Core Insights - The article discusses cost optimization strategies for developers using OpenAI API, highlighting a 43% reduction in costs after consuming 9.4 billion tokens in one month [1]. Group 1: Model Selection - Choosing the right model is crucial, as there are significant price differences between models. The company found a cost-effective combination by using GPT-4o-mini for simple tasks and GPT-4.1 for more complex ones, avoiding higher-priced models that were unnecessary for their needs [4][5]. Group 2: Prompt Caching - Utilizing prompt caching can lead to substantial cost savings and efficiency. The company observed an 80% reduction in latency and nearly 50% decrease in costs for long prompts by ensuring that variable parts of prompts are placed at the end [6]. Group 3: Budget Management - Setting up billing alerts is essential to avoid overspending. The company experienced a situation where they exhausted their monthly budget in just five days due to not having alerts in place [7]. Group 4: Output Token Optimization - The company optimized output token usage by changing the output format to return only position numbers and categories instead of full text, resulting in a 70% reduction in output tokens and decreased latency [8]. Group 5: Batch Processing - For non-real-time tasks, using Batch API is recommended. The company migrated some night processing tasks to Batch API, achieving a 50% cost reduction despite the 24-hour processing window being acceptable for their needs [8]. Group 6: Community Feedback - There were mixed reactions from the community regarding the strategies shared, with some questioning the necessity of consuming 9.4 billion tokens and suggesting that best practices should have been considered during the system design phase [9][10].