CloudKeeper Makes AI/LLM Cost Tracking Easier for Businesses with Full Stack FinOps for AI

CloudKeeper, an AI and cloud cost optimization partner, today announced the launch of Full Stack FinOps for AI, a platform designed to help businesses track AI-related expenditure across models, applications, software licenses, and cloud infrastructure.

As organizations adopt AI across different functions, their spending can span multiple model providers, applications, teams, and infrastructure environments. Understanding the total cost of these deployments can be challenging when usage and expenditure are tracked across separate systems.

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Full Stack FinOps for AI brings these cost elements together through a unified dashboard, providing 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 Utilization 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), allowing businesses to connect their own large language models (LLMs) and access AI cost insights and recommendations within their existing AI workflows.

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“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.

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“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.