5 Strategies for Reducing LLM Inference Costs
Practical strategies for reducing your LLM inference costs, from simple optimizations to advanced techniques like distillation.
LLM inference is expensive. Here are five strategies for reducing costs, ordered from simplest to most impactful.
1. Prompt Optimization
The simplest optimization: use fewer tokens. Review your prompts for unnecessary verbosity. Remove examples that don't improve output quality.
Impact: 10-30% cost reduction
2. Response Caching
Cache responses for identical or similar queries. This works well for FAQs and common questions.
Impact: Varies widely (0-50% depending on query patterns)
3. Model Routing
Route simple queries to cheaper models, complex queries to expensive ones. Requires building a classifier.
Impact: 20-40% cost reduction
4. Batching
Batch multiple requests together when latency allows. Most providers offer discounts for batch processing.
Impact: 10-20% cost reduction
5. Distillation
Train a small, specialized model on your use case. This is the most impactful but requires more setup.
Impact: 50-80% cost reduction
Conclusion
Start with prompt optimization and caching. As you scale, invest in routing and distillation for maximum savings.