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Representative Outcomes for UK Startups

These are benchmark-aligned modelled scenarios to illustrate typical outcomes; results vary.

See how a 6-Week FinOps Sprint™ typically identifies ≈15–35% optimisable spend, improves forecast accuracy, and builds predictable cloud budgets — using benchmark-based modelled scenarios aligned to FinOps best practices.

⚠️Important: These examples are modelled from industry benchmarks and Zyorix Sprint methodology simulations. They are not Zyorix client results.

≈15-35%
Optimisable spend (modelled)
≈6-8 weeks
Typical ROI realisation (modelled)
≈90-95%
Cost allocation accuracy (modelled)
FS
Fintech (Series A): ≈28% Projected Savings in 8 Weeks
Fintech

Representative Scenario (Modelled)

≈28% projected cost reduction (modelled)

The Challenge

Rapid growth from seed to Series A led to cloud costs growing 400% in 12 months. Engineering team had no visibility into cost drivers, and finance couldn't forecast accurately. Investors demanded predictable burn rates.

The Solution

Implemented the 6-Week FinOps Sprint™ focusing on immediate waste reduction (idle dev/staging resources), reserved instance strategy for predictable workloads, and cost allocation by team/feature.

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
Retail Tech (Multi-Cloud): ≈35% Projected Savings in 5 Weeks
Retail Tech

Representative Scenario (Modelled)

≈35% projected cost reduction (modelled)

The Challenge

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.

The Solution

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)

The Challenge

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.

The Solution

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 representative and modelled, based on FinOps benchmarks and Zyorix Sprint methodology simulations, not verified Zyorix client engagements.

Realised outcomes depend on the changes you implement (e.g., rightsizing, RIs/SPs/CUDs, decommissioning). We measure against a documented baseline per the Zyorix Methodology.

Results vary by environment complexity, current FinOps maturity, and implementation pace.

Typical Outcomes (Benchmark-Based)

What to expect from the 6-Week FinOps Sprint™

Based on FinOps industry benchmarks and Sprint methodology simulations

≈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™

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Honest ROI assessment
Quick wins identified