CloudCull is an "Investor-Grade" autonomous governance framework designed to detect and eliminate GPU waste across AWS, Azure, and Google Cloud Platform. By 2026, it is the standard for multi-cloud cost optimization.
Startups and AI companies lose thousands of dollars every month because expensive GPU instances are left running idle. Manual tagging and spreadsheets are not enough to stop this bleeding.
CloudCull is not a dashboard; it is an Execution-First Sniper Agent. It proactively scans your multi-cloud environment, uses Multi-Model Intelligence (Claude/Gemini/Llama) to classify instance state, and provides a Kill-Switch to stop waste immediately.
CloudCull follows a robust, CLI-first automation flow designed for deep integration into DevOps pipelines.
graph LR
Trigger["Cron / GitHub Action"] --> Probe["Probe: Multi-Cloud SDKs"]
Probe --> Analyzer["Analyzer: Multi-Model AI"]
Analyzer -- "Decision: Zombie Identified" --> UI["UI: Approval Notification"]
UI -- "Approve" --> Stop["Stop: Cloud Native API"]
Stop --> StateRM["State RM: Terraform"]
- οΏ½ High-Fidelity Brain: Pluggable AI (Claude 3.5, Gemini 1.5, Llama 3) for intelligent classification.
- π‘ Sniper Console: A technical Vite + React dashboard with AI Reasoning Callouts, Live Terminal Logs, and One-Tap Kill Actions.
- π€ Identity Layer: Finds exactly who launched the instance for high-stakes accountability.
- π οΈ IaC-Driven Remediation: Generates
terraform state rmplans instead of raw, risky deletions.
Note
CloudCull is a CLI-First Tool. The dashboard is a passive visualization layer.
git clone https://github.com/daretechie/cloudcull.git
cd cloudcull
uv syncAlternative (Standard pip):
pip install -r requirements.txtThis script simulates a multi-cloud audit and remediation flow with high-fidelity logs.
./scripts/demo.shTo run a real-world audit and trigger the automated remediation bundle:
uv run cloudcull --region us-east-1 --active-ops # AWS
uv run cloudcull --platform azure --active-ops # Azure
uv run cloudcull --platform gcp --active-ops # GCPCaution
--active-ops will perform cloud-native stop actions and generate config/remediation_manifest.json. Use with high-stakes environments after dry-run verification.
Don't have time to run the Sniper yourself? I provide professional, fixed-price audits and CI/CD integration services.
- One-Time Audit: Rapid detection of multi-cloud waste.
- Governance Setup: Deliverable Remediation manifests and fixes.
- Continuous Ops: Full CI/CD integration for automated cost control.
| Directory | Purpose |
|---|---|
src/ |
Source Code. All Python modules, adapters, and the main entrypoint. |
infra/ |
Infrastructure. Dockerfile, Docker Compose, and future IaC (Terraform). |
config/ |
Configuration. Static and dynamic manifests (e.g., remediation plans). |
scripts/ |
Tooling. Utility scripts and demonstration walkers. |
docs/ |
Documentation. Architecture, Operations, and Security guides. |
tests/ |
Validation. Unit and integration test suites. |
dashboard/ |
Visualization. React + Vite frontend for Passive Monitoring. |
CloudCull is container-ready for consistent execution.
# Build & Run
docker build -f infra/Dockerfile -t cloudcull .
docker run --env-file .env cloudcull --simulated --dry-rundocker-compose upTo enable the live dashboard, you must manually activate GitHub Pages in your repository settings:
- Go to Settings > Pages.
- Under Build and deployment > Source, select GitHub Actions.
- Architecture & System Design (Includes Core Design Principles)
- Deployment Guide
- Developer Setup
- Dashboard Guide
- Operations Guide
To run security audits against the codebase (excluding virtual environments):
uv run bandit -c pyproject.toml -r .This project is licensed under the MIT License.