PromptAuditTrail: LLM Cost & Usage Logger
Automatically tracks and categorizes every API call to Claude, GPT, and other LLMs, breaking down costs by feature/team/project so engineering teams can see exactly where their AI spend is leaking.
The Problem
Engineering teams building AI-powered features have no visibility into which features are actually expensive to run. A single poorly optimized prompt in production can cost thousands monthly, but teams only discover this when the bill arrives—with no way to trace which product feature caused the spike.
Target Audience
Startup CTOs and engineering leads (50-500 person companies) shipping multiple AI features; specifically those using Claude, GPT-4, or Anthropic APIs in production.
Why Now?
LLM API costs are now material enough that CFOs are asking engineers to justify spend. Token pricing volatility (Claude 3.5 price cuts) means last month's budget is wrong. Teams need real-time visibility yesterday.
What's Missing
Existing APM/observability tools treat LLM calls as generic transactions. They don't understand token economics, model swaps, or provide cost-per-feature rollups that engineering teams actually care about.
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