AI, the LLM needs to come to the data. This is where Elastic comes in, with our ability to help organizations store and manage all of their data in very cost-effective ways, and by providing accurate, real-time context to AI by searching through all of this organizational data in real time. Furthermore, Elastic is capable of doing this consistently across cloud and self-managed environments. This hybrid flexibility allows sensitive data and workloads to remain in their preferred environments, eliminating the need for costly replatforming. This unique flexibility is why we continue to displace legacy vendors and niche cloud native players alike. And the results are clear. The number of commitments for over a million dollars in annual commitment value signed this quarter grew over 30% compared to the same period last year, driven by new logos and customer expansion. Consolidation and AI are powerful tailwinds. As organizations manage exploding data volumes, they are turning to Elastic to drive both innovation and efficiency to their search, observability, and security needs. For example, we signed a seven-figure new logo deal with the Fortune 100 insurance institution for Elastic Security. Seeking to modernize their security operations, the company initiated a competitive process to replace a legacy SIM solution that was plagued by slow query speeds, inefficient data retention, and rigid SOC workflows. By leveraging features like LogsDB and searchable snapshots, they are consolidating data into a single cyber data lake with integrated AI-powered SIM workflows, all powered by Elastic and its capabilities, including AI Assistant, attack discovery, and AI-driven orchestration. This transition enables their analysts to achieve markedly faster cybersecurity detection and remediation outcomes while meeting strict regulatory requirements. In another large deal from the quarter, a global leader in data resiliency software chose Elastic Observability to power the monitoring layer for its new cloud offering. As they migrate their vast user base to the cloud, they are leveraging our full observability suite, including AI Assistant and LogsDB, to transform from reactive troubleshooting to intelligent semantic-aware analysis. By integrating OpenTelemetry and our vector search capabilities, the customer is now able to proactively detect anomalies and remediate issues using natural language queries, significantly reducing mean time to resolution. They chose Elastic over Incumbence due to our deep integration flexibility, superior handling of unstructured data, and the ability to provide a single source of truth across the organization. Crucially, as companies navigate their cloud migrations, they require a platform that doesn't force them to choose between their existing data centers and the cloud. Our asymmetric advantage in supporting modern cloud and hybrid environments drove a significant win with a global financial group During the quarter, we closed a seven-figure expansion deal for Elasticsearch, which serves as the core of their online banking application for tens of millions of users. They needed a central data repository capable of supporting both cloud and self-managed architectures, allowing them to run mission-critical workloads in their preferred environment without compromising performance. Elastic succeeded with their existing MongoDB implementation failed to provide the scalable retriever and precision necessary to move beyond simple search into production-grade context engineering. Moving forward, they are integrating semantic search and advanced AI features to further personalize the user experience through faster, more accurate retrieval. Central to these enterprise engagements is the rise of agentic AI. Customers are moving from passive Q&A to active agents that drive workflows. Precise action requires precise data. The conversation has shifted from which model to use to how to feed it the most accurate context. Enterprises realize that to unlock the value of AI, they must bridge the gap between their LLMs and their proprietary unstructured and structured data. Elastic makes this AI work. We are the engine that allows enterprises to build production-grade AI systems that are actually worthy of their business. While others offer simple vector databases, We know that vectors alone are not enough. We deliver the full retrieval toolkit from hybrid search to advanced re-ranking, ensuring that agents have the relevant context they need to take precise actions. This ability to bridge enterprise data to the LLM with our platform is directly translating into expanded AI adoption. In Q3, new customer commitments with AI continue to grow. and we now have over 2,700 customers on Elastic Cloud using us as a vector database, with additional customers using us for broader AI capabilities, including agent builder and attack discovery, bringing our total count of AI customers to over 3,000. We now have over 470 customers with an ACV of 100,000 or greater using us for AI. This includes more than 410 using us as a vector database. Cumulatively, AI use cases have now penetrated over a quarter of our 100K ACV customer cohort. We are seeing sustained demand from the largest companies in the world alongside interest from the new wave of AI-native companies. During the quarter, we closed multiple new logo and expansion deals with AI-first innovators, validating that our platform is the standard for both established enterprises and disruptors. A leading AI recruiting platform used by large enterprises and startups alike, chose Elastic's vector database to power their core customer-facing software because our search performance at scale was better than competitors. An AI-enabled driver and fleet safety company expanded their use of Elasticsearch in Q3 as they scale into new global regions. Elastic provides the real-time retrieval necessary to power their platform, ensuring they