Elastic N.V. (ESTC) Stock Analysis and Metrics

Elastic

NYSE: ESTC

$58.68 ▲ +1.35 (+2.35%)

At close: Jul 24, 2026 · 3:59 PM UTC

Financial Ratios

Market Cap6.04 Bn

P/E16.42

P/S3.47

Div. Yield0.00

Total Debt (Qtr)570.90 Mn

Revenue Growth (1y) (Qtr)16.03

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About

Elastic N. V. enables its customers to transform data into answers actions and outcomes with Search AI. The company provides a platform that combines search technology with artificial intelligence to help organizations find insights from large volumes of data. Its core offering is the Elasticsearch engine which serves as a scalable document store and search engine. Built on this platform are three solutions Elasticsearch Elastic Observability and Elastic Security that address search observability and security use cases. The platform is available as a cloud service or as self managed software and can be deployed in public private hybrid or multi cloud environments.

Elastic N. V. generates revenue primarily through subscription based offerings. The company sells access to its platform via Elastic Cloud Hosted and Elastic Cloud Serverless which are paid services delivered on major cloud providers such as AWS GCP and Azure. It also offers Elastic Self Managed software which can be used free of charge or purchased with paid features. Subscription pricing is based on resource consumption and provides access to all three solutions. Subscriptions accounted for approximately ninety three percent of total revenue in the fiscal years ended April 30 2025 and 2024. The remaining revenue comes from professional services and other minor sources.

Elastic N. V. competes in a highly fragmented market that includes search observability and security vendors. Its main rivals for search capabilities include Apache Solr Lucidworks Fusion MongoDB Atlas Algolia Coveo Google Pinecone and Microsoft Azure Cognitive Search. For observability the company faces competition from AppDynamics Datadog Dynatrace New Relic and Splunk. In the security space its competitors include Azure Sentinel CrowdStrike Google SecOps Palo Alto Networks and Splunk. Elastic differentiates itself through the speed scale and relevance of its platform its extensible architecture and its open source development model. The company also benefits from a large active developer community and a flexible deployment model that supports public private hybrid and multi cloud environments.

Elastic N. V. serves a diverse customer base that includes enterprises educational institutions and government entities across many industries. As of April 30 2025 the company reported approximately twenty one thousand five hundred customers. The customer base is broad with no single customer accounting for ten percent or more of total revenue except for a channel partner that represented about twelve percent of revenue in the fiscal year ended April 30 2025. The company’s products are used for use cases such as website search enterprise observability log analytics application performance monitoring and threat detection.

