DEMO

FinOps Intelligence

Enterprise interactive showcase of how IBM Apptio and IBM Turbonomic deliver closed-loop cloud cost management, pairing multi-cloud cost allocation and unit economics with performance-aware automated resource rightsizing.

Building Blocks used:

automation.optimize

Skills used

Download skills for IBM Bob →

automated-resource-mgmt-turbonomic

Products Used

1 Review use case

Modern enterprises struggle with surging multi-cloud expenditures and decentralized infrastructure decisions. Finance teams face multi-million dollar cloud bills with limited attribution, while engineering teams defensively overprovision virtual machines and container workloads to safeguard against latency and downtime. Static cost recommendations often pile up in spreadsheets because organizations lack the operational confidence to execute changes without risking service SLAs.

Financial Visibility & Accountability (IBM Cloudability): - Full multi-cloud cost allocation across AWS, Microsoft Azure, Google Cloud Platform, and IBM Cloud. - Tag-based showback/chargeback, ML-powered budget variance forecasting, and anomaly detection. - Business unit metrics and unit economics (e.g., cost-per-checkout, cost-per-transaction, cost-per-query).

Performance-Aware Actionable Optimization (IBM Turbonomic): - AI-driven supply-chain analytics that continuously map application resource demand to underlying cloud infrastructure. - Non-disruptive rightsizing across VMs, Kubernetes container requests/limits, databases, and unattached volumes. - Automated workload parking schedules for idle non-production environments. - Single-click or policy-driven execution of optimization actions that guarantee performance while eliminating cloud waste.

Interactive Capabilities & Enterprise Tooling: - Live KPI simulation demonstrating tangible spend reduction, budget convergence, and multi-million dollar annualized savings. - Interactive REST API Explorer illustrating native integration patterns with IBM Cloudability and IBM Turbonomic APIs. - Dynamic scenario modeling allowing teams to upload custom JSON workload topologies or test enterprise configurations.

Start Here

Watch this short overview

Preview the application

Explore key screens from the demo

2 Try the interactive application

Experience the interactive demo and explore its features. A demo script is provided to help when presenting the application to partners.

Try the demo

Have questions, want a walkthrough or found a bug?

Contact the demo owner for assistance.

Submit a request →

3 Explore how it was built

The Continuous FinOps Closed-Loop Architecture connects IBM Cloudability and IBM Turbonomic into an automated, four-stage lifecycle (INFORM → OPTIMIZE → OPERATE → MEASURE). IBM Cloudability ingests multi-cloud billing and usage to provide financial visibility, showback attribution, ML forecasting, and unit economics (INFORM). IBM Turbonomic dynamically analyzes full-stack application demand to generate and execute performance-aware rightsizing, container scaling, and workload parking actions (OPTIMIZE & OPERATE). The cycle closes as Cloudability validates realized cost reductions and updates executive reporting without risking application SLOs (MEASURE).

Architecture

4 Download and modify with IBM Bob

Download the source code and specifications for this application to set up and run it locally. Once downloaded, open the code in IBM Bob to modify and adapt it to your own use case.

Source Code

FinOps Intelligence — Demo Script

Step 1: Set the Stage & Enterprise Context

Screen / Tab to Open: Click on the Overview (Closed-Loop FinOps) tab in the top navigation.

On-Screen Actions:

Point to the top metric header cards:
- Current Monthly Spend: $2,000,000/mo ($24M annual run-rate across AWS, Azure, GCP).
- Identified Waste / Optimization Opportunity: $460,000/mo ($5.52M/year).
Point to the interactive Closed-Loop Workflow diagram (INFORM → OPTIMIZE → OPERATE → MEASURE).

Step 2: INFORM — Financial Intelligence & Attribution

Screen / Tab to Open: Click on the Financial Intelligence tab.

On-Screen Actions:

Highlight the Spend vs. Budget Variance Chart.
Select/filter the Retail E-Commerce Platform card in the spend allocation breakdown.
Point out the category cost split: Compute VMs ($180k), Kubernetes Pods ($80k), and Databases ($60k).

Step 3: Advanced Capabilities — ML Forecasting & Anomaly Detection

Screen / Tab to Open: Switch to the Capabilities Hub / Advanced Capabilities sub-tab.

On-Screen Actions:

Point to the Machine Learning Spend Forecast curve showing the unconstrained $2.75M run-rate trajectory.
Hover over the Real-Time Anomaly Alerts (e.g., cross-region data transfer spike and orphaned unattached storage volumes).

Step 4: OPTIMIZE & OPERATE — Automated Resource Actions

Screen / Tab to Open: Switch to the Resource Optimization tab.

On-Screen Actions:

Filter the action list by Retail E-Commerce.
Walk through the specific AI-generated recommendations:
- Rightsize AWS EC2 m5.2xlarge → m5.xlarge (+$28,500/mo savings)
- Scale down Kubernetes Container Pod CPU/Memory limits (+$22,000/mo savings)
- Workload Parking for non-prod staging during off-hours (+$14,500/mo savings)
- Database instance rightsizing & orphaned volume deletion (+$15,000/mo savings)
Click “Execute All Actions for Retail” (or click individual Execute buttons).
Show the live status change to EXECUTED and the real-time spend counter dropping from $370,000/mo to $290,000/mo ($80,000/mo savings | $960,000/year).

Step 5: MEASURE — Verified ROI & Business Outcomes

Screen / Tab to Open: Switch to the Executive Value tab.

On-Screen Actions:

Point to the Realized Savings Summary:
- Retail realized savings: $80,000/mo ($960,000/yr).
- Enterprise total potential: $460,000/mo ($5.52M/yr).
Point to the Unit Economic Improvement: Cost per checkout dropped by 21.6%.
Highlight the 4 Strategic Executive Pillars:
- Lower Cost: Elimination of recurring multi-cloud waste.
- Financial Accountability: Precise showback and unit economics.
- Automated Operations: Non-disruptive, policy-driven execution.
- Assured Performance: Workloads sized continuously for actual demand.

Step 6: Enterprise Extensibility & Next Steps

Screen / Tab to Open: Switch to the API Explorer or Config modal.

On-Screen Actions:

In the REST API Explorer, select a sample endpoint (e.g., /reporting/reports/spend-by-dimension or Turbonomic /api/v3/actions) and click “Send Request” to show the live payload.
Open the Environment Config modal to demonstrate loading a customer’s custom environment JSON.