Keeping pace with news and developments in the real-time analytics and AI market can be a daunting task. Fortunately, we have you covered with a summary of the items our staff comes across each week. And if you prefer it in your inbox, sign up here!
The Linux Foundation launched the Tokenomics Foundation to focus on establishing open industry standards, benchmarks, and best practices for the economics of AI. The Tokenomics Foundation roadmap includes initial work on a number of items, including:
- Definitions: Publish what tokenomics is, and define token value/density, including input, output, reasoning, and cache.
- A reference model for the full cost of AI: Not just tokens. Shared terms and the complete component picture, so the token line is understood as one part of the bill rather than the whole of it.
- Cost to serve: A standard method for measuring the whole bill of materials, expressed as cost per call rather than cost per token, so the number maps to work actually performed.
- Value measurement: A framework for relating AI spend to outcomes, starting with the share of work completed without human involvement, measured against what the process costs today.
- Education and certification: A foundational course and credential, so practitioners and the teams around them can apply all of the above.
- Projects like the Big-T Framework: Provides a methodology for classifying the token and related AI cost complexity of workloads ahead of model routing activities between the most cost-effective frontier or open source models.
- Token Cost Telemetry: Improved AI cost reporting schemas in FOCUS v1.5 and beyond (FinOps Open Cost and Usage Specification) to better understand AI TCO
- AI Value Frameworks: The Foundation is laying out a roadmap for showing ROI from the TCO (Total Cost of Ownership) of AI, as well as how Tokenomics impacts business models, COGS, and labor planning.
The founding member organizations include Accenture, BNY, Broadcom, Calero, Cast.ai, DoiT, Finout, Flexera, GoDaddy, Greenpixie, Hitachi, IBM, JPMorganChase, Kion, Lenovo, Nebius, North Cloud, Oracle, Pay-i, Pointfive, Revenium, SAP, ServiceNow, SHI, Stacklet, Vantage, WWT, XOsphere and Yarken.
Red Hat announced the formation of asago (AI Safety and Governance Orchestration), an open-source community project intended to automate how AI governance policies become product-ready, safely deployed AI systems. asago connects the fragmented steps, tools, and requirements of engineering and compliance teams to create an automated, auditable, and traceable workflow. The intent is to help deliver safer, production-ready AI systems that fuel innovation in days, not months or years.
asago plans to use a standardized open-source platform and automated workflow to translate complex corporate and regulatory AI governance policies into actual operational controls. It builds on Red Hat and NVIDIA’s work as members of the Open Secure AI Alliance and aligns developer flexibility with operator governance across four primary stages, including risk mapping, risk assessment, risk mitigation, and production deployment.
Real-time analytics news in brief
Alteryx announced that Alteryx One is now available on Snowflake Marketplace, giving joint customers direct access to Alteryx One within the Snowflake AI Data Cloud. This helps organizations close the last-mile gap between trusted enterprise data and the decisions, workflows, and actions that run the business. Snowflake provides a governed foundation for enterprise data, while Alteryx enables business teams to prepare, analyze, automate, and apply AI to that data where it already resides.
Databricks announced that it is making Unity AI Gateway, the tool the company unveiled in June to help control AI costs with multi-AI smart routing and cost modeling, generally available on its platform. The solution gives enterprises the cost, control, and choice levers to manage AI spend and enforce security across AI agents and assets everywhere, whether it is a coding agent, an MCP, an external agent, a skill, or a model.
DDN announced a collaboration with NVIDIA within the Storage-Next initiative focused on helping organizations achieve greater business and financial value from their AI investments. Specifically, the companies are working to create a faster, more efficient connection between NVIDIA accelerated computing and the DDN Infinia data intelligence platform. By enabling GPUs to access the data they need more directly, the collaboration is designed to reduce delays and keep valuable computing resources productive.
DTEX announced DTEX Deep Researcher, an AI security agent that identifies human and AI risk before it escalates to a security alert. The solution helps organizations identify risk that has generated no alert and matches no existing detection. It uncovers hidden connections across users, endpoints, data, and AI activity. It also helps expose shadow activity beyond standard shadow AI and shadow IT. Additionally, the solution produces investigative findings and executive-ready reports with evidence-backed detail.
Pinecone announced the general availability of Pinecone Nexus, a knowledge engine that transforms an enterprise’s proprietary data and workflows into governed, agent-ready knowledge. The solution delivers a knowledge layer purpose-built for agents. It compiles an enterprise’s data into governed, domain-specific knowledge that agents query through KnowQL, a declarative query language built for agents. Pre-compiling knowledge lowers token costs, answers faster, and completes tasks with more accuracy.
Runware unveiled the Sonic Inference Pod, a modular data center designed and built to rapidly increase access to AI inference infrastructure for model providers. Unlike traditional data centers, which take years to build and cost $9-19 billion per gigawatt, Runware’s Sonic Inference Pod delivers 1 megawatt of compute in a 20-foot shipping container, with custom servers, PCBs, and a closed-loop cooling system engineered for inference performance. The result is facilities costs that are significantly lower than a traditional gigawatt-scale data center build.
Sandisk announced the release of the HBF (High Bandwidth Flash) technical specification through the Open Compute Project (OCP) with the goal of driving HBF standardization for the AI inference era. The specification was developed through the HBF technology workstream under OCP, with Sandisk and SK hynix serving as primary contributors. Notably, Google and Tenstorrent joined as consortium members during this standardization process, contributing significantly to technology validation and the establishment of the standard.
Sentra announced Shadow AI DLP, a new capability within the Sentra Platform for AI Data Readiness that helps organizations securely adopt AI while preventing sensitive data from being shared with generative AI applications. The solution gives security and governance teams visibility into AI application usage and enforces data-aware controls before sensitive content leaves the browser. The capability combines Shadow AI Discovery and AI Browser DLP, allowing organizations to understand which AI applications employees are using, assess the risks of those applications, and prevent sensitive data from being shared.
Teradata announced the availability of its enterprise-grade Data Analyst Agent in AWS Marketplace, bringing AI-assisted, conversational analytics directly into existing AWS environments. The solution enables advanced, multi-step analytics by orchestrating SQL queries on the Teradata platform and performing iterative statistical analysis using Python within AWS.
VDURA and Wasabi Technologies announced a technology alliance to connect GPU-adjacent AI data infrastructure with predictably priced S3-compatible cloud storage. Through the alliance, the companies will collaborate to help AI factories, neoclouds and enterprise HPC environments place active datasets and checkpoints close to GPUs while extending long-term retention, protection and data reuse into the cloud.
If your company has real-time analytics news, send your announcements to ssalamone@rtinsights.com.
In case you missed it, here are our most recent weekly real-time analytics news roundups:
- Real-time Analytics News for the Week Ending August 1
- Real-time Analytics News for the Week Ending July 25
- Real-time Analytics News for the Week Ending July 18
- Real-time Analytics News for the Week Ending July 11
- Real-time Analytics News for the Week Ending July 4
- Real-time Analytics News for the Week Ending June 27
- Real-time Analytics News for the Week Ending June 20
- Real-time Analytics News for the Week Ending June 13
- Real-time Analytics News for the Week Ending June 6
- Real-time Analytics News for the Week Ending May 30
- Real-time Analytics News for the Week Ending May 23
- Real-time Analytics News for the Week Ending May 16
- Real-time Analytics News for the Week Ending May 9