Investment Thesis

▲ Bull case

Elastic is positioning itself as the essential data and context layer for enterprise AI, leveraging its foundational strengths in data gravity, context engineering, specialized agents, and platform consolidation to capture long-term growth in the AI-driven infrastructure stack. The company's data gravity advantage ensures that LLMs come to the data rather than moving petabytes of proprietary information, which is cost-prohibitive and poses security risks for enterprises. By optimizing Elasticsearch as the most efficient data store for unstructured data—including logs, metrics, vectors, text, audio, and video—through massive compression and significant ingest speedups, Elastic provides the price scalability and speed that make it the preferred data store for AI workloads. This is further reinforced by innovations like cross-project search in serverless, which eliminates the need for costly data centralization by allowing users to query disparate projects where they live, addressing a critical pain point in large enterprises with data scattered across teams and regions. These capabilities are not incremental improvements but structural advantages that deepen customer entrenchment, as evidenced by the 7-figure win with a global financial information provider managing over 2 billion documents, where Elastic displaced a legacy dual-vendor setup by proving superior relevancy for high-volume workloads through hybrid search, enhanced by the Jina AI acquisition for multilingual support across 30-plus languages.
The company is successfully transitioning customers toward multi-year, high-value commitments that are accelerating CRPO and RPO growth, creating a durable revenue runway that the market is underestimating. In Q4 FY26, CRPO grew 20% (20% constant currency) to $1.2 billion, while RPO accelerated to $1.98 billion, growing 28% as reported and 27.4% in constant currency—driven by an exceptional quarter for multi-year commitments and the highest year-over-year growth in total RPO over the last four years. Critically, non-current RPO (representing obligations beyond 12 months) grew 43% year-over-year in Q4, signaling a deepening of customer relationships and a shift toward longer-term contracts. This was achieved without material changes to discount practices, underscoring genuine customer commitment to Elastic’s value proposition. The shift is particularly pronounced in the public sector, where the CISA SIEM as a Service partnership is growing as civilian agencies switch from legacy security offerings to Elastic Cloud, with commitments ramping over the year to translate into future revenue traction. Commercial analogs are evident in the Fortune 50 global financial services firm’s 8-figure win to consolidate disparate cyber data silos into a unified AI-driven SIEM, leveraging Elastic for improved data retention, accessibility, and long-term infrastructure cost optimization—demonstrating how Elastic’s AI-driven capabilities in attack discovery and AI Assistant are enabling proactive threat mitigation and productivity savings. These multi-year, high-value commitments are not transient but reflect a structural shift in how enterprises view Elastic as a long-term AI infrastructure partner, setting up accelerated revenue recognition in FY27 as existing commitments convert to sales.
Elastic’s AI adoption is broadening beyond search into security and observability, creating a self-reinforcing flywheel where increased platform usage drives higher ACV expansion and margin expansion, with the market failing to fully appreciate the scale of this opportunity. Over 600 customers with $100k+ ACV are now using Elastic’s AI capabilities—a figure that includes more than 40 serverless customers previously uncaptured—and AI use cases have penetrated more than a third of the $100k ACV customer cohort. Importantly, the AI-attached cohort is growing at a rate roughly 5% faster than the rest of the customer base, a trend that is continuing and accelerating as AI adoption becomes universal across organizations of every scale. This growth is not confined to search; newer AI products are penetrating security and observability, with traction in AI-driven SOC and SRE capabilities, agent builder (now in GA), and MCP apps for security and observability that embed interactive workflows into tools like Claude, VS Code, and GitHub Copilot. The company’s blog demonstrating a 70% reduction in token usage and improved accuracy when using Elastic as a context layer versus naive RAG alone highlights a durable cost and performance advantage that becomes increasingly critical as data volumes and agent deployment grow. As AI becomes embedded in every mission-critical business process, Elastic’s role as the grounding layer for real-time business reality ensures that its platform is not just a tool but a foundational component of the AI stack, driving expansion across its three solutions and supporting the platform consolidation trend where organizations favor single vendors capable of leveraging AI across security and observability at lower TCO.

