TokenBooks Cross-Provider AI Spend Dashboard

TokenBooks Cross-Provider AI Spend Dashboard

See where your AI money goes. Track spending across OpenAI, Anthropic, Google, and more. Per-provider breakdowns, per-model costs, budget tracking, waste detection.

See where your AI money goes. Track spending across OpenAI, Anthropic, Google, and more. Find waste. Stay under budget.

TokenBooks imports billing data from multiple AI providers, aggregates everything into one view, and shows you exactly where every dollar went. Per-provider breakdowns, per-model costs, time series trends, budget tracking, and waste detection — all in one tool, all local, zero cloud dependencies.

The Problem

You use OpenAI for coding, Anthropic for research, Google for experiments. Each has its own billing dashboard. You have no idea:

Manually exporting CSVs and combining them in Excel is tedious and error-prone.

What TokenBooks Does

Unified Dashboard

Budget Tracking

Waste Detection

Time Series Analysis

Task-Level Tracking

Quick Start

# 1. Import billing data
python3 token_import.py openai_billing.csv --provider openai --output data.json
2. Analyze spending
python3 token_books.py data.json --budget 100
3. Generate HTML dashboard
python3 token_report.py data.json --output dashboard.html --budget 100

Open dashboard.html in a browser to see:

Usage Guide

Import Data

From OpenAI:

# Download CSV from OpenAI usage dashboard
python3 token_import.py openai_usage.csv --provider openai --output data.json

From Anthropic:

# Download JSON from Anthropic console
python3 token_import.py anthropic_usage.json --provider anthropic --output data.json

From Custom CSV:

# Provide column mapping
python3 token_import.py my_billing.csv --provider custom --column-map mapping.json --output data.json

Column mapping format (mapping.json):

{
  "timestamp": "Date",
  "provider": "API_Provider",
  "model": "Model_Name",
  "input_tokens": "Prompt_Tokens",
  "output_tokens": "Response_Tokens",
  "cost": "Cost_USD"
}

Analyze Spending

# Basic analysis
python3 token_books.py data.json
With budget tracking
python3 token_books.py data.json --budget 100
JSON output for scripting
python3 token_books.py data.json --output json > analysis.json

Generate Dashboard

# Generate HTML dashboard
python3 token_report.py data.json --output dashboard.html
With budget status
python3 token_report.py data.json --output dashboard.html --budget 100

Use Cases

1. Multi-Provider Cost Tracking

2. Budget Enforcement

3. Model Optimization

4. Task Cost Allocation

5. Team Reporting

6. Vendor Comparison

What Gets Tracked

Per-Provider

Per-Model

Per-Task

Time Series

Waste Detection

Configuration

See config_example.json for reference constants and example values

# Set monthly budget
MONTHLY_BUDGET = 100.0
Update pricing for accurate cost calculation
MODEL_PRICING = {
    'gpt-4': {'input': 30.0, 'output': 60.0},
    'claude-3-opus': {'input': 15.0, 'output': 75.0},
}
Budget alert thresholds
ALERT_THRESHOLDS = {
    'warning': 80,  # 80% utilization
    'critical': 95  # 95% utilization
}
Waste detection threshold
WASTE_THRESHOLD = 0.10  # $0.10 per 1k tokens
Number of top models to show
TOP_MODELS_LIMIT = 10

See config_example.json for full options.

Examples

Example 1: Monthly Budget Tracking

# Import January data
python3 token_import.py openai_jan.csv --provider openai --output jan.json
Check budget status
python3 token_books.py jan.json --budget 100
Output:
Total cost: $87.42
📊 Budget Analysis:
  Monthly budget: $100.00
  Spent this month: $87.42
  Remaining: $12.58
  Utilization: 87.4%

Example 2: Waste Detection

python3 token_books.py data.json
Output:
💡 Potential Waste Detected:
  gpt-4: $45.23 (127 requests)
    → Consider using a cheaper model for simple tasks

Example 3: Multi-Provider Dashboard

# Import from multiple providers
python3 token_import.py openai.csv --provider openai --output combined.json
python3 token_import.py anthropic.json --provider anthropic --output temp.json
Merge manually or import again (appends)
... (or use custom tooling to merge JSON files)
Generate unified dashboard
python3 token_report.py combined.json --output dashboard.html

What's Included

| File | Purpose | |------|---------| | token_books.py | Main aggregation engine (CLI + library) | | token_import.py | Import parsers for OpenAI, Anthropic, custom CSV | | token_report.py | HTML dashboard generator | | config_example.json | Configuration template | | README.md | This file | | LIMITATIONS.md | What it doesn't do | | LICENSE | MIT License |

Requirements

Python API

Use TokenBooks in your own scripts:

from token_import import ImportManager, UsageRecord
from token_books import CostAggregator
from token_report import DashboardGenerator
Import data
manager = ImportManager()
manager.import_openai('openai.csv')
records = manager.get_all_records()
Analyze
aggregator = CostAggregator(records)
print(f"Total cost: ${aggregator.get_total_cost():.2f}")
print(f"Total tokens: {aggregator.get_total_tokens():,}")
By provider
by_provider = aggregator.by_provider()
for provider, breakdown in by_provider.items():
    print(f"{provider}: ${breakdown.total_cost:.2f}")
Generate dashboard
html = DashboardGenerator.generate(records, monthly_budget=100.0)
with open('dashboard.html', 'w') as f:
    f.write(html)

quality-verified

FAQ

Q: Does it work with Google Cloud billing? A: Not directly. Google Cloud billing CSVs can be imported via custom CSV parser with column mapping. See config_example.json for mapping format.

