OpenTelemetry-Native LLM Cost Allocation and Chargeback Platform
KubeCon EU 2026 highlighted the need for LLM workload observability on Kubernetes. Teams running multi-tenant AI platforms cannot attribute LLM API costs to specific teams, features, or customers. An OpenTelemetry-native cost allocation tool that traces token usage from request to invoice would solve the emerging FinOps gap for AI spend.
Problem Statement
Platform teams operating shared LLM infrastructure cannot attribute costs to individual teams, features, or customers. Shared API keys obscure per-request cost responsibility. Monthly invoices from OpenAI/Anthropic show total spend but not allocation. Teams exceed budgets without warning because there is no real-time cost visibility at the granularity needed for chargeback.
The Idea
An OpenTelemetry-native platform that traces LLM API costs from individual requests through to team/feature/customer attribution, enabling accurate chargeback and budget management for AI spend.
Why Now
Enterprise AI spend is growing 3-5x annually but cost attribution is broken: shared API keys, pooled model endpoints, and multi-tenant gateways make it impossible to answer 'which team spent $50K on GPT-4 last month'. OpenTelemetry's LLM semantic conventions (finalized 2025) now provide the instrumentation standard needed.
Target User
AI platform engineers and FinOps teams at organizations with $50K+/month in LLM API spend across multiple teams
Target Market
Multi-team organizations using LLM APIs with shared infrastructure (estimated 5,000+ organizations globally)
The full brief is free to read
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- Score rationale across 11 dimensions
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