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LLM Cost Tracker

$19

Python LLM cost tracker with token counting, per-request cost calculation, and budget alerts.

📁 11 files
MarkdownPythonLLM

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📁 File Structure 11 files

llm-cost-tracker/ ├── LICENSE ├── README.md ├── examples/ │ ├── basic_usage.py │ └── sample_costs.jsonl ├── free-sample.zip ├── guide/ │ ├── 01_features.md │ ├── 02_project-structure.md │ ├── 03_usage-examples.md │ └── 04_license.md ├── index.html └── src/ └── llm_cost_tracker.py

📖 Documentation Preview README excerpt

LLM Cost Tracker

Python LLM cost tracker: token counting, per-request cost calculation, budget alerts, usage reports by model, and cost forecasting. Zero dependencies.

Part of the AI Toolkit collection by [CodeVault](https://ai-toolkit.codevault.dev).

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


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

FlagDescription
--demoRun demo with simulated data
--pricingShow pricing table
--db FILEPath to cost database (default: llm_costs.jsonl)
--log JSONLog a request with model, input_tokens, output_tokens

... continues with setup instructions, usage examples, and more.

📄 Code Sample .py preview

examples/basic_usage.py#!/usr/bin/env python3 """ Basic usage example for the LLM Cost Tracker. Demonstrates: - Logging API requests and calculating costs - Checking budget alerts - Generating usage reports - Forecasting future costs - Using the pricing table and cost calculator """ import json import sys import tempfile from pathlib import Path # Allow running from the examples/ directory sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "src")) from llm_cost_tracker import ( PRICING, CostTracker, calculate_cost, estimate_tokens_from_text, ) def demo_cost_calculation() -> None: """Calculate costs for individual API calls.""" print("=== Cost Calculation ===\n") # Compare costs across models for the same usage models = ["gpt-4o", "gpt-4o-mini", "claude-sonnet-4", "claude-3-haiku", "llama-3-70b"] input_tokens = 1000 output_tokens = 500 print(f" Cost for {input_tokens} input + {output_tokens} output tokens:\n") print(f" {'Model':<25} {'Cost':>10}") print(f" {'-' * 35}") for model in models: cost = calculate_cost(model, input_tokens, output_tokens)
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