Cost Optimization
- How can I reduce my LLM API costs?
Cut LLM API costs by using smaller models, caching, batching, and optimizing prompts. Also monitor usage and set budgets. - What is the best way to optimize prompt length for cost?
Keep prompts as short as possible while preserving meaning. Remove filler words, use concise instructions, and avoid repeating context. This directly reduces token count and cost. - Why does caching help lower LLM API bills?
Caching stores previously generated responses or processed prompts, so repeated requests don't incur full token costs. Many providers offer prompt caching that discounts repeated input tokens by 50% or more. - Can batching API requests save money?
Yes, batching multiple requests into a single API call can reduce overhead and sometimes cost. Some providers offer batch discounts, but savings depend on the provider and use case. - Is it cheaper to use smaller models for simple tasks?
Yes, smaller models are almost always cheaper per token than larger ones. For simple tasks like classification, extraction, or short summaries, they can cut costs by 10x or more with little quality loss. - How do I set a budget for LLM API spending?
Start by estimating your monthly token usage, then multiply by the per-token price of your chosen model. Set a hard limit in your provider's dashboard and monitor usage weekly.