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genpark-enterprise-cloud-finops-inference-cost-governor-skill

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SUMMARY

Enterprise Cloud FinOps & Multi-Model LLM Inference Cost Governor. Monitors tenant token burn rates across Tencent Cloud and multi-model runtimes, routes queries across Flash-Lite, Flash, and Pro reasoning tiers based on task complexity and | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-enterprise-cloud-finops-inference-cost-governor-skill

GenPark AI
LinkedIn
License: MIT
Dependencies
MCP Compliant

Enterprise Cloud FinOps & Multi-Model LLM Inference Cost Governor. Monitors tenant token burn rates across Tencent Cloud and multi-model runtimes, routes queries across Flash-Lite, Flash, and Pro reasoning tiers based on task complexity and budget ceilings, and enforces automated cost circuit breakers.

🌐 Official GenPark AI Ecosystem Links

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

"""Example usage for EnterpriseCloudFinOpsInferenceCostGovernor."""
import sys
import json
from client import EnterpriseCloudFinOpsInferenceCostGovernor

sys.stdout.reconfigure(encoding='utf-8')

def main():
    print("=== Tencent Cloud & WorkBuddy LLM Inference FinOps Governor Demo ===")
    governor = EnterpriseCloudFinOpsInferenceCostGovernor()

    # 1. Route a routine lookup task
    print("\n--- 1. Routing Routine Table Lookup (50k Input / 5k Output Tokens) ---")
    r1 = governor.route_and_govern_inference(
        tenant_id="marketing_dept",
        task_complexity="SIMPLE_LOOKUP",
        estimated_input_tokens=50000,
        estimated_output_tokens=5000,
        daily_budget_usd=0.50
    )
    print(f"Routed Tier: {r1['routed_model_tier']} | Call Cost: ${r1['estimated_call_cost_usd']:.4f} (Saved ${r1['cost_saved_vs_pro_usd']:.4f} vs Pro)")

    # 2. Simulate nearing budget limit and requesting Deep Reasoning
    print("\n--- 2. Triggering 85% Soft Cap Budget Protection ---")
    governor.tenant_ledger["marketing_dept"]["accumulated_spend_usd"] = 0.45
    r2 = governor.route_and_govern_inference(
        tenant_id="marketing_dept",
        task_complexity="DEEP_ARCHITECTURAL_REASONING",
        estimated_input_tokens=40000,
        estimated_output_tokens=8000,
        daily_budget_usd=0.50
    )
    print(f"Ideal Tier: {r2['ideal_model_tier']} -> Enforced Tier: {r2['routed_model_tier']}")
    print(f"FinOps Policy Action: {r2['finops_policy_action']} (Utilization: {r2['budget_utilization_pct']}%)")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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