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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.

≈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

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