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Labs & Demos

Internal demonstrations and lab simulations showcasing Zyorix's FinOps frameworks: dashboards, tagging compliance models, budget/forecast workflows, and optimisation backlogs.

⚠️Important: These are lab simulations and demonstrations, not client engagements. Frameworks are validated in lab settings and designed to help organisations target optimisation outcomes.

≈15-35%
Target optimisation range (lab models)
≈6-8 weeks
Target ROI window (lab benchmarks)
≈90-95%
Target allocation coverage (lab standards)
FS
Azure Cost Management: Budget & Forecast Variance — Lab Simulation
Fintech

Lab Simulation (Fintech Profile)

≈28% projected cost reduction (modelled)

Lab Scenario

Lab scenario: Rapid growth profile where cloud costs grow 400% in 12 months. Engineering lacks cost visibility, finance cannot forecast accurately. Typical Series A pressure for predictable burn rates.

Lab-Validated Framework

Lab-validated 6-Week FinOps Sprint™ framework: waste identification (idle dev/staging resources), reserved capacity planning for steady workloads, and team/feature cost allocation model.

The Results

Monthly Spend (modelled)
£45,000 £32,400
−£12,600
Annual Value (modelled)
≈£151,200
Forecast Accuracy (post-Sprint, modelled)
±6% variance
Cost Allocation (modelled)
≈92% tagged
ROI Payback (modelled)
≈8 weeks

Why this scenario is representative:

Seed→Series A teams often experience 3–5× spend growth without allocation or RI/SP strategy. Model assumes baseline idle dev/staging, partial tagging, and steady workload components suitable for commitments.

What Was Delivered:

  • 90-day FinOps roadmap
  • Reserved Instance recommendations (18-month coverage)
  • Cost allocation model (by team & feature)
  • Automated anomaly detection alerts
  • Monthly forecast model
EP
Power BI FinOps Overview: Ownership, KPIs, Trends — Lab Demo
Retail Tech

Lab Simulation (Retail Tech Multi-Cloud)

≈35% projected cost reduction (modelled)

Lab Scenario

Multi-cloud architecture (AWS for compute, GCP for data) with no unified cost view. Multiple teams sharing infrastructure led to allocation disputes and budget overruns. Previous attempt with a SaaS FinOps platform failed due to complexity.

Lab-Validated Framework

Created unified cost allocation framework across AWS + GCP, implemented showback reports for each product team, optimised data transfer costs between clouds, and established governance policies to prevent future waste.

The Results

Monthly Spend (modelled)
£68,000 £44,200
−£23,800
Annual Value (modelled)
≈£285,600
Multi-Cloud Allocation (modelled)
≈95% accuracy
Team Accountability (modelled)
100% showback coverage
ROI Payback (modelled)
≈5 weeks

Why this scenario is representative:

Multi-cloud environments (AWS+GCP) frequently lack unified cost allocation and cross-cloud commitment strategies. Model assumes moderate waste across both platforms with fragmented tagging practices.

What Was Delivered:

  • Unified multi-cloud cost dashboard
  • Showback/chargeback framework
  • Data transfer optimisation playbook
  • Team-specific budget alerts
  • Quarterly governance review process
BS
B2B SaaS (Post-M&A): ≈31% Projected Waste Elimination
Enterprise Software

Representative Scenario (Modelled)

≈31% projected waste elimination (modelled)

Lab Scenario

Post-acquisition integration led to duplicate infrastructure and orphaned resources across 15+ AWS accounts. No cost ownership, and engineering team spent more time firefighting than optimising. CFO demanded 20% cost reduction to hit profitability targets.

Lab-Validated Framework

Consolidated AWS accounts using AWS Organizations, identified and decommissioned orphaned resources (34% of total spend), implemented rightsizing for over-provisioned RDS and EC2 instances, and created cost-aware culture through training.

The Results

Monthly Spend (modelled)
£92,000 £63,480
−£28,520
Annual Value (modelled)
≈£342,240
Orphaned Resources (modelled)
~£18K/month eliminated
Rightsizing Impact (modelled)
≈18% compute optimisation
ROI Payback (modelled)
≈4 weeks

Why this scenario is representative:

Post-M&A scenarios typically inherit duplicate infrastructure, abandoned projects, and orphaned resources. Model assumes moderate architectural debt with opportunity for consolidation and modernization.

What Was Delivered:

  • AWS account consolidation strategy
  • Orphaned resource audit & decommissioning plan
  • Rightsizing recommendations (50+ instances)
  • Cost-aware engineering training (2 workshops)
  • Ongoing quarterly health checks

Important Disclosure:

These scenarios are lab simulations demonstrating Zyorix Sprint methodology, not client engagements.

Lab-validated frameworks designed to help organisations target cost optimisation outcomes (e.g., rightsizing, reserved capacity planning, waste elimination). Baselines documented per the Zyorix Lab Methodology.

Actual outcomes depend on your environment complexity, current FinOps maturity, and implementation commitment.

Target Outcomes (Lab Benchmarks)

What the pilot-ready 6-Week FinOps Sprint™ is designed to help you achieve

Based on FinOps industry benchmarks and lab-validated Sprint methodology

≈15-35%
Cost Reduction (modelled)
Typically achieved within ~60 days (varies by implementation pace)
≈±5-10%
Forecast Accuracy (modelled)
Monthly spend variance after governance setup
≈90-95%
Cost Allocation (modelled)
Tagged and allocated to teams/features
≈4-12 weeks
ROI Payback (modelled)
Time to recover Sprint investment (benchmark-based)
≈23-34%
Idle Waste Found (modelled)
Orphaned resources, over-provisioned instances
100%
Team Visibility
Engineering & finance aligned on cloud costs

See Your Optimisation Potential

Book a free 30-minute discovery call with Sohil to identify potential savings opportunities with the 6-Week FinOps Sprint™

No obligations
Honest ROI assessment
Quick wins identified