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:

• How much you're spending total across all providers
• Which models are eating your budget
• Whether you're on track for your monthly limit
• If you're using expensive models for simple tasks

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

What TokenBooks Does

Unified Dashboard

• Import from OpenAI (CSV), Anthropic (JSON), Google Cloud, or custom formats
• See total spending across all providers in one place
• Per-provider, per-model, per-task breakdowns
• Visual HTML reports with CSS-based charts (zero JavaScript)

Budget Tracking

• Set monthly budgets
• See remaining balance and utilization percentage
• Alerts when you hit 80% or 95%

Waste Detection

• Identify expensive models used for simple tasks
• Calculate cost-per-1k-tokens for each model
• Suggest cheaper alternatives

Time Series Analysis

• Daily, weekly, or monthly spending trends
• See when costs spiked
• Compare this month vs. last month

Task-Level Tracking

• Tag API calls with task names ("code_review", "documentation", etc.)
• See which tasks cost the most
• Optimize task-to-model assignments

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:

• Total spend and token usage
• Provider and model breakdowns
• Daily spending trend (last 30 days)
• Budget status
• Waste detection alerts

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

You use OpenAI, Anthropic, and Google. Import all billing data into TokenBooks, see total spend in one place.

2. Budget Enforcement

Set a $100/month budget. TokenBooks tells you when you've spent $80 (warning) or $95 (critical). Avoid surprise bills.

3. Model Optimization

See which models cost the most. If GPT-4 is 80% of your spend but only used for simple tasks, switch to GPT-3.5 Turbo.

4. Task Cost Allocation

Tag API calls with task names. See that "code_review" costs $30/month while "documentation" costs $5. Optimize accordingly.

5. Team Reporting

Generate monthly HTML dashboards for your team. Share spending trends and optimization opportunities.

6. Vendor Comparison

Compare costs across providers for similar workloads. Maybe Anthropic is cheaper for your use case than OpenAI.

What Gets Tracked

Per-Provider

• Total cost
• Total tokens (input + output)
• Request count

Per-Model

• Total cost
• Token usage
• Request count
• Cost per 1k tokens

Per-Task

• Total cost (if you tag API calls)
• Token usage
• Request count

Time Series

• Daily, weekly, or monthly trends
• Spot spending spikes
• Compare periods

Waste Detection

• Models with high cost-per-token ratios
• Suggestions for cheaper alternatives
• Identify over-provisioned tasks

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

Requirements

• Python 3.7+
• Zero external dependencies (stdlib only)
• Works on Linux, macOS, Windows

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.

• The author(s) are NOT liable for any damages, losses, or consequences arising from the use or misuse of this software — including but not limited to financial loss, data loss, security breaches, business interruption, or any indirect/consequential damages.
• This software does NOT constitute financial, legal, trading, or professional advice.
• Users are solely responsible for evaluating whether this software is suitable for their use case, environment, and risk tolerance.
• No guarantee is made regarding accuracy, reliability, completeness, or fitness for any particular purpose.
• The author(s) are not responsible for how third parties use, modify, or distribute this software after purchase.

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.

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