LLM Cost Optimization Techniques: A Production Playbook

Author: BigData Boutique · Type: practitioner · Status: draft · Published: 2026-07-16 · URL: https://bigdataboutique.com/blog/llm-cost-optimization-techniques

Licence: Consultancy blog; standard copyright, quote with attribution.

Summary

Production playbook for LLM spend: model right-sizing and routing (5-20x savings on routed traffic; cascades that escalate only on low confidence reach 85-98% reduction in published research), provider prompt caching (90% discount on cached input reads), batch APIs (~50% discount), output token caps, and semantic response caching. Frames model selection as the single highest-leverage cost decision.

Insights

• Most recurring production tasks (classification, extraction, formatting, short answers) do not need a frontier model, and routing them to smaller tiers is the largest single cost lever. (high, draft) cost model routing

  Most production traffic - classification, extraction, routing, short answers, formatting - does not need the strongest model. — Model selection and routing section

• A cascade pattern - cheap model first, confidence scoring, escalate only when uncertain - preserves quality while cutting cost by large factors, and suits recurring agent tasks like tagging or triage. (high, draft) cost model routing orchestration

  A cascade that queries a cheap model first, scores the answer’s reliability, and escalates to a stronger model only when confidence is low. — Routing/cascading section

• Prompt caching and batch APIs give order-of-magnitude discounts on exactly the workload shape agent operations produce (stable long system prompts, non-urgent bulk jobs). (high, draft) cost agents in production

  Cache read costs 0.1x the base input price (a 90% discount)… Batch APIs: ~50% discount on token pricing. — Caching and batching sections