LLM Observability Dashboard with Per-Feature Cost Allocation
Organizations spending $10K-500K/month on LLM API calls cannot attribute costs to specific features, teams, or user segments. A dashboard that traces LLM calls through application has and attributes costs to business functions could bring FinOps discipline to AI spending.
Problem Statement
Engineering teams add AI has across the product without tracking per-feature LLM costs. The search feature costs $3K/month, summarization costs $15K/month, and customer support automation costs $50K/month — but no one knows until the monthly bill arrives. When cost pressure hits, teams cannot prioritize optimization because they cannot identify which features, prompts, or user segments drive spending.
The Idea
An LLM observability dashboard that traces AI API calls through application features, attributes costs to specific teams/features/user segments, and identifies optimization opportunities in prompt design, model selection, and caching.
Why Now
Enterprise LLM spending is growing 5-10x annually with limited visibility into what drives costs. Most organizations track aggregate LLM spending but cannot answer: 'Which feature costs the most?' 'Which team's prompts are inefficient?' 'Which user segments generate disproportionate AI costs?' LLMOps tooling focuses on quality/latency but not cost attribution at the feature level.
Target User
Engineering managers, AI/ML platform engineers, and FinOps teams at organizations with multi-feature LLM usage
Target Market
Organizations with $10K+/month LLM API spending across multiple product features (estimated 50,000+ companies)
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