How to Control LLM API Spend Beyond Token Cost: Models, Retries, and Go

Описание и применение

Контроль расходов на LLM‑API достигается через подсчёт токенов, выбор модели по качеству, ограничение попыток и отключение ненужных функций.

Задача

High-tier model selection, unnecessary feature usage, redundant calls, and aggressive retries inflate LLM API spend beyond token cost.

Результат

Cost controls were introduced at the request boundary, leading to predictable token usage, reduced retries, and elimination of unnecessary features, thereby lowering overall API spend.

Рабочий процесс

• 1. Classify question type (lookup, synthesis, ambiguous).
• 2. Count tokens of the request using Infrai token count endpoint before model selection.
• 3. Choose the smallest model that meets the quality target for the classified question.
• 4. Apply an attempt budget and bounded backoff for retries.
• 5. Disable unnecessary features (long context, tool use, richer output) unless required.
• 6. Log request ledger with question, context size, model tier, attempts, and output length for future analysis.

Доказательства

The factors that increase LLM API spend beyond token cost are high-tier model selection, unnecessary feature usage, redundant calls, and retries that repeat accepted work; control them with token budgets, quality gates, and bounded backoff.

Источник

For a gaming knowledge base, classify questions before answering them: factual lookup, multi-document synthesis, and ambiguous or high-risk explanation.

Источник

The first useful control is token counting before model selection. The verified route is POST /v1/ai/tokens/count.

Источник