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Checklist, in AI in Markets

LLM Cost Control Checklist

LLM cost scales with volume in ways that are invisible until the first big invoice. This checklist governs the cost of a finance LLM workflow before and after launch.

12 items. Published May 26, 2026. By AI Fin Hub Research.

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Sections

Work in focused batches instead of one long wall

Section 1

Phase 1: Model right-sizing

3 items

Use the Model Selector for Finance: Input task, latency budget, cost budget, context size, and quality sensitivity; get ranked model recommendations with rationale — grounded in published.

Use the Token-Cost Optimizer: Compute the dollar cost of a trading research loop across Claude, GPT, and Gemini. Prompt length × model × retry × call volume → cost per idea and per.

Section 2

Phase 2: Caching and batching

3 items

Use the Financial Document Token Estimator: Paste a 10-K, 10-Q, 8-K or earnings transcript and see token count + one-pass extraction cost across ten frontier LLMs, with cache-hit toggle.

Use the Batch vs Real-Time Cost Calculator: Jobs per day, tokens per job, model, deadline — get real-time vs batch cost side-by-side with savings estimate and batch-eligibility flag. Based.

Section 3

Phase 3: Agent-loop bounds

3 items

Use the Agent Cost Envelope Calculator: Model an LLM research loop end-to-end — steps, tool calls, convergence checks, markets per day — and see per-loop, daily, and monthly cost with cost-cap.

Section 4

Phase 4: Monitoring

3 items

Tips

Small moves that make the checklist easier to finish

The default of using the biggest model on every call is the most expensive habit in production. Most finance tasks are routine, and routing them to a cheaper model is free money.
An uncapped agent loop is a financial liability, not just a latency one. The step limit and spend cap are the difference between a bug costing pennies and a bug costing a mortgage payment.
Cache the stable context. The system prompt and reference documents you send on every call are pure repeated cost, and caching them is one of the largest single savings available.

Sources

  1. Artificial intelligence in UK financial services 2024 , Bank of England and Financial Conduct Authority (2024)
  2. FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance , Chen, Zaharia, Zou, arXiv (2023)

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