CloudKeeper Launches Full Stack FinOps for AI to Track LLM Spending
CloudKeeper, an AI and cloud cost optimization partner, has launched Full Stack FinOps for AI, a platform that pulls AI spending on models, licenses, tokens and GPU infrastructure into a single dashboard. The company says it is meant to help businesses understand the total cost of their AI deployments.
- CloudKeeper launched Full Stack FinOps for AI on Oct. 29, 2026.
- The platform unifies AI costs for models, licenses, infrastructure, tokens and GPUs in one dashboard.
- It consolidates cost and usage data across AWS, Microsoft Azure and Google Cloud.
- MCP support lets businesses connect their own LLMs to access cost insights and recommendations.
- Businesses can try the platform with 30 days of free access.
One dashboard for scattered AI costs
According to CloudKeeper, companies that adopt AI across different functions often end up with spending spread over several model providers, applications, teams and infrastructure environments. When usage and expenditure sit in separate systems, working out the total cost of a deployment can be difficult.
Full Stack FinOps for AI is designed to address that by bringing these cost elements together in a unified dashboard. The announcement was made in New York on Oct. 29, 2026.
What the platform covers
CloudKeeper lists six areas of visibility in the platform:
- AI Inference and Model Costs: spending across AI models, providers, workloads and applications.
- AI License Costs: software licenses, providers, teams and associated expenditure.
- AI Infrastructure Costs: the cloud infrastructure costs tied to AI workloads.
- Token Usage Analytics: token consumption and spending patterns across AI models.
- GPU Cost and Utilization Analytics: GPU usage, infrastructure costs and potential underutilization.
- Multi-Cloud Dashboard: cost and usage data consolidated across AWS, Microsoft Azure and Google Cloud.
The platform also supports the Model Context Protocol (MCP). This lets businesses connect their own large language models (LLMs) and access AI cost insights and recommendations inside their existing AI workflows.
Company comments and trial
CloudKeeper CEO Deepak Mittal said businesses need a clear picture of their AI spending to make confident decisions about adopting and scaling AI. He said the aim is to "bring all the cost insights together into one view".
Sanjeev Mittal, CPTO at CloudKeeper, said AI and LLM costs go beyond model and token usage alone, because the licenses, GPUs and cloud infrastructure behind AI workloads also add to the total. According to him, putting these elements together helps teams examine their AI expenditure in context, and MCP support lets them reach cost insights within their existing AI environments.
Businesses can try the platform with 30 days of free access.
About CloudKeeper and what it means for buyers
CloudKeeper describes itself as an outcome-driven AI and cloud cost optimization partner that combines FinOps expertise, proprietary technology and hands-on execution. The company says it has over 15 years of cloud cost optimization experience and has helped 400+ global companies reduce cloud and AI spend by an average of 20% across AWS, Microsoft Azure and Google Cloud. It also says its outcome-based model delivers ongoing savings without upfront costs or long-term commitments. These figures are CloudKeeper's own claims.
For businesses running AI on more than one provider or cloud, the launch targets a practical problem: costs from models, licenses, tokens and GPUs are often reported in different places. A single view, plus the free 30-day trial, gives teams a way to test whether consolidated tracking helps them see where AI money goes. The source gives no pricing beyond the trial, so cost after that period is not stated.
