Optimization Strategies That Cut LLM Costs by 80%

This introduces a step-by-step optimization framework and techniques that can reduce costs by up to 80% when operating LLM services.

Key Optimization Techniques and Expected Effects

• Prompt Caching: Up to 90% reduction in input token costs
• Model Routing: 60-95% reduction through routing based on model complexity
• Semantic Caching: 100% cost reduction for repeated queries
• Reranking (RAG): 70-85% reduction in input tokens through context optimization
• Batch APIs: 50% cost reduction through batch processing

Step-by-Step Implementation Framework

1. Observe: Cost tracking via LiteLLM and trace-based monitoring using Langfuse.
2. Optimize: Utilizing tools such as GPTCache (caching), LLMLingua-2 (compression), and RouteLLM (routing).
3. Operate: Applying per-project budget routing and context engineering.

Settings for Immediate Cost Savings Immediate cost savings can be achieved simply by introducing a LiteLLM proxy, adding cache_control: {"type": "ephemeral"} to system prompts, and enabling the token-efficient-tools beta feature.