The investment narrative around Elastic (ESTC) has gotten more upbeat thanks to increasing interest in the company’s latest AI initiatives.
Last week, shares of Elastic, a provider of an AI-powered data analytics platform centered around the Elasticsearch engine, hit a new 52-week high of $84.04. The stock has rebounded sharply from the 52-week low of $46.18 reached in January and is up 56% YTD.
At the Goldman Sachs tech conference in early September, Elastic CEO Ash Kulkarni said generative AI has provided a “huge resurgence of interest and excitement” across the customer base for search. He explained that Elastic is carving out a niche in AI, as its software enables users to bring in all kinds of unstructured data and quickly make that data searchable. Then users are able to apply analytics and machine learning to get real value out of the data.
Elastic is already seeing more workloads focused around generative AI. But the tailwind from this new technology is just getting started. While the company currently has hundreds of paying customers working with generative AI, these implementations and workloads are in the very early stages of production. Since Elastic is on a consumption-based model, it will recognize revenue when more data is moved into these AI-based workloads and users run actual queries.
In early September, Elastic shares jumped 20% in one session because of better-than-expected fiscal Q1 (ended July) results and general excitement about AI. In the July quarter, total revenue rose 17% to $294 million, 3.3% above the consensus estimate. Elastic Cloud revenue gained 24% to $121 million (representing 41% of total revenue). The total customer base rose 6% to 20,500.
Elastic in FQ1 saw significant activity around generative AI, with numerous customers selecting its Elasticsearch Relevance Engine (ESRE) as their platform for building generative AI applications using vector search and hybrid search capabilities. Vector search uses machine learning to transform unstructured data (text and images) into numeric representation. Elastic started investing in vector database functionality nearly three years ago.
Launched in May, ESRE is designed to bring the power of AI to enterprise data. With ESRE, organizations can use their own or third-party machine learning models to generate embeddings from their data, store these embeddings in a vector store at a very large scale and then efficiently search across these vectors in real-time to enhance large language model (LLM) responses by providing context using retrieval augmented generation.
The ESRE platform scales well and creates secure deployments for enterprise customers. It’s flexible enough to enable hybrid search, which uses a combination of vector, semantic and text search techniques to ensure the most relevant results. The platform uses reciprocal rank fusion to combine multiple result sets with different relevance indicators into a single result set. By reranking the results from the different search techniques, the final output is fully optimized.
Elastic is steadily adding AI technology to its security and observability products. The company recently launched its AI Assistant for Observability. Using generative AI and proprietary data, this new offering delivers context-aware and more accurate remediation recommendations for site reliability engineers (SREs).
Powered by ESRE, AI Assistant for Observability accelerates time to resolution by providing help with application errors, log message interpretation and alert analysis. The interface improves speed and collaboration across teams by allowing users to interactively chat and visualize all relevant telemetry cohesively in one place, while also leveraging proprietary data and remediation playbooks.
Elastic also made its Universal Profiling product generally available to customers. This new offering helps SRE teams deal with blind spots within complex cloud-native environments that are created by a lack of instrumentation on many components. Featuring always-on instrumentation, Universal Profiling pinpoints performance bottlenecks with visibility into third-party libraries, enabling expedited issue resolution.
For FQ2 (Oct.), Elastic’s total revenue outlook of $303 million to $305 million was above the consensus of $301.8 million. The latest top-line guidance for FY’24 (April) indicates growth of 17%. While revenue growth continues to be pressured by customers watching their consumption costs, Elastic’s profitability is improving. In FQ1, adjusted free cash flow totaled $49 million, up from $26 million in the previous quarter. For FY’24, Elastic expects EPS of $1.01 to $1.11.
Some Wall Street firms have been getting more bullish on Elastic. Canaccord raised its price target to $85 from $79, noting Elastic’s product capabilities in areas such as vector search and generative AI workloads.
Both Baird and BofA lifted their Elastic targets to $90 after attending the ElasticON AI conference, which showcased the company’s latest AI advancements and applications in search. Baird believes Elastic’s total addressable market continues to be substantial across the search, security and observability segments.
This summer, Gartner placed Elastic in the Visionary segment of the Magic Quadrant for application performance monitoring (APM) and observability for the third consecutive year.
BofA is more confident in Elastic’s positioning as a “strategic generative AI tech stack disruptor.” The firm said it is applying a higher valuation to its Elastic estimates given that it now has greater confidence in the company’s competitive differentiation.
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