Elastic beat Q3 guidance, reporting total revenue of $450 million (up ~18%) and sales-led subscription revenue of $376 million (up ~21%), with CRPO crossing $1 billion and management raising full-year fiscal 2026 revenue and non-GAAP EPS guidance.
Enterprise AI adoption is accelerating and Elastic is positioning as a “context engine” beyond vectors — the company has >2,700 Elastic Cloud vector-database users and >3,000 total AI customers, with Agent Builder now GA, expanded Inference Service, and Elastic Workflows in preview.
Customer and capital momentum: customers with >$100k ACV rose 14% to over 1,660 and $1M+ commitments grew >30% YoY, while Elastic repurchased about $186 million in Q3, completing roughly 60% of its $500 million buyback program.
Elastic (NYSE:ESTC) executives highlighted accelerating customer adoption of its “Search AI Platform” and solid third-quarter fiscal 2026 financial performance, pointing to strong demand tied to enterprise AI initiatives, continued large-deal momentum, and raised full-year guidance.
Chief Financial Officer Navam Welihinda said the company “outperformed the high end of revenue and profitability guidance ranges,” citing consistent execution, strong consumption, and customer commitments across search, security, and observability.
Total revenue: $450 million, up about 18% as reported and 16% in constant currency.
Sales-led subscription revenue: $376 million, up 21% as reported and 19% in constant currency.
CRPO (current remaining performance obligations): $1.06 billion, up 19% as reported and 15% in constant currency; Welihinda said this crossed $1 billion for the first time.
RPO: up 22% as reported and 18% in constant currency.
Non-GAAP margins: subscription gross margin of 82%, total gross margin of 78%, and non-GAAP operating margin of 18.6%.
Adjusted free cash flow: about $54 million, or roughly a 12% margin.
Elastic’s fourth-quarter fiscal 2026 guidance calls for total revenue of $445 million to $447 million and sales-led subscription revenue of $371 million to $373 million. Welihinda noted Q4 has three fewer days than each of the first three quarters, which he said creates a roughly $14 million to $15 million revenue headwind that is reflected in guidance. The company guided for non-GAAP operating margin of approximately 14.5% and non-GAAP diluted EPS of $0.55 to $0.57.
Management raised full-year fiscal 2026 expectations to total revenue of $1.734 billion to $1.736 billion and sales-led subscription revenue of $1.434 billion to $1.436 billion. Elastic also updated full-year non-GAAP operating margin guidance to approximately 16.3% and non-GAAP diluted EPS guidance to $2.50 to $2.54.
Chief Executive Officer Ash Kulkarni said Elastic beat “the high end of guidance across all key metrics,” describing sustained platform demand and strong sales execution. Kulkarni said Q3 marked the company’s “sixth consecutive quarter of strong field execution,” supporting “healthy” CRPO growth and a “strong pipeline” heading into Q4.
Welihinda said the quarter’s strength was “balanced across all geographies,” with continued multi-year commitments. Elastic ended the quarter with more than 1,660 customers with annual contract value above $100,000, up 14% year-over-year, and added about 60 net new $100,000 ACV customers sequentially.
AI adoption was a major theme. Kulkarni said Elastic sees “context” as critical for enterprise AI, arguing that “the LLM needs to come to the data.” He emphasized Elastic’s ability to support cloud and self-managed environments as a competitive differentiator, particularly for regulated industries and sensitive data.
On AI-specific metrics, Kulkarni said Elastic has:
More than 2,700 Elastic Cloud customers using Elastic as a vector database.
More than 3,000 total “AI customers” when including broader capabilities such as Agent Builder and Attack Discovery.
More than 470 customers with $100,000+ ACV using Elastic for AI, including more than 410 using it as a vector database.
Welihinda added that 28% of the $100,000+ ACV cohort now uses Elastic for AI, which he said includes incremental capabilities such as Attack Discovery and Agent Builder.