Elastic is positioning itself as the essential data and context layer for enterprise AI, leveraging its foundational strengths in data gravity, context engineering, specialized agents, and platform consolidation to capture long-term growth in the AI-driven infrastructure stack. The company's data gravity advantage ensures that LLMs come to the data rather than moving petabytes of proprietary information, which is cost-prohibitive and poses security risks for enterprises. By optimizing Elasticsearch as the most efficient data store for unstructured data—including logs, metrics, vectors, text, audio, and video—through massive compression and significant ingest speedups, Elastic provides the price scalability and speed that make it the preferred data store for AI workloads. This is further reinforced by innovations like cross-project search in serverless, which eliminates the need for costly data centralization by allowing users to query disparate projects where they live, addressing a critical pain point in large enterprises with data scattered across teams and regions. These capabilities are not incremental improvements but structural advantages that deepen customer entrenchment, as evidenced by the 7-figure win with a global financial information provider managing over 2 billion documents, where Elastic displaced a legacy dual-vendor setup by proving superior relevancy for high-volume workloads through hybrid search, enhanced by the Jina AI acquisition for multilingual support across 30-plus languages.
The company is successfully transitioning customers toward multi-year, high-value commitments that are accelerating CRPO and RPO growth, creating a durable revenue runway that the market is underestimating. In Q4 FY26, CRPO grew 20% (20% constant currency) to $1.2 billion, while RPO accelerated to $1.98 billion, growing 28% as reported and 27.4% in constant currency—driven by an exceptional quarter for multi-year commitments and the highest year-over-year growth in total RPO over the last four years. Critically, non-current RPO (representing obligations beyond 12 months) grew 43% year-over-year in Q4, signaling a deepening of customer relationships and a shift toward longer-term contracts. This was achieved without material changes to discount practices, underscoring genuine customer commitment to Elastic’s value proposition. The shift is particularly pronounced in the public sector, where the CISA SIEM as a Service partnership is growing as civilian agencies switch from legacy security offerings to Elastic Cloud, with commitments ramping over the year to translate into future revenue traction. Commercial analogs are evident in the Fortune 50 global financial services firm’s 8-figure win to consolidate disparate cyber data silos into a unified AI-driven SIEM, leveraging Elastic for improved data retention, accessibility, and long-term infrastructure cost optimization—demonstrating how Elastic’s AI-driven capabilities in attack discovery and AI Assistant are enabling proactive threat mitigation and productivity savings. These multi-year, high-value commitments are not transient but reflect a structural shift in how enterprises view Elastic as a long-term AI infrastructure partner, setting up accelerated revenue recognition in FY27 as existing commitments convert to sales.
Elastic’s AI adoption is broadening beyond search into security and observability, creating a self-reinforcing flywheel where increased platform usage drives higher ACV expansion and margin expansion, with the market failing to fully appreciate the scale of this opportunity. Over 600 customers with $100k+ ACV are now using Elastic’s AI capabilities—a figure that includes more than 40 serverless customers previously uncaptured—and AI use cases have penetrated more than a third of the $100k ACV customer cohort. Importantly, the AI-attached cohort is growing at a rate roughly 5% faster than the rest of the customer base, a trend that is continuing and accelerating as AI adoption becomes universal across organizations of every scale. This growth is not confined to search; newer AI products are penetrating security and observability, with traction in AI-driven SOC and SRE capabilities, agent builder (now in GA), and MCP apps for security and observability that embed interactive workflows into tools like Claude, VS Code, and GitHub Copilot. The company’s blog demonstrating a 70% reduction in token usage and improved accuracy when using Elastic as a context layer versus naive RAG alone highlights a durable cost and performance advantage that becomes increasingly critical as data volumes and agent deployment grow. As AI becomes embedded in every mission-critical business process, Elastic’s role as the grounding layer for real-time business reality ensures that its platform is not just a tool but a foundational component of the AI stack, driving expansion across its three solutions and supporting the platform consolidation trend where organizations favor single vendors capable of leveraging AI across security and observability at lower TCO.

