I built Shadow AI because I experienced this problem firsthand. We were spending real money on OpenAI and Anthropic APIs, but nobody could answer a simple question: **who was actually spending it, and on what?** Engineering didn’t know. Finance didn’t know. And the AI tools people were using in their browsers were completely invisible. We tried spreadsheets, Grafana dashboards, and custom scripts. None of them gave us the full picture. Then I started looking for a product that could track both employee AI usage (ChatGPT, Claude, etc.) and production API spend in one place. I couldn’t find one. So I started building Shadow AI. The funny part is that I’m now experiencing the exact problem I’m trying to solve every day. I use AI coding tools heavily to build the company, so I constantly see how quickly AI usage and costs can spread across different tools, models, APIs, and people. One thing that has really surprised me since starting this: **the problem seems much bigger than I initially thought.** We’ve talked to engineering leaders who discovered $30K+ AI spending spikes only when the invoice arrived — sometimes weeks after the spending happened. And when we demo Shadow AI, the reaction we hear most often isn’t “why would I need this?” It’s: “When can we install this?” That’s been one of the strongest signals that we’re onto something. What Shadow AI does Shadow AI brings employee AI usage and production AI spend into one place: * Browser tracking: Our Chrome extension automatically captures usage across tools like ChatGPT and Claude. * API tracking: Our SDK tracks OpenAI and Anthropic usage with zero latency impact. * No-code billing integrations: Read-only billing API polling gives you visibility without changing your code. * Unified dashboard: See spend by team, user, tool, model, and application. * Guardrails & alerts: Set budgets, detect anomalies, and catch unexpected spending before it becomes a surprise invoice. * Policy enforcement: Understand and control which AI tools are being used across the organization. * Duplicate subscription detection: Find overlapping or unused AI subscriptions. * Rightsizing recommendations: Identify opportunities to reduce unnecessary AI spend. * MCP server: AI assistants like Claude Code, Claude Desktop, and Cursor can query live spend data conversationally. Your own AI agents can also report their usage directly back to Shadow AI. The goal is simple: make AI spend as visible and controllable as cloud spend. We’re launching on Product Hunt today, and I’d genuinely love feedback — especially from teams already spending meaningfully on AI. Demo Credentials: Email: sarah@nimbust.ai ▎Password:demo123 How are you tracking AI spend today? Spreadsheets? Grafana? Cloud billing? Internal tooling? Or are you mostly waiting for the invoice? I’d love to hear what’s working, what isn’t, and what you wish existed.

Shadow AI

AI spend visibility and control for teams

AI spend visibility and control for teams