GPU/Neocloud Billing using Rafay’s Usage Metering APIs
Cloud providers offering GPU or Neo Cloud services need accurate and automated mechanisms to track resource consumption.
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Provide developers with seamless access to LLMs, while streamlining the experience of deploying, interacting with, and managing Generative AI (GenAI) models
Provide developers and data scientists with centralized API access to a curated list of enterprise approved, public cloud and self-hosted LLMs for use in their GenAI applications.
Allow 1-click deployments of self hosted LLMs such as Llama 3.1, Vicuna, and more from an integrated catalog with support for GPUs and auto scaling infrastructure
Seamlessly connect to internal and external data sources such as databases, cloud storage, and data lake systems. This ensures that the AI models are trained on accurate, up-to-date data, and simplifies the process of preparing datasets for training
Allow developers to iteratively design and evaluate LLM prompts, maintain history, compare performance and cost across models
Get detailed insights into the costs associated with model usage, allowing teams to track costs down to individual projects, users, and models. This capability enables organizations to monitor and control spending, set budgets, and implement cost-saving measures while ensuring that resources are allocated efficiently
By providing GenAI playgrounds to developers and data scientists, Rafay customers realize the following benefits:
GenAI playgrounds from Rafay enable rapid experimentation and prototyping, allowing teams to quickly test and refine AI models, driving faster innovation and breakthroughs
By providing a shared environment for developers and data scientists, Rafay fosters cross-functional collaboration and unlock creative potential, leading to more diverse and innovative AI solutions
Rafay offers cost visibility and governance tools that help track and control model usage expenses, ensuring efficient allocation of resources and maximizing ROI in AI investments
Cloud providers offering GPU or Neo Cloud services need accurate and automated mechanisms to track resource consumption.
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Agentic AI is the next evolution of artificial intelligence—autonomous AI systems composed of multiple AI agents that plan, decide, and execute complex tasks with minimal human intervention.
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Whether you’re training deep learning models, running simulations, or just curious about your GPU’s performance, nvidia-smi is your go-to command-line tool.
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See for yourself how to turn static compute into self-service engines. Deploy AI and cloud-native applications faster, reduce security & operational risk, and control the total cost of Kubernetes operations by trying the Rafay Platform!