LLM Cost Optimizer

Category: Engineering Domain: AI Cost Management

Overview

The LLM Cost Optimizer skill provides tools for counting tokens, estimating costs across different LLM providers, and optimizing prompts to reduce token usage without sacrificing quality. Essential for teams managing LLM API budgets at scale.

Quick Start

# Count tokens in a prompt file and estimate costs
python scripts/token_counter.py --file prompt.txt --models gpt-4o claude-sonnet

# Count tokens from stdin
echo "Hello world" | python scripts/token_counter.py --stdin --models all

# Analyze a prompt for optimization opportunities
python scripts/prompt_optimizer.py --file system_prompt.txt

# Optimize with target reduction
python scripts/prompt_optimizer.py --file prompt.txt --target-reduction 30

Tools Overview

Tool	Purpose	Key Flags
token_counter.py	Count tokens and estimate costs across models	--file, --text, --stdin, --models
prompt_optimizer.py	Analyze prompts for token reduction opportunities	--file, --target-reduction, --format

Workflows

Cost Estimation for New Project

1. Collect sample prompts (system prompt + user messages)
2. Run token_counter.py with target models
3. Multiply per-request cost by expected daily volume
4. Compare models on cost-quality tradeoff

Prompt Optimization Sprint

1. Identify highest-cost prompts from usage logs
2. Run prompt_optimizer.py on each
3. Apply suggested optimizations
4. Re-count tokens to verify reduction
5. A/B test optimized vs. original for quality

Reference Documentation

• LLM Pricing Guide - Current pricing for major LLM providers, token estimation methods

Common Patterns

Token Reduction Techniques

• Remove redundant instructions and examples
• Use shorter variable names in few-shot examples
• Compress verbose system prompts
• Replace repeated context with references
• Use structured output formats (JSON) to reduce response tokens
• Batch multiple requests into single prompts where possible

Cost-Effective Model Selection

• Use smaller models for classification/extraction tasks
• Reserve large models for complex reasoning
• Implement model routing based on query complexity
• Cache responses for identical or similar queries