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Enterprise Multi-Cloud Cost Intelligence System

A custom-built multi-cloud cost intelligence system deployed entirely to client infrastructure. Automated anomaly detection, reporting, and executive dashboards across GCP, AWS, and Azure.

3
Cloud Providers
100%
Data Sovereignty
Enterprise
Production Scale

Client Overview

Industry

Large Enterprise Technology Company

Region

Turkey (Data Sovereignty Required)

Cloud Footprint

GCP (150+ projects), AWS (100+ accounts), Azure (220+ subscriptions)

Project Duration

3 months (development + deployment)

The Challenge

Critical Business Problems:

  • No Real-Time Cost Visibility: Unexpected spending spikes discovered weeks after they occurred, leading to budget overruns
  • Manual Reporting Burden: Finance team spent days each month manually compiling cost reports across three cloud providers
  • No Executive Visibility: Leadership lacked a consolidated view of multi-cloud spending and trends
  • Data Sovereignty Requirements: All cost data must remain within their infrastructure (no SaaS tools allowed)
  • Complex Multi-Cloud Architecture: 470+ separate billing entities across three cloud providers

Our Solution

We built a custom multi-cloud cost intelligence system deployed entirely on the client's infrastructure. The solution provides automated anomaly detection, departmental reporting, and unified executive dashboards—all running within their own cloud accounts with complete data sovereignty.

Three Core Components

1. Multi-Cloud Anomaly Detection
Automated real-time cost anomaly detection across GCP, AWS, and Azure
  • Custom algorithms running as batch jobs in each cloud provider's native compute (Cloud Functions, Lambda, Container Apps)
  • 15-day rolling data refresh to capture late-arriving cost data and validate anomalies
  • Real-time alerts sent to Microsoft Teams with detailed anomaly cards
  • SQL databases (BigQuery, RDS, Azure SQL) store aggregated costs and flagged anomalies
2. Automated Departmental Reporting
Monthly cost reports automatically generated and distributed
  • Automated Excel reports showing monthly consumption per department for each cloud provider
  • Month-over-month comparison and progress against annual budgets
  • Automatic distribution to stakeholders via email and Microsoft Teams
  • Eliminated manual reporting burden (previously 3-5 days per month)
3. Unified Executive Dashboard
Single consolidated view of all cloud spending with AI-generated insights
  • Power BI dashboard consolidating costs from all three cloud platforms
  • LLM-generated executive summaries highlighting key cost trends and anomalies
  • All data processing within client infrastructure (no external API calls)
  • Strategic visibility for leadership into multi-cloud financial performance

Technology Stack & Architecture

GCP Implementation

  • Cloud Functions (Python)
  • BigQuery (data warehouse)
  • Cloud Scheduler (orchestration)
  • Billing export to BigQuery

AWS Implementation

  • AWS Lambda (Python)
  • Amazon RDS (PostgreSQL)
  • EventBridge (orchestration)
  • Cost and Usage Reports (CUR)

Azure Implementation

  • Azure Container Apps
  • Azure SQL Database
  • Cost Management Exports
  • Blob Storage (Parquet files)

Results & Business Impact

Proactive Cost Management

Shifted from reactive (discovering issues weeks later) to proactive (real-time alerts). Anomalies now caught within hours, not weeks.

Manual Work Eliminated

Finance team saved 3-5 days per month previously spent on manual report compilation. Reports now auto-generated and distributed.

Complete Data Sovereignty

All cost data remains within client's infrastructure. No external SaaS tools. Full compliance with data sovereignty requirements.

Executive Visibility

Leadership now has consolidated, AI-enhanced view of multi-cloud spending. Strategic decisions backed by real-time data.

Key Technical Achievements

Multi-Cloud Native Architecture

Built separate, optimized pipelines for each cloud provider using their native services. No forced abstractions—leverages each platform's strengths.

LLM Integration for Executive Insights

Integrated LLM to generate logical highlights and summaries of monthly cost trends. Processed entirely within client infrastructure (no external API calls).

Containerized Deployment & IaC

All components deployed via infrastructure-as-code and container images. Fully reproducible, version-controlled, and maintainable.

Teams Integration for Real-Time Alerts

Anomaly notifications sent directly to Microsoft Teams with rich cards. Finance team gets actionable alerts in their existing workflow.

Why Data Sovereignty Mattered

Unlike SaaS FinOps tools that require sending billing data to external providers, this solution runs entirely within the client's infrastructure. This meant:

  • No data ever leaves their environment - stored in their own databases
  • Compliance with internal data policies - no third-party data sharing
  • Client owns all infrastructure - full control over data retention and access
  • No vendor lock-in - they control the entire system

Need Custom AI Built on Your Infrastructure?

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