cost-aware-engineering

Prototype FinOps platform for cost-aware engineering, unit economics, and automated cloud cost accountability.

Overview

This repository demonstrates a modular FinOps execution pipeline that moves cloud cost management left — from reactive billing review to proactive engineering action.

Instead of stopping at cost visibility, the system translates cost signals into:

• driver-aware recommendations
• owner and team routing
• priority assignment
• Jira-style execution artifacts

🚀 Capabilities

1. Cost Signal → Action Engine

• Detect anomalies and cost drivers
• Generate structured remediation actions
• Route to owners (Jira-style output)

2. Tagging Governance Engine

• Detect missing required tags (owner, environment, product, cost_center)
• Prioritize tagging gaps
• Enable cost accountability

3. Showback Engine

• Aggregate cloud spend by owner
• Surface unassigned cost
• Support financial accountability and chargeback models

4. Architecture Cost Advisor

This module simulates how architecture choices affect monthly cloud cost and cost per transaction before deployment.

It is designed to support shift-left FinOps by helping teams evaluate design tradeoffs earlier, instead of waiting for billing surprises.

What it evaluates

• estimated monthly cost
• transaction volume
• cost per transaction
• architecture guidance

Example output

[
  {
    "scenario": "baseline",
    "estimated_monthly_cost": 600,
    "transactions": 10000,
    "cost_per_transaction": 0.06,
    "advice": "Architecture cost profile looks reasonable. Continue monitoring growth and efficiency."
  },
  {
    "scenario": "database_heavy_design",
    "estimated_monthly_cost": 870,
    "transactions": 7000,
    "cost_per_transaction": 0.1243,
    "advice": "High cost per transaction. Review architecture efficiency, caching, and scaling strategy."
  }

### 5. Kubernetes Cost Allocation Engine
- Allocate shared Kubernetes cluster cost to workloads, namespaces, teams, and products
- Use request-based allocation for reserved platform demand
- Compare request-based cost vs usage-based cost
- Surface inefficiency and over-allocation
- Directly attribute persistent volume cost
- Create a foundation for container showback, chargeback, and unit economics]
## 🔗 Unified FinOps System

This repository implements a full FinOps control loop:

```text
Cost Signals
   ↓
Tagging Governance (Who owns the cost?)
   ↓
Showback (Where is cost allocated?)
   ↓
Unit Economics (What is cost per business outcome?)
---

## 🧠 Example: Tagging + Showback Output

### Tagging Issues
```json
[
  {
    "resource": "db-1",
    "missing_tag": "owner",
    "priority": "high"
  }
]

## Why this matters

Many FinOps workflows stop at reporting. This prototype is designed to show how FinOps can operate as an engineering enablement system:

- Detect cost anomalies
- Interpret the underlying cost driver
- Generate context-aware actions
- Route actions to the correct team
- Simulate execution through ticket creation

## Current working pipeline

The current prototype implements this flow:

```text
Detection Layer
-> Driver Intelligence Layer
-> Action Generation Layer
-> Routing Layer
-> Jira Simulation Layer


---

## Example output

### Action output

```json
{
  "driver": "RDS",
  "resource": "db-1",
  "owner": "team-b",
  "team": "database-team",
  "priority": "high",
  "ticket": "DB-AUTO",
  "action": "Review database sizing and utilization"
}




### Jira ticket output

```json
{
  "ticket_id": "DB-AUTO-XXXXXX",
  "assigned_team": "database-team",
  "owner": "team-b",
  "status": "OPEN",
  "summary": "RDS cost anomaly - db-1",
  "description": "Review database sizing and utilization",
  "priority": "HIGH"
}