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Spend less on LLM tokens.
Get better results.
Practical strategies for reducing token usage across Claude, GPT, and other AI models — without sacrificing output quality.
Read the guides →Context Engineering
Token optimization is a context problem, not a prompt-shortening problem. Learn session management, JIT retrieval, and repo memory.
Read more →Prompt Caching
Cache reads cost 90% less than regular input tokens. Design your prompt architecture for maximum cache hits.
Read more →Tool Overhead
MCP servers and tool definitions can add 55K–134K tokens before any work starts. On-demand loading cuts that by 85%.
Read more →Latest guides
Last updated Jun 14, 2026Designing for Prompt Cache Hits: How to Save 90% on LLM Input TokensUpdated Jun 14LLM Token Optimization Strategies: The Complete Guide for 2026Updated Jun 14How to Measure and Monitor LLM Token Usage (Before You Can Optimise It)Updated Jun 145 Ways to Reduce Your LLM API Costs TodayUpdated Jun 14How to Reduce OpenAI and Claude API Token Costs: A Developer's GuideUpdated Jun 14
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