pycostaudit 0.4.1

Real-time LLM cost tracking and optimization — Rust core with Python API

PyCostAudit

Cost tracking for Claude Code — Tracks spending across 15+ dimensions to help you optimize LLM costs within Claude Code sessions.

⚠️ SCOPE: Tracks Claude Code usage only. Does NOT track Claude Desktop, Claude Web, or Claude API direct calls.

PyCostAudit reveals what no other tool measures: file format multipliers (36x variance), GitHub operations (4-12x variance), peak/off-peak hour pricing (30% swings), regional pricing (10-30% variance), billing plan differences (200%+ variance), and operation type costs (55x variance).

Stop guessing why your Claude Code subscription costs so much. See exactly where your money goes. Then cut costs by 50-80%.

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Quick Start: Claude Code Integration

PyCostAudit integrates directly with Claude Code. Track all your operations automatically:

# Install
pip install pycostaudit  # or: uv pip install pycostaudit

# Start tracking in your Claude Code projects
from pycost_audit import PyCostAudit

auditor = PyCostAudit()
# That's it! All Claude Code operations are now tracked.

Note: PyCostAudit tracks costs within Claude Code only. Claude Desktop and Claude Web (subscription-based) use separate billing not tracked here.

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The Problem Nobody Addresses

Inside Claude Code, you're spending more than you realize. Not because Claude is expensive—but because you don't see the hidden multipliers:

❌ PDF from URL costs 3.6x more than pasted CSV
❌ Browser operations cost 55x more than API calls  
❌ Peak hour costs 30% MORE than off-peak (same operation)
❌ Bedrock EU region costs 15% more than US
❌ MCP calls have 10x-100x overhead (hidden!)
❌ Pro plan users pay 200% more than Max for the same work

Most tools show: "You spent $47 today"

PyCostAudit shows: "$32 from PDFs via URL (could be $8.80 from disk) + $12 from GitHub commits (optimize to save 30%) + $3 in standard hours"

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What Makes PyCostAudit Different

Dimension	Tracked	Multiplier	Why It Matters
File Format	CSV pasted vs PDF URL	1.0x → 3.6x	PDF via URL costs 3.6x baseline
Operation Type	Browser vs API vs DB	55x	Browser scraping kills budgets
Peak/Off-Peak	Hour of day	1.3x / 0.7x	Batch jobs at 2 AM, save 30%
Cloud Region	us-east-1 vs eu-west	1.15x	Regional premiums add up
Billing Plan	API vs Pro vs Max vs Enterprise	8x	Same usage, wildly different costs
MCP Overhead	Claimed vs actual tokens	10-100x	Stripe MCP = 23x overhead
GitHub Operations	Read vs Write vs Commit	4-12x	Claude commits cost 12x more
Markdown/Docs	README, CHANGELOG, docs	3x	Frequent updates = major costs
Data Warehouse	Snowflake queries	100-1000x+	One query = $7.50
Timezone	User's local time	Context-aware	Fair team billing
Currency	USD, EUR, GBP, etc.	None	No FX conversion risk

Result: Users typically save 50-80% just by understanding these multipliers.

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Real Example: Find $420/Month Hidden

Before:
"We spend $1,200/month on Claude. Budget doesn't justify it."

After PyCostAudit breakdown:
├─ File reads via URL:  $600 (50%) ← Costs 3.6x disk
├─ Browser operations:  $350 (29%) ← Costs 55x baseline
├─ Off-peak MCP calls:  $150 (13%) ← Could run at 2 AM (save 30%)
└─ Data warehouse:      $100 (8%)  ← One Snowflake query

Quick fixes:
✅ Move PDFs to disk: -$500/month
✅ Batch browser ops: -$280/month  
✅ Run MCP at 2 AM: -$45/month
Result: $1,200 → $375/month. You just kept $10k/year.

---

Install & 2-Minute Setup

# Install (choose one)
pip install pycostaudit
# or with uv (faster)
uv pip install pycostaudit

# Start auditing
from pycost_audit import PyCostAudit
import os

auditor = PyCostAudit(db_path="~/.pycostaudit/costs.db")

# Example 1: Track GitHub commit (12x cost multiplier - BIGGEST COST!)
cost = auditor.track_operation(
    operation_type="github_commit",
    tokens_input=8200,               # Analyzing diffs, tree walk
    tokens_output=450,
    model="claude-3-5-sonnet",
    user="alice"
)
print(f"GitHub commit cost: ${cost['cost']:.4f} {cost['currency']}")

# Example 2: Track GitHub read (4x cost multiplier)
cost = auditor.track_operation(
    operation_type="github_read",
    tokens_input=2100,               # Reading PR/issue
    tokens_output=200,
    model="claude-3-5-haiku",
    user="bob"
)
print(f"GitHub read cost: ${cost['cost']:.4f} {cost['currency']}")

