Cloud and AI used to be separate concerns. You had AWS, GCP, or Azure for infrastructure. You had OpenAI or Anthropic for AI APIs. Different teams, different invoices, different tools.
That's changing. AI is embedded in cloud workloads. AWS Bedrock, GCP Vertex AI, and Azure OpenAI are cloud services with AI pricing. GitHub Copilot and Actions drive AI and compute spend. Cursor costs scale with your dev team. The line between "cloud" and "AI" has blurred. So should your cost tracking.
The New Reality: Cloud + AI Everywhere
AWS—Compute, storage, and Cost Explorer. Plus Bedrock for foundation models. Lambda, SageMaker, and API Gateway for AI pipelines. All in one bill, but visibility varies.
GCP—BigQuery, Cloud Run, and Vertex AI. Billing export to BigQuery gives you data, but aggregation across services and AI products takes work.
Azure—VM, storage, and Cost Management. Plus Azure OpenAI, Cognitive Services, and AI Studio. Another set of dashboards, another billing cycle.
AI APIs—OpenAI, Anthropic, Cursor. Token-based, usage-driven. Separate from cloud, but part of the same technology spend.
GitHub—Actions, Copilot, Codespaces. Developer tools with usage-based pricing. Often overlooked until the bill spikes.
When each of these is tracked in isolation, you miss the forest for the trees. Total technology spend becomes a spreadsheet exercise. Trends are hidden. Forecasts are guesswork.
Why a Single Dashboard Matters
A unified view answers:
- What's our total technology spend? Cloud + AI + dev tools, in one number.
- Where did it change? Did AWS grow? Did OpenAI spike? Did GitHub Actions blow up?
- Where are we heading? A forecast for the whole stack, not per-provider fragments.
- When is something wrong? Anomaly detection across all sources, not just one.
Without it, you're reconciling multiple dashboards, invoices, and billing exports. By the time you have the full picture, the month is over.
What to Consolidate
At minimum:
- Cloud providers: AWS, GCP, Azure—via Cost Explorer, BigQuery billing export, or Cost Management API
- AI APIs: OpenAI, Anthropic, Cursor—via provider billing APIs
- Developer tools: GitHub—Actions, Copilot, Codespaces, Storage
Optional but increasingly relevant:
- Hugging Face—Inference Endpoints, Spaces, Jobs for open-source model deployment
- Twilio—if communications and AI (e.g., voice AI) are part of the same stack
The goal isn't to track everything. It's to track everything that materially affects technology spend. If it's on the invoice, it should be in the dashboard.
How to Get There
- Connect each provider to a cost-tracking tool that supports multiple sources.
- Normalize the data—different providers use different structures; you need a common schema (date, provider, service, amount).
- Aggregate by date—daily rollups across all providers give you comparable trends.
- Add forecasting—project total spend based on current pace and trends.
- Set alerts—anomaly detection and threshold alerts across the unified view.
Unified cost tracking doesn't replace provider-native tools. It complements them. Use AWS Cost Explorer for deep AWS analysis. Use a unified dashboard for total spend, trends, and early warning. One view for the big picture; provider tools for the details.
Get started: Connect AWS, GCP, Azure, OpenAI, and GitHub to StackSpend for a single dashboard. Learn more about cloud and AI cost monitoring.