How to Reduce Your LLM API Costs by 80%

1. Prompt Engineering for Token Efficiency

The simplest cost reduction comes from writing better prompts. Shorter, more specific instructions produce better results with fewer tokens. Avoid repeating context that the model already has. Remove unnecessary examples from few-shot prompts once the model demonstrates consistent understanding.

Measure your average input and output token counts. Set targets to reduce both by 30% through prompt optimization alone. Most teams achieve this within a week of focused effort.

2. Implement Prompt Caching

Both Anthropic and OpenAI now offer prompt caching for system prompts and repeated context. Cached tokens are significantly cheaper -- up to 90% less for Anthropic. If your application uses consistent system prompts across many requests, caching is the highest-impact single optimization.

3. Model Routing and Fallbacks

Not every request needs your most expensive model. Implement a routing layer that classifies incoming requests by complexity and routes simple ones to budget models. For example, a basic Q&A response might use GPT-4o Mini at $0.15/M input tokens instead of GPT-4o at $2.50/M.

Build fallback chains: try the cheapest viable model first, and only escalate to premium models when the initial response does not meet quality thresholds.

4. Response Caching

Many LLM requests are near-duplicates. Implement semantic caching that identifies similar requests and returns cached responses. Even a 20% cache hit rate can save significantly at scale. Use embeddings to match similar queries rather than exact string matching.

5. Batch Processing

If your workload allows it, batch non-urgent requests. Most providers offer 50% discounts for batch API usage. This is ideal for background tasks like content moderation, data extraction, or document processing that does not require real-time responses.