Cursor seats, Copilot licenses, and Claude API tokens rarely live in one budget line. Engineering pays for tools; finance sees fragmented invoices; neither side can answer a simple question: what did AI-assisted development cost us last month?
The visibility gap
Most organizations track cloud spend with Datadog or CloudHealth—but AI development stacks evolved faster than observability. Teams run multiple tools in parallel, developers self-serve API keys, and agent loops can burn tokens overnight without triggering alerts.
What belongs in an AI engineering spend dashboard
- Total spend by provider (Cursor, Copilot, OpenAI, Anthropic)
- Per-developer breakdown to compare adoption and coach laggards
- Per-repo or team views for chargeback and capacity planning
- Model-level detail to spot expensive defaults (e.g., opus for routine reviews)
- Trend lines and forecasts before quarterly true-ups surprise the CFO
Start with official data sources
The best dashboards connect via vendor APIs—Cursor Enterprise, GitHub Copilot Metrics, provider billing APIs—rather than proxying developer traffic. That keeps security reviews short and avoids storing source code.
Try ForgeMeter
ForgeMeter, from Saabsa Solutions, unifies these sources in under five minutes. Start free for small teams or explore the live demo to see daily spend trends, AI summaries, and budget alerts.