AI Cost Optimization: Spending Smart on LLMs, Compute, and Infrastructure

The excitement of AI is giving way to budget reality. Token costs, GPU hours, and infrastructure expenses add up fast. Here is how to get the same results for significantly less.

The Cost Pyramid

• Level 1: Model selection — Use the smallest model that meets your quality bar. GPT-4o-mini handles 80% of use cases at 1/10th the cost of GPT-4o. Phi and Mistral 7B handle classification and extraction cheaper still.
• Level 2: Prompt optimization — Shorter prompts cost less. Remove redundant instructions. Use structured outputs instead of asking the model to format. Fewer tokens in, fewer tokens out.
• Level 3: Caching — Identical or similar queries should not trigger new LLM calls. Semantic caching (cache responses for semantically similar questions) can reduce LLM calls by 30-50% in production.
• Level 4: Architecture — Cascade patterns (small model first, large model only when needed). Batch processing instead of real-time where acceptable. Asynchronous pipelines that can use spot instances.

The Measurement Framework

Track cost per successful outcome — not cost per token. A cheap model that requires 3 retries and human correction is more expensive than a costly model that gets it right the first time. Optimize for total cost of quality, not unit token price.

The Quick Win

Audit your current LLM usage. You will almost certainly find that 20% of your prompts account for 80% of your spend. Optimize those first. The savings often fund your next AI initiative.