How to Estimate LLM API Cost Before Shipping Your AI App
Estimating LLM API costs before deploying an AI app is crucial to avoid unexpected expenses in production. Many developers underestimate costs by focusing on single API calls rather than the full workflow, which includes input and output tokens, retries, tool calls, and conversation history. Architectural choices like model size, response structure, and the use of RAG or agentic workflows significantly impact overall cost.
- ▪LLM costs depend on input tokens, output tokens, requests per user, daily users, retry rates, tool calls, prompt caching, conversation history, and model choice.
- ▪Output tokens are often more expensive than input tokens, and verbose responses increase both cost and latency.
- ▪RAG systems and agentic workflows can drastically increase token usage by adding retrieved context and multiple LLM calls per user request.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3422276) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Bhanu Pratap Singh Posted on May 16 • Originally published at superml.dev How to Estimate LLM API Cost Before Shipping Your AI App #ai #architecture #llm #machinelearning Most AI app prototypes look cheap. Then production happens. A developer tests an LLM feature with 20 prompts, gets a few good responses, and assumes the cost is manageable. But production cost is not based on one prompt.
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