AWS Cost Optimizer

Automated AWS cost optimization recommendations based on production experience achieving 60% cost reduction.

Features

• DynamoDB Analysis: Identify tables that should use provisioned capacity
• Lambda Analysis: Find functions without reserved concurrency limits
• S3 Analysis: Detect buckets missing lifecycle policies
• CloudFront Analysis: Identify distributions with suboptimal cache settings
• Rich CLI: Beautiful terminal output with actionable recommendations

Installation

For a CLI installation on macOS, use pipx. It manages an isolated Python environment and puts the command on your PATH:

brew install pipx
pipx ensurepath
# Open a new terminal after ensurepath, then:
pipx install aws-cost-optimizer
aws-cost-optimizer --help

Upgrade an existing pipx installation with pipx upgrade aws-cost-optimizer.

Alternatively, install into a virtual environment (Python 3.9+):

python3 -m venv .venv
source .venv/bin/activate
python -m pip install aws-cost-optimizer

Version 0.1.4 and later install Boto3 with AWS Common Runtime (CRT) support for credentials created by aws login. AWS credentials and a default region must still be configured. Named profiles can be selected with AWS_PROFILE.

For an older installation showing the missing botocore[crt] dependency error, run python -m pip install 'botocore[crt]' inside its active virtual environment.

Homebrew distribution

PyPI publication does not create a Homebrew formula. A dedicated Homebrew tap can provide brew install mjfxjas/tap/aws-cost-optimizer once a mjfxjas/homebrew-tap repository and its formula have been published. This tap is not set up by this repository. Until then, use the pipx commands above. See the Homebrew guide for release, tap creation, dependency generation, testing, and publishing steps for this and your other programs.

The formula should install a released source archive into its own virtual environment using Language::Python::Virtualenv, declare Python and all transitive dependencies (including awscrt) with checksummed resources, and verify aws-cost-optimizer --help without needing AWS credentials.

Quick Start

# Analyze all services
aws-cost-optimizer analyze

# Analyze specific service
aws-cost-optimizer analyze --service dynamodb

# Bulk apply workflow (safe-first)
aws-cost-optimizer apply --all --service all --dry-run

# Live execute (requires explicit --execute)
aws-cost-optimizer apply --all --service all --execute

# Single-resource workflow
aws-cost-optimizer apply --service dynamodb my-table --dry-run
aws-cost-optimizer apply --service dynamodb my-table --execute

# Interactive menu/hub (includes "Apply all (EXECUTE)")
aws-cost-optimizer menu

Common Operator Flows

A few high-signal commands to show what the tool is for:

# Full analysis pass across supported services
aws-cost-optimizer analyze

# Guided terminal experience
aws-cost-optimizer menu

# Safe bulk apply preview
aws-cost-optimizer apply --all --service all --dry-run

Smoke Test

Quick verification that install and CLI wiring are healthy:

python3 -m pip install --upgrade aws-cost-optimizer
aws-cost-optimizer --help
python3 -c "from importlib.metadata import version; print(version('aws-cost-optimizer'))"

Security Checks

• CI runs Bandit static security analysis on src/aws_cost_optimizer (Python 3.11 job).
• Failing threshold is set to medium-or-higher severity/confidence.

bandit -r src/aws_cost_optimizer --severity-level medium --confidence-level medium

Example Output

Service: DynamoDB | Resource: my-table | Issue: Using on-demand | Savings: ~40-60% | Action: Switch to provisioned
Service: Lambda | Resource: my-function | Issue: No concurrency limit | Savings: Prevent overruns | Action: Set reserved concurrency
Service: S3 | Resource: my-bucket | Issue: No lifecycle policy | Savings: ~20-30% | Action: Add lifecycle rules

Real-World Results

This tool is based on optimizations that achieved:

• 60% cost reduction on production serverless application
• 90% reduction in Lambda invocations via CloudFront caching
• Predictable costs through provisioned capacity

Requirements

• Python 3.9+
• AWS credentials configured
• IAM permissions for read access to analyzed services

Development

git clone https://github.com/mjfxjas/aws-cost-optimizer
cd aws-cost-optimizer
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e .
python -m unittest discover -s tests -v

License

MIT. See LICENSE.

Changelog

See CHANGELOG.md for versioned release notes.

Author

Jonathan Schimpf - jon@theatrico.org

AWS Solutions Architect Associate with production experience optimizing cloud costs.