How to Reduce LLM API Costs

Reducing LLM API costs is critical for any production application. Here are proven strategies that can cut your spend by 50-90%:

1. Use Prompt Caching

Anthropic and OpenAI offer prompt caching that stores system prompts and repeated content. Cached tokens cost 50-90% less than regular input tokens. This is especially effective for applications with consistent system prompts.

2. Batch Non-Urgent Requests

Both OpenAI and Anthropic offer batch APIs at ~50% of standard pricing. Use batch processing for: content moderation, bulk classification, overnight data processing, and report generation.

3. Route to Cheaper Models

Use smaller models for simple tasks. Phi-4 at $0.070/M input can handle classification, extraction, and summarization. Reserve premium models for complex reasoning and coding.

4. Optimize Prompt Length

Shorter prompts = lower costs. Remove unnecessary context, use concise instructions, and avoid repeating information the model already knows. A well-crafted prompt can be 50% shorter with the same quality.

5. Cache Responses

Cache API responses for identical or similar inputs. Use semantic caching to serve previously generated responses for queries with the same intent.

6. Set Max Tokens

Always set max_tokens to prevent runaway generation. For structured outputs, this can dramatically reduce output token costs.

7. Use Streaming for Early Stopping

With streaming, you can detect when you have enough output and cancel the request early, saving on output tokens.

8. Monitor and Alert

Set up cost monitoring and alerts. Track per-feature costs, identify expensive queries, and optimize the worst offenders first.