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optimize-llm-cost-latency

Reduce LLM token usage and tail latency via prompt caching and model routing.

9|3|Updated Jun 13, 2026
Search Tags:#optimization#llm#latency#prompt-caching#token-management#cost-reduction
One-click install
npx skills add https://github.com/Sir-chawakorn/sanook-cli --skill optimize-llm-cost-latency
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: optimize-llm-cost-latency
Source: https://github.com/Sir-chawakorn/sanook-cli/tree/main/skills/optimize-llm-cost-latency
Command: npx skills add https://github.com/Sir-chawakorn/sanook-cli --skill optimize-llm-cost-latency

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses high operational costs and slow response times in LLM-powered applications by implementing systematic optimization strategies that reduce token consumption and improve perceived performance.

Core Features & Use Cases

  • Token Economics: Provides a framework for measuring and reducing input/output token usage through context trimming and prompt caching.
  • Latency Reduction: Implements streaming, model routing, and semantic caching to improve time-to-first-token and overall response speed.
  • Use Case: If your application is scaling and the monthly LLM bill is becoming unsustainable, use this skill to audit your usage and implement tiered routing and prefix caching to cut costs by up to 90 percent.

Quick Start

Analyze the current LLM usage patterns and identify the highest impact optimization levers by running the cost and latency measurement protocol on your production logs.

Frequently Asked Questions about optimize-llm-cost-latency

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce LLM token costs and latency in a production application?▼

You can reduce LLM token costs and latency by applying context trimming, provider-level prompt caching, and intelligent model routing to systematically lower token consumption and improve response times.

What is provider-level prompt caching and how does it optimize LLM usage?▼

Provider-level prompt caching optimizes LLM usage by storing frequently used context prefixes, which reduces input token processing and significantly improves time-to-first-token for repeated queries.

How do I measure p50 and p95 tail latency for LLM API calls?▼

You measure p50 and p95 tail latency by running a cost and latency measurement protocol on your production logs, establishing a rigorous baseline to track optimization performance over time.

Can I preserve response quality while implementing model routing for cost reduction?▼

Yes, intelligent model routing preserves response quality by directing complex queries to high-tier models and simpler tasks to cost-efficient models, ensuring high-performance response times without degrading output.

What is the best way to audit high LLM API bills for scaling applications?▼

The best way to audit high LLM API bills is to analyze current usage patterns and identify high-impact optimization levers like semantic caching and tiered routing to cut costs by up to 90 percent.

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