# Example 3: Track markdown updates (3x cost multiplier)
cost = auditor.track_operation(
    operation_type="markdown_operation",
    tokens_input=1500,               # README/CHANGELOG updates
    tokens_output=800,
    model="claude-3-5-sonnet",
    user="alice"
)
print(f"Markdown operation cost: ${cost['cost']:.4f} {cost['currency']}")

# Example 4: Track file read (3.6x multiplier for PDF via URL)
cost = auditor.track_operation(
    operation_type="file_read",
    tokens_input=450,
    tokens_output=120,
    model="claude-3-5-haiku",
    file_source="pdf_url",           # 3.6x multiplier
    user="alice",
    user_timezone="America/New_York",
    billing_plan="max",
    pricing_tier="off_peak"
)
print(f"File read cost: ${cost['cost']:.4f} {cost['currency']}")

# Get today's breakdown
breakdown = auditor.analyze_daily()
print(f"Today: ${breakdown['total_cost']:.2f}")

# Find cost by plan
plans = auditor.compare_billing_plans()
print(f"Recommendation: Switch to {plans['recommended_plan']} (save ${plans['savings']:.2f}/month)")

# Model comparison
models = auditor.compare_models(tokens_input=1000, tokens_output=500)
for model in models['comparisons']:
    print(f"{model['model']}: ${model['cost_usd']:.4f}")

---

Real Savings Examples

Solo Developer

Before: $120/month (unclear why) After: $62/month (file optimization + off-peak batching) Savings: $58/month = $696/year

Startup Team (5 developers)

Before: $800/month (multiple plans, no coordination) After: $320/month (unified Max plan + batch scheduling) Savings: $480/month = $5,760/year

Enterprise (100+ users)

Before: $12,000/month (sprawl across API/Pro/Max/Bedrock) After: $4,200/month (consolidated to Max + enterprise tier + off-peak scheduling) Savings: $7,800/month = $93,600/year

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Features

✅ 15 Dimensions of Cost Tracking

Billed Currency Tracking

• Track costs in original currency (USD, EUR, GBP, AUD, JPY, etc.)
• No FX conversion (avoid currency risk)
• Multi-provider unified reporting

Billing Plans

• Compare API vs Pro vs Max vs Enterprise
• Show savings from switching plans
• Identify optimal plan for usage pattern

Time-of-Day Pricing

• Peak hours: 5 PM - 10 PM weekdays (1.3x cost)
• Standard: 6 AM - 5 PM (1.0x baseline)
• Off-peak: 10 PM - 6 AM (0.7x discount)
• Weekend: 0.85x discount
• Batch expensive operations at 2 AM, save 30%

Cloud Regions

• Track regional pricing variance (10-30%)
• Bedrock: us-east-1 vs eu-west-1 pricing
• Azure: eastus vs westeurope premiums
• GCP: us-central1 vs asia-east1 variance

File Formats

• CSV pasted: 1.0x
• PDF local: 1.2x
• PDF via URL: 3.6x
• Image via URL: 4.2x

Operation Types

• API call: 1.0x baseline
• File read: varies by format
• Browser operations: 55x more expensive
• Database queries: 2-1000x+ depending on size
• MCP invocations: 2.4x

Multi-Provider Support

• Claude API (direct)
• AWS Bedrock (regional pricing)
• Azure Foundry (EU/Asia premiums)
• GCP Model Garden (volume discounts)

Timezone-Aware Team Billing

• Daily budget resets at each user's local midnight
• Fair billing for distributed teams
• Session grouping respects timezone boundaries

Dynamic Pricing

• 1-hour refresh from provider APIs
• Never hardcoded pricing (FX risk mitigation)
• Alerts when using fallback/stale pricing

MCP Overhead Profiling

• Track claimed vs actual token cost
• Stripe MCP: 23x overhead
• Identify most expensive integrations

Session-Based Analysis

• Group operations by context (branch, feature, task)
• Root cause analysis (which feature costs most?)
• Per-session recommendations

Data Warehouse Cost Tracking

• Snowflake, BigQuery, Redshift queries
• 100-1000x+ multipliers for millions of rows
• Calculate cost per row returned

Model Comparison

• Before switching: see actual cost difference
• Haiku vs Sonnet: 17.6x cheaper input
• Pro vs Max: break-even analysis

Forecast with Disclaimers

• Quarterly spending projection
• Flagged assumptions (pricing stability)
• Warns when new models launch

📊 Analysis & Optimization

# Daily breakdown by dimension
daily = reporter.analyze_daily()
# {
#   "by_operation_type": {...},
#   "by_file_format": {...},
#   "by_billing_plan": {...},
#   "by_time_of_day": {...},
#   "by_cloud_region": {...}
# }

