The Hidden Cost of AI Agents: Why Your LLM Pipeline Is Bleeding Money

Short summary

Author shares production patterns for reducing LLM pipeline costs from 10k+ daily jobs. Key strategies: Batch API (50% cheaper for non-urgent work), multi-tier model routing by task complexity, smart fallbacks to cheaper models, and caching embeddings/prompts. Includes code examples and cost-per-job metrics.

• •OpenAI Batch API cuts costs 50% for non-urgent workloads (hours vs seconds latency)
• •Route simple tasks to GPT-4o-mini, medium to DeepSeek (23x cheaper than GPT-4), complex only to GPT-4o
• •Cache prompt results and embeddings by hash; build fallback chains to escalate expensive models only on failure

Generated with AI, which can make mistakes.