Play 66
AI Infrastructure Optimizer
High✅ Ready
AI-driven cloud cost optimization with right-sizing, GPU analysis, and FinOps reporting.
Intelligent infrastructure optimization agent that queries Azure Resource Graph and Monitor to provide right-sizing recommendations, GPU utilization analysis, idle resource detection, and reserved instance purchase guidance. Generates auto-scaling policies, detects cost anomalies in real time, and produces monthly FinOps reports with actionable savings breakdowns. Uses Azure Advisor signals enriched with GPT analysis to explain optimization impact in business terms.
Architecture Pattern
FinOps analysis agent: resource graph queries, anomaly detection, savings reports
Azure Services
Azure OpenAIAzure MonitorAzure AdvisorCost ManagementResource GraphAzure Functions
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — FinOps Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (232 lines), evaluate (112 lines), tune (164 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI key input + envFile
TuneKit (AI Config)
- config/openai.json — gpt-4o for optimization analysis
- config/guardrails.json — cost-optimization focus, savings validation
- evaluation/eval.py — Savings accuracy >85%, Adoption rate >60%
Tuning Parameters
Savings threshold percentUtilization floor percentScaling aggressivenessAnomaly sensitivityReport frequency days
Estimated Cost
Dev/Test
$50–100/mo
Production
$1K–3K/mo