can manage increasing data volumes without sacrificing performance. And a leading AI native cybersecurity company focused on AI automated penetration testing has integrated our SIM solution into their product. Elastic centralizes all of their logs without complication, allowing them to effortlessly scale through their massive growth trajectory. At the heart of these wins is the performance of our Search AI platform. We aren't just adding features, we are aggressively optimizing our engine. focusing our development efforts on delivering market-leading relevance, speed, and efficiency. In the last 18 months, we have driven two orders of magnitude less RAM required for vector search through innovations like better binary quantization, or bbq, disk bbq, and our ACORN filtering algorithm, among other things. This investment makes Elasticsearch vector search up to eight times faster than open search. Our superior performance led to one seven-figure deal with the global heavy equipment manufacturer. The customer continues to migrate mission-critical workloads over to Elastic Cloud from OpenSearch to improve scalability and performance. They are relying on our platform to power their high-speed search for telemetry data collected via the Starlink network. By leveraging LogsDB, they have achieved a significant reduction in cloud costs while managing seven years of historical customer data. Our focus on performance extends to our partnership with NVIDIA as well, where together we help enterprises deploy AI applications faster without draining IT infrastructure. We recently announced the technical preview of our Elasticsearch GPU plugin for a GPU-accelerated vector database, which allows for 12 times faster indexing. Additionally, the Dell AI data platform, now with NVIDIA and Elastic, delivers a tightly integrated AI stack that streamlines the ability to build, deploy, and scale AI. By making Elasticsearch a core component of the Dell and NVIDIA AI factories, we are meeting the critical demand for building AI on customer-controlled infrastructure. As we deepen these technical advantages, we strengthen our technical moat while removing friction from scaling AI. This quarter, we reached several product milestones designed to simplify the path from data to action for our customers. We are providing an end-to-end framework for building the next generation of intelligent applications. First, we officially launched the general availability of Agent Builder. Agent Builder allows developers to build secure, context-driven AI agents in minutes. Unlike consumer apps that surf the web, Our focus is on internal business applications using company data. We piloted AgentBuilder with a global 100 financial group to investigate and troubleshoot its production infrastructure, demonstrating an order of magnitude improvement in performance for complex issues and democratizing the specialized expertise necessary for rapid troubleshooting. An international entertainment and media company created a chat interface for customer interactions They found the agent builder results to be significantly more reliable and accurate than the other LLM-centric approaches they had tried. Building an agent is only half the battle. The other half is ensuring that agent has the most relevant information at its fingertips. This quarter, we expanded our elastic inference service to include Jina AI's multilingual re-ranking models. Jina AI delivers a best-in-class model for search accuracy with JINA V3 currently the number one re-ranker in its model size category on the MTEB English Retrieval Benchmark, a gold standard for search and RAG relevance. JINA AI's V5 Nano and V5 Small models continue to outpace peers as well, scoring high in retrieval, re-ranking, and other tasks. By making these models available natively, we are allowing our customers to tune their AI applications for maximum precision and recall. is the critical next step in a context engineering pipeline that ensures the most relevant data is presented to the LLM. Gina's state-of-the-art models deliver superior performance across over 80 languages. While AI provides the reasoning, enterprises still require the reliability of rule-based automation for critical business tasks. This is why we introduced Elastic Workflows in Technical Preview. Workflows adds automation capability directly into our platform, allowing agents to orchestrate actions across internal and external systems like Slack or ServiceNow. It moves Elastic from being a search box to a complete system of action. Finally, we are delivering on our promise of hybrid flexibility with Cloud Connect for self-managed customers. We recognize that many of our largest customers, particularly in financial services and government, maintain data on-premises for regulatory or sovereignty reasons. However, procuring and managing GPU hardware for AI is a massive hurdle for these teams. Cloud Connect allows customers to keep their data local while securely bursting to Elastic Cloud to leverage NVIDIA GPUs for high-performance inference. This ability to bridge modern AI capabilities with rigorous enterprise requirements is exactly why we are winning large-scale displacements against legacy providers. As organizations prioritize both innovation and operational efficiency, they're moving away from fragmented legacy tools in favor of Elastic's unified Search AI platform. The results of this quarter, accelerating growth, large deal momentum, and major competitive displacements, confirm that our strategy is resonating and that we are winning the race to become the essential infrastructure for the next generation of AI-powered businesses. I want to thank our customers for their partnership, our shareholders for their trust, and most importantly, our employees for their tireless spirit of innovation. With that, I will turn the call over to Navam to review our financial results in more detail. Thank you, Ash.