If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):A new embedding model cuts vector DB costs by ~200x.It also outperforms OpenAI and Cohere models.Here's a complete breakdown (with visuals): ...

Compared to OpenAI-v3-large (float, 3072d). voyage-context-3 (binary, 512):- 99.48% lower vector DB costs.- 0.73% better retrieval quality.Check this 👇 https://t.co/7pLYG2Vkot ...

voyage-context-3 supports 2048, 1024, 512, and 256 dimensions with quantization.Compared to OpenAI-v3-large (float, 3072d), voyage-context-3 (int8, 2048):- delivers 83% lower vector DB costs- provides 8.60% better retrieval qualityCheck this 👇 https://t.co/OqBhucXCN5 ...

A new embedding model cuts vector DB costs by ~200x.It also outperforms OpenAI and Cohere models.Here's a complete breakdown (with visuals): ...

[Music] Okay, thanks everyone for coming today. Uh, so today's talk is called Building Alice's Brain. How we built an AI sales rep that learns like a human.Uh, my name is Sherwood. I am one of the tech leads here at 11X. I lead engineering for our Alice product and I'm joined by my colleague Saw.So 11X for those of you who are unfamiliar is a company that's building digital workers for the go to market organization. We have two digital workers today. We have Alice who is our AI SDR and then we also have Jul ...

[Music] We are talking about graph rack today. That's the graph rack trick of course. Uh and we want to look at patterns for successful graph applications uh for um making LLMs a little bit smarter by putting knowledge graph into the picture.My name is Michael Hunga. I'm VP at of product innovation at Neo Forj. My name is Steven Shin.I lead the developer relations at Neo Forj. And um actually we're we're both co-authoring. This is fun because we're both already authors and finally we've been friends for yea ...

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]

Core Viewpoint - The primary challenge for storage vendors is how to store data for artificial intelligence (AI) access, ensuring that AI models and agents can quickly retrieve this data through efficient data pipelines [1][3]. Group 1: AI Integration in Storage - AI is being utilized in storage management to enhance efficiency and is crucial for cybersecurity [1]. - Storage hardware and software vendors are adopting Nvidia GPUDirect support to expedite raw data transmission to GPUs, which has expanded from file support to include object storage via RDMA [3][4]. - Data management software can transition from storage array controllers to databases or data lakes, and can be hosted in public clouds like AWS, Azure, or GCP [3][4]. Group 2: Data Processing and Storage Solutions - Data must be identified, located, selected, and vectorized before being usable by large language models (LLMs), with vector storage options including specialized vector databases [4][5]. - Vendors like VAST Data are developing their own AI pipelines, contrasting with companies like Qumulo that focus on internal operations enhancement without GPUDirect support [5][10]. - Major storage vendors such as Cloudian, Dell, and IBM support GPUDirect for file and object storage, although support may vary across product lines [8][9]. Group 3: Advanced AI Capabilities - Nvidia's BasePOD and SuperPOD GPU server systems have been certified by several vendors, indicating a trend towards deeper integration with Nvidia's AI software [9][10]. - Companies like Hammerspace and VAST Data support Nvidia GPU server's key-value (KV) cache offloading, which is essential for optimizing AI model performance [11]. - Cloud file service providers are also exploring AI data pipelines to support GPU-based inference, although collaboration with Nvidia remains limited [12]. Group 4: Challenges in Data Accessibility - Backup and archive data pose challenges for AI model access, as many backup vendors are reluctant to provide API access to their stored data [13][14]. - Organizations with diverse storage vendors and systems may face difficulties in creating a unified strategy for AI model data accessibility, potentially leading to vendor consolidation [14].