What is Token Optimization?
Token optimization reduces the number of tokens consumed by AI API calls. Learn techniques for minimizing token usage and OpenClaw costs.
Definition
Token Optimization
Token optimization is the practice of minimizing the number of tokens consumed by AI API calls while maintaining output quality. It encompasses techniques like prompt compression, efficient prompt design, model routing, and context management to reduce costs.
Why It Matters
Why You Should Care
AI API costs scale linearly with token usage. For OpenClaw users making thousands or millions of API calls, even small per-request optimizations compound into significant savings. Token optimization can reduce AI infrastructure costs by 80-93%.
How It Works
Under the Hood
Token optimization combines multiple techniques: prompt compression reduces input tokens, model routing selects cost-effective models, efficient prompt design minimizes unnecessary instructions, and context management avoids sending redundant information. claw.zip automates the first two — compression and routing — as a transparent proxy for OpenClaw users.
Related Terms
Keep Learning
Prompt Compression
Prompt compression reduces the number of tokens in AI prompts while preserving meaning. Learn how it works and why it matters for OpenClaw API costs.
Token Counting
Token counting measures how many tokens are in a prompt or response. Learn why token counts matter for OpenClaw API costs and context windows.
AI Model Routing
AI model routing automatically selects the cheapest capable model for each query. Learn how it reduces OpenClaw costs without sacrificing quality.
AI Cost Reduction
Comprehensive overview of strategies to reduce OpenClaw API costs: compression, routing, caching, prompt engineering, and more.
See Token Optimization in Action
Try claw.zip free and experience the difference for yourself.