Dev.to
7/14/2026
Practical strategies for reducing LLM API costs by 95%: model routing, caching, and batching
Original: I Cut My AI API Bill by 95% — Here's How You Can Too
Short summary
A practical guide to cutting LLM API costs by 95% through model-task matching, tiered routing, prompt caching, compression, and batching. The author provides specific price comparisons (e.g., GPT-4o at $10/M vs DeepSeek V4 Flash at $0.25/M for chat) and includes Python code for a routing map that classifies input complexity and selects the cheapest sufficient model. The biggest single lever is using cheap models for the 95% of calls that don't require heavy reasoning.
- •Model-task matching alone saves 90%+ by routing simple tasks to cheaper models like DeepSeek V4 Flash ($0.25/M) vs GPT-4o ($10/M)
- •Tiered routing tries cheapest model first, escalates only if needed
- •Additional savings from prompt caching, compression, and batching; includes Python code examples
Generated with AI, which can make mistakes.
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