CloudKeeper has introduced Full Stack FinOps for AI, a platform that enables businesses to track AI-related spending across models, applications, software licenses, and cloud infrastructure.
“Businesses need a clear understanding of their AI spending to make confident decisions about how they adopt and scale AI. With Full Stack FinOps for AI, we want to help them bring all the cost insights together into one view, understand their spending and make better-informed decisions about their AI investments,” said Deepak Mittal, CEO, CloudKeeper.
Full Stack FinOps for AI
Full Stack FinOps for AI consolidates these cost elements in a unified dashboard, offering visibility into key areas of AI expenditure, including:
- AI Inference and Model Costs: Track spending across AI models, providers, workloads, and applications.
- AI License Costs: View software licenses, providers, teams, and associated expenditure.
- AI Infrastructure Costs: Understand the cloud infrastructure costs associated with AI workloads.
- Token Usage Analytics: Monitor token consumption and spending patterns across AI models.
- GPU Cost and Utilisation Analytics: Examine GPU usage, infrastructure costs, and potential underutilization.
- Multi-Cloud Dashboard: Consolidate cost and usage data across AWS, Microsoft Azure, and Google Cloud.
The platform also supports the Model Context Protocol (MCP), enabling businesses to connect their own large language models (LLMs) and access AI cost insights and recommendations within existing AI workflows.
“AI/LLM costs span beyond model and token usage alone. The licenses, GPUs, and cloud infrastructure supporting AI workloads also contribute to the overall cost. By bringing these elements together, the platform helps teams examine their AI expenditure in context. MCP support also allows them to access cost insights within their existing AI environments,” said Sanjeev Mittal, CPTO, CloudKeeper.
