PromptAudit: LLM Cost Intelligence
Automatically tracks and alerts teams when their Claude/GPT API spending deviates from baseline, identifying wasteful prompt patterns and token inefficiencies
The Problem
AI teams building with Cursor, Lovable, and similar tools often have no visibility into which features or workflows are burning through token budgets. A single poorly-optimized prompt loop can silently inflate monthly costs by thousands without anyone noticing until the bill arrives, and there's no way to retroactively identify which product flows caused the spike.
Target Audience
Startup founders and indie hackers building AI-powered apps with Claude/OpenAI APIs; small AI product teams (2-10 people) who need cost governance but lack traditional FinOps infrastructure
Why Now?
LLM API costs are now the primary operational expense for AI startups, yet most lack native cost controls; recent awareness of 'token bloat' in prompt chains makes founders actively hunting for solutions
What's Missing
Existing observability tools (Langfuse, Helicone) don't surface actionable cost optimization insights, and native OpenAI dashboards show spending but not anomalies or predictions. Teams need a 'cost diff' tool that flags when spending deviates from trend and suggests which prompts to optimize.
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