Despite their compact size, they outperform significantly larger models with 7B to 14B parameters and achieve best-in-class results on the MMTEB (Multilingual MTEB) benchmark among models of comparable size and purpose. Their small footprint enables outstanding hybrid search at lower infrastructure cost, faster query response, and new deployment scenarios where memory and compute budgets are tight - including edge devices and resource-constrained environments. The Jina v5 models are now available through th ...

Core Viewpoint - Elastic is set to announce its third quarter fiscal 2026 earnings results on February 26, 2026, after the U.S. market closes, followed by a conference call to discuss the financial results and business outlook [1]. Group 1: Earnings Announcement - The financial results for the third quarter fiscal 2026, which ended on January 31, 2026, will be released after market close on February 26, 2026 [1]. - A conference call will be held at 2:00 p.m. PT / 5:00 p.m. ET on the same day to review the financial results and business outlook [1]. Group 2: Company Overview - Elastic, known as the Search AI Company, integrates search technology with artificial intelligence to transform data into actionable insights [1]. - The Elastic Search AI Platform supports various solutions, including search, observability, and security, and is utilized by over 50% of Fortune 500 companies [1].

Core Insights - Elastic has launched the Elastic Inference Service (EIS) via Cloud Connect, enabling self-managed Elasticsearch deployments to access cloud-hosted inference capabilities without the need for GPU infrastructure management [1][3] - The EIS allows organizations to implement advanced semantic search capabilities efficiently while keeping their existing architecture and data on-premises [2][3] Group 1 - The EIS is now available for self-managed customers using Elastic Stack 9.3, providing access to GPU-based embedding and reranking models, including those from Jina.ai [2][3] - This service simplifies the adoption of semantic search for self-managed customers by eliminating the complexity associated with GPU infrastructure [3] - Users can benefit from a range of cloud services, including automated diagnostics and fast AI inference, while maintaining data security on-premises [3] Group 2 - Elastic integrates its expertise in search technology with artificial intelligence to transform data into actionable insights, serving thousands of companies, including over 50% of the Fortune 500 [4]

Core Insights - Elastic NV's stock surged by 8% in after-hours trading following significant announcements, rising to $88.07 from $81.55 [1] Group 1: Acquisition of Jina AI - Elastic has completed the acquisition of Jina AI, a company specializing in open-source tools for handling diverse data types and languages [2] - This acquisition enhances Elastic's capabilities in vector search, retrieval-augmented generation (RAG), and context engineering for agentic AI, incorporating dense vector models for text and image processing [3] - The former CEO of Jina AI, Han Xiao, has joined Elastic as vice-president of AI, emphasizing the importance of search in generative AI [4] Group 2: Introduction of GPU-Accelerated Inference Service - Elastic launched the Elastic Inference Service (EIS), utilizing NVIDIA GPUs to achieve up to 10x faster data processing compared to CPU-based options [5] - The EIS offers an API-based inference service and features consumption-based pricing per million tokens, available on Serverless and Elastic Cloud Hosted deployments [6] Group 3: Stock Buyback Program - The board of Elastic has authorized a share repurchase program of up to $500 million with no expiration date, reflecting management's confidence in the company's business strength [7] - Over the past year, Elastic's stock has increased by 2.10%, although it has decreased by 17.69% in 2025, with a market capitalization of $8.67 billion [7]

Core Insights - Elastic has launched the Elastic Inference Service (EIS), a GPU-accelerated inference-as-a-service designed for Elasticsearch semantic search, vector search, and generative AI workflows [1][2]. Group 1: Service Features - EIS provides an API-based inference service utilizing NVIDIA GPUs, integrated with Elasticsearch's vector database for low-latency and high-throughput inference [3]. - The first text-embedding model available on EIS is the Elastic Learned Sparse EncodeR (ELSER), with plans to support additional models for multilingual embeddings and reranking soon [3][5]. - EIS is designed to streamline the developer experience by eliminating model downloads, manual configuration, and resource provisioning, integrating directly with semantic text and the Inference API [7]. Group 2: Performance and Scalability - The service offers improved end-to-end semantic search capabilities, compatible with both sparse and dense vectors, as well as semantic reranking [7]. - GPU-accelerated inference provides consistent latency and up to 10x higher throughput for ingestion compared to CPU-based alternatives [7]. - EIS is available on Serverless and Elastic Cloud Hosted deployments, accessible across all cloud service providers and regions [5]. Group 3: Pricing and Support - EIS features consumption-based pricing, charged per model per million tokens, making it easy for users to get started and access support [7]. - Elastic provides intellectual property indemnity for all models offered on EIS, ensuring peace of mind for users [7].

Core Insights - Elastic has completed the acquisition of Jina AI, enhancing its capabilities in retrieval, embeddings, and context engineering for agentic AI [1][2] - The acquisition positions Elastic as a leading Search AI Platform, emphasizing its commitment to open and accessible Search AI solutions [2][3] Group 1: Acquisition Details - The acquisition deepens Elastic's capabilities in vector search, retrieval-augmented generation (RAG), and context engineering [2] - Jina AI's technology adds dense vector, multilingual, and multimodal embeddings models, enhancing Elastic's ELSER model [3] - Advanced rerankers from Jina AI improve retrieval quality for visual and long-context multilingual documents [3] Group 2: Strategic Goals - The integration of Jina AI's models aims to enhance relevance for unstructured data, enabling developers to deliver higher-quality context to generative AI systems [3] - Elastic plans to continue Jina AI's practice of releasing models on Hugging Face and publishing academic research [4] - The models will be available through the Elastic Inference Service (EIS) on Elastic Cloud, allowing enterprise customers to utilize embeddings and rerankers alongside Elastic's vector database [4] Group 3: Leadership and Vision - The former CEO of Jina AI, Han Xiao, has joined Elastic as VP of AI, indicating a strategic alignment in advancing search foundation models [4] - Elastic's CEO, Ash Kulkarni, highlighted the importance of search in generative AI and the expanded capabilities brought by Jina AI [3]