openfinops 0.1.2

Open Source FinOps Platform for AI/ML Cost Observability and Optimization

🌟 OpenFinOps

Open Source FinOps Platform for AI/ML Cost Observability and Optimization

🎯 Overview

OpenFinOps is a comprehensive, open-source platform for tracking, analyzing, and optimizing costs across AI/ML infrastructure and operations. It provides real-time visibility into:

• LLM Training Costs - Track GPU utilization, training jobs, and compute expenses
• RAG Pipeline Monitoring - Monitor vector databases, embeddings, and retrieval costs
• Multi-Cloud Cost Tracking - Unified view across AWS, Azure, GCP
• AI API Usage - Track OpenAI, Anthropic, and other LLM API costs
• Executive Dashboards - Role-based views for CFO, COO, Infrastructure Leaders
• Cost Attribution - Per-model, per-team, per-project cost breakdown
• AI-Powered Optimization - Intelligent recommendations for cost savings

✨ Key Features

📊 Comprehensive Observability

• Real-time Monitoring: Live metrics for all AI/ML operations
• Multi-Cloud Support: AWS, Azure, GCP telemetry integration
• LLM Training Tracking: GPU utilization, loss curves, cost per epoch
• RAG Analytics: Document processing, embedding generation, retrieval accuracy
• API Cost Tracking: OpenAI, Anthropic, and custom API endpoints

💰 FinOps Intelligence

• Cost Observatory: Centralized cost tracking and analysis
• Cost Attribution: Per-model, per-project, per-team breakdowns
• Budget Management: Set budgets, track spending, get alerts
• Optimization Recommendations: AI-powered cost-saving suggestions
• Trend Analysis: Historical cost patterns and forecasting

📈 Professional Visualizations

• VizlyChart: Built-in visualization library for charts and graphs
• Executive Dashboards: Role-based dashboards (CFO, COO, Infrastructure)
• Real-time Charts: Live updating metrics and KPIs
• Custom Reports: Generate and export custom cost reports

🔐 Enterprise-Grade Security

• Role-Based Access Control (RBAC): Fine-grained permissions
• IAM Integration: Identity and access management system
• Audit Logging: Complete audit trail for all operations
• Secure API: Authentication and authorization built-in

🚀 Easy Deployment

• Docker Support: Containerized deployment ready
• Kubernetes Ready: Helm charts for K8s deployment
• Cloud-Native: Deploy on any cloud provider
• On-Premise: Run in your own datacenter

🏗️ Architecture

openfinops/
├── observability/          # Core observability platform
│   ├── observability_hub.py        # Central monitoring hub
│   ├── llm_observability.py        # LLM training & RAG monitoring
│   ├── finops_dashboards.py        # FinOps dashboards
│   ├── cost_observatory.py         # Cost tracking
│   ├── cost_reporting.py           # Cost reporting
│   ├── ai_recommendations.py       # AI-powered optimization
│   └── alerting_engine.py          # Intelligent alerting
│
├── vizlychart/             # Visualization library
│   ├── charts/                     # Chart implementations
│   ├── rendering/                  # Rendering engine
│   └── core/                       # Core utilities
│
├── dashboard/              # Role-based dashboards
│   ├── cfo_dashboard.py            # CFO financial view
│   ├── coo_dashboard.py            # COO operations view
│   ├── infrastructure_leader_dashboard.py
│   └── iam_system.py               # Access control
│
└── agents/                 # Cloud telemetry agents
    ├── aws_telemetry_agent.py
    ├── azure_telemetry_agent.py
    ├── gcp_telemetry_agent.py
    └── generic_telemetry_agent.py

🚀 Quick Start

Installation

# Install from source
git clone https://github.com/openfinops/openfinops.git
cd openfinops
pip install -e .

# Or with all features
pip install -e ".[all]"

# Or specific cloud providers
pip install -e ".[aws,azure,gcp]"

Basic Usage

from openfinops.observability import ObservabilityHub
from openfinops.observability import LLMObservabilityHub

# Initialize observability
hub = ObservabilityHub()
llm_hub = LLMObservabilityHub()

# Register training cluster
llm_hub.register_training_cluster(
    cluster_name="gpu-cluster-1",
    nodes=["node-1", "node-2"]
)

# Track training metrics
llm_hub.track_training_metrics(
    model_id="gpt-custom",
    epoch=1,
    step=100,
    loss=0.5,
    gpu_memory_usage=8000
)

# Get cost summary
cost_summary = hub.get_cost_summary()
print(f"Total spend: ${cost_summary['total']}")

Running the Dashboard

# Start the web dashboard
openfinops-dashboard

# Or with custom port
openfinops-dashboard --port 8080

# Access at http://localhost:8080

📚 Documentation

• Installation Guide - Detailed installation instructions
• Quick Start - Get started in 5 minutes
• API Reference - Complete API documentation
• Architecture - System architecture overview
• Deployment Guide - Production deployment guide
• Contributing - How to contribute

🎯 Use Cases

For Engineering Teams

• Monitor LLM training job costs in real-time
• Track GPU utilization and optimize resource allocation
• Debug cost anomalies and inefficiencies
• Set up alerts for budget overruns

For Finance Teams

• Get complete visibility into AI/ML spending
• Track cost attribution by team, project, or model
• Generate reports for stakeholders
• Forecast future AI infrastructure costs

For Leadership

• Executive dashboards with key metrics
• ROI analysis for AI initiatives
• Budget vs. actual spending tracking
• Strategic cost optimization recommendations

🌐 Multi-Cloud Support

OpenFinOps supports telemetry from:

• AWS: CloudWatch metrics, Cost Explorer, EC2, SageMaker
• Azure: Azure Monitor, Cost Management, Azure ML
• GCP: Cloud Monitoring, Cloud Billing, Vertex AI
• AI Platforms: OpenAI, Anthropic, Hugging Face, Custom APIs

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository
git clone https://github.com/openfinops/openfinops.git
cd openfinops

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black src/ tests/
ruff check src/ tests/

📊 Roadmap

• Kubernetes operator for auto-scaling based on cost
• Integration with Prometheus and Grafana
• Support for more AI platforms (Cohere, AI21, etc.)
• Advanced anomaly detection with ML models
• Mobile app for cost monitoring
• Slack/Teams integration for alerts
• Custom webhook support
• Cost allocation tagging system

📄 License

OpenFinOps is licensed under the Apache License 2.0. See LICENSE for details.

🙏 Acknowledgments

OpenFinOps is built with inspiration from the FinOps Foundation principles and best practices from the cloud cost optimization community.

📝 Citing OpenFinOps

If you use OpenFinOps in your research, please cite it using the following references:

BibTeX

@software{openfinops2024,
  title = {{OpenFinOps}: Open Source FinOps Platform for AI/ML Cost Observability and Optimization},
  author = {Murugan, Durai and {OpenFinOps Contributors}},
  year = {2024},
  month = {10},
  version = {0.1.0},
  url = {https://github.com/rdmurugan/OpenFinOps},
  license = {Apache-2.0},
  keywords = {finops, cost-optimization, observability, ai-ml, cloud-cost, llm-monitoring}
}


## 📞 Support

- **GitHub Issues**: [Report bugs or request features](https://github.com/openfinops/openfinops/issues)
- **Discussions**: [Community discussions](https://github.com/openfinops/openfinops/discussions)
- **Email**: [e-mail]

## ⭐ Star Us!

If you find OpenFinOps useful, please consider giving us a star on GitHub! It helps the project grow and reach more users.

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**Made with ❤️ by the OpenFinOps community**