Q: Can it track API calls in real-time? A: No. TokenBooks analyzes exported billing data, not live API usage. Export from your provider, then import.

Q: Does it send data anywhere? A: No. Everything runs locally. Zero network calls.

Q: How do I tag API calls with task names? A: Add a "task" field to your billing exports. If your provider doesn't support this, manually add it to the CSV before importing.

Q: Can I combine multiple months of data? A: Yes. Import each month's data into the same JSON file, or merge JSON files manually.

Q: Does it support non-USD currencies? A: Not directly. Convert to USD before importing, or manually adjust cost values.

License

MIT — See LICENSE file.

Author

Shadow Rose

Built for people who want to understand and control their AI spending without enterprise-grade analytics platforms.

⚠️ Disclaimer

This software is provided "AS IS", without warranty of any kind, express or implied.

USE AT YOUR OWN RISK.

By downloading, installing, or using this software, you acknowledge that you have read this disclaimer and agree to use the software entirely at your own risk.

TRADING DISCLAIMER: This software is a tool, not a trading system. It does not make trading decisions for you and does not guarantee profits. Trading cryptocurrency, stocks, or any financial instrument carries significant risk of loss. You can lose some or all of your investment. Past performance of any system or methodology is not indicative of future results. Never trade with money you cannot afford to lose. Consult a qualified financial advisor before making investment decisions.

Support & Links

| | | |---|---| | 🐛 Bug Reports | TheShadowyRose@proton.me | | ☕ Ko-fi | ko-fi.com/theshadowrose | | 🛒 Gumroad | shadowyrose.gumroad.com | | 🐦 Twitter | @TheShadowyRose | | 🐙 GitHub | github.com/TheShadowRose | | 🧠 PromptBase | promptbase.com/profile/shadowrose |

*Built with OpenClaw — thank you for making this possible.*

🛠️ Need something custom? Custom OpenClaw agents & skills starting at $500. If you can describe it, I can build it. → Hire me on Fiverr

Example 1: Monthly Budget Tracking

# Import January data
python3 token_import.py openai_jan.csv --provider openai --output jan.json
Check budget status
python3 token_books.py jan.json --budget 100
Output:
Total cost: $87.42
📊 Budget Analysis:
  Monthly budget: $100.00
  Spent this month: $87.42
  Remaining: $12.58
  Utilization: 87.4%

Example 2: Waste Detection

python3 token_books.py data.json
Output:
💡 Potential Waste Detected:
  gpt-4: $45.23 (127 requests)
    → Consider using a cheaper model for simple tasks

Example 3: Multi-Provider Dashboard

# Import from multiple providers
python3 token_import.py openai.csv --provider openai --output combined.json
python3 token_import.py anthropic.json --provider anthropic --output temp.json
Merge manually or import again (appends)
... (or use custom tooling to merge JSON files)
Generate unified dashboard
python3 token_report.py combined.json --output dashboard.html

See config_example.json for reference constants and example values

# Set monthly budget
MONTHLY_BUDGET = 100.0
Update pricing for accurate cost calculation
MODEL_PRICING = {
    'gpt-4': {'input': 30.0, 'output': 60.0},
    'claude-3-opus': {'input': 15.0, 'output': 75.0},
}
Budget alert thresholds
ALERT_THRESHOLDS = {
    'warning': 80,  # 80% utilization
    'critical': 95  # 95% utilization
}
Waste detection threshold
WASTE_THRESHOLD = 0.10  # $0.10 per 1k tokens
Number of top models to show
TOP_MODELS_LIMIT = 10

See config_example.json for full options.

Q: Does it work with Google Cloud billing? A: Not directly. Google Cloud billing CSVs can be imported via custom CSV parser with column mapping. See config_example.json for mapping format.

Q: Can it track API calls in real-time? A: No. TokenBooks analyzes exported billing data, not live API usage. Export from your provider, then import.

Q: Does it send data anywhere? A: No. Everything runs locally. Zero network calls.

Q: How do I tag API calls with task names? A: Add a "task" field to your billing exports. If your provider doesn't support this, manually add it to the CSV before importing.

Q: Can I combine multiple months of data? A: Yes. Import each month's data into the same JSON file, or merge JSON files manually.

Q: Does it support non-USD currencies? A: Not directly. Convert to USD before importing, or manually adjust cost values.