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

Description and use

LLM API spend is controlled by token counting, model selection based on quality, bounded retries, and feature gating.

Problem

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

Outcome

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.

Workflow

• 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.

Evidence

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.

Source

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

Source

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

Source