Track Cloud Spending → Forecast AI Costs → Generate Budget Reports
Monitor your organization's cloud infrastructure costs and predict future AI workload expenses using spending data from major providers.
Workflow Steps
CloudHealth
Aggregate multi-cloud spending data
Connect CloudHealth to your AWS, Google Cloud, and Azure accounts to pull daily spending data, focusing on AI/ML services like SageMaker, Vertex AI, and Azure OpenAI
Google Sheets
Import and organize cost data
Set up automated data import from CloudHealth API into a Google Sheet with columns for date, provider, service type, and cost, categorizing AI-specific services
ChatGPT (via OpenAI API)
Generate cost forecasts and insights
Use GPT-4 to analyze spending trends, identify cost spikes, and generate 3-month forecasts based on usage patterns, including recommendations for optimization
Looker Studio
Create interactive dashboard
Build a dashboard connected to your Google Sheet showing spending trends, provider comparisons, and AI service costs with filters for time periods and departments
Gmail
Send weekly executive summary
Configure automated weekly emails to finance and engineering leadership with dashboard screenshots, key metrics, and AI-generated insights about spending trends
Workflow Flow
Step 1
CloudHealth
Aggregate multi-cloud spending data
Step 2
Google Sheets
Import and organize cost data
Step 3
ChatGPT (via OpenAI API)
Generate cost forecasts and insights
Step 4
Looker Studio
Create interactive dashboard
Step 5
Gmail
Send weekly executive summary
Why This Works
Provides end-to-end visibility into AI spending patterns while leveraging AI itself to generate predictive insights, helping organizations make informed infrastructure investment decisions
Best For
IT directors, finance teams, and engineering managers who need to monitor and forecast AI infrastructure costs across multiple cloud providers
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