Features

This chapter covers the core features and capabilities of LLM Cost Tracker.

Features

• Cost calculation — Automatic per-request cost from token counts and model pricing
• Pricing table — Built-in pricing for 14+ models (GPT-4o, Claude, Llama, Mixtral, etc.)
• Budget alerts — Real-time budget monitoring with ok/warning/critical/exceeded levels
• Usage reports — Per-model breakdown, daily cost charts, and top expensive requests
• Cost forecasting — Predict future costs based on recent usage patterns
• JSONL database — Append-only log for durability and easy integration
• Prefix matching — Automatically matches model versions (e.g., "gpt-4o-2025-01-01" → "gpt-4o")
• CLI interface — Log requests, check budgets, generate reports from the terminal

Quick Start

# Run demo with simulated API calls
python src/llm_cost_tracker.py --demo

# Show pricing table
python src/llm_cost_tracker.py --pricing

# Log a request
python src/llm_cost_tracker.py --log '{"model":"gpt-4o","input_tokens":500,"output_tokens":200}'

# Check budget
python src/llm_cost_tracker.py --budget 50.00 --db costs.jsonl

# Generate usage report
python src/llm_cost_tracker.py --report --db costs.jsonl

# Forecast next 30 days
python src/llm_cost_tracker.py --forecast 30 --db costs.jsonl

Project Structure

Follow this guide to get LLM Cost Tracker up and running in your environment.

Project Structure

llm-cost-tracker/
├── README.md
├── LICENSE
├── src/
│   └── llm_cost_tracker.py    # Core engine (~420 lines)
└── examples/
    ├── basic_usage.py          # Programmatic usage example
    └── sample_costs.jsonl      # Sample cost data

CLI Reference