Six Node.js Guardrails to Track Per-Tenant AI Costs in Invoice RAG Systems

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A software engineering guide outlines six guardrails for controlling embedding and LLM costs in Node.js-based invoice retrieval-augmented generation (RAG) systems. The core principle is that every indexing and retrieval operation must be attributed to a specific tenant before any text is sent to an AI model. Developers are advised to count tokens using the model's own tokenizer rather than character or word estimates, and to store raw usage integers separately from pricing to allow accurate cost reconciliation. The guide warns that batching does not eliminate unattributed token usage, and that a single aggregated cost column can mask one tenant consuming a disproportionate share of resources. A sample Go admission service interface is provided to enforce tenant ownership and model selection before any work is processed.

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