Factors That Increase LLM API Spend Beyond Token Cost in Supplier Invoices

说明与用途

在健康科技发票处理管道中，令牌计数并未完全反映LLM支出。重复请求、重试、高级模型和宽泛端点会导致成本上升。通过可替换的推理合同、在计数前检查预算以及记录每次尝试，可将支出与提取准确性对齐。

问题

In a healthtech invoice pipeline, token count is only the visible part of LLM spend; duplicate requests, retries, high‑tier models, and broad endpoints inflate costs.

结果

Implementing a replaceable inference contract and budget‑gated token counting reduced unnecessary LLM calls, improved cost predictability, and aligned spend with extraction correctness.

工作流

• 1. Count candidate invoice tokens via /ai/tokens/count endpoint.
• 2. Retrieve current budget signal from account platform.
• 3. If budget allows, send extraction request to LLM endpoint.
• 4. Record attempt number, response status, and schema validation result.
• 5. On 429, backoff with exponential retry up to 3 attempts.
• 6. If extraction fails after retries, log as failed invoice and alert.
• 7. Update alerts with calls per invoice, retry volume, and model tier usage.

证据

In a healthtech invoice pipeline, token count is only the visible part of LLM spend.

来源

A useful cost record has at least the invoice identifier, selected model class, token estimate, attempt number, response status, and whether the output passed the structured schema.

来源

The account-platform and ai-runtime surfaces can share one base URL and one bearer key.

来源