Token Usage Optimization: Best Practices

Token usage directly impacts both performance and cost in AI applications. Through analysis of thousands of workflows on our platform, we've identified key optimization patterns that can reduce token consumption by 40-60% while maintaining or improving output quality.

Understanding Token Consumption Patterns

Not all AI operations consume tokens equally. Here's how different workflow components impact your usage:

Caching Strategies

Implement intelligent caching to avoid re-processing identical or similar inputs. Our built-in caching system can automatically identify similar requests and return cached results, reducing token usage by up to 40% for typical workflows.

{
  "cacheConfig": {
    "enabled": true,
    "ttl": 3600,
    "similarityThreshold": 0.85,
    "keyFields": ["input", "options.temperature"]
  }
}

Batching and Parallel Processing

For high-volume applications, batching requests can significantly improve efficiency. Process multiple inputs together when possible, and use parallel processing for independent operations.

Performance Benchmarks

We tested these optimization strategies across different workflow types:

These optimizations not only reduce costs but often improve response times through better resource utilization and reduced model load.