deburger 1.0.0

Static analysis tool that detects expensive cloud code patterns and estimates cost impact before deployment

deburger

Catch expensive cloud code before it ships. Static analysis tool that scans your codebase, detects costly patterns, and estimates your monthly cloud bill impact.

The Problem

Developers write code. Cloud bills arrive. Nobody connects the dots until it's too late. A single for loop with an S3 call inside costs $120/month. Multiply that across a team and you're burning thousands.

The Solution

deburger analyzes your code during development and tells you exactly which lines will cost money in production. No cloud credentials needed. No runtime overhead. Just static analysis that catches expensive patterns before they deploy.

Install

pip install deburger

Quick Start

cd your-project/
deburger init --provider aws
deburger check .

You'll see:

issues found
┏━━━━━━━━┳━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ file   ┃ line ┃ type            ┃ severity ┃ monthly cost ┃ savings ┃
┡━━━━━━━━╇━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━┩
│ app.py │   42 │ s3 in loop      │ high     │      $120.00 │ $108.00 │
│ api.py │   15 │ unbounded query │ critical │       $25.00 │  $22.50 │
└────────┴──────┴─────────────────┴──────────┴──────────────┴─────────┘

total monthly waste: $15,301
after optimization: $37
savings: 99.8%

What It Detects

S3/Storage in Loops - Individual API calls instead of batch operations
Missing Connection Pools - New database connections per request
Unbounded Queries - SELECT * without LIMIT causes timeouts and cost
Cold Start Issues - Heavy imports in Lambda functions add latency
Sequential Async - Awaiting in sequence instead of parallel execution
Expensive Logging - High-volume logs in hot paths

Languages Supported

• Python (AST-based analysis)
• JavaScript/TypeScript (pattern detection)

Cloud Providers

• AWS
• Google Cloud Platform
• Microsoft Azure

Commands

deburger init       # Create config file
deburger check      # Scan code for issues
deburger diff       # Compare cost between branches
deburger blame      # Show cost by developer
deburger hook       # Install pre-commit hook
deburger optimize   # Get fix suggestions

Git Hook Integration

Automatically check commits:

deburger hook --install

Now expensive code gets caught before it's committed:

git commit -m "Add feature"
# Hook runs automatically
# Commit blocked if expensive patterns found

CI/CD Integration

Add to GitHub Actions:

- name: Cost Analysis
  run: |
    pip install deburger
    deburger check . --json > cost-report.json
    deburger check . || exit 1

Configuration

Customize .deburger.yml for your traffic patterns:

provider: aws
region: us-east-1

traffic:
  requests_per_day: 100000
  avg_duration_ms: 500
  avg_memory_mb: 512

hooks:
  fail_on_critical: true
  max_monthly_cost: 200

More accurate traffic estimates = more accurate cost predictions.

Real-World Impact

• Startup (5 devs): Found $2,400/month waste, fixed in 2 days
• Mid-size (50 devs): Prevented 23 expensive patterns from reaching production in month 1
• Enterprise (200+ devs): Saved $18,000/year by catching issues in PR reviews

Why deburger?

Static Analysis - No runtime overhead, no performance impact
No Credentials Required - Analyzes code locally, never touches your cloud
Developer-First - Integrates into existing workflows (git, CI/CD, IDE)
Language Agnostic - Works with Python and JavaScript, more coming
Provider Agnostic - Supports AWS, GCP, Azure with same patterns

How It Works

1. Parses your code into an AST (Abstract Syntax Tree)
2. Detects expensive patterns using static analysis rules
3. Calculates cost based on your traffic configuration
4. Suggests optimizations with estimated savings

No magic. No AI guessing. Just pattern matching against known expensive antipatterns.

License

MIT

Links

Repository: github.com/sahilnyk/deburger
Issues: github.com/sahilnyk/deburger/issues
PyPI: pypi.org/project/deburger

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Built by @sahilnyk

Stop deploying expensive code. 🍔