LLM and AI Cost Optimization
Strategies for managing and reducing costs in LLM-powered applications, from token economics to RAG architectures.
npx skills add majiayu000/claude-skill-registry --skill llm-cost-optimization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LLM and AI Cost Optimization ## Overview Artificial Intelligence, specifically Large Language Models (LLMs), introduces a new variable cost component to software engineering: **Inference tokens**. Unlike traditional API costs which are often fixed or volume-tiered, LLM costs scale linearly with usage, context length, and model complexity. **Core Principle**: "Spend on intelligence where it matters, optimize where it doesn't." --- ## 1. LLM Pricing Models (2024 Context) Pricing is typically structured around **Tokens** (units of text, roughly 4 characters or 0.75 words). | Provider | Model | Input Price (per 1M) | Output Price (per 1M) | Unit | | :--- | :--- | :--- | :--- | :--- | | **OpenAI** | GPT-4o | $5.00 | $15.00 | Tokens | | **OpenAI** | GPT-4o-mini | $0.15 | $0.60 | Tokens | | **Anthropic**| Claude 3.5 Sonnet | $3.00 | $15.00 | Tokens | | **Anthropic**| Claude 3 Haiku | $0.25 | $1.25 | Tokens | | **Google** | Gemini 1.5 Pro | $3.50* | $10.50* | Characters/Tokens | | **Cohere** | Command R+ | $3.00 | $15.00 | Tokens | *\*Google often prices differently based on prompt size (e.g., higher for > 128k context).* ### The Cost Disparity Note that **GPT-4o-mini** is ~33x cheaper t
- Overview
- 1. LLM Pricing Models (2024 Context)
- The Cost Disparity
- 2. Token Economics
- Input vs. Output
- Context Window Usage
- 3. Cost Optimization Strategies
- A. Model Routing (The Tiered Approach)
- B. Prompt Engineering for Brevity
- C. Caching Frequent Prompts
- D. Semantic Caching (Advanced)
- 4. Embedding and Vector Database Costs
- Layer 1: Embedding Generation
- Layer 2: Vector Storage
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What does the LLM and AI Cost Optimization skill do?
Strategies for managing and reducing costs in LLM-powered applications, from token economics to RAG architectures.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill llm-cost-optimization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.