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Make Your LangSmith Deployment Multi-Tenant
LangChain· 2026-04-14 16:27
Make your LangSmith deployments multi-tenant in just a few lines of code. In this video, we're going to walk through how you can add user authentication to your deployed agents in LangSmith deployment so that each user will get their own scope threads, runs, and conversation history, all managed by the deployment itself. Let's go ahead and jump right into it.With every LangSmith deployment, you get access to over 30 plus API endpoints that come as part of our agent server. This agent server will include end ...
The DDN Product Roadmap | DDN AI Data Summit 2026
DDN· 2026-04-09 19:32
My name is Omar. I'm about six months old at DDN. Um I focus on product and engineering.Uh today I'm just going to be taking you through a few key themes that we're going to be talking about uh across uh 2026 and some of the new innovations that we're going to be bringing about. uh as sort of you saw through the course of the presentation uh the the the core uh innovations at DDNS are mainly focused around uh you know AI native companies here we're developing a lot of SDKs in order to give you native integr ...
Meet DDN Infinia The Platform for End to End AI
DDN· 2025-09-18 19:04
Infinia Platform Overview - Infinia is a software-defined, metadata-driven, containerized, cloud-native data intelligence platform designed for scalability, performance, and efficiency across core, cloud, and edge environments [1] - The platform supports critical data protocols like object and block, integrating with AI data acceleration libraries like TensorFlow and PyTorch [1] - Infinia enhances AI execution engines by serving data in its native form, reducing the need for data conversion and speeding up applications [1] Metadata and Multi-Tenancy Capabilities - Infinia allows for tagging massive amounts of metadata to objects, enabling faster data discovery and processing, with no limitations on metadata capability [1] - The platform has built-in multi-tenancy capabilities, providing SLAs for individual tenants and sub-tenants on capacity and performance, ensuring quality of service [1] Scalability and Cloud Native Design - Infinia is fully containerized, allowing for scale-out at web scale, starting from a few terabytes and scaling to exabytes [1] - The product is designed to be cloud-native and will soon be available in leading cloud provider marketplaces [2] AI Data Challenges and Solutions - Infinia addresses the complexity of managing large amounts of distributed multimodal data across core, cloud, and edge environments by creating a unified platform [1] - It tackles the demand for extremely low latency required to run AI applications, as well as the high costs associated with running AI [1] - The platform ensures data protection at any time and at any scale [1] Performance Metrics - Infinia can deliver time to first byte in less than a microsecond [2] - It can deliver 30 to 40 million objects per second in list object operations [2] - Infinia can deliver terabytes per second throughput at large scale [2] Efficiency and Sustainability - Infinia can achieve 10x data reduction, fitting over 100 petabytes of storage into a single rack [2] - It can reduce the overall data center footprint by a quarter compared to competitors, saving 10x power and cooling costs [2] Security Features - Infinia focuses on security authentication and access control, preventing unauthorized data access [2] - Data is always encrypted, and all actions within the system are audited [2] - The platform provides 99.9999% uptime enabled by reliability-focused features [2] Key Business Outcomes - Infinia aims to reduce complexity and achieve more accurate results on a unified platform for AI inference, data analytics, and model training [2] - It accelerates innovation by running AI apps faster, enabling businesses to beat the competition [2] - The platform enables rapid deployment across the cloud core and the edge to increase productivity, boost efficiency, and maximize ROI [2]