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llm-cost-optimizer

Reduce LLM API spend through routing, caching, and prompt optimization.

Updated Apr 9, 2026
Search Tags:#llm#observability#cost-optimization#cost-analysis#prompt-caching#token-management#model-routing
One-click install
npx skills add https://github.com/Patasse97/claude-skills --skill llm-cost-optimizer-patasse97
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-cost-optimizer
Source: https://github.com/Patasse97/claude-skills/tree/main/engineering/llm-cost-optimizer
Command: npx skills add https://github.com/Patasse97/claude-skills --skill llm-cost-optimizer-patasse97

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reduces LLM API spend by routing, caching, and prompt optimization to lower costs while preserving user-facing quality.

Core Features & Use Cases

  • Cost Audit: instrument requests to identify top spend drivers and provide actionable insights.
  • Cost Optimization: implement model routing, caching, prompt compression, and semantic caching to cut per-request costs.
  • Architecture & Observability: design budgets, dashboards, and graceful degradation patterns to control spend in production.

Quick Start

Run an initial cost-audit on your current LLM usage to identify top spend drivers.

Frequently Asked Questions about llm-cost-optimizer

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

FAQPage Schema
How do I reduce LLM API spend without degrading user-facing quality?▼

Reduce LLM API spend by applying model routing, prompt caching, and prompt optimization across providers. These techniques lower per-request costs while preserving the quality of your production AI features.

What is semantic caching and how does it cut LLM token usage?▼

Semantic caching cuts LLM token usage by storing and retrieving previous model responses. It bypasses redundant API calls for similar prompts, significantly lowering per-request costs without regenerating outputs.

How do I identify top spend drivers in my LLM application?▼

Identify top spend drivers by running a cost audit with per-request instrumentation. This analyzes token usage across production AI features to provide actionable insights into your highest API expenses.

Can I apply model routing across different LLM providers to control costs?▼

Model routing can be applied across different LLM providers to control costs. It dynamically directs requests to cheaper models based on task complexity, cutting spend while preserving output quality.

What's the best way to design budget envelopes for production AI features?▼

Design budget envelopes by implementing architecture patterns for graceful degradation and spend limits. Combine this with observability dashboards to maintain cost control over your production AI features.

When do I need LLM observability for cost analysis?▼

LLM observability is needed when you require spend visibility across production AI features. It uses per-request instrumentation to monitor token usage, routing decisions, and caching effectiveness for cost analysis.

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