Kulkarni positioned Elastic as more than a vector database provider, arguing that “vectors alone are not enough.” In response to an analyst question about what makes Elastic a “context engine,” Kulkarni outlined key requirements: ingesting structured and unstructured data, hybrid search that blends text and vector search, re-ranking for accuracy, tools to assemble agents, workflows to take actions, and monitoring including LLM observability. He also pointed to Elastic’s Inference Service as a way to connect to various LLMs.
Among product updates discussed on the call:
Agent Builder GA: Elastic launched Agent Builder into general availability. Kulkarni cited pilots and early customer examples, including a financial group using it to investigate production infrastructure and a media company building a chat interface for customer interactions.
Inference Service expansion: Elastic added Jina AI multilingual re-ranking models, describing re-ranking as an important step in improving relevance for enterprise AI applications.
Elastic Workflows (technical preview): A preview feature intended to help orchestrate actions across systems like Slack or ServiceNow.
Cloud Connect for Self-Managed customers: A capability described as enabling customers to keep data on-premises while “bursting” to Elastic Cloud to use NVIDIA GPUs for inference.
Kulkarni also emphasized performance work in vector search, describing innovations such as Better Binary Quantization (BBQ), DiskBBQ, and an ACORN filtering algorithm. He said these efforts reduced RAM requirements for vector search and made Elasticsearch vector search “up to 8x faster than OpenSearch.”
Management cited several large customer wins and expansions tied to security, observability, and search use cases. Kulkarni said the number of commitments over $1 million in annual commitment value grew more than 30% versus the year-ago period.
Examples discussed included:
A seven-figure new logo with a Fortune 100 insurance institution for Elastic Security, described as a legacy SIEM replacement, using features including logsdb and searchable snapshots, plus AI Assistant, Attack Discovery, and AI-driven orchestration.
A large deal with a global data resiliency software company choosing Elastic Observability for a new cloud offering, leveraging OpenTelemetry and vector search capabilities.
A seven-figure expansion with a global financial group using Elasticsearch at the core of an online banking application, where Elastic was positioned as enabling “production-grade context engineering” and hybrid deployments.
A seven-figure deal with a heavy equipment manufacturer migrating workloads from OpenSearch to Elastic Cloud, using logsdb and managing multi-year historical data.
On competition, Kulkarni said Elastic does not view frontier LLM providers as direct competitors, describing models as “reasoning engines” that still require systems to deliver real-time enterprise context. He said Elastic integrates broadly with LLM providers and supports protocols that allow agent-to-agent communication.
In another competitive discussion, Kulkarni addressed a deal where a customer moved away from a MongoDB implementation for search, describing it as a case where the customer had trouble scaling basic search and needed improved performance for hybrid search.
Welihinda said Elastic made “significant progress” on its $500 million share repurchase program announced in October. In Q3, the company repurchased about $186 million of shares, or roughly 2.4 million shares. Cumulatively, Elastic has repurchased 3.8 million shares, completing 60% of the authorization by the end of Q3. Welihinda said the repurchase program is continuing in Q4.
In Q&A, management reiterated that sales-led subscription revenue is the key metric they want investors to focus on, noting that AI workloads can be purchased as self-managed and deployed in customers’ cloud or hybrid environments. Welihinda also said guidance incorporates prudence and risk adjustments, including the possibility that some large deals shift timing, and noted that enterprise seasonality tends to be “tail-end weighted” in Q3 and Q4.
Elastic N.V. operates as a search and analytics company, offering a suite of open source and subscription-based solutions for search, observability and security use cases. Its flagship product, Elasticsearch, enables fast and scalable full-text search and analytics across large volumes of structured and unstructured data. Complementary tools such as Kibana provide visualization capabilities, while Beats and Logstash serve as lightweight data shippers and data processing pipelines, respectively.
The company was founded in 2012 by Shay Banon, who serves as chief technology officer, and Steven Schuurman.
