How to optimize LLM inference costs in production?

Cost optimization is crucial for sustainable AI products. Here's a tiered approach:

Tier 1: Quick Wins (Implement Today)

1. Model routing: Use GPT-3.5-turbo for simple queries, GPT-4 only for complex ones. Can reduce costs by 50-70%. Implement a classifier to route requests.
2. Prompt optimization: Shorter prompts = fewer tokens. Remove unnecessary examples. Use abbreviations where possible.
3. Response caching: Cache common queries. Redis/Memcached for frequent questions. Can save 20-30% on repeated queries.
4. Token limits: Set max_tokens to prevent runaway costs. Analyze actual needs vs. defaults.

Tier 2: Medium-Term Solutions

1. Fine-tune GPT-3.5: Often matches GPT-4 quality for domain-specific tasks. Training cost: ~$100. Inference: 10x cheaper than GPT-4.
2. Batch processing: Group non-urgent requests. OpenAI offers batch API with 50% discount.
3. Streaming: Stop generation when you have enough. Use streaming API and stop early.

Tier 3: Advanced Strategies

1. Self-hosted models: Llama 2, Mistral on your infrastructure. High upfront cost, but $0 per token. Good for high-volume, predictable workloads.
2. Hybrid approach: Self-hosted for 80% of queries, GPT-4 for edge cases.
3. Distillation: Train smaller model from GPT-4 outputs.

Expected Savings:

• Model routing: -60%
• Caching: -25%
• Fine-tuning: -50% (for fine-tuned queries)
• Total potential savings: 70-80%

Start with Tier 1 this week. You should see immediate cost reduction.