Preview — 4 of 12 items
Are you using prompt caching for repeated system prompts?
Are you routing simple classification tasks to cheaper models?
Are you batching non-urgent API calls to unlock 50% discounts?
Have you measured your actual cost-per-feature, not just total spend?
Are you doing the thing that saves a lot of money?
Are you doing the thing that saves a lot of money?
Are you doing the thing that saves a lot of money?
Are you doing the thing that saves a lot of money?
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All 12 items, plus notes on how to check each one and estimated savings impact.
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Want to go deeper?
Read our guides on the highest-leverage cost optimizations for production LLM workloads.
Prompt Caching: The Fastest Way to Cut LLM Costs by Up to 80%
How OpenAI and Anthropic caching works, when to use it, and real cost examples.
LLM Model Routing: How to Cut Your AI Costs by 50%
Match each task to the cheapest model that passes your quality bar.
Batch API vs Real-Time LLM Calls: When to Use Each (And Save 50%)
When to move workloads to async batch processing and how to do it safely.
GPT-4o vs GPT-4o-mini: When to Downgrade and Save 15x
A decision framework for the most common OpenAI model downgrade opportunity.