AI Agent: Gen AI FinOps
GenAIFinOps was born as a project to solve the complexity of managing multiple models and the volatility of token prices, functioning as a true Kubernetes for AI Costs.
How to scale large language models (LLMs) without operational costs undermining ROI? We developed an abstraction layer that allows dynamic switching between models, ensuring technical performance with radical financial efficiency.
RESOURCES
Oracle (Pricing Chat)
Ask natural language questions about AI model pricing: “What is the cheapest GPT model?”, "Compare the prices of GPT-4 and GPT-3.5", "Which models are compatible with vision?"
Architect (Cost Optimizer)
Receive AI-based recommendations: Analyze your use case. Calculate costs for different models. See potential savings (monthly/annual). Compare alternatives with graphs.
Control Panel
Monitor your optimization platform: System health metrics. Provider overview. Model statistics. Quick start guide.
BUSINESS VALUE
Scenario: Customer support chatbot handling 10 million tokens/month
See the cost accumulate over time
Total savings (12 months)
$4,767.60
A 99.3% cut in costs
GPT-4 (current)
$4,800
GPT-4o-mini
$32.40
For developers
- ✓Save time researching prices
- ✓Data-driven model selection
- ✓Optimize cost without losing quality
For companies
- ✓Cut AI costs by 30 to 99%
- ✓Avoid budget overruns
- ✓Track and forecast AI spending
- ✓Justify AI investments to stakeholders
Technologies used
- Backend:
Python + FastAPI + ChromaDB + RAG - Frontend:
React + TypeScript + Tailwind CSS - AI:
litellm (support for LLM from multiple providers)