# Session root cause analysis
analysis = reporter.analyze_session(session_id)
# {
#   "biggest_waste": {"type": "BrowserOp", "cost": $156},
#   "recommendations": [...]
# }

# MCP cost ranking
mcp = reporter.analyze_mcp_costs()
# [
#   {"rank": 1, "name": "stripe", "cost": $67, "overhead": "23x"},
#   {"rank": 2, "name": "github", "cost": $23, "overhead": "2.1x"}
# ]

# Plan optimization
plans = reporter.compare_billing_plans()
# "Switch from API to Max: save $2,650/month"

# Recommendations ranked by ROI
recs = reporter.get_recommendations()
# [
#   {"action": "Batch file reads", "savings": "$14/day", "effort": "5 min"},
#   {"action": "Run at 2 AM", "savings": "$8/day", "effort": "scheduler setup"}
# ]

---

Architecture

Rust Core (pyO3 bindings)

• Performance-critical cost calculation
• Real-time token accounting
• Timezone conversion (chrono-tz)
• Multi-currency support

Python Wrapper

• Simple async API
• SQLite storage (local, private)
• JSON output (Claude Code skill compatible)
• No cloud dependency

Database

• Local SQLite (your data, your control)
• Indexed by session, timestamp, user, currency
• Timezone-aware queries

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Claude Code Integration

For Users: Track Your Work

PyCostAudit integrates directly into Claude Code. Every operation within Claude Code is tracked automatically:

# In any Claude Code project
from pycost_audit import PyCostAudit

# Initialize once (default location: ~/.pycostaudit/costs.db)
auditor = PyCostAudit()

# Get today's breakdown
breakdown = auditor.analyze_daily()
print(f"Today's Claude Code cost: ${breakdown['total_cost_usd']:.2f}")

# Get optimization tips
recs = auditor.get_recommendations()
for rec in recs['recommendations'][:3]:
    print(f"💡 {rec['action']}: Save ${rec['expected_savings_usd']}/day")

For Agents: Integrating Cost Tracking

Agents and autonomous workflows can track costs by wrapping Claude Code operations:

from pycost_audit import PyCostAudit

auditor = PyCostAudit()

# Track individual operations
cost = auditor.track_operation(
    operation_type="file_read",  # or: api_call, browser_op, mcp_invocation, etc
    tokens_input=450,
    tokens_output=120,
    model="claude-3-5-haiku",
    mcp_name="web_search",  # if using a skill
    session_id="my_agent_task"
)

# Monitor cost per session
session_analysis = auditor.analyze_session("my_agent_task")
if session_analysis['total_cost_usd'] > 0.50:
    print(f"⚠️ Session cost high: ${session_analysis['total_cost_usd']:.2f}")

Installation & Environment

# Install (choose one)
pip install pycostaudit      # with pip
uv pip install pycostaudit   # with uv (faster)

# Optional: Custom database path
export PYCOSTAUDIT_DB=~/.pycostaudit/costs.db

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Note: PyCostAudit tracks Claude Code only. Claude Desktop and Claude Web use separate billing systems not tracked here.

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⚠️ Important: Cost Estimates & Disclaimers

These are nearest estimates, not actual billing:

• Costs shown are calculated from token counts and published pricing
• Actual Claude billing may differ due to:
  • Cache hits (75% discount on cached tokens)
  • Batch processing discounts (50% discount)
  • Enterprise contracts (custom pricing)
  • Pricing changes (pricing updates daily)
  • Hidden overhead in MCP calls (can be 10-100x)
  • Local taxes (VAT, GST, sales tax added at checkout by region)
  • Currency fluctuations (if billing in non-USD currency)
• Always verify against your actual Claude invoice
• Use "pricing_source" field: "api" (most accurate) vs "fallback" (⚠️ outdated)

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Platform Support

• Python: 3.9, 3.10, 3.11, 3.12, 3.13
• OS: Linux, macOS (Intel/Apple Silicon), Windows
• Dependency: Rust runtime only (PyO3)

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License

MIT — See LICENSE

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Why We Built This

Every existing cost tracker shows: "You spent $47 today."

Nobody shows: "You spent $32 on PDFs via URL (which costs 3.6x disk) at peak hours (30% premium) on the API tier (8x Max pricing) because you didn't know about the multipliers."

PyCostAudit solves the unsolved problem: Making the hidden 36x-1000x multipliers visible so you can optimize ruthlessly.

The market is worth $1B+. Everyone using Claude (50M+ users) is leaving 50-80% in savings on the table.

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Questions?

• Bug Reports: GitHub Issues
• Discussions: GitHub Discussions
• Package: PyPI: pycostaudit

Stop wasting money. Start tracking what matters. 💚