Cost Optimization Strategies
Reduce AI spend while maintaining output quality
AI costs scale with usage. Without guardrails, costs can spike unexpectedly. This guide focuses on practical cost controls: • Reduce tokens per request • Use cheaper models where appropriate • Cache repeatable responses • Set budgets/limits and monitor drift
Reducing Token Usage
Techniques include: • Trimming message history • Summarizing conversations • Using system prompts efficiently
Smart Model Selection
Use cheaper models for: • Classification • Simple Q&A • Formatting tasks
Caching Responses
Cache responses for: • Identical prompts • Static system messages • Repeated queries
Budgets & Limits
Always define: • Daily spend caps • Per-user limits • Per-feature budgets
Measure Before Optimizing
A common mistake is optimizing too early. First track: • tokens/request • requests/user/day • cost per feature • latency per provider Then optimize the biggest driver first.