LLM Cost Optimization

LLM costs can spiral quickly in production. Token-based pricing means every prompt and response has a cost. Here's how to optimize spending while maintaining quality.

Understanding LLM Costs

LLM pricing is typically based on:

• Input tokens: Your prompts, context, and system instructions
• Output tokens: Model responses
• Model tier: GPT-4 costs 10-30x more than GPT-3.5

Cost Optimization Strategies

1. Right-Size Your Model

Not every task needs GPT-4. Use smaller models for simple tasks:

• Classification and routing: GPT-3.5 or smaller
• Simple Q&A: Fine-tuned smaller models
• Complex reasoning: GPT-4 or Claude

2. Optimize Prompts

• Remove unnecessary context and instructions
• Use concise system prompts
• Limit few-shot examples to minimum needed

3. Implement Caching

• Cache responses for identical or similar queries
• Use semantic caching for near-duplicate requests
• Cache embeddings for RAG applications

4. Limit Output Length

• Set max_tokens appropriately for each use case
• Instruct models to be concise
• Use structured output formats

Cost Monitoring

Track costs to identify optimization opportunities:

• Monitor token usage per endpoint
• Track cost per user or feature
• Set alerts for spending anomalies
• Review high-cost queries for optimization

DriftRail Cost Tracking

DriftRail captures token usage for cost analysis:

await client.ingest({
  model: 'gpt-4',
  provider: 'openai',
  metadata: {
    tokensIn: 500,
    tokensOut: 200,
    latencyMs: 850
  }
});

The dashboard shows token usage trends, helping identify cost optimization opportunities.

FAQ