Financial Data and Key Metrics Changes - Total revenue in Q4 was $388 million, growing 16% year-over-year on an as-reported and constant currency basis [30] - Subscription revenue in Q4 totaled $362 million, also growing 16% as reported and 17% in constant currency [30] - Elastic Cloud revenue grew 23% on an as-reported and constant currency basis [30] - Non-GAAP operating margin for Q4 was 15%, with a gross margin of 77% [35][36] - Adjusted free cash flow margin improved by approximately 600 basis points to end the year at 19% [36] Business Line Data and Key Metrics Changes - The number of customers with over $1 million in annual contract value grew approximately 27%, adding about 45 net new customers [34] - Customers with over $100,000 in annual contract value grew approximately 14%, adding about 180 net new customers [34] - Subscription revenue excluding Monthly Cloud was $315 million, growing 19% in Q4 [32] Market Data and Key Metrics Changes - Strong growth was observed in the APJ region, followed by EMEA and The Americas, while some pressure was noted in the U.S. Public sector [34] - Over 2,000 Elastic Cloud customers are using Elastic for Gen AI use cases, with over 30% of these customers spending $100,000 or more annually [12] Company Strategy and Development Direction - The company is focusing on leveraging AI to automate business processes and drive innovation, positioning itself as a strategic partner for enterprises [11][18] - Elastic aims to strengthen its position as the preferred vector database, enhancing its offerings with new technologies like better binary quantization [13][19] - The company is committed to maintaining a balance between growth and profitability while continuing to innovate and expand its product offerings [40][43] Management's Comments on Operating Environment and Future Outlook - Management acknowledged potential uncertainty in the macro environment but expressed confidence in the healthy pipeline and demand signals [39] - The company expects continued growth and strong margins in FY 2026, projecting total revenue in the range of $1.655 billion to $1.670 billion [42] Other Important Information - Elastic Cloud now accounts for over 50% of subscription revenue, with strong growth in cloud adoption [18] - The company announced a strategic collaboration agreement with AWS to enhance solution integrations and accelerate AI innovation [25] Q&A Session Summary Question: Guidance and Metrics - Inquiry about the conservativeness of guidance and leading indicators of business performance [45] - Response highlighted the balance of positive demand signals with macro uncertainty, emphasizing the importance of CRPO and subscription revenue metrics [46][49] Question: Partnerships and Market Opportunities - Question regarding the impact of recent partnerships, particularly with AWS and NVIDIA, on market opportunities [53] - Management noted the growing acceptance of Elastic as a leading vector database and the importance of partnerships for driving cloud adoption [54] Question: Retrieval Augmented Generation (RAG) - Inquiry about the durability of RAG architectures and Elastic's positioning [59] - Management affirmed the critical role of retrieval in enterprise data management and the growing adoption of their vector database for RAG use cases [60][61] Question: Cloud Performance and Consumption Hesitation - Question about the sequential growth in cloud performance and the impact of the leap year [62] - Management clarified that the leap year and fewer days in Q4 affected consumption rates, but normalized growth rates remained strong [64][66] Question: Go-to-Market Strategy and Changes - Inquiry about the effectiveness of go-to-market changes made in the previous fiscal year [69] - Management confirmed that the changes have settled and are yielding positive results, with plans to continue hiring sales capacity [70][72] Question: AI Commitments and Emerging Use Cases - Question about the $1 million AI commitments and emerging use cases [93] - Management clarified that 25% of $1 million customers are using Elastic for AI workloads, with a variety of sophisticated use cases emerging across industries [94][96]

如果您希望可以时常见面，欢迎标星收藏哦~ 在存储管理中使用人工智能几乎是轻而易举的事。它提高了存储管理员的效率，并且对网络安全至 关重要。关键挑战在于存储人工智能数据，以便模型和即将上线的代理能够通过人工智能数据管道 快速访问这些数据。存储硬件或软件供应商是否为此做出了特殊安排，或者依赖于在光纤通道、以 太网和 NVMe 上运行的标准块、文件和对象访问协议，并由中间人工智能管道软件使用这些协议 从其存储中选择和提取数据？ 基础存储硬件和软件供应商都采取了不同程度的特殊安排，首先是采用 Nvidia GPUDirect 支持， 以便更快地将原始数据发送到 GPU。这项服务最初仅限于文件，但现在已扩展到通过 RDMA 传 输 S3 的对象。其他 GPU 或 AI 加速器硬件供应商没有与 GPUDirect 等效的服务。在流水线的每 个阶段，原始数据都会逐步转换为 AI 模型可用的最终数据集和格式，这意味着非结构化文件和对 象数据的向量嵌入。 数据仍然存储在磁盘或 SSD 驱动器硬件上，但管理这些数据的软件可以从存储阵列控制器变为数 据库或数据湖，再变为矢量存储，可以是独立的，也可以是数据仓库、数据湖或湖屋的一部分 ...