▼ Bear case

Elastic’s revenue growth is increasingly dependent on a shifting mix of cloud commitments that delay revenue recognition, creating near-term headwinds that the market may be overlooking despite strong CRPO and RPO metrics. While the company highlights accelerating CRPO (20% growth in Q4 FY26) and RPO (28% growth), the shift toward Elastic Cloud—particularly driven by U.S. public sector adoption of SIEM as a Service—means that a larger portion of commitments are back-end loaded, as cloud revenue ratably recognizes over the subscription term compared to self-managed licenses, which have a portion of revenue recognized upfront. This dynamic was explicitly acknowledged in the earnings call, where Navam Welihinda noted that Q4’s significantly larger mix of cloud commitments impacted in-quarter revenue, and that revenue from cloud commitments ramps over the year. Although this shift is positive for future revenue as commitments ramp toward their levels, it creates a near-term drag on quarterly revenue growth that could lead to periodic misses if the market expects linear conversion of CRPO to sales. The guidance for FY27 already reflects this, with Q1 revenue growth guided at only 13.1% YoY (midpoint) and Q4 expected to show the highest growth, implying a pronounced back-half acceleration that relies on flawless execution of ramped sales capacity and consumption against existing commitments. If sales ramp does not proceed as planned or if consumption lags behind commitment levels—especially in the public sector where agency onboarding may face bureaucratic delays—the back-half acceleration may not materialize, leaving the company vulnerable to disappointing quarterly results despite strong leading indicators.
The company’s dependence on large, multi-year deals with high-ACV customers introduces concentration risk and execution vulnerability, particularly as the sales cycle for these enterprise wins lengthens and becomes more complex, potentially slowing the conversion of commitments to revenue. Elastic celebrated record Q4 performance in $1M+ deals and added over 30 net new customers to its $1M+ ACV cohort in FY26, bringing the total to over 240, with the >$5M ACV cohort growing 30%. While this reflects strong traction with strategic accounts, it also means that a material portion of growth is tied to a small number of large, complex deals—such as the 8-figure win with a Fortune 50 global financial services firm consolidating cyber data silos or the 7-figure wins with a global financial information provider and a workplace AI software firm. These deals require significant internal customer alignment, extensive proof-of-concepts, and often involve displacing entrenched legacy vendors, which can extend sales cycles and increase the risk of delays or renegotiations. The earnings call revealed that the company is seeing success in displacing incumbents in security and observability, but did not address whether the increasing sophistication of AI use cases is raising the bar for customer validation or if competitors are accelerating their own AI-native offerings to counter Elastic’s gains. If the sales team cannot maintain the velocity of these high-value wins—or if macroeconomic pressures cause enterprises to delay or downsize strategic initiatives—the growth engine could falter, especially given that the company’s midterm targets rely on sustaining 20%+ sales-led subscription revenue growth through FY29, a target that assumes continued success in landing and expanding with the most demanding enterprise clients.
Elastic’s operating margin expansion plans are predicated on uncertain AI-driven internal productivity gains and organizational restructuring, which may not deliver the anticipated efficiency improvements and could instead create execution risks that offset top-line momentum. The company expects to expand its non-GAAP operating margin from 16.4% in FY26 to approximately 19% in FY27—a 2.5 percentage point increase—driven by AI-enabled productivity gains across functions and organizational changes aimed at reducing complexity as it evolves to align with an age of AI automation. Ashutosh Kulkarni stated that these changes are expected to simplify operations, reduce operational complexity, and scale more effectively as the business grows, with Navam Welihinda confirming that the firm is raising its medium-term FY29 non-GAAP operating margin target from over 20% to approximately 25%. However, the transcript provides little concrete detail on how these AI-driven productivity gains will be measured or realized, particularly in functions like sales where enterprise selling still requires significant interpersonal interactions and headcount is expected to grow meaningfully. The reliance on internal AI adoption to fund margin expansion introduces execution risk: if the productivity gains from tools like coding platforms, marketing automation, or financial analysis fall short of expectations, or if the organizational restructuring creates disruption during transition, the company may fail to achieve its margin targets. This is especially concerning given that the adjusted free cash flow margin is already projected to rise to 21.5% in FY27, and any shortfall in margin expansion would directly impact the Rule of 40 trajectory, which the company aims to exceed by FY29. Without clear, quantifiable milestones for internal AI ROI, the market may be overestimating the ease and speed of this operational transformation.

Elastic’s revenue growth is increasingly dependent on a shifting mix of cloud commitments that delay revenue recognition, creating near-term headwinds that the market may be overlooking despite strong CRPO and RPO metrics. While the company highlights accelerating CRPO (20% growth in Q4 FY26) and RPO (28% growth), the shift toward Elastic Cloud—particularly driven by U.S. public sector adoption of SIEM as a Service—means that a larger portion of commitments are back-end loaded, as cloud revenue ratably recognizes over the subscription term compared to self-managed licenses, which have a portion of revenue recognized upfront. This dynamic was explicitly acknowledged in the earnings call, where Navam Welihinda noted that Q4’s significantly larger mix of cloud commitments impacted in-quarter revenue, and that revenue from cloud commitments ramps over the year. Although this shift is positive for future revenue as commitments ramp toward their levels, it creates a near-term drag on quarterly revenue growth that could lead to periodic misses if the market expects linear conversion of CRPO to sales. The guidance for FY27 already reflects this, with Q1 revenue growth guided at only 13.1% YoY (midpoint) and Q4 expected to show the highest growth, implying a pronounced back-half acceleration that relies on flawless execution of ramped sales capacity and consumption against existing commitments. If sales ramp does not proceed as planned or if consumption lags behind commitment levels—especially in the public sector where agency onboarding may face bureaucratic delays—the back-half acceleration may not materialize, leaving the company vulnerable to disappointing quarterly results despite strong leading indicators.
The company’s dependence on large, multi-year deals with high-ACV customers introduces concentration risk and execution vulnerability, particularly as the sales cycle for these enterprise wins lengthens and becomes more complex, potentially slowing the conversion of commitments to revenue. Elastic celebrated record Q4 performance in $1M+ deals and added over 30 net new customers to its $1M+ ACV cohort in FY26, bringing the total to over 240, with the >$5M ACV cohort growing 30%. While this reflects strong traction with strategic accounts, it also means that a material portion of growth is tied to a small number of large, complex deals—such as the 8-figure win with a Fortune 50 global financial services firm consolidating cyber data silos or the 7-figure wins with a global financial information provider and a workplace AI software firm. These deals require significant internal customer alignment, extensive proof-of-concepts, and often involve displacing entrenched legacy vendors, which can extend sales cycles and increase the risk of delays or renegotiations. The earnings call revealed that the company is seeing success in displacing incumbents in security and observability, but did not address whether the increasing sophistication of AI use cases is raising the bar for customer validation or if competitors are accelerating their own AI-native offerings to counter Elastic’s gains. If the sales team cannot maintain the velocity of these high-value wins—or if macroeconomic pressures cause enterprises to delay or downsize strategic initiatives—the growth engine could falter, especially given that the company’s midterm targets rely on sustaining 20%+ sales-led subscription revenue growth through FY29, a target that assumes continued success in landing and expanding with the most demanding enterprise clients.
Elastic’s operating margin expansion plans are predicated on uncertain AI-driven internal productivity gains and organizational restructuring, which may not deliver the anticipated efficiency improvements and could instead create execution risks that offset top-line momentum. The company expects to expand its non-GAAP operating margin from 16.4% in FY26 to approximately 19% in FY27—a 2.5 percentage point increase—driven by AI-enabled productivity gains across functions and organizational changes aimed at reducing complexity as it evolves to align with an age of AI automation. Ashutosh Kulkarni stated that these changes are expected to simplify operations, reduce operational complexity, and scale more effectively as the business grows, with Navam Welihinda confirming that the firm is raising its medium-term FY29 non-GAAP operating margin target from over 20% to approximately 25%. However, the transcript provides little concrete detail on how these AI-driven productivity gains will be measured or realized, particularly in functions like sales where enterprise selling still requires significant interpersonal interactions and headcount is expected to grow meaningfully. The reliance on internal AI adoption to fund margin expansion introduces execution risk: if the productivity gains from tools like coding platforms, marketing automation, or financial analysis fall short of expectations, or if the organizational restructuring creates disruption during transition, the company may fail to achieve its margin targets. This is especially concerning given that the adjusted free cash flow margin is already projected to rise to 21.5% in FY27, and any shortfall in margin expansion would directly impact the Rule of 40 trajectory, which the company aims to exceed by FY29. Without clear, quantifiable milestones for internal AI ROI, the market may be overestimating the ease and speed of this operational transformation.

Geographical Breakdown of Revenue (2026)

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Geographical Breakdown of Revenue (2026)

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Investment Thesis

Product and Service Breakdown of Revenue (2026)

Geographical Breakdown of Revenue (2026)

Peer